Copernicus Land Monitoring Services

Transcription

1 Copernicus Land Monitoring Services TASK 3: CONTINUATION OF SNOW & ICE TASK D 3.2: Summary Report to Support EEA Date: June 2017 Version: 1.0 Prepared by: Marketa Jindrova (GISAT) Joan Bauzà Llinàs Alaitz Zabala Torres (Universitat Autonoma de Barcelona)

2 TABLE OF CONTENTS 1 OBJECTIVES SNOW AND ICE PARAMETERS: BACKGROUND SNOW COVER Definition Data availability Current services Conclusions and needs FRACTIONAL SNOW COVER Definition Data availability Current services Conclusions and needs SNOW WATER EQUIVALENT Definition Data availability Current service Conclusions and needs LAKE/ RIVER ICE Definition Data availability Current Status Conclusions and needs OBSERVATION SYSTEMS IN-SITU SATELLITES Synthetic Aperture Radar (SAR) SAR Interferometry (InSAR) Passive Microwave Altimeters Radar Scatterometers Visible to Thermal Infrared AIRBORNE MODELLING, DATA ASSIMILATION, REANALYSIS DATA ASSIMILATION, DATA MANAGEMENT SUMMARY OF OBSERVATION SYSTEMS USER REQUIREMENTS- UPDATE USER REQUIREMENTS: QUESTIONNAIRES FROM INTERNATIONAL USERS New Survey Gathered information Summary of National Services & Other Users Requirements EUROPEAN POLICIES Flood Directive Water Framework Directive Summary of European Policies KEY EXPERTS IGOS CRYOSPHERE THEME REPORT (2007) POLAR REGIONS Polar Space Task Group (PSTG): Strategic Plan: i

3 4.5.2 Polar View and Polaris Copernicus Polar Mission GLOBAL CRYOSPHERE WATCH (GCW) Snow Watch SNOWPEX WHITE PAPER ON PERSPECTIVES FOR A EUROPEAN SATELLITE SNOW MONITORING STRATEGY (GLOBSNOW) RESEARCH PROJECTS- UPDATE CRYOLAND GLOBSNOW CRYOCLIM ESSEM COST ACTION ES EUROPEAN SERVICES- UPDATE COPERNICUS CLIMATE CHANGE SERVICE (C3S) C3S Summary GLOBAL LAND GAP ANALYSIS SELECTION OF SNOW AND ICE PARAMETERS SELECTION OF SPATIAL AND TEMPORAL RESOLUTION SELECTION OF OBSERVATION SYSTEMS SUMMARY OF GAP ANALYSIS Snow Cover & Fractional Snow Cover Snow Water Equivalent CLOUD COVER ANALYSIS SENTINEL-2 CLOUD COVER ANALYSIS MODIS AND SENTINEL-3 ANALYSES SUMMARY TECHNICAL SPECIFICATION OF SNOW & ICE MONITORING SNOW COVER & FRACTIONAL SNOW COVER LAYERS Narrative Description Technical Specifications General Advices REFERENCES ANNEX I.: QUESTIONNAIRES... I ii

4 TABLE OF TABLES Table 1: Overview of selected satellites and sensors monitoring snow and lake/river ice variables (IGOS) Table 2: Snow & Ice parameters vs type of sensors Table 3: Summary of advantages and disadvantages of monitoring SC and SWE by different methods Table 4: List of most desired user requirements regarding specifications of snow related parameters based on the Polar Expert Group meeting discussion Table 5: Table summarising status and gaps regarding selected snow parameters according to the Polar Expert Group meeting discussions Table 6: Potential contributions by current Sentinel and Metop as seen by the Polar Expert Group Table 7: List of in-situ datasets covering Europe (adopted from GCW Snow Watch in 2015) Table 8: List of satellite datasets covering Europe (adopted from GCW Snow Watch in 2015) Table 9: List of products provided by CryoClim project Table 10: Main characteristics and innovations in ERA5 with respect to ERA-Interim Table 11: Analysis showing the importance of monitoring of a selected snow and ice parameters as seen by the users Table 12: Analysis of spatial and temporal resolution regarding Snow Cover (SC) based on RS data Table 13: Analysis of spatial and temporal resolution regarding Snow Water Equivalent (SWE) based on RS data Table 14: Analysis of the means of obtaining information in order to monitor SC/FSC Table 15: Specifications of satellite data selected for analyses within the Snow Portal Table 16: Specifications (AOI, composite frequency) for the Sentinel-2 data analyses Table 17: Specifications (AOI, composite frequency) for the MODIS data analyses Table 18: Specifications (AOI, composite frequency) for the Sentinel-3 data analyses Table 19: Specifications (AOI, composite frequency) for the MODIS & Sentinel-3 data analyses Table 20: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Europe for different n- days composites for winter seasons: , , and the average of the two winter seasons Table 21: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Austria for different n- days composites for a winter season , , and the average of the two winter seasons Table 22: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Sweden for different n- days composites for a winter season , , and the average of the two winter seasons Table 23: Overview of a percentage of Snow, NoSnow and Cloud-covered pixels over Europe for winter season calculated for composites of different time length. The table compares results for data retrieved from different satellites: MODIS, Sentinel-3 and the combination of MODIS & Sentinel-3 data iii

5 TABLE OF FIGURES Figure 1: Overview of satellite missions by GCW Figure 2: Overview listing a qualitative assessment of current capabilities for monitoring cryosphere from space Figure 3: Snow Extent datasets being compared within the SnowPEx project Figure 4: Snow Water Equivalent datasets being compared within the SnowPEx project Figure 5: Graph showing the % of Cloud cover vs Cloud-free area over Europe for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data Figure 6: Graph showing the % of Cloud cover vs Cloud-free area over Austria for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data Figure 7: Graph showing the % of Cloud cover vs Cloud-free area over Sweden for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data Figure 8: Example maps displaying the average cloud-free cover (brown) vs cloud cover (grey) over Europe when different time length mosaic is created based on Sentinel-2A data. From left to right, and top to bottom: 1 day, 7 days, 14 days, 21 days, 1 month, 2 months, 3 months, and 6 months mosaics Figure 9: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the MODIS data Figure 10: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the Sentinel-3 data Figure 11: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the combination of MODIS & Sentinel-3 data Figure 12: Example maps displaying the average Snow (blue), NoSnow (green), and Cloud (grey) coverage over Europe when different time length mosaic is created based on the combination of MODIS and Sentinel-3 data. From left to right, and top to bottom: 1 day, 3 days, 7 days, 10 days, 14 days mosaic iv

6 LIST OF ABBREVIATIONS AATSR AMSRE AOI ASTER ATSR AVHRR CEOP CSC C3S DFSC DG DMSP D4SC ECMWF EEA EO ERA ERS ESA ETM EU FGGE FMI FP7 FSC GCOS GCW GEO GEOSS GTS IR JRC LIE MFSC MERIS MODIS NASA Advanced Along-Track Scanning Radiometer Advanced Microwave Scanning Radiometer- Earth Observing System Area of Interest Advanced Spaceborne Thermal Emission and Reflection Radiometer Along Track Scanning Radiometer Advanced Very High Resolution Radiometer Coordinated Enhanced Observing Period Copernicus Space Component Copernicus Climate Change Service Daily Fractional Snow Cover Directorate General Defense Meteorological Satellite Program Daily 4-classes Snow Cover European Centre for Medium-Range Weather Forecast European Environment Agency Earth Observation ECMWF Re-Analysis European Remote Sensing (satellite) European Space Agency Enhanced Thematic Mapper European Union First GARP (Global Atmospheric Research Program) Global Experiment Finnish Meteorological Institute 7 th Framework Programme Fractional Snow Cover Global Climate Observing System Global Cryosphere Watch Group on Earth Observations Global Earth Observation System of Systems Global Telecommunications System Infra-Red Joint Research Centre Lake Ice Extent Monthly Aggregated Fractional Snow Cover Medium Resolution Imaging Spectrometer Moderate Resolution Imaging Spectroradiometer The National Aeronautics and Space Administration v

7 NDSI NIR NPP NOAA NRT OLCI OLI PSTG R & D RBD RS SAR SC SE SEVIRI SLSTR SMMR SMOS SSM/I SSMIS SWE SYKE TIRS TM UN USGS VIIRS WCRP WFSC WIGOS WIS WMO Normalised Difference Snow Index Near Infra-Red National Polar-orbiting Partnership National Oceanic and Atmospheric Administration Near-Real Time Ocean and Land Cover Instrument Operational Land Imager Polar Space Task Group Research & Development River Basin District Remote Sensing Synthetic Aperture Radar Snow Cover Snow Extent Spinning Enhanced Visible and Infrared Imager Sea and Land Surface Temperature Radiometer Scanning Multichannel Microwave Radiometer Soil Moisture and Ocean Salinity Special Sensor Microwave/Imager Special Sensor Microwave Imager/Sounder Snow Water Equivalent Finnish Environment Institute Thermal Infrared Sensor Thematic Mapper United Nations U. S. Geological Survey Visible Infrared Imaging Radiometer Suite World Climate Research Programme Weekly Aggregated Fractional Snow Cover WMO Integrated Global Observing System WMO Information System World Meteorological Institute vi

8 This report builds on and is a carry-on of the Copernicus Service contract n. 3436/B2015/R0- COPERNICUS/EEA.56195: Task II.9: Report on the state of European national snow and ice cover services and Framework projects. Please see that report for reference. vii

9 1 OBJECTIVES The main objective of this report is to draft a specification of a new snow and ice monitoring service at the European level. In order to achieve that several steps have been taken. First, the selected snow and ice parameters were reviewed together with the various available observation systems. Second, a user requirements were collected for a future snow and ice monitoring service. User requirements are based on the opinion of individual users and key experts, and it also includes general ideas/ trends recorded during several previous snow and ice related activities which have performed a similar user requirement analyses. Third, a review of similar monitoring services is performed in order to avoid duplication in specifying the upcoming monitoring service. Based on the previous a gap analysis is performed reviewing the gaps in current monitoring services and the possible opportunities on how to complement them by using the resources of the current observing systems. Finally, a specifications of the future snow and ice monitoring service are given, together with general advices to be considered whilst setting up such a monitoring service. 1

10 2 SNOW AND ICE PARAMETERS: BACKGROUND Observation of the daily geographical extent of snow cover (SC) is essential because it enables inference of several first-order effects of snow on many Earth systems. It is used in many models as a fundamental control on hydrologic, atmospheric and ecosystem processes. Observations of Snow Water Equivalent (SWE) is also essential. SWE provides quantitative information about both mass and energy that defines the volume of water storage and establishes memory and first-order predictability for hydrologic and atmospheric systems. SWE is the principal snow observation needed for most water resource applications and it complements precipitation observations (IGOS). Snow properties are observed using a wide variety of instruments and systems on surface, and on airborne and spaceborne platforms. These observations are used and combined in many different ways, depending on application, required scales and accuracies, and the types of observations available. 2.1 SNOW COVER Regarding SC observations are reasonably mature but there are remaining difficulties in discrimination of snow and clouds. The spectral resolution, bandwidth and dynamic range of available optical sensors on both low Earth-orbit and geostationary satellites present fundamental constraints. Improved multi-spectral imaging is needed to address this problem. Observation of cloud motion from GEO sensors can improve the discrimination. In this case improved spatial resolution is needed to resolve textural differences (IGOS) Definition Snow Cover represents the surface of snow that accumulates on the ground in a given location, including that from snowfall, snowdrift, and avalanche Data availability The general preference of monitoring snow cover is at a high spatial resolution scale or better. Such information can therefore be obtained from e. g. Landsat-8 and Sentinel-2 satellites. Landsat-8: This satellite developed as a collaboration between NASA and the U.S. Geological Survey (USGS) launched in February 2013 consists of two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). These two sensors provide seasonal coverage of the global landmass at a spatial resolution of 30 metres (visible, NIR, SWIR), 100 metres (thermal), and 15 metres (panchromatic). Compared to the previous Landsat missions Landsat-8 collects data for two new bands: a coastal band (band 1) and a cirrus band (band 9). Sentinel-2: Sentinel-2 is an EO mission developed by the ESA as part of the Copernicus Programme. It consists of two identical satellites operating on opposite sides of the orbit: Sentinel-2A launched in June 2015, and Sentinel-2B launched in March Both satellites carry a multi- 2

11 spectral instrument (MSI) with 13 spectral channels (visible, NIR, SWIR) with a spatial resolution of 10 (visible, NIR), 20 and 60 metres. Temporal resolution of the satellite constellation is 5 days at the equator (i.e. less in higher latitudes). Another option of monitoring SC is by using the microwave sensors, such as SMMR, SSM/I, AMSR-E (passive microwave) or radar/ SAR data (active microwave). However, both come with a disadvantage. Passive microwave cannot provide information on high spatial resolution scale, and SAR can only detect snow when it is wet Current services a/ GlobSnow GlobSnow does not generate snow cover extent products since it focuses on the FSC. b/ CryoLand CryoLand also does not generate SC extent products since it focuses on fractional snow cover. Nevertheless, a snow cover extent of wet snow has been tested from multi-temporal SAR data, including Sentinel-1. At the CryoLand Portal 1 there is a product Multitemp SCAW Alps Radar providing detected wet snow from Sentinel-1. Each product shows regions of wet snow for the specific day of acquisition. Areas not detected as covered by wet snow can be covered by dry snow or be snow-free. Forested areas as well as areas affected by radar shadow or layover are masked. The spatial coverage varies from day to day depending on data availability. The service is delivered during the melting and summer season over the Alps. Demonstration products are available for the melting and summer season 2015, a pilot service starts in April The pilot service is part of the EU FP7 project SEN3APP demonstration. c/ ESSEM COST Action ES1404 Results of the project regarding Snow Cover are not published yet but should be considered in the future Conclusions and needs The SC extent is given in a binary format showing snow presence or absence. The fact that most products are based on medium resolution optical satellite data makes it more appropriate to represent the variable as FSC. It is possible to monitor high resolution SC by using Landsat-8 data, however, the potential was not fully exploited due to the satellite s low repeatability. Nevertheless, if used together with other data sources Landsat-8 could present an important data source. The potential of using Sentinel-2 data for SC monitoring has not yet been fully investigated. However, several studies has proven that it can provide a sound data source on a frequent basis thanks to its tandem operation, even though it is lacking thermal channels. On the other hand, Sentinel-2 has a dedicated cirrus band (Band 10, SWIR) in 60 metres spatial resolution. 1 CryoLand Portal: 3

12 According to Dr. Kari Luojus, the lack of snow products at high spatial resolution presents a major gap in snow cover monitoring at this moment. This information can be retrieved from both Sentinel-2 and/or Landsat-8 satellites. R & D projects concerning Sentinel-2 data could potentially find easier ways of funding because these could naturally fall under the EEA framework. If SC monitoring could be provided in high spatial resolution, the information on SC given in a binary format (snow presence/ snow absence) would be more useful and has more relevance on its own (even without the knowledge of FSC) than it has now. As explained in the introduction of this chapter the SAR sensors are not working on the optimal frequencies to detect dry snow. It is not known if any technical problems prevent SAR sensors to work in frequencies capable to monitor dry snow this could be an area to explore in future programs. Homogenized criteria on dataset format and metadata of all snow variables together within one portal at a European scale hosting and providing the data would facilitate the access to most users. This is an important user requirement that has been gathered from the reviewed questionnaires. This suggestion is applicable to all reviewed variables. 2.2 FRACTIONAL SNOW COVER Definition The fractional snow cover (FSC) is the percent snow cover (from 0 to 100% or from 0 to 1) in each pixel (Hall et al. 2005) Data availability Regarding Fractional Snow Cover (FSC) the data are available as follows: Globsnow: The ESA ERS-2 ATSR-2 and Envisat AATSR data are the main employed data sources. Data generated for the years o ERS-2: launched April 1995, end of mission September 2011 o Envisat: launched March 2002, end of mission May 2012 Cryoland: The product uses medium resolution optical satellite data from Terra MODIS. The first data available at their portal is from August 2014 o Terra: launched December Current services a/ GlobSnow The GlobSnow Snow Extent (SE) product portfolio includes maps of Fractional Snow Cover (FSC, range 0 100% or 0 1) on a geographical grid which covers the Northern Hemisphere between latitudes 25 N 84 N and longitudes 168 W 192 E. GlobSnow SE products are based on optical measurements in the visual-to-thermal part of the 4

13 electromagnetic spectrum provided by ERS-2/ATSR-2 ( ) and Envisat / AATSR ( ), so that a continuous dataset spanning 17 years is obtained. Three different versions of the dataset are available: v1.2 as an outcome of GlobSnow-1 ( ) and versions 2.0 and 2.1 as an outcome of GlobSnow-2. Starting from 2013, GlobSnow SE production has been relying on data provided by Suomi NPP VIIRS, with hemispheric daily coverage. The FSC is presents in four different formats: Daily Fractional Snow Cover (DFSC), snow fraction (%) per grid cell for all satellite overpasses of a given day. Daily 4-classes Snow Cover (D4SC), snow cover classified into four categories (0%-10%), (10%-50%), (50%-90%) and (90%-100%) per grid cell for all satellite overpasses of a given day. Weekly Aggregated Fractional Snow Cover (WFSC) for all satellite overpasses within a 7-day period based on aggregation of daily products. Available for each day based on a 7-day sliding time window giving the highest priority to the most recent observations. Monthly Aggregated Fractional Snow Cover (MFSC) for all satellite overpasses within a calendar month period providing the average, standard deviation, minimum and maximum FSC for the period. The products are based on the SCAmod method described in (Metsämäki et al. 2005). The semi-empirical reflectance model-based method originates from the radiative transfer theory and describes the scene reflectance as a mixture of three major constituents opaque forest canopy, snow, and snow-free ground which are interconnected through the apparent forest transmissivity and the snow fraction. SCAmod employs top-of-atmosphere reflectance acquisitions of ATSR-2/AATSR Band 1 ( nm) as input. Since the SCAmod reflectance model method relies on a single band, additional rules for detecting snow-free ground conditions were established in GlobSnow-2. These include the employment of Normalized Difference Snow Index (NDSI) and surface brightness temperature in determination of thresholds for assigning a pixel as snow-free. The further development of the algorithm also included the advanced consideration of snow-free ground reflectance. The datasets are available at the GlobSnow Data Access page 2 and information on the different products is explained in (Luojus et al. 2013). b/ Cryoland The Pan-European FSC is a homogenized product for large areas developed by ENVEO 3 in cooperation with the Finnish Environment Institute (SYKE 4 ). The product uses a medium resolution optical satellite data from Terra MODIS (Sentinel-3 in the future) and extends from 2 GlobSnow Data Access Page: 3 ENVEO: 4 SYKE: 5

14 Scandinavia in the north to northern Africa in the south, and from Portugal in the west to the Caspian Sea in the east (from 72 N/11 W to 35 N/50 E). The algorithm compensates for the effects of the forest and estimates the below-tree FSC. The product and its associated accuracy estimation per pixel is provided daily with spatial resolution (approx. 500 m). Operational FSC monitoring is based on single sensor approaches. The synergistic usage of the spectro-radiometer 'Ocean and Land Colour Instrument' (OLCI) and the 'Sea and Land Surface Temperature Radiometer' (SLSTR) on board of the future Sentinel-3 will provide a new data base enabling an improved snow monitoring and snow and cloud discrimination system. Processing lines for mapping fractional snow cover and improving the cloud screening based on these new sensors have been developed and tested by ENVEO using synergistically archived Envisat MERIS and AATSR data, which have very similar spectral and geometric characteristics as the instruments on-board of Sentinel-3. The resulting snow maps have a significantly improved spatial resolution, and the synergistic usage of the two sensors enables an improved snow and cloud discrimination, which is a critical issue for an operational snow monitoring service. The FSC data is available at the Cryoland Portal 5. The data is divided in three different formats: Daily FSC Pan European Optical: is a near real time product from Optical Satellite data providing daily Fractional Snow Cover with x (ca. 500 m x 500 m) pixel size for the Pan-European area, extending from 72 N / 11 W to 35 N / 50 E. The cloud covered pixels are masked over land. Daily FSC Baltic Optical: describes the snow-covered area over the Baltic area given as percentage (0-100%) of the land surface covered by snow with a spatial resolution of 500 m. The underlying method uses a transmissivity map (a parameter describing the passage of light back and forth through forest canopy) enabling the estimation of areas covered by vegetation (trees and tall scrubs). The estimate is given with an uncertainty of 15% percentage points on a single pixel scale. Daily FSC Alps Optical: This near real time product from Optical Satellite data provides the daily Fractional Snow Cover over the Alps and lowlands with x (ca. 250 m x 250 m) pixel size. Cloud covered pixels are masked. c/ ESSEM COST Action ES1404 Results of the project regarding FSC are not published yet but should be considered in the future Conclusions and needs FSC monitoring is based on the visual-to-thermal spectrum at a medium spatial resolution. 5 CryoLand Portal: neso1.cryoland.enveo.at/cryoclient/ 6

15 If the gap of high spatial resolution SC is filled by using e.g. information from Sentinel-2 and/or Landsat-8 data this could potentially also improve the information about FSC because these two products are closely related and they can complement each other. At this moment SAR technology allows for detection of wet snow only, however, it does not provide the capability of distinguishing between dry snow and soil. Therefore, when such a technology is developed in the future this will present a huge improvement in the current snow monitoring. 2.3 SNOW WATER EQUIVALENT The monitoring of SWE is critically important for many users and field areas. Nevertheless, ground-based SWE monitoring is not very consistent in Europe. SWE observations are rather sparse in many areas or the information is missing completely. SWE values show large variability depending on many different parameters, spatial variability being one of them. Therefore, it is not very effective to interpolate information from only a few isolated SWE observations to a bigger area because this can introduce a significant uncertainty of the interpolated values due to the lack of nearby supporting observations. The capabilities of SWE retrieval using available microwave sensors and algorithms are generally insufficient. This is due to the low resolution of the sensors and the complexly mixed signals resulting from sub-footprint variability in snowpack and land surface characteristics. Regional tuning of empirical algorithms has been successful in some cases but this approach has not been widely adopted and has some of the same risks of inconsistencies as groundbased networks. Satellite based SWE observations have greater potential than what is currently achieved. In order to improve SWE monitoring capabilities a more effective use of microwave and ancillary observations is needed Definition Snow water equivalent is the equivalent depth of liquid water that would result from complete melting of a snowpack (Singh, V. et al., 2011) Data availability SWE data are available as follows: Globsnow: Combining of space-borne passive radiometer data (SMMR, SSM/I and SSMIS) with data from ground-based synoptic weather stations. The first data available at their portal is from September o SMMR (sensor on board Nimbus-7 and Seasat satellites) Nimbus-7: launched October 1978, end of mission August 1994 Seasat: launched June 1978, end of mission October 1978 o SSM/I (sensor on board DMSP-F8 and DMSP F10-15 satellites) DMSP-F8: part of a series launched DMSP F10-15: part of a series launched o SSMIS (sensor on board DMSP-F16 DMSP-F19) 7

16 2.3.3 Current service a/ GlobSnow DMSP-F16 DMSP-F19: part of a series launched The GlobSnow SWE record based on a methodology by Pulliainen (Pulliainen 2006) and Takala (Takala et al. 2011) utilizes a data-assimilation based approach combining space-borne passive radiometer data (SMMR, SSM/I and SSMIS) with data from ground-based synoptic weather stations. The satellite sensors utilized provide data at K- and Ka-bands (19 GHz and 37 GHz respectively) at a spatial resolution of approximately 25 km. The SWE record is produced on a daily, weekly and monthly basis. SWE information is provided for terrestrial non-mountainous regions of Northern Hemisphere, excluding glaciers and Greenland. Due to the nature of the radiometer observations the SWE product is reliably detected in areas with seasonal dry snow cover. Areas with sporadic wet snow or a thin snow layer are not reliably detected and typically not present on the SWE product. The areas marked as snow free may thus include areas with occasional wet snow cover. SWE is presented in three different formats: Daily Snow Water Equivalent (Daily L3A SWE), snow water equivalent (mm) for each grid cell for all evaluated land areas of the Northern Hemisphere. Weekly Aggregated Snow Water Equivalent (Weekly L3B SWE), calculated for each day based on a 7-day sliding time window aggregation of the daily SWE product. Monthly Aggregated Snow Water Equivalent (Monthly L3B SWE) a single product for each calendar month providing the average and maximum SWE, calculated from the weekly aggregated SWE product. b/ Cryoland The SWE product is based on the combination of satellite-based passive microwave radiometer and ground-based weather station data applied in a SWE model. Nimbus-7 SMMR ( ), DMSP SSM/I (1987 to present) and Aqua AMSR-E are the main data sources. Non-mountainous areas within the pan-european domain are covered by the CryoLand implementation of the SWE service. The pan-european domain SWE maps are provided with 0.1 spatial resolution (approx. 10 km). The SWE (coarse resolution) data is available at the Cryoland Portal 6. The data, named daily_swe_paneuropean_microwave is a daily Pan-European SWE product based on the combination of satellite-based microwave radiometer (DMSP SSM/I from 1987 present) and ground-based weather station snow depth data provided by ECMWF. The product covers nonmountainous areas between 72 N/11 W and 35 N/50 E, with a pixel size of 0.10 x The product is provided daily with 2 days delay during the winter season, from the beginning of October until the end of July. 6 CryoLand Portal: neso1.cryoland.enveo.at/cryoclient/ 8

17 c/ ESSEM COST Action ES1404 Results of the project regarding SWE are not published yet but should be considered in the future Conclusions and needs SWE monitoring is based on satellite-based passive microwave radiometer combined with ground-based weather station data. As mentioned in the introduction the main limitation of using passive microwave radiometers is the coarse resolution (i.e. tens of kilometres) and the fact that there is no algorithm that is ideal globally, the basic algorithm must be tuned to individual land cover types (Hall et al. 2005). Monitoring of the SWE from space-borne data is limited to the wet snow only. This can be obtained by using SAR instruments. Monitoring of SWE in areas with dry snow is currently missing due to the previous and the fact that passive microwave only provides data in coarse resolution and such resolution is insufficient for SWE monitoring. 2.4 LAKE/ RIVER ICE In Europe the main negative implication of the lake ice is the risk to cause ice-jam flooding which can cause serious damage to infrastructure and property. In the sub-arctic regions lakes represent a major component of terrestrial landscape. They have the highest evaporation rates of any high latitude surface which makes their monitoring relevant for different purposes. STATUS OF OBSERVATIONS The extent measure defines a section of water body as either ice-covered or ice-free and from remote sensing data each pixel is usually coded in binary mode with a value of 1 or 0. Ground-based observations were once the most important source of information but there is a recent declining trend in the ground-based network. Satellite observation can provide information about some parameters no longer observed insitu. In-situ measurements are, however, still needed in order to validate remote sensing approaches and refine models. Climatically and hydrologically relevant parameters, such as the dates of the first appearance of ice, complete freeze-over, beginning of thaw when water body becomes free of ice are not available from remote sensing products. MODIS 500-m daily snow product is particular interesting for lake and river ice monitoring, however, extensive cloud cover and period of darkness in early winter in high latitudes limit its use. Active microwave, SAR and scatterometer date, as well as the higher frequency passive microwave may help during the freeze-up and break-up periods. Spatial resolution of most satellite sensors providing daily temporal resolution is too coarse to monitor most of the river ice parameters. 9

18 2.4.1 Definition The surface of ice that covers a lake/ river Data availability Regarding Lake Ice the data are available as follows: Cryoland: The Lake ice data is currently not available at the Cryoland Portal since, as a demonstration product, is still under evaluation or development. Since the products are generated from optical and radar images once the algorithm is consolidated it could be possible to go backwards to the time of the satellites operating period Current Status a/ GlobSnow Glob Snow does not include lake ice monitoring. b/ Cryoland Lake and river ice products for northern Europe are generated in near real time from optical satellite data and radar images. The primary lake / river ice products present the extent and concentration of lake ice, and ice cover on rivers. Lake Ice and River Ice Services offer information in four classes (full snow cover, partial snow/white ice, clear ice and open water). Lake Ice Extent (LIE) with optical Earth Observation (EO) Data product was developed. The method uses Terra/MODIS 250 m reflectance data as input and can be transferred to other optical satellite sensors. Later an improved automated processing line was developed and implemented. Automated pre-processing of optical EO data for the Lake Ice Extent product is carried out including cloud detection from MODIS/Terra data (500 m and 1 km channels) to create cloud mask. The MODIS/Terra 250m channel 2 ( nm) was used for lake ice interpretation. The initial validation of the product gave overall accuracies of 86% and 74% for two high-resolution datasets from Landsat TM and SPOT-4, respectively. For the Lake Ice Extent with SAR data, River Ice Extent and Flood Inundation Area products state of the art image processing algorithm is used to identify pixels that are covered by ice and pixels not covered by ice (binary classifier). River ice extent is a product derived from SAR images that gives a binary classification (ice/ open water) of river stretches. The classification algorithm for RIE is the same as the lake ice extent algorithm but using higher resolution SAR data (< 25 m and better). Glacier lakes maps are generated from high (Landsat, Sentinel-2) and very high (SPOT-5, Quickbird, Ikonos) resolution satellite imagery. The Lake ice data is currently not available at the Cryoland Portal 7 since, as a demonstration product, is still under evaluation or development. Three different products are described: 7 CryoLand Portal: neso1.cryoland.enveo.at/cryoclient/ 10

19 Daily LIE Baltic Optical: Describes the lake ice extent on the large lakes ( > 45 km², i.e. 500 pixels) and rivers over the Baltic Sea drainage basin derived from daily optical satellite data (currently MODIS Terra). View 10 day LIE Baltic Cloud free (a 10-day LIE Composite Cloudfree (Viewing Product)): This layer is a daily updated aggregated LIE map based on the most recent cloud free pixel information of the past 10 days of the daily Baltic LIE product. This layer is intended as a VIEWING service only. LIE Analysed Lakes Baltic (Areas analysed for Lake Ice occurrence): The lake boundaries have been derived from GlobCover dataset (due.esrin.esa.int/globcover/), where a 250m buffer towards the interior of the lakes is used to exclude areas of shallow water and mixed pixels of water and land. The spatial resolution of the underlying satellite data (MODIS Terra) and the product is 250m. C/ ESSEM COST Action ES1404 Results of the project regarding Lake Ice are not published yet but should be considered in the future Conclusions and needs At the moment, the Lake Ice products are based on optical medium spatial resolution data while high spatial resolution data is still under development. The gap on high spatial resolution datasets will be routed with the use of high (Landsat, Sentinel-2) and very high (SPOT-5, Quickbird, Ikonos) resolution satellite imagery announced by Cryoland. 11

20 3 OBSERVATION SYSTEMS Snow properties are observed using a wide variety of instruments and systems on surface, and on airborne and spaceborne platforms. A large number of spaceborne based snow products exist varying in: - Temporal resolution - Spatial resolution - Spatial coverage (global, European, regional) - Parameter monitored - Data source (in-situ, RS, model data) - Retrieval algorithm - Product provider (commercial, research) - Etc. SC and FSC are usually derived from passive optical sensor data (e.g. MODIS, (A)ATSR(2), AVHRR, SUOMI NPP VIIRS, SEVIRI, etc.) with a spatial resolution usually between 500 m and 5 km and are available in near-real time (NRT) at a daily/ weekly/ monthly temporal resolution. SWE is usually derived from passive-microwave radiometers (e.g. AMSRE, SSM/I, SSMIS, AMSR2, etc.) at a spatial resolution of km on a daily basis, bearing in mind only wet snow is being detected. Table 1 shows selected satellites and sensors used for monitoring the snow and lake & river ice variables (adopted from IGOS report). Table 1: Overview of selected satellites and sensors monitoring snow and lake/river ice variables (IGOS). Sensor type Satellites and Sensors Snow Lake and River Ice Radar Altimeter High-res (SAR) Low-res (Scat) Radar Radar High-res vis/ir Sentinel-3 ERS-2 Envisat CryoSat-2 Sentinel-1 ERS-2 SAR Envisat ASAR Radarsat 1, 2 TerraSAR-X ERS2-Wind Scat QuikScat MetOp-ASCAT Sentinel-2 SPOT 1-5 Accumulation Accumulation Accumulation Extent Motion Extent Extent Melt/Freeze Onset Floe Size Distribution 12

21 Landsat Meltponds ASTER Mid-res vis/ir AVHRR Extent Extent Landsat-TM/ETM+ MODIS VIIRS Poassive Microwave SSMI AMSR-E Extent Thickness Extent Snow thickness SMOS 3.1 IN-SITU Many in-situ observation networks worldwide have diminished or have been completely lost. This is due to sometimes difficult accessibility or the cost of maintaining these ground-based stations. However, those stations which are still operational provide valuable snow observations. Mostly the in-situ stations consist of a small network operated at local scales and often are heavily dependent on cooperative observers (usually volunteers) to collect and report observations. The most common parameter to be measured from the in-situ stations is snow depth. Far fewer SWE observations are available, however, for a detailed SWE monitoring the in-situ stations are essential. Usually in-situ based meteo stations result in a single snow measurement at individual point, whilst point observations are only representative of a small area. Different measurement tools and instruments are used with widely varying accuracy and precision (IGOS). A major gap regarding in-situ monitoring is due to the sparse and inconsistent network of meteo stations and due to different methods of data acquirement. A unified in-situ monitoring network collecting data of the same specifications over the whole of Europe would significantly improve the snow and ice cover monitoring. Most ground-based snow-observing systems exist for specific users and applications without larger purpose or oversight. Internationally accepted observation standards and guidelines exist but awareness of these is often low (IGOS). 3.2 SATELLITES Greater spatial coverage and sampling over larger areas (compared to in-situ stations) is provided by remote sensing (RS). Observations of SC from visible, near-infrared (NIR) and microwave sensors satellites have been used since The longest satellite-derived record (> 40 years) of weekly SC over the Northern Hemisphere is monitored by NOAA, the continuation is ensured by AVHRR, MODIS, SSM/I and AMSR-E. Satellite instruments are essential for delivering sustained, consistent observations of the global cryosphere. But no one all-encompassing sensor exists. It is rather the combination and 13

22 synthesis of data from different yet complementary sensors which is essential and underlines the critical importance of maintaining key synergic elements of the system. Satellites are key to extending local in-situ measurements. Cost of in-situ observations in polar and mountain regions is very high and satellite monitoring is able to address only a partial suite of key geophysical variables. Regarding SWE the satellite based capabilities are more limited. Microwave sensors appear to be ideal for this purpose, however, current sensors lack the optimal combinations of frequencies and resolutions. Passive microwave radiometers provide useful estimates of SWE but they operate with low spatial resolution and are poorly suited for mountainous areas. SAR observations overcome the resolution problem but current sensors operated at frequencies too low to be useful for most snow-packs. Space-based observations are the only viable means of filling gaps in the information acquired from ground- networks and can provide additional information not easily observable on the ground. The consistent, systematic nature of satellite remote sensing helps overcome some of the functional inconsistencies of surface-based observations (IGOS). In general terms, optical sensors observe snow-surface properties (with solar illumination, in cloud-free conditions), and are used for mapping SC extent (Bokhorst et al. 2016), particularly in the short-wave infrared band ( 1.6 µm) which allows snow/cloud discrimination (Hall et al. 2005). Passive Microwave sensors are sensitive to snow properties and operate independently from solar illumination with a weak sensitivity to the atmosphere (Bokhorst et al. 2016). Current satellite SAR sensors are not ideal for measuring snow cover since their usual bands X-band ( cm, GHz) or lower frequencies (longer wavelengths) are not generally useful for detecting and mapping thin, dry snow because the size of snow particles is much smaller than the size of the wavelength. Currently, only passive microwave sensors operate at higher frequencies than X-band (Hall et al. 2005). The main limitation of using microwave radiometers is the coarse resolution (i.e. tens of kilometres) and there is no algorithm that is ideal globally, the basic algorithm must be tuned to individual land cover types (Hall et al. 2005). Existing radar sensors which can provide information on SC with fine resolution are able to work only in the presence of wet snow (Bokhorst et al. 2016). The main handicap of monitoring the SWE with passive microwave sensors is its coarse spatial resolution. Takala et al., 2017 present an algorithm to improve the spatial resolution to approximately 5 km. However, in situ measurements are needed for this algorithm and therefore the use of this algorithm backwards in time depends on the availability of in-situ data. Table 2 shows the snow and ice parameters in regards to the sensor type. 14

23 Table 2: Snow & Ice parameters vs type of sensors. Snow Cover & Lake Ice Type of sensor Sensor Advantages Sensor Disadvantages Optical High Spatial Resolution Limited by solar illumination and atmosphere conditions Passive Microwave Radiometer Operate independently of solar illumination and atmosphere conditions Coarse Spatial Resolution SAR Operate independently of solar illumination and atmosphere conditions Limited to wet snow High Spatial Resolution Snow Water Equivalent Passive Microwave radiometer Operate independently of solar illumination and atmosphere conditions Coarse Spatial Resolution Synthetic Aperture Radar (SAR) SAR technology obtains data at high resolution and has an all-weather and all-seasons monitoring capability which makes it in many ways the most powerful tool for observing the cryosphere. Example of SAR missions are Radarsat-2 and TerraSAR-X which are both commercial satellites, and Envisat Advanced SAR (ASAR). Parameters monitored by SAR: - Ice velocity tracking - Mapping ice sheet facies and margins - Detecting and tracking icebergs - Detailed sea-ice information - Mapping fast ice - Mapping the onset and extent of ice melt and refreeze - Detection and monitoring of river and lake ice break-up - SWE: further research needed 15

24 3.2.2 SAR Interferometry (InSAR) Sentinel-1 satellite with a C-band SAR mission in order to ensure continuity of existing ESA data sources, i.e. ERS-1, ERS-2, and Envisat. Parameters monitored by InSAR: - Largescale estimates of ice discharge - Distinguishing glacier and ice-sheet thinning caused by changes in ice flow - Measurements of ice sheets, ice caps and glaciers Passive Microwave The parameters monitored by this technology: - Terrestrial snow cover areal extent - Terrestrial snow cover depth - Terrestrial snow cover wet/dry state - Global sea ice concentration - Global sea ice extent - Ice season length - Snow depth on sea ice - Ice temperature - Maps of surface melt onset and refreeze Example mission: SSM/I Altimeters The parameters monitored by this technology: - Benchmarks of ice-sheet change - Monitoring sea ice thickness - Ice sheet DEM (combining satellite radar and laser altimetry data) Radar Scatterometers Low-resolution radars which can complement SAR thanks to their broader swath and more frequent coverage. In terrestrial regions a major challenge in remotely mapping snow cover is the identification of contributions to the measured signal from vegetation, snow and underlying soil which is not possible using a single-frequency scatterometer alone. A potential help would be a scatterometer operating at multiple frequencies and a dual-frequency radar altimeter, e.g. Envisat. The parameters monitored by this technology: - Terrestrial snow cover - Sea ice snow cover 16

25 - Large iceberg tracking - Studies of ice sheet near-surface characteristics - Surface melt/freeze detection Visible to Thermal Infrared High resolution optical sensor is fundamental for mapping and monitoring cryosphere. Unlimited access to archived cryospheric data and continual acquisition of future scenes is crucial in key regions. 3.3 AIRBORNE Equally important are surface and airborne observations because they provide key data which cannot be measured from space at the moment. They also provide more detailed information in critical areas and observations which can be used for calibration and validation of satellite retrievals. Airborne measurements of naturally occurring terrestrial gamma radiation can be used to observe SWE along flight line transects. Gamma radiation emitted from isotopes in mineral soil is measured over both snow covered and snow-free area. The attenuation of gamma radiation by water in any phase is well known. However, this approach can only be used by low-flying aircraft and cannot be applied to spaceborne aircrafts because the radiation signal is fully attenuated by the tropospheric water column. 3.4 MODELLING, DATA ASSIMILATION, REANALYSIS The operating resolution of most snow and land-surface modelling has far surpassed that of microwave sensors with significant capability to observe SWE and snow depth. Large-scale Hydrometeorological models operating at 1 5 km resolution are now common and further resolution improvements are expected. However, high-frequency microwave sensors necessary to observe SWE and snow depth have not improved comparably which significantly limits their use in terrestrial modelling applications (IGOS). 3.5 DATA ASSIMILATION, DATA MANAGEMENT An example model of the data integration engine for the Cryosphere Observing System is the WCRP CEOP (Coordinated Enhanced Observing Period) which has been accepted as the main water and energy cycle data procession engine for GEOSS. Data sets are produced by using four-dimensional data assimilation of satellite and in-situ observations supplemented by calibration and validation on the base of high quality measurements at a set of reference stations. Model output for the area around reference stations is merged with local observations and integrated with high-level satellite products. In this case data products come from several different organisations and data integration and archiving is done by another organisation. CEOP could be used as a prototype system as it demonstrated state of the art capabilities of merging satellite data, surface based observations, and model output to produce useful hydrological cycle data products (IGOS). 17

26 3.6 SUMMARY OF OBSERVATION SYSTEMS A general recommendations regarding observing systems on their improvement are listed below. IN-SITU MONITORING: IGOS report: A coordinated plan for surface-based snow-observation networks needs to be developed at national and international level Need for improved consistency in observing methods, reporting standards, improved data exchange GlobSnow White Paper: Existing networks should be strengthened, maintained and coordinated on a national, regional and global level (e.g. GCW Snow Watch) Common reference sites in varying regions with sustained and standardized observing programs for snow variables should be established (e. g. GCW CryoNet) SPACEBORNE MONITORING: Retrieval of snow parameters over all types of terrain (especially mountainous regions) is challenging when using operational NRT satellite systems. But especially in mountain regions the information on snow/ice parameters is vital to be provided in high spatial and temporal resolution. Satellite data is key to extending local in-situ measurements, but there is no all-encompassing sensor, therefore the combination and synthesis of data from different but complementary sensors is essential. IGOS report: Capability of satellite observation needs improvement Development/validation of satellite remote sensing techniques (including validation of existing products), support of new systems and algorithm development is required High-frequency active and passive microwave observations (which suit well SWE observing) have low spatial and spectral resolution, thus improved instruments with higher spatial and spectral resolution are required High-frequency (Ku, X-band) SAR should be considered a priority for global SWE observation Research and development of algorithms and new sensors to measure SWE needed Mapping ice on lakes and rivers require a finer spatial resolution 18

27 SAR IGOS report: Further research needed to realise the retrieval of SWE from SAR because SAR is the only instrument capable of mapping wet snow cover at fine spatial resolution required in mountainous terrain Optimal sensor for lake and river ice monitoring but development of operational methods based primarily on the use of high-resolution SAR imagery is needed GENERAL IGOS report: Techniques to merge in-situ and satellite measurements required Integrated multi-sensor data fusion and global analysis systems blending snow observations from all sources must be improved The ideal snow observing system would use observations from all relevant sources in coherent, consistent high-resolution analyses Improved algorithms for the objective, optimal combination of snow observations from widely disparate sources must be developed In-situ observations of freeze-up and break-up regarding lake and river ice need to be compared with satellite-derived time series, this would ensure continuity in the transition between the in-situ and satellite observations Potential of passive and active microwave data to map lake and river ice parameters needs to be examined. The advantages and disadvantages of different observation systems in regards to the monitoring of SC, SWE and lake/ river ice are listed in Table 3. 19

28 Table 3: Summary of advantages and disadvantages of monitoring SC and SWE by different methods. IN-SITU SNOW COVER SNOW WATER EQUIVALENT LAKE ICE ADVANTAGES: Provides more detailed information in critical areas (e.g. mountainous regions) Provides observations which can be used as validation/calibration of satellite retrievals DISADVANTAGES: Cost of observations very high, especially in polar and mountain regions Inconsistent network, lack of consistency in reporting standards In-situ stations expensive to operate Often dependent on volunteers Point measurement only representative to a small area Different measurement tools and instruments are used with widely varying accuracy and precision A coordinated plan for in-situ networks needs to be developed at national and international level ADVANTAGES: DISADVANTAGES: The most precise information on SWE Recent declining trend in the surface-based DISADVANTAGES: networks Only few SWE observations available In the context of large spatial variability interpretation of a few isolated snow observations is very difficult The lack of nearby supporting observations increases the uncertainty of available observations Sparse observations limit interpretation to large regions and do not resolve important local-scale variability 20

29 SPACEBORNE GENERAL ADVANTAGES: Sustained, consistent observations High spatial coverage and sampling over large areas Key to extend local in-situ measurements and fill gaps in surface networks Provides the only means for timely and complete observations of the global snow cover DISADVANTAGES: Not a single all-encompassing sensor exists Can only obtain partial suite of key geophysical variables (especially in mountainous areas) Capability of satellite observation needs improvement (mainly in spatial and temporal resolution) ADVANTAGES: ADVANTAGES: Long record (> 40 years) of weekly SC over Northern hemisphere The satellite observations have greater potential than is currently achieved Spaceborne observations of SC are reasonably mature Advancements in retrieval methods need to use microwave and ancillary observation DISADVANTAGES: more effectively Successful identification of contributions to the measured signal from vegetation, snow, and underlying soil remains a challenge (solution could be a scatterometer operating at multiple frequencies and a dual-frequency radar altimeter, e.g. Envisat) Difficulties in discrimination of snow and clouds DISADVANTAGES: Limited capabilities SWE obtained from spaceborne sensors require sound validation Algorithms require regional tuning to account for variable landscape and physical properties effects Algorithms for regional tuning has been successful in some cases but this approach has not been widely adopted and has a risk of inconsistencies ADVANTAGES: Can provide some of the parameters no longer observed in-situ DISADVANTAGES: In-situ data needed to validate the RS approaches and refine models Climatically and hydrologically relevant parameters, such as the dates of the first appearance of ice, complete freeze-over, beginning of thaw, when water body becomes free of ice are not available from remote sensing products Spatial resolution of most satellite sensors providing daily temporal resolution is too coarse to monitor most of the river ice parameters 21

30 OPTICAL ADVANTAGES: High spatial resolution DISADVANTAGES: Limited by solar illumination and atmosphere conditions DISADVANTAGES: MODIS 500-m daily snow product is particular interest for lake and river ice monitoring, however, extensive cloud cover and period of darkness in early winter in high latitudes limit its use SPACEBORNE: PASIVE MICROWAVE SAR ADVANTAGES: Operates independently of solar illumination and atmosphere conditions DISADVANTAGES: Coarse spatial resolution ADVANTAGES: High spatial resolution Operates independently of solar illumination and atmosphere conditions DISADVANTAGES: Limited to wet snow ADVANTAGES: Operates independently of solar illumination and atmosphere conditions DISADVANTAGES: Low spatial and spectral resolution Poorly suited for mountainous areas ADVANTAGES: Adequate spatial resolution The only instrument capable of mapping wet snow cover at fine spatial resolution required in mountainous terrain DISADVANTAGES: Operate at too low frequencies to be useful for most snow-packs Further research needed to realise the SWE retrieval from SAR ADVANTAGES: Active microwave, SAR, and scatterometer data, as well as the higher frequency passive microwave may help during the freeze-up and break-up periods Optimal sensor for lake and river ice monitoring thanks to its higher spatial resolution and the ability to penetrate through cloud and monitor during darkness MODELLING ADVANTAGES: Large-scale Hydrometeorological models operate at 1 5 km resolution DISADVANTAGES: Limited by the lack of detail information from microwave sensors 22

31 AIRBORN E ADVANTAGES: Can obtain information on SWE based on measuring terrestrial gamma radiation 23

32 4 USER REQUIREMENTS- UPDATE The objective of the following chapter is to provide an update of the situation regarding monitoring of snow and ice parameters in Europe. An important part on this topic was already performed and reported in the previous Task II.9 (see the deliverable report for more details). While Task II.9 focused on collecting information from the different National Services in European Countries in this report we have extended the analysis to some Research Centres, EO Companies, Universities and Protected Areas in Europe. Also, two important European Union Directives related to water have been analysed from the perspective of how a future Snow and Ice Monitoring System in Europe could help to accomplish their aim: The Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks and the Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. More user requirements were collected from key experts and user requirements recorded during past activities were also reviewed, e.g. IGOS Cryosphere Theme Report, Snow Watch, SnowPEx, etc. 4.1 USER REQUIREMENTS: QUESTIONNAIRES FROM INTERNATIONAL USERS New Survey In order to complement the information supplied in the previous report (Task II.9) by the different National Meteorological Institutes in the European Countries members of the EEA a list of partners at the European funded H2020 Ecopotential Project 8 was contacted, as well as, a list of International River Basin districts (RBDs 9 ) from the EEA. Ecopotential project partners feedback complements the National Meteorological Institutes since the project focuses its activities on a targeted set of internationally recognized Protected Areas, blending Earth Observations from remote sensing and field measurements, data analyses, and modelling of current and future ecosystem conditions and services. A total of 38 between public or private Research Centres and Universities involved in Ecopotential project received the questionnaire: Consiglio Nazionale delle Ricerque (CNR) - Italy; Università del Salento -Italy; The Research Institute for Earth Observation (EURAC)- Italy; Centro Superior de Investigaciones Científicas (CSIC) -Spain; Helmholtz Centre for Environmental Research (UFZ) -Germany; Universität Bayreuth -Germany; German Aerospace Center (DLR) -Germany; Centre National de la reserche scientifique (CNRS) -France; University of Leeds -United Kingdom; Environment Systems -United Kingdom; University of Bucharest - Romania; Instituto de Ciencias, Tecnologias e Agroambiente da Universidade do Porto (ICETA) -Portugal; Instituto Superior Técnico de Lisboa (IST) -Portugal; Centre for Research and 8 Ecopotential Project: 9 RBD: 24

33 Technology (CERTH) -Greece; Foundation for Research and Technology (FORTH) -Greece; École Polytechnique Fédérale de Lausanne -Switzerland; Hydrobiological Institute Ohrid - Macedonia; Council for Scientific and Industrial Research (CSIR) -Zambia; Instituto Superiore per la Potezione e la Ricerca Ambientale (ISPRA) -Italy; Politecnico de Milano (POLIMI) -Italy; Ecological and Forestry Applications Research Centre (CREAF) -Spain; Universitat Autònoma de Barcelona (UAB) -Spain; Universidad de Granada -Spain; Austria Environment Agency - Austria; Universität Potsdam -Germany; Museum für Naturkunde Berlin -Germany; Tour du Valat (Institut de reserche por la conservation des zones humides méditerranées)-france; Deltares Institute -The Netherlands; Arathos Technologies- Greece; Starlab company -Spain; German Centre for Integrative Biodiversity Research (idiv) -Germany; Netherlands Institute for Sea Research (NIOZ) -The Netherlands; Centre d'etudes Spatiales de la BIOsphère -France; London School of Economics -United Kingdom; United Nations Environment Programme (UNEP) Vienna Office -Austria; ETH Zurich University -Switzerland; Environmental and water Agency of Andalucia -Spain and the University of Geneva -Switzerland. The questionnaire was also sent to nine Mountain Ecosystems included in the Ecopotential project: Gran Paradiso -Italy; Northern Limestone NP -Austria; Peneda-Gerês -Portugal; Sierra Nevada -Spain; Bayerischer Wald -Germany; Lakes Ohrid/Prespa -Former Yugoslav Republic of Macedonia; High Tatra Mts.-Poland/Slovakia; Hardangervidda -Norway; Abisko -Sweden and La Palma Island - Spain. Finally the questionnaire was received by the following International River Basin districts (RBDs 10 ) from the EEA: Danube River Basin Permanent Secretariat 11 ; Rhône River Basin; Po River Basin Bacino di Po 12 ; Rhine River Basin International Commission for the Protection of the Rhine 13 and the Water Framework Directive implementation 14 in Norway Gathered information A total of seven Ecopotential partners answered the questionnaire, out of which five are using remote sensing data to monitor the snow: The Remote Sensing Lab (Foundation for Research and Technology)- Greece; The University of Norway; Starlab company in Barcelona- Spain; The University of Granada- Spain; and The Research Institute for Earth Observation at Bolzano- Italy. The other two partners are working with meteorological stations and in-situ data only: The National Park Kalkalpen- Austria and the Environmental and Water Agency of Andalucia (AMAYA) - Spain. One issue to consider in the meteorological stations and in-situ data gathering in mountain terrains is the complexity and cost derived from the difficulties presented by the terrain. The most commonly measured product is the Snow Cover, followed by the Snow Water Equivalent. AMAYA, despite not using remote sensing data is measuring the snow albedo and the snow sublimation and melting to feed hydrological models. The Remote Sensing Lab is also interested in the runoff from the snow. 10 RBD: 11 Danube River Basin Permanent Secretariat: 12 River Basin Bacino di Po: 13 International Commission for the Protection of the Rhine: 14 Water Framework Directive implementation in Norway: 25

34 The satellites/sensors used are Terra and Aqua MODIS in the optical, and Envisat ASAR and Sentinel-1 in microwave. As future requests, there is a consensus in the need to unify criteria about the data and to centralize all data in a portal. Also, higher spatial resolution products (i.e. Sentinel-2) an integration of SAR and Optical products to estimate water resources from snow are requested. Regarding the questionnaires sent to the EEA International River Basin districts (RBDs 15 ) no questionnaire was received back. Questionnaires received can be found in Annex I Summary of National Services & Other Users Requirements As expected the user requirements significantly exceed what is currently technically feasible. A new polar dedicated mission will solve some of the issues and requests, however, by the time it is operational users will have to make do with what is currently available and accept related limitations. Based on the collected questionnaires received back from the national services within EEA Member and Cooperating countries, and from organisations involved in the Ecopotential project the following can be concluded: Absolute majority of European countries performs their own national level snow and ice monitoring for national purposes. Different parameters are being monitored. Out of 43 questionnaires SWE was reported to be measured in 15 cases (about 1/3 of users), about ¼ of respondents reported Snow Cover monitoring, FSC was reported only in 4 cases, Lake Ice in 2 cases. The majority of monitoring is based on in-situ measurements, automatic weather stations or meteo-stations. Remote sensing at the national level is being used in some of the cases (optical data, radar). By some of the other users it is seen as a strong instrument but cannot be utilised because of resource purposes. SWE in most of the cases is based on in-situ data measurements. SC monitoring is performed differently in each case using various methods (in-situ, meteo-stations, radar, and optical data). Some of the countries has a long data tradition with records over 100 years, some countries started the monitoring more recently. Absolute majority of countries is concentrating on monitoring the area of their own country, but would find it useful to have access to data from other (especially neighbouring) areas. Spatial resolution varies a lot amongst the national services from under 1 km resolution (when a dense in-situ network is established) to 50 km. The users of snow & ice data are mostly the following: 15 RBD: 26

35 o Hydrological services, authorities, o Water management, flood forecasting o Hydropower companies o Meteorology & forecasting, numerical weather predictions o Climate analysis o Avalanche warning systems o Scientists, research, universities o Road maintenance services o Engineering, projecting buildings, roof maintenance o Agriculture, forestry o Insurance companies o Seafarers o Winter tourism o Etc. The most common drawbacks countries are experiencing (mostly related to in-situ monitoring which is the most common practise): o In-situ station network is not dense enough (especially in mountain regions which are often inaccessible) o Maintenance of meteo-stations is costly o Devices are not resilient enough to perform in harsh weather conditions o Lack of unified approach for monitoring in both mountainous and forest areas o Optical satellite data limited by cloud cover o Low temporal and spatial resolution regarding remote sensing data Best practices reported by national services: o Integration of all available data (model, in-situ, radar, optical data) o Existing WMO standards seems to be efficient Besides what each country monitors themselves the following parameters were mentioned to be desired to be monitored: o SWE o Liquid water content o Snow cover o Snow temperature o Snow density o Snow depth o Snow quality from RS data o Surface roughness o Spectral reflectivity o River ice o Sea ice 27

36 o Real time avalanche activity o Snowflake size o Snowfall intensity o Etc. Most of the respondents replied positively to the question of whether or not they would find European snow & ice cover monitoring service useful. The case of those few who replied negatively is that these were local bodies concentrating on local monitoring. If there was a European snow & ice cover monitoring service the countries would be interested in the following: o Easy and free access to the data in a unified format o Data stored in one central database o Optimise data processing, management and exchange o Homogenous data consistent in space and time o Consistent and robust methodology o Availability of comparative data from other countries o More direct interaction with service provider o Near-real time access to European wide station data o Data accessibility would allow for: testing methodologies in different climates support for validation and calibration o Daily product derived from a combination of in-situ and RS data o NRT satellite-derived SC and SWE daily 4.2 EUROPEAN POLICIES Flood Directive The Floods Directive reference is: Parliament, E., & Committee, S. (2007). DIRECTIVE 2007/60/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 October 2007 on the assessment and management of flood risks, (2455), Different cases are considered where ice and snow monitoring through remote sensing could contribute to the Directive targets: - Ice-jam in rivers: According to (Lindenschmidt et al. 2016) high run-off generation in rivers may happen due to snowpack melting. This high run-off may result in ice breakup followed by secondary ice cover consolidations that can lead to river ice jamming and subsequent flooding. High spatial resolution EO data for Ice cover measurement and ice cover break-up detection in rivers and lakes could be useful to predict the ice- 28

37 jams. - Snowmelt-related flood risk: different authors mention this topic i.e. (Graybeal & Leathers 2006); (Grippa et al. 2005). According to (Dziubanski & Franz 2016) an accurate snow and ice monitoring service could generate products that combined with hydrologic prediction models would help to estimate future snowmelt, water supplies, and also flooding potential. - Permafrost melting: A topic mentioned by many authors i.e. (Jepsen et al. 2013) or (Sakai et al. 2012). High latitude regions are experiencing the greatest climate warming and the increase in air temperature thaws permafrost which underlays 25% of the northern hemisphere. After ice wedge melting due to the increased air temperature, the melting water gushes from the ground. Then, the melting water flows into nearby rivers, leading to floods. Also, the permafrost melting will cause a problem of ground subsidence. EO SAR data could help to detect when the permafrost thaws thanks to the changes in the dielectric constant of the soil. - Sea-level rises due to melting glaciers and ice sheets: this is a topic widely discussed at the scientific community in a Global Warming scenario i.e. (Chen 2006); (Howat et al. 2007). According to (Kerr 2013) some glaciers might not be losing ice or might even be gaining it but all 19 glacierised regions of the world lost ice between 2003 and 2009 contributing to global sea-level rise. The study used the two satellites of the Gravity Recovery and Climate Experiment (GRACE) that measured the minuscule changes of gravity as ice melted away beneath them. The other satellite used was the Orbiting Ice, Cloud, and Elevation Satellite that uses a laser to measure changing ice height and therefore volume Water Framework Directive The Water framework directive reference is: European Community, Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official Journal of the European Parliament, L327(September 1996), pp The water framework directive is quite general. Those paragraphs of the directive where snow and ice monitoring could help for a better compliance of the directive are listed: - The purpose of the Directive: to establish a framework for the protection of inland surface waters, transitional waters, coastal waters and groundwater which contributes to mitigating the effects of floods and droughts and thereby contributes to: the provision of the sufficient supply of good quality surface water and groundwater 29

38 as needed for sustainable, balanced and equitable water use. - Article 5 Each Member State shall ensure that for each river basin district or for the portion of an international river basin district falling within its territory: an analysis of its characteristics is undertaken (Snow & Ice are essential variables of a river basin and their monitoring improves its mapping). - Article 7 Member States shall identify, within each river basin district: - Article 8 all bodies of water used for the abstraction of water intended for human consumption providing more than 10 m 3 a day as an average or serving more than 50 persons, and those bodies of water intended for such future use. Monitoring of surface water status, groundwater status and protected areas. Member States shall ensure the establishment of programmes for the monitoring of water status in order to establish a coherent and comprehensive overview of water status within each river basin district. (Similar as in Article 5, Snow & Ice are essential variables of the water status in a river basin). - Article 13 River basin management plans Member States shall ensure that a river basin management plan is produced for each river basin district lying entirely within their territory. River basin management plans may be supplemented by the production of more detailed programmes and management plans for sub-basin, sector, issue, or water type, to deal with particular aspects of water management. - ANNEX II Surface Waters Characterization of surface water body types Member States shall identify the location and boundaries of bodies of surface water and shall carry out an initial characterization of all such bodies in accordance with the following methodology. Member States may group surface water bodies together for the purposes of this initial characterization. The surface water bodies within the river basin district shall be identified as falling within either one of the following surface water categories rivers, lakes, transitional waters or coastal waters or as artificial surface water bodies or heavily modified surface water bodies 30

39 (It may be considered there is a gap in the directive since there is no reference to the snow cover) Summary of European Policies Based on the two reviewed European policies related to water (Flood Directive, Water Framework Directive) the following would be potentially useful in the land snow & ice monitoring: Flood Directive SWE monitoring in order to help predicting snowmelt-related flood risk River and lake ice monitoring to help predicting ice-jams in rivers Permafrost thaw monitoring by the use of EO SAR data Water Framework Directive According to the WFD an analysis of the characteristics of each River Basin District needs to be undertaken. Snow and ice are essential variables of a river basin and their monitoring could improve the river basin mapping. The fact that snow and ice are not mentioned in the WFD can be considered as a gap in the directive itself. 4.3 KEY EXPERTS Several European experts on snow & ice were contacted in order to obtain opinions and advices on the new European snow & ice monitoring service which were at the end collected from two experts: Dr. Thomas Nagler (ENVEO, Project Coordinator of CryoLand project) Dr. Nagler considers the following gaps/ opportunities which could be addressed by the future snow and ice monitoring service: High resolution daily snow product for Europe, e.g. from Sentinel-2 or Landsat 8 (although Europe does not have a complete daily coverage) Melt snow extent from Sentinel-1 for mountain regions Synergistic snow product for mountain regions using Sentinel-2A/B and Sentinel-1 A/B Glacier mapping service for Europe (Alps, Scandinavia, etc.) based on Sentinel-2/ Landsat-8: time series of snow on glaciers for water management, hydrological applications. Dr. Kari Luojus (FMI, Technical Manager of GlobSnow project) 31

40 The findings and conclusions obtained during the GlobSnow project related to near-real time monitoring service are listed below: Both the information on Snow Extent (fractional information required) and Snow Water Equivalent on a timely basis for European domain is needed by the end user community. The information on SE and SWE can be acquired for pan-european scale with the existing satellite sensors but there are significant differences in retrieval accuracy based on ESA GlobSnow and preliminary results of ESA SnowPEx assessments. For a pan-european outlook there is a need to choose a retrieval approach that has been established for a continental scale, not a regionally tuned one. The chosen services should preferably have a strong basis on prior research conducted and should rely on established (and published) mature retrieval approaches. There are several prior European initiatives that have led to state-of-the-art approaches on both SE and SWE monitoring. Utilization of the results from these earlier initiatives is strongly recommended. There should be a focus on utilizing the upcoming Sentinel satellite series in conjunction with the existing space-based assets. Fusion of SE and SWE -based information to overcome the sensor limitations of each respective approach should be investigated and encouraged for future services. 4.4 IGOS CRYOSPHERE THEME REPORT (2007) Integrated Global Observing Strategy (IGOS) is a strategic planning process initiated by a partnership of international organisations concerned with the observational component of global environmental change issues. The main objectives are to address how well user requirements are being satisfied by the existing observations systems and how they could be met more effectively in the future through better integration and optimisation of satellite, airborne, and in-situ observation systems. The report does not propose to establish a new cryosphere observing system but rather proposes measures to develop and coordinate cryospheric components of the World Meteorological Organisation s (WMO) Integrated Global Observing System, the Global Climate Observing System (GCOS), the Global Ocean Observing System (GOOS), the Global Terrestrial Observing System (GTOS), and other systems in order the set of cryospheric products to be developed meets most identified user requirements. It proposes arrangements to ensure the existing cryospheric data and products are known, available and accessible. Some of the information from this report are included in chapters 2 and 3 of this report. 4.5 POLAR REGIONS Even though this report should focus on the situation over the whole Europe it is important to mention a unique and extremely sensitive component of the Earth which is represented by 32

41 polar regions. This area refers mainly to Arctic and Antarctic but it also partly covers the far north part of Europe. Therefore, it is important to mention the current status of those areas and the plans for future. The following initiatives were undertaken regarding this topic Polar Space Task Group (PSTG): Strategic Plan: The Polar Space Task Group was established in 2011 and operates under the auspices of the World Meteorological Organisation (WMO) Executive Council Panel of Experts on Polar and High Mountain Observations, Research, and Services (EC-PHORS). It seeks to coordinate the efforts of space-faring nations in the joint endeavour of providing scientific information about the polar regions and the cryosphere in support of science and applications. Strategic priorities for regarding snow: - Assure continuity in routine continental scale monitoring of Snow extent and SWE - Plan SAR data as complement to passive microwave (AMSR-E, AMSR2) and > 500 m optical data (MODIS, Sentinel-3 OLCI, SLSTR) for continental scale Snow extent and SWE, and in Alpine regions and rugged topography where other methods fail - Establish < 3 days repeat SAR monitoring of European Alpine region and other selected mountain regions during seasonally-limited snow melt time window - Establish common polarization/mode observation strategy between SAR missions - Demonstrate routine snow melt data processing - Pilot a snow melt service - Expand temporal/spatial revisit to operationalize services Polar View and Polaris Another activity focused on Polar regions is led by Polar View team. Polar View is a global organisation providing leading-edge satellite based information and data services in the polar regions and the cryosphere. In 2015 Polar View group was awarded a contract by the ESA for the Polaris Project which will guide the development of the next generation of space infrastructure to support both scientific and operational information needs in the rapidly evolving Polar regions. In April 2016 a Summary report was prepared on user needs and high-level requirements for the next generation of observing systems for the polar regions. Regarding Snow parameters the required information is: Snow extent, Snow structure/age, Snow depth, Snow freeze-thaw, Snow surface state/albedo, Snow water equivalent, Snow chemistry/particulates. Regarding River and Lake Ice the requested parameters are: Ice thickness, Ice extent, Ice structure/age, Ice freeze-thaw, Ice dammed lakes and rivers. Within the project a network of service providers delivered regional satellite snow mapping products for Central Europe, Alps, Baltic Region and Scandinavia. This activity, however, is focused on the future missions and tailor those new future satellite missions to the user needs. 33

42 4.5.3 Copernicus Polar Mission The European Commission (EC) responsible for the Copernicus programme has initiated an action with the objective of gathering user requirements for the Next Generation of the Copernicus Space Component (CSC). This is based on the joint Communication by the EC and the High Representative of the Union for Foreign Affairs and Security Policy issued on 27 April 2016 to the European Parliament and the Council proposing An integrated European Union policy for the Arctic. Based on the user requirements collected in the recent years it was concluded that a dedicated Polar and Snow satellite mission with highly elliptical orbit will bring an answer. In order to gather, examine and consolidate user requirements related to this matter and confront these with major gaps to be potentially addressed by the Next Generation of the CSC a workshop was held in Brussels in June In the second step a Polar Expert Group was established which met up twice in spring 2017 in order to further specify the user requirements for the new polar mission. First step: Initial report (June 2016) The following was stressed out: - The importance of monitoring seasonal snow cover (for water cycle, water resources, energy and radiation budget) - The crucial role played by snow and ice processes for the improvement of land surface models and of climate models - The need to monitor snow cover and its links to permafrost evolution and carbon exchange - The importance of in-situ measurements for some critical parameters, such as SWE, river discharge estimates, snow height, etc. - There is a progressive reduction in availability of snow related observations and in-situ networks which are crucial for the monitoring of some of the snow parameters (e.g. snow depth) - A multi-mission approach has been identified as being suitable to address a large number of needs for both polar regions and snow cover monitoring Second step: Polar Expert Group (May 2017) A report was drafted based on Polar Expert Group meetings. This initiative is focused on the issue of polar regions in general. For the purposes of this report only topics related to snow and ice on land will be mentioned. A long list of desired parameters to be monitored was written down based on user needs. Out of those the high priority ones were highlighted. One of the key parameters to be monitored on land is Snow (SC, FSC and SWE) and Permafrost (extent, fraction, topography, deformation monitoring). Regarding snow SWE seems to be of the most interest. 34

43 During the first Polar Expert Group meeting the following table was created summarising the user needs regarding SC, FSC and SWE (see Table 4). 35

44 Table 4: List of most desired user requirements regarding specifications of snow related parameters based on the Polar Expert Group meeting discussion. Param eter Domain AOI Resoluti on Seaso nality Frequ ency Timeli ness Unit Rang e Accu racy Availability of In-situ observations Status: is it currently monitored by EO means? Continuity- what are the expectations with respect to future availability of this variable? Priority SC, FSC General Europ e 100 m regiona l 500 m global All year round Daily NRT (withi n 6 hours) Continu ous scale in [%] Various sources exist and (nonharmonised) data are made available on a regular basis Operational service Current status of EO and in-situ data ensured or likely to improve High, improvements are essential for progress in the domain SWE General Global 50 m (nice to have: 1 m) Snow cover ed seaso n Annu ally (nice to have: weekl y) Non time critica l Continu ous scale in [cm] Hardly accessible Experiment al research ongoing Availability/qualit y of both in-situ and EO data at risk to deteriorate High, improvements are essential for progress in the domain General Europ e 100 m regiona l 500 m global All year round Daily NRT (withi n 6 hours) Continu ous scale in [mm] 0-10, mm Various sources exist and (nonharmonised) data are made available on a regular basis Operational service Current status of EO and in-situ data ensured or likely to improve High, improvements are essential for progress in the domain Hydrology Global 1 km (nice to Nov- May 15 days (nice NRT (withi Continu ous For SWE < Irregular measurements available Experiment al research ongoing Availability/qualit y of both in-situ and EO data at High, improvements are essential for 36

45 have: 100 m) to have: 1 day) n 6 hours) scale in [m] 200 mm: 40 mm; for SWE > 200 mm: 20 % risk to deteriorate progress in the domain Hydrology 50 m (nice to have: 1 m) Snow cover ed perio d Annu ally (nice to have: weekl y) Non time critica l Continu ous scale in [cm] 10 cm (nice to have : 1 cm) Hardly accessible Experiment al research ongoing Availability/qualit y of both in-situ and EO data at risk to deteriorate High, improvements are essential for progress in the domain Meteorol ogy global 5 km (nice to have: 1 km) Nov- May 3 days (nice to have: 1 day) NRT (withi n 6 hrs) QRT (withi n 1 h) Continu ous scale in [m] For SWE < 200 mm: 30 mm; for SWE > 200 mm: 15 % Irregular measurements available Experiment al research ongoing Availability/qualit y of both in-situ and EO data at risk to deteriorate High, improvements are essential for progress in the domain 37

46 A real worry has been expressed about the long term continuity of space observations from European and non-european satellite missions. Strong and close coordination between space agencies (through different mechanisms, e.g. CEOS, GEO/GEOSS, WMO, etc.) would be desirable in order to tackle this issue and ensure that at least a minimum optimal number of dedicated space missions is planned. During the second Polar Expert Meeting the Status and Gaps regarding each parameter were put together. The following was concluded regarding snow (see Table 5). There is an urgent need for high resolution SWE which is not feasible to monitor by the current spaceborne sensors. Table 6 summarises potential contributions by the current Sentinel and Metop missions to improve the snow and ice monitoring. 38

47 Table 5: Table summarising status and gaps regarding selected snow parameters according to the Polar Expert Group meeting discussions. SEASONAL SNOW STATUS GAPS Total Snow Area VIS, NIR & TIR imager Issues with cloud/snow discrimination Available products show significant differences Higher resolution required for complex terrain (mountains) Cloudiness/polar night issues In some products filled with coarse IMWR Snow Mass (SWE) on land Low spatial resolution SWE maps available from IMWR, but at comparatively large uncertainty Operational products available (GlobSnow, etc) IMWR SWE: accuracy needs to be improved Problems with spatial resolution in complex terrain, forests, saturation over deep snow Continuity of PMW on METOP High resolution product needed, not covered by current sensors Snow Melt Extent C band SAR (S1, ERS, ENVISAT) provide snapshot Algorithms mature for mountain regions Problems in forest Melt extent depends on acquisition time Table 6: Potential contributions by current Sentinel and Metop as seen by the Polar Expert Group. Sentinel-1 Sentinel-2 Sentinel-3 METOP SAR MSI OLCI SLSTR SRAL VII Metimage IRS SCA MWI 3MI Snow extent Snow Melt area coarse coarse Snow Water Equivalent coarse coarse 39

48 4.6 GLOBAL CRYOSPHERE WATCH (GCW) The World Meteorological Institute (WMO) represents a specialised agency of the United Nations (UN) for meteorology, operational hydrology and related geophysical sciences. GCW is an activity initiated by the WMO with the aim to support all key cryospheric in-situ and remote sensing observations. GCW provides authoritative, clear and useable data, information and analyses on the past, current and future state of the cryosphere. It provides a framework for observing the cryosphere through a coordinated approach on national to global scales to the Members and partners in order so they can deliver quality-assured global and regional products and services. The observing component of the GCW in called WIGOS (WMO Integrated Global Observing System) Via WIGOS and WMO Information System (WIS) the GCW provides a fundamental contribution to the GEOSS (Global Earth Observation System of Systems). GCW activities (examples): Developing CryoNet, a network of surface observations Developing measurement guidelines and best practices Refining observational requirements for the WMO Rolling Review of Requirements Engaging in and supporting inter-comparison of products, e.g. GCW Snow Watch Contributing to WMO s space-based capabilities database (with PSTG) Creating unique products, e.g. the SWE Tracker Engaging in historical data rescue, e.g. snow depth Building a snow and ice glossary Developing international training and outreach materials Providing up-to-date information on the state of the cryosphere Providing access to data via a portal CryoNet CryoNet is the core component of the GCW surface network. It builds on exiting cryosphere observing programmes. There are also Contributing stations which provide useful measurements but are not part of CryoNet. There are 87 CryoNet Stations and Sites (out of which 31 are in Europe) and 43 Contributing Stations (out of which 25 are in Europe). CryoNet sites can collectively provide the range of snow and ice conditions needed to validate satellite retrievals. Satellites The satellite observation network for the cryosphere is robust (see Figure 1) and GCW is utilizing many satellite products that exist in order to provide higher-level information and services. 40

49 Figure 1: Overview of satellite missions by GCW. 41

50 As shown on the GCW websites although there are many satellites and sensors monitoring cryosphere not all methods are mature. Figure 2 gives a qualitative assessment of current capabilities for measuring cryosphere from space. Figure 2: Overview listing a qualitative assessment of current capabilities for monitoring cryosphere from space. The role of satellites in GCW includes: Collaborating with the Polar Space Task Group (PSTG) on observing requirements Development of satellite product trackers which are also useful for monitoring product quality Facilitating, supporting, and devising best practices for product inter-comparisons, both large scale and for focus areas Providing data from various CryoNet sites for validation of satellite products Snow Watch The main goal of Snow Watch activity which started in 2013 is to assess the maturity and accuracy of snow products, improve the reporting of and access to in-situ snow measurements, promote the exchange of snow data and information for snow cover monitoring, and identify critical snow related issues that need to be addressed in GCW. Current Snow Watch activities include: A satellite snow product validation and inter-comparison project (ESA SnowPEx) A product inventory and self-assessment of products by principal investigators 42

51 A global snow data rescue project Snow Trackers for SC and SWE Effort to standardize snow related nomenclature Improving reporting practices and real-time data for in-situ snow measurements on the GTS network enabling wider access to these observations The Snow Watch team compiled an inventory 16 of satellite-derived, in-situ, and re/analysis snow datasets in The datasets covering Europe are in the tables below (see Table 7 and Table 8) divided by the type, i.e. in-situ and satellite. 16 Snow Watch inventory: 43

52 Table 7: List of in-situ datasets covering Europe (adopted from GCW Snow Watch in 2015). Product(s) Organization Description Period Areal Coverage Resolution Variables Frequency Data Sources Global Historical Climate Network - Daily (GHCN-D) NOAA NCDC Daily snow depth observation from various sources subject to a common set of QC protocols Mainly countries reporting realtime snow depth observations to the WMO GTS/WIS Spatial coverage highly variable in space and time; most dense coverage over NH mid-latitudes SD, snowfall, SWE; other climate variables such as temperature, precipitation and pressure are also included in the dataset daily Station numbers vary in time. Data updated in near real-time French Alpine snow data, Meteo-France Snow depth, snow properties and climate observations from mountain stations run by Meteo-France partners for avalanche risk monitoring French Alps 156 stations snowfall, SD, liquid water content of snowpack, snowpack density, info on snow grain size 12 hours Operational program Integrated Surface Database (ISD) NCDC The Integrated Surface Database (ISD) consists of global hourly and synoptic observations compiled from numerous sources into a single common ASCII format and common data model. ISD integrates data from over 100 original data sources, including numerous data formats that were key-entered from paper forms during the 1950s?1970s time frame Global ~20,000 station; NH coverage most dense over midlatitudes SD, snowfall sub-daily, daily Western Italian Alps Monthly Snowfall and Snow Cover Duration, NSIDC G01186 NSIDC Monthly snowfall amounts and monthly snow cover duration in days stations. Available stations range from 565 meters to 2720 meters in elevation. Snow cover duration monthly The period of record varies with each station, with the longest station record including data from 1877 to The average station record duration is approximately 61 years. Snow Survey of Great Britain British Glaciological Society ( ), Met Office ( ) Manual observation of the elevation of the local snowline (> 50% snow cover) The data for Scotland are digitised; Great Britain data still paper records 145 stations in digitized dataset for Scotland Snowline elevation Daily (Oct- May) Not all stations reported each year or for whole record 44

53 Table 8: List of satellite datasets covering Europe (adopted from GCW Snow Watch in 2015). Product(s) Organization Description Period Areal Coverage Resolutio n Variables Freque ncy Data Sources Comments GlobSnow SWE ESA, Finnish Meteorologic al Institute (FMI) Combination of climate station snow depth observations and forward microwave emission model simulations with SMMR and SSM/I satellite passive microwave data Non-alpine Northern Hemispher e 25 km SWE Daily; weekly ; monthl y SMMR; SSM/I; AMSR-E; SSM/I- S Evaluation with surface obs over Eurasia gave an RMSE of 30 to 40mm for SWE values below 150 mm; systematically underestimates SWE for snowpacks > 150 mm; SWE masked out over areas with complex topography, glaciers and ice sheets GlobSnow Snow Extent ESA, Finnish Meteorologic al Institute (FMI) Estimation of fractional snow covered area from SCAmod algorithm Northern Hemispher e 0.01 deg Fractional Snow Cover Daily; weekly ; monthl y ERS-2/ATSR-2 and EnviSat/AATSR Global daily coverage not possible due to narrow swath width of AATSR; snow cover fraction may be overestimated over very dense forest due to low reflectance dynamics NASA Standard AMSR-E NASA 19 and 37 GHz Tb difference; enhancements for vegetation and grain size evolution; distinction between shallow and deep snow Northern Hemispher e 25 km SWE Daily; pentad ; monthl y AMSR-E, AMSR2 NASA Prototype AMSR-E NASA Combination of numerical techniques, snow emission modeling and climatology Northern Hemispher e 25 km SWE Daily; monthl y AMSR-E NOAA AMSR2 Snow Products NOAA Variation of NASA AMSR-E methodology Global 25 km Snow Cover, Depth, SWE Daily AMSR2 This is a new set of products that is generated routinely at CIMSS/University of Wisconsin, but will not be operational in NOAA until later in 2015 JAXA AMSR2 Snow Products US National Ice Center Operational Global Snow Products (IMS) JAXA NIC Rosenkrantz (1998) (11 yr RAOB + RT) Trained analyst interpretation of satellite imagery and derived mapped products IMS 24 km 1997-, IMS 4 km 2004-, IMS 1 km Nearglobal (daily) Northern Hemispher e, Global 10 & 25 km, swath 1 km, 4 km, 24 km Snow Cover, SWE, SD snow /no snow Daily Daily AMSR2 (18, 36, GHz) AVHRR, MODIS, GOES, SSMI, as well as derived mapped products (USAF Snow/Ice Analysis, AMSU, AMSR-E, NCEP models, etc.) and surface observations 45

54 Global 4KM Multisensor Automated Snow/Ice Map Product (GMASI) CryoClim Multi- Sensor Snow Cover Product Cryoland PanEuropean Fractional Snow Extent Product European Alps AVHRR SE dataset NOAA NESDIS ESA, Norwegian Computing Centre, Norwegian Met. Inst. ESA, ENVEO Remote Sensing Research Group, University of Bern, Switzerland Combined optical and microwave retrievals with recurrent gap filling to maintain data continuity Combined optical and microwave fusion with SCE estimation based on Hidden Markov Model approach Snow cover fraction estimated from NDSI thresholding 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985? Global 4 km Nov Northern Hemispher e Pan- European Domain; 35-72?N 11-50?E European Alps + Europe 5 km ~500 m 1 km snow / no snow snow / no snow Fractional Snow Cover and uncertainty snow / no snow Daily Daily, monthl y, annual Daily (latenc y < 1 day) MetOp AVHRR, MSG SEVIRI, GOES Imager and DMSP SSMIS SMMR, SSM/I, AVHRR GAC MODIS (VIIRS; Sentinel-3) AVHRR An update of the dataset will take place in 2015/16 together with a more comprehensive validation Some misclassification of patchy snow in spring/summer related to MODIS Cloud Product; larger uncertainties in rugged terrain as only one band is used in the snow cover calculations EURACSnowAlps Institute for Applied Remote Sensing, EURAC, Bolzano, Italy Higher resolution snow cover estimates from MODIS using topographic correction for MODIS bands European Alpine Arch (43-48?N, 5-15?E) 250 m Fractional snow cover Daily MODIS, band 1 (RED) and band 2 (NIR) (for snow) Improved mapping of snow in mountain regions compared to standard MODIS products especially patchy snow; Topographic correction cannot eliminate completely shadow effect in very steep slope especially during the morning acquisition (TERRA);Some limitations in detecting snow in forested areas; Some misclassification of snow-cloud Japanese Global Snow Cover Extent for Climate Dataset (GHRM5C) JAXA (Japan Aerospace Exploration Agency) Estimate of global SCE from 5 channels of AVHRR and MODIS to create climate record from Global 0.05 deg snow / no snow / wet snow Daily, weekly, monthl y AVHRR ( ), MODIS ( ) Higher resolution coverage than NOAA CDR product; Discriminates between dry and wet snow; Some missing months in 1980, 1981, 1994, 1995, 2001; Values over Antarctic are not valid; degraded performance in dense forest, cold desert and high mountains 46

55 MODIS Snow Products Collection 6 NASA Goddard Snow cover fraction estimated from the spectral characteristics of reflected solar energy. Daily data are cloud gap filled. Terra: 24 Feb Aqua: 4 July Global 500 m swath; 0.05? by 0.05? Snow cover fraction (%), cloud persistence count, QA assessment day, 8- day, month MODIS bands 1,2,4,6 More accurate mapping of spring and summer snow cover in mountainous areas achieved by removing the surface temperature screen MODIS MODSCAG Products NOAA-CDR MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0 NASA JPL Rutgers University, NOAA, NCDC NSIDC Snow cover fraction estimated from spectral mixture analysis MODSCAG method 89 x 89 Polar Stereographic grid (190.5 km spacing) Snow cover presence/absence from three satellite sources plus a merged source estimate March Some missing months in period 1999? 2012; MODIS in period Global Northern Hemispher e Northern Hemispher e km 25 km Snow cover fraction (%) snow presence/ab sence based on 50% snow cover threshold (weekly); snow cover duration fraction % (monthly) snow cover presence/ab sence Resear ch Datase t weekly, monthl y daily TERRA, AQUA Mainly visible imagery (VHRR, AVHRR, VISSR, VAS); Based on IMS 24 km from June IMS, MODIS Collection 5 Cloud Gap Filled Snow Cover, PMW brightness temperature, merged estimate Data extensively used for climate monitoring (e.g. IPCC, BAMS State of Climate), model evaluation and snow cover-climate studies. Mainly recommended for continentalhemispheric scale studies. Changes in charting procedures and satellite resolution and frequency have occurred over time that may impact data homogeneity. Note that the monthly snow cover fraction is a duration fraction not an areal fraction. MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Weekly 100km EASE-Grid 2.0 NSIDC Snow cover presence/absence from two satellite sources plus a merged source estimate ; PMW in period Northern Hemispher e 100 km Snow cover presence/ab sence weekly NOAA-CDR, PMW brightness temperature, merged estimate GRACE NASA JPL, German Space Agency, ESA, University of Texas at Austin Area-averaged estimates of terrestrial water storage anomalies (snow + ice + soil + ground water) March Global 1x1 deg. and 4X4 deg. grids Water storage anomalies (depth) monthl y Requires expert knowledge to use these data correctly. See Frappart et al (2011) and Forman et al (2012) for examples of GRACE applications related to snow water storage. 47

56 4.7 SNOWPEX In part as a response to the recommendations put forth in the first GCW Snow Watch workshop the ESA is supporting a snow product inter-comparison study called SnowPEx. The objectives of this projects are to: - Inter-compare and evaluate global / hemispheric (pre-) operational products (SC and SWE) derived from different EO sensors and generated by means of different algorithms, assessing the product quality by objective means - Evaluate and inter-compare temporal trends of seasonal snow parameters from various EO based products in order to achieve well-founded uncertainty estimates for climate change monitoring - Elaborate recommendations and needs for further improvements in monitoring seasonal snow parameters from EO data The project will support the setup of a consolidated operational satellite snow observation system for the WMO Global Cryosphere Watch and help to improve the snow cover database for climate monitoring. The datasets being compared are shown on Figure 3 and Figure 4. Figure 3: Snow Extent datasets being compared within the SnowPEx project. 48

57 Figure 4: Snow Water Equivalent datasets being compared within the SnowPEx project. 4.8 WHITE PAPER ON PERSPECTIVES FOR A EUROPEAN SATELLITE SNOW MONITORING STRATEGY (GLOBSNOW) The paper written based on the experience from the GlobSnow project summarises the needs for progress towards a Satellite-based Snow Monitoring Strategy based on user consultation workshops, review processes within the snow community, and literature: - Establish a cross-continental Group on Satellite Snow Monitoring Perspectives - Improve the user-interaction in all product phases from the development phase to data dissemination for a better user acceptance of satellite-based snow products - Perform regular product intercomparison, validation and product assessment exercises (e.g. WMO GCW endorsed ESA SnowPEx project), coordinated within the community - Communicate and provide quantified product uncertainties following common definitions and establish rules and procedures in consultation with the end-users - Consider and assure in time a transfer from R&D products and services into future sustainable initiatives to guarantee a continuity for the end-users - Promote successful demonstration projects and pilot-products through various channels to exploit the improved capabilities of new EO sensors in the upcoming national and international space programs. 49

58 5 RESEARCH PROJECTS- UPDATE GlobSnow and CryoLand are two H2020 projects already reviewed in October Fully details can be found in the report deliverable SC56195 Task II.9. Both projects have officially finished but their websites (and automatic processing chains) are still online for various end users from those projects. The websites and automatic production scripts have been maintained with minimal effort by internal resources of some of the project partners. 5.1 CRYOLAND The aim of the FP7 project was to prepare the software and processing lines for generating snow, glaciers and lake/river ice products using EO data from Sentinel satellites. It is set up for a potential cryospheric component of the Copernicus/GMES Land Monitoring Service. CryoLand project coordinated by ENVEO provides the following products and services (with listed temporal and spatial resolution, as well as the source of data in brackets) build upon data from a variety of EO satellites and are ready to fully exploit the potential of the new Sentinel satellite constellation: SNOW SERVICES Pan-European o Fractional Snow Cover: Daily, 500 m (Optical RS data, DEM, NDSI threshold map, Surface classification, Transmissivity map) o Snow Water Equivalent: Daily, 25 km (Passive Microwave, Mountain mask) o Standardized Snow Pack Indicator: Daily, km (SWE product) Regional o Fractional Snow Cover- Scandinavia: Daily, 250 m (Multi-temporal optical and Multitemporal/Multi-sensor (optical & radar RS data) o Fractional Snow Cover- Baltic area: Daily, 500 m (Optical RS data, Transmissivity map) o Fractional Snow Cover- Alps: Daily, 250 m (Optical RS data, DEM) o Wet Snow Cover- Scandinavia: Daily, 50 m (Radar data) o Wet Snow Cover- Alps: Multi-temporal, 100 m (Radar RS data, DEM) LAKE AND RIVER ICE SERVICES o River Ice Extent- Scandinavia: Annually, 2 50 m (High-res Radar RS data) o Lake Ice Classification- Baltic Area: Daily, 250 m (Optical RS data) According to Dr. Kari Luojus of the Finnish Meteorological Institute (FMI) the following products of the CryoLand project will be delivered via the JRC Global Land service (in the list 50

59 above these are underlined): Daily Northern Hemisphere 5 km SWE Daily Pan-European 500 m fraction of snow cover extent Daily Baltic Lake Ice Extent All of the parameters follow-on from the CryoLand activity. The SWE has heritage on GlobSnow and CryoLand with a hemispherical coverage. The developed services and methods are capable to immediately utilize data from the various satellites of the European Copernicus Sentinel satellite constellation. 5.2 GLOBSNOW The ESA funded project coordinated by FMI (the Finnish Meteorological Institute) ran from 2008 until 2014 in two phases (GlobSnow-1 and GlobSnow-2), whilst the first phase resulted in two long-term datasets at the hemispherical scale: 1) SE (Areal Snow Extent) & FSC (Fractional Snow Cover) o provided for a period of 17 years o based on optical data from Envisat AATSR and ERS-2 ATSR-2 sensors o period: o Daily/ Weekly/ Monthly basis o moderate resolution (0.01 x0.01 ) o NetCDF CF format o The SCAmod method used to produce the product provides high quality FSC and detects fractional snow in forests better than any other products available at this scale o First hemispheric daily SC dataset generated from ESA ATSR-2 and AATSR observations o FSC limited for observations at sun zenith angle < 73 o Sensor revisit time is 3 days and the swath of daily data is narrow (ca 500 km) resulting in gaps 2) SWE (Snow Water Equivalent) o provided for a period of 33 years o based on time series of measurements by two different spaceborne passive microwave sensors: SMMR and SSM/I o combination of satellite-based passive microwave data with ground-based weather station data in a data assimilation scheme o period: o Daily/ Weekly/ Monthly basis o Spatial resolution 25 km 51

60 o NetCDF CF format As a result of the project an operational near-real time snow information service is also running since GlobSnow-2 project had the following objectives: - Further enhancement of the retrieval methodologies for SE and SWE products - Reprocessing of the long-term datasets utilizing the improved retrieval algorithms - Investigation of utilizing AVHRR and NPP Suomi VIIRS data as a gap filler before the launch of Sentinel-3 SLSTR. - Development of a new product combining the high-res SE data with the lowerresolution SWE product The products are based exclusively on ESA s satellites. Data are freely accessible. USE OF PRODUCTS: The SWE product is utilised by the WMO GCW initiative to provide a daily near-real time tracker of hemisphere snow conditions. It has been also applied in the JRC Drought Observatory as a drought indicator called Standardised Snow Pack Index. 5.3 CRYOCLIM The project objective was to develop a new operational services for long-term systematic climate monitoring of the cryosphere. The system and services proposed are designed to be integrated into GEOSS (its climate monitoring part) and to contribute to the GCW according to the climate monitoring principles recommended by the Global Climate Observing System (GCOS). The list of product provided by the project is listed in Table 9. The success with the multi-sensor/multi-temporal algorithm for snow in CryoClim has contributed to triggering the development of a new algorithm and snow product for Scandinavia, a product that will be probably provided commercially (company TBD). The algorithm will fuse Sentinel-1 (SAR) and Sentinel-3 (optical) data and provide daily snow maps for Scandinavia of 300 or 250 m spatial resolution. The SAR is contributing when the snow is wet (melting season), while the use of SAR and a multi-temporal approach will to a large degree mitigate the limitations given by cloud-cover at the northern latitudes. Pilot product provision is expected to take place during the winter

61 Table 9: List of products provided by CryoClim project. PROVIDER PRODUCT COVERAGE FREQUENCY RESOLUTION TIME SPAN Norwegian Meteorological Institute Sea Ice Sea Ice Concentration Global Monthly 10 km present Sea Ice Edge Global Norway 10 km present Snow Snow Cover Extent (Passive Microwave) Global Daily 10 km present Snow Cover Extent (Optical) Global Daily 5 km present Snow Cover Extent (Multi-sensor) Global Daily 5 km present Norwegian Water Resources and Energy Directorate Norwegian Polar Institute Glacier Norway Glacier Svalbard Glacier Area Outline Norway 1 30 years 30 m 1930/1984- present Glacier-dammed Lake Outline Norway 1 30 years 30 m 1930/1984- present Glacier Periodic Photo Norway Annual N/A present Glacier Surface Type Svalbard Annual 30 m present Glacier Balance Area Svalbard Annual 30 m present 53

62 5.4 ESSEM COST ACTION ES1404 Another project related to snow has been identified: ESSEM COST Action ES A European network for harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction. This COST Action on SNOW aims at building a better connection between snow measurements and models, between snow observers, researchers and forecasters, for the benefit of various stakeholders and the entire society. The Aim of the Action is to enhance the capability of the research community and operational services to provide and exploit quality-assured and comparable regional and global observation-based data on the variability of the state and extent of snow. Overall Objectives & Benefits: Establish a European-wide science network on snow measurements for their optimum use and applications benefitting on interactions across disciplines and expertise. Assess and harmonize practices, standards and retrieval algorithms applied to ground, air-borne and space-borne snow measurements in order to foster their acceptance by key snow network operators at the international level. Develop a rationale and long term strategy for snow measurements, their dissemination and archiving. Advance snow data assimilation in European numerical weather prediction and hydrological models and show its benefit for relevant applications. Establish a validation strategy for climate, numerical weather prediction and hydrological models against snow observations and foster its implementation within the European modelling communities. Training of a new generation of scientists on snow science and measuring techniques with a broader and more holistic perspective linked with the various applications. The management Committee Chair belongs to Dr. Ali Nadir Arslan from the Finnish Meteorological Institute and the Vice-Chair to Patricia de Rosnay from the European Centre for Medium Range-Weather Forecasts in the United Kingdom. The project is still on its initial moments and there is no available information on the different variables and methodologies to be revised yet. 17 ESSEM COST Action ES1404: 54

63 6 EUROPEAN SERVICES- UPDATE 6.1 COPERNICUS CLIMATE CHANGE SERVICE (C3S) The Copernicus Climate Change Service 18 (C3S) responds to environmental and societal challenges associated with human-induced climate changes. The service will give access to information for monitoring and predicting climate change and thus will help supporting adaptation and mitigation. It benefits from sustained network of in-situ and satellite based observation, re-analysis of the Earth climate and modelling scenarios based on a variety of climate projections. In 2014 a Delegation Agreement was signed with the European Centre for Medium-Range Weather Forecast (ECMWF) to implement the service which is still under development. C3S is already providing comprehensive monthly data and maps for the average global and European temperatures; these maps can be a found in the Products 19 section of the website. The products are divided into three areas: 1. Monthly maps and graphs o With the global average surface air temperature since Climate reanalysis o Including snow variable. 3. Seasonal forecast o The C3S is developing seasonal forecast products with a target publication date of 15th of each month. These products are based on data from several stateof-the-art seasonal prediction systems. o The current proof-of-concept phase includes graphical forecast products for a number of variables (air and sea-surface temperature, atmospheric circulation and precipitation); the forecasts are updated every month and cover a time range of 6 months. o The centers currently providing forecasts to C3S are ECMWF, The Met Office and Météo-France; at a later stage Deutscher Wetterdienst and Centro Euro- Mediterraneo sui Cambiamenti Climatici will be added to the list. 2. Climate reanalysis with ERA 5 Climate Reanalysis Data: a numerical description of the recent climate produced by combining models with observations. It contains estimates of 18 Copernicus Climate Change Service (C3S): 19 C3S Products section: 55

64 atmospheric parameters such as air temperature, pressure and wind at different altitudes, and surface parameters such as rainfall, soil moisture content, sea-surface temperature and wave height. ERA5 is the fifth generation of ECMWF atmospheric reanalysis of the global climate. ECMWF climate reanalysis started with the FGGE reanalysis produced in the 1980s, followed by ERA- 15, ERA-40 and most recently ERA-Interim. ERA5 included three groups of parameters: atmosphere, land and wave. It is in a 31 km global resolution providing uncertainty estimates. Both global and regional reanalysis benefit from advances in modelling and data assimilation activities. Observations (truth) are needed. The maps and graphs in the Products section of the C3S are based on pre-release data from ECMWF's ERA-Interim reanalysis. The ERA-Interim data can be accessed via the ECMWF 20 website. The observations (satellite and in-situ) used as input into ERA5 are listed in the file ERA5 observation inputs.xlsx 21 Table 10 shows the main characteristics and innovations in ERA5 with respect to ERA-Interim. Table 10: Main characteristics and innovations in ERA5 with respect to ERA-Interim. ERA-Interim ERA5 Period present present Production Period August 2006 end 2018 Jan 2016 end 2017, then continued in near real-time Assimilation system IFS Cycle 31r2 4D-Var IFS Cycle 41r2 4D-Var Model input As in operations (inconsistent SST) Appropriate for climate (e.g. CMIP5 greenhouse gases, volcanic eruptions, SST and sea-ice cover) Spatial resolution 79 km globally, 60 levels to 0.1 hpa 31 km globally, 137 levels to 0.01 hpa 20 ECMWF ERA-Interim data: 21 The file is available at 56

65 Uncertainty estimates None From a 10-member Ensemble of Data Assimilations (EDA) at 63 km resolution Output frequency 6-hourly analysis, 3-hourly forecast fields Hourly analysis and forecast fields, 3-hourly for the EDA Input observations As in ERA-40 and from Global Telecommunication System In addition, various newly reprocessed datasets and recent instruments that could not be ingested in ERA-Interim Variational bias scheme Satellite radiances Also ozone, aircraft and surface pressure data Satellite data RTTOV-7, clear-sky, 1D- VAR rainy radiances RTTOV-11, all-sky for various components Additional innovations Long-term evolution of CO2 in RTTOV, cellpressure correction SSU, improved bias correction for radio sondes, EDA perturbations for sea-ice cover C3S Summary C3S focuses on climate change and meteorological forecasting. It provides an increasing volume of atmospheric composition and climate information relevant for polar regions and snow covered areas based on a combination of satellite and in-situ observations using the latest methods and tools. Terrestrial snow properties are highly sensitive to changes in temperature and precipitation regimes and are recognised to provide a fundamental indicator of climate variability and change, and also provide a significant feedback effect in a warming climate. Snow data assimilation is crucial for numerical weather prediction. In the absence of satellite dedicated missions in-situ snow depth observations is by far the most relevant information for snow data assimilation. However, there is a gap in the ground truth station network because of the areas with poor reporting and areas with seasonal reporting. Some countries provide data in near-real time but some countries have data policy issues. ERA5 represents the latest generation of atmospheric reanalysis of the global climate. Snow is one of the essential climate variables within ERA5 and it is based on both in-situ and satellite data. However, ERA5 provides means for global re-analysis, not for earth observations. 57

66 6.2 GLOBAL LAND Copernicus Land Monitoring Service consists of three main components: Global, Pan- European and Local component. The Global component is coordinated by the European Commission DG Joint Research Centre (JRC), whilst Pan-European and Local components are coordinated by the EEA. So far no information regarding snow & ice within the Global Land activity is officially available. We have tried to contact JRC directly several times but did not succeed to obtain any information regarding the plans for snow & ice monitoring. Indirectly the information about the specifications of the planned cryosphere parameters to be monitored within Global component was obtained from Dr. Kari Luojus of the Finnish Meteorological Institute (FMI), the Project Manager of GlobSnow project, and it is as follows: Daily Northern Hemisphere 5 km SWE Daily Pan-European 500 m fraction of snow cover extent Daily Baltic Lake Ice Extent All of the parameters follow-on from the CryoLand activity. The SWE has heritage on GlobSnow and CryoLand with a hemispherical coverage. 58

67 7 GAP ANALYSIS 7.1 SELECTION OF SNOW AND ICE PARAMETERS The following parameters were advised to focus on by the EEA after the first report (Task II.9) was submitted: Snow Cover (SC), Fractional Snow Cover (FSC), Snow Water Equivalent (SWE), and Lake and River Ice. Based on the Polar Expert Group meeting in spring 2017 in Brussels it was confirmed that the most important and needed parameters regarding snow are: Snow cover, Fractional snow cover, and Snow water equivalent. This can be taken as a relevant opinion because the meeting was attended by snow and ice experts from all over Europe, expert users and representatives from both Copernicus services and Commission DGs. This then confirms what the EEA expressed as their main interest at an internal meeting at the beginning of this task. In order to draft the specifications of the new snow and ice monitoring service for Europe a gap analysis exercise was carried out resulting in the following tables. Table 11 analyses the need for the selected snow and ice parameters as seen by the user community. The rows represent a list of users or activities which have been reviewed during this task and which can feed into the user requirement exercise. Each parameter gets a score from 0 to 2 based on the description below: - Score 0: Parameter not mentioned or mentioned very briefly (by users) - Score 1: Parameter would be useful/nice to have (as expressed by the users) - Score 2: Parameter is essential to have (as seen by the users) Table 11: Analysis showing the importance of monitoring of a selected snow and ice parameters as seen by the users. SC & FSC SWE Lake/ River Ice National services Key experts European policies IGOS PSTG Polar View Polar Expert Group CryoLand GlobSnow CryoClim TOTAL

68 Based on the first analysis it is obvious that the Snow Cover (including Fractional Snow Cover) and Snow Water Equivalent parameters are essential for most of the users, thus it is highly advised to concentrate on those two parameters which are from this reason also considered in the further analyses. 7.2 SELECTION OF SPATIAL AND TEMPORAL RESOLUTION The following two tables (see Table 12, Table 13) analyse the temporal and spatial resolution of each of the selected parameters. The scoring systems was designed to contain three scores, first score representing the user requirements, second score indicating the existence of a monitoring service at European level, and third score indicating the feasibility of monitoring such a parameter with the use of a current observation systems: 1) Desirability: o Score 0: Not desired by users o Score 1: Nice to have o Score 2: Desired by users, essential 2) Monitoring service existence: o Score 0: Monitoring service/s exist at the European level o Score 1: Monitoring service exists but with certain limitations o Score 2: Monitoring service does not exist at the European level 3) Feasibility: o Score 0: Not feasible to monitor by the current observation means o Score 1: Potentially feasible to monitor with limitations; research needed o Score 2: Feasible to monitor by the current observation means The higher the total sum of scores, the more benefit such a monitoring could bring (in the table indicated in bold red). Table 12: Analysis of spatial and temporal resolution regarding Snow Cover (SC) based on RS data. SPATIAL RESOLUTI ON SC TEMPORAL RESOLUTION 1 day 2 7 days 8 days 100 m m > 500 m Regarding the Snow Cover, at a spatial resolution of 500 metres and lower there are monitoring services available, one example is the result of the GlobSnow project. At the spatial resolution between m there is also a monitoring service available (or will be in a near future) - the JRC is planning to base their new snow monitoring service on the results of 60

69 the CryoLand project. Designing another monitoring service of those spatial resolutions would therefore result in a duplication. However, based on the table above there is an obvious gap in SC (and FSC) monitoring which is at very high spatial resolution of 100 m and higher. There is a huge demand for a product of such a high spatial resolution, yet there is no European monitoring service currently providing such a data. At the same time with the new Sentinel-2 satellite this information is now possible to obtain, although with certain limitations. The main limitation is the cloud cover which, where present, makes any data acquisition from optical sensors impossible in such area. In order to demonstrate the cloud cover limitations a Snow Portal was developed for purposes of performing various analyses based on selected optical sensors and time windows. See Chapter 8: Cloud Cover Analysis for further information. Table 13: Analysis of spatial and temporal resolution regarding Snow Water Equivalent (SWE) based on RS data. SPATIAL RESOLUTI ON SWE TEMPORAL RESOLUTION 1 day 2 7 days 8 days 1 km km 10 km > 10 km As for the SWE monitoring this topic is more complicated. There are monitoring services available at a low spatial resolution, e.g. from GlobSnow and CryoLand projects at a spatial resolution of 25 km. Regarding the medium spatial resolution JRC is planning to provide a SWE product within the Global Land activity of a spatial resolution of 5 km. Therefore, proposing another monitoring service of spatial resolution of 5 km or lower would result in a duplication. There is a high demand for SWE data at high spatial resolution, i.e. below 1,000 metres, however, such a monitoring is not feasible to perform with a use of a single currently available spaceborne sensor due to the known limitations of each of the observation systems as mentioned in Chapter 3. The main data source is provided by the SAR data, however, it cannot act as a single data source due to its limitations. Therefore, a data assimilation from several data sources is necessary. The best other data source seem to be the data from in-situ stations. The information can be further improved by the conjunction with optical data, passive microwave and modelling. The capabilities of current spaceborne data are not fully utilised yet so there is a space for further research and development. Several local activities are looking into data assimilation of different data sources in order to retrieve SWE values of high spatial resolution. However, none of them can provide a mature enough methodology for an SWE retrieval of a high spatial resolution at the European level, as far as we are aware. 61

70 A significant improvement in this issue could be made if there is a unified, dense network of in-situ stations obtaining SWE data using the same methodology. However, such a network does not exist yet and a retrieval of SWE with a spatial resolution of < 1,000 m seems to be unfeasible with the use of technologies and methodologies available at the moment. 7.3 SELECTION OF OBSERVATION SYSTEMS In Table 14 an analysis is drafted based on the means of obtaining data, i.e. observation systems. Only the specifications determined within the previous chapters are being taken further, i.e. SC (&FSC) of spatial resolution 100 m. Three criteria were considered in the scoring exercise within this chapter: 1) the feasibility if used as a single data source, i.e. is it possible to obtain given data via the observation system?, 2) feasibility if in conjunction with other data sources; 3) improvement in data information if used in a conjunction with other data sources. 1) Feasibility if used as a single data source o Score 0: Not possible o Score 1: Possible with limitations o Score 2: Possible 2) Feasibility if used in a conjunction with other data source o Score 0: Not possible o Score 1: Possible- conjunction necessary in order to get meaningful data o Score 2: Possible 3) Improvement if used in a conjunction with other data sources o Score 0: No improvement o Score 1: Improvement foreseen o Score 2: Significant improvement foreseen; not possible without conjunction The higher the total sum of scores the more benefit such a monitoring could bring (in table indicated by bold red). Table 14: Analysis of the means of obtaining information in order to monitor SC/FSC. SC & FSC In-situ data Remote Sensing Optical data Passive Microwave SAR Modelling

71 Based on the observation systems analysis the following was deduced. Regarding the SC and FSC the main data source is represented by the optical data (Sentinel-2 or Landsat-8). This information can be improved by the use of other sources, e.g. in-situ data, SAR data or modelling if assimilated in a correct way. 7.4 SUMMARY OF GAP ANALYSIS Snow Cover & Fractional Snow Cover One of the main user requirements which is not fulfilled yet, but with the launch of the Sentinel-2 constellation it is now feasible, is to retrieve a SC including FSC at a high spatial resolution. Based on the available information it was decided that the main data source shall be represented by the optical spaceborne data, namely the Sentinel-2 satellite constellation. Other data sources can be used to achieve better results (e.g. Landsat-8 optical data, SAR, or in-situ data) but this is dependable on the budget and also on the service provider and the methodology proposed. Users are asking for a daily data of high spatial resolution. However, the use of optical data brings a limitation presented by the cloud cover which is especially extensive during winter months and over mountainous areas where snow occurs the most. Therefore, before defining the service specifications of snow monitoring which would use the optical data as the main (and possibly solely) source of information, it was decided that an analysis will be performed to quantify the extent of the cloud cover in space and time. See the next chapter (Chapter 8: Cloud cover analysis). In the SC & FSC retrieval there does not exist a single methodology which could be applied with a certain best results. Therefore, the specifications are not strictly set in every respect giving the service providers a space for their own suggestions. One needs to keep in mind that the Sentinel-2 has only been launched recently, therefore, there is not enough information to strictly set service specifications together with selecting the most convenient methodology to be applied. A space is suggested to be left for certain specifications to be set by the service providers themselves Snow Water Equivalent SWE is the principal snow observation needed for most water resource applications and it complements precipitation observations. Monitoring of SWE presents a very challenging task because currently it is not technically feasible to obtain this information with a high temporal and spatial resolution on a timely basis. There are several services providing the information on SWE, each using slightly different methodology but none of them provide a daily information with a spatial resolution of < 5 km at the European level as requested by the users. For a detailed SWE monitoring the in-situ stations are essential because they provide the most precise information available, however, the network of in-situ stations is insufficient, 63

72 inconsistent or completely lacking in some areas which results in the interpretation of SWE to be missing at a local scale. Satellite based SWE monitoring is limited to the retrieval from microwave sensors but this is insufficient due to the low spatial resolution and mixed signals. Passive microwave data operate independently of solar illumination (polar night) and atmosphere conditions (cloud cover) but provide coarse spatial resolution. SAR provides adequate spatial resolution and it represents the only instrument capable of mapping wet snow cover at fine spatial resolution over mountainous areas. However, it is limited to wet snow and further research is needed in order to realise the SWE retrieval from SAR. Based on the previous it is essential to base the SWE monitoring on a retrieval from different data sources (in-situ, passive microwave, SAR) and combine the information by an adequate and sound data assimilation algorithm. A good idea would be to use a fusion of SC and SWE information to overcome the limitations of each of them. There are experimental researches ongoing looking into this issue. However, given the complexity of the matter, it is not within our capacity to find enough information about those individual methodologies, assess them, and predict possible results if applied to the continental scale. Therefore, it is not advised to aim for setting up an SWE monitoring service of a high spatial resolution over Europe until the technology allows for adequate data retrieval and until there is a sound and tested methodology of data assimilation. 64

73 8 CLOUD COVER ANALYSIS As mentioned before the main limitation of optical remote sensing data is the cloud cover which means no data can be obtained for certain area and time if it is obscured by cloud cover. Therefore, before designing a new monitoring service which is to use optical data as the main source of information it is desirable to perform several analyses in order to quantify the average frequency of cloud cover over Europe. Recognising these analyses as critical in order to be able to fulfil this task and draft meaningful snow service specifications based on facts (rather than on a subjective feeling ) a portal has been developed. Since this extends well beyond the original scope of this task a so called Snow Portal has been developed from the resources of EartH2Observe project and tailored to fit the purposes of the analyses of this report. The Snow Portal proved to provide a very useful tool in analysing cloud cover and it will be ready for use within several months. At a request more analyses can be run and be accommodated for the purposes of this task. The main objectives of the Snow portal analyses was to show: - How is the cloud cover proportion developing based on the composite frequency - How many days is needed for data collection in order to create a mosaic of certain cloud-free proportion, or if possible a cloud-free mosaic Optical data from three different satellite missions has been selected to be included in this analysis: MODIS (Terra, Aqua), Sentinel-3 and Sentinel-2. The period for these analyses was chosen to be November April (incl.) which represents a majority of the European winter season. This is also because the Sentinel-3 data are only available since November 2017, therefore winter season is the first winter period when data are available for all mentioned satellite sensors. Data from Sentinel-2 are also available for winter season which was included in the analyses. The specifications of those satellites is in Table 15. Table 15: Specifications of satellite data selected for analyses within the Snow Portal. SATELLITE SENSOR SPATIAL RESOLUTION TEMPORAL RESOLUTION Terra, Aqua MODIS 250 m (bands 1-2) 500 m (bands 3-7) 1 km (bands 8-36) Sentinel-3 SLSTR 500 m (visible/nir, short wavelength bands) 1 km (thermal IR channels) Sentinel-2 MSI 10 m (4 visible & IR bands) 20 m (6 red-edge/shortwave-ir bands) 60 m (3 atmospheric correction bands) 1 2 days 1 day 5 days (from 2 satellites above equator) 65

74 Several analyses were performed, the list of which is given in the four tables below (see Table 16, Table 17, Table 18, Table 19). There are three areas of interest (AOI) for which the analyses were performed: Europe, Austria and Sweden. Austria was selected as a country representing the Central Europe Alpine area where clouds are more likely to occur due to the presence of high mountains. Sweden was chosen as a country representing higher latitudes and a mountainous Scandinavian area where clouds are occurring with a higher frequency due to proximity to the sea. Table 16: Specifications (AOI, composite frequency) for the Sentinel-2 data analyses. Sentinel-2 1 day 7 days 14 days 21 days 1 month 2 months Europe Austria Sweden Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud 3 months Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud 6 months Cloud/ NoCloud Cloud/ NoCloud Cloud/ NoCloud Table 17: Specifications (AOI, composite frequency) for the MODIS data analyses. MODIS (Aqua, Terra) Europe Austria Sweden 1 day 3 days 7 days 10 days 14 days Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Table 18: Specifications (AOI, composite frequency) for the Sentinel-3 data analyses. Sentinel-3 1 day 3 days 7 days 10 days 14 days Europe Austria Sweden Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud 66

75 Table 19: Specifications (AOI, composite frequency) for the MODIS & Sentinel-3 data analyses. MODIS Sentinel-3 Europe Austria Sweden & 1 day 3 days 7 days 10 days 14 days Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud Snow/ NoSnow/ Cloud For each satellite (or their combination as indicated in the tables above) data is available with different temporal resolution. For some of them the revisit time is daily but the spatial resolution is lower (e.g. Sentinel-3). For Sentinel-2 (which was suggested to be used for the snow monitoring by many users) the spatial resolution is high but the revisit time is 5 days (above equator, meaning in higher latitudes the temporal resolution is higher) from a combination of two Sentinel-2 satellites. For our analysis only data from Sentinel-2A was taken into account because Sentinel-2B data is not available yet at the time of this analysis. At this point there is a trade-off between temporal and spatial resolution depending on the user needs. Data from Sentinel-3 and MODIS were classified into three classes: Snow, NoSnow, Cloud, meaning: - Snow: Pixel on the land which is classified as a snow cover based on the satellite data - NoSnow: Pixel on the land which is classified as snow-free based on the satellite data - Cloud: Pixel above the land which is classified as cloud based on the satellite data Each pixel is assigned a value based on the rule: Snow > NoSnow > Cloud, i.e. if at least once a pixel classified as snow is present then value Snow will be assigned. If there is no Snow value for the whole period but there is at least one NoSnow value then NoSnow is assigned to that pixel. If the pixel is permanently classified as Cloud then value Cloud will be assigned. Data from Sentinel-2 satellite was classified into two classes: Cloud, NoCloud, based on the cloud mask downloaded (no snow mask is present at this moment based on the Sentinel-2 data): - Cloud: Pixel above the land which is covered by cloud based on the cloud mask downloaded - NoCloud: Pixel above the land which is not covered by cloud based on the cloud mask downloaded For each satellite daily scenes were mosaicked together to create a 1-day composite, and then daily composites were mosaicked again to create n-days composites with a frequency as indicated in the tables above and based on the rules mentioned above. 67

76 8.1 SENTINEL-2 CLOUD COVER ANALYSIS Sentinel-2A data is available for the last two winter seasons, i.e and For the general purposes of the cloud cover analyses a winter period was set to be from the beginning of November until the end of April. Table 20 shows the results of the two winter seasons in regards to the cloud cover. There are several rows each of them indicating a number of days/ months for which a mosaic based on daily data was calculated. For each n-days mosaic there is a number indicating an average number of cloud-free pixels and an average number of pixels covered by cloud, those two numbers adding up to 100 %. The columns are showing results for winter season , , and the average of the two winter seasons. 68

77 Table 20: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Europe for different n-days composites for winter seasons: , , and the average of the two winter seasons / NoCloud Cloud NoCloud Cloud NoCloud Cloud 1 day 32.85% 67.15% 35.00% 65.00% 33.93% 66.08% 7 days 37.85% 62.15% 42.35% 57.65% 40.10% 59.90% 14 days 48.97% 51.03% 58.67% 41.33% 53.82% 46.18% 21 days 60.76% 39.24% 71.17% 28.83% 65.97% 34.04% 1 months 68.23% 31.77% 79.55% 20.45% 73.89% 26.11% 2 months 86.24% 13.76% 93.85% 6.15% 90.05% 9.96% 3 months 94.34% 5.66% 96.71% 3.29% 95.53% 4.48% 6 months 99.71% 0.29% 99.94% 0.06% 99.83% 0.18% A graph is shown on Figure 5 displaying the average values for the two winter seasons. Axis x is proportionate to the time length. Figure 5: Graph showing the % of Cloud cover vs Cloud-free area over Europe for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data. 69

78 The number of scenes used for the analyses at the European level is 24,806 scenes for winter season with the average scene cloud cover of % and the average scene cloud-free cover of %. For winter season there are 31,084 scenes available, the average scene cloud cover is % and the average scene cloud-free cover being %. The same analysis as described above was performed for Austria and Sweden, the results can be seen in Table 21 and Table 22, and on Figure 6 and Figure 7 below. Table 21: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Austria for different n-days composites for a winter season , , and the average of the two winter seasons / NoCloud Cloud NoCloud Cloud NoCloud Cloud 1 day 36.16% 63.84% 35.85% 64.15% 36.01% 64.00% 7 days 36.35% 63.65% 43.21% 56.79% 39.78% 60.22% 14 days 48.64% 51.36% 58.88% 41.12% 53.76% 46.24% 21 days 60.21% 39.79% 70.75% 29.25% 65.48% 34.52% 1 months 70.84% 29.16% 78.94% 21.06% 74.89% 25.11% 2 months 92.33% 7.67% 95.38% 4.62% 93.86% 6.15% 3 months 97.68% 2.32% 98.09% 1.91% 97.89% 2.12% 6 months 99.26% 0.74% 99.88% 0.12% 99.57% 0.43% Table 22: Overview of a percentage of cloud-free pixels vs cloud-covered pixels over Sweden for different n-days composites for a winter season , , and the average of the two winter seasons / NoCloud Cloud NoCloud Cloud NoCloud Cloud 1 day 34.42% 65.58% 33.78% 66.22% 34.10% 65.90% 7 days 34.32% 65.68% 36.48% 63.52% 35.40% 64.60% 14 days 50.60% 49.40% 54.00% 46.00% 52.30% 47.70% 21 days 61.39% 38.61% 67.88% 32.12% 64.64% 35.37% 1 months 76.31% 23.69% 71.91% 28.09% 74.11% 25.89% 2 months 87.25% 12.75% 86.86% 13.14% 87.06% 12.95% 3 months 93.33% 6.67% 89.84% 10.16% 91.59% 8.42% 6 months 99.87% 0.13% 99.94% 0.06% 99.91% 0.10% 70

79 Figure 6: Graph showing the % of Cloud cover vs Cloud-free area over Austria for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data. Figure 7: Graph showing the % of Cloud cover vs Cloud-free area over Sweden for composites of different time length. Axis x is proportionate to the composite length. An average values were calculated from two winter seasons: , based on the Sentinel-2A data. 71

80 Figure 8 displays an example cloud cover/ cloud-free data over the whole of Europe based on the Sentinel-2A data for each of the chosen mosaic periods as mentioned above, i.e. (from top to bottom, and left to right): 1 day mosaic, 7 days mosaic, 14 days mosaic, 21 days mosaic, 1 month mosaic, 2 months mosaic, 3 months mosaic, and 6 months mosaic. Please note that the subsequent pictures on Figure 8 do not cover the same time window (do not build on each other). They were selected for the demonstration purpose of average Cloud/ Cloud-free coverage over Europe for selected mosaic time periods. 72

81 Figure 8: Example maps displaying the average cloud-free cover (brown) vs cloud cover (grey) over Europe when different time length mosaic is created based on Sentinel-2A data. From left to right, and top to bottom: 1 day, 7 days, 14 days, 21 days, 1 month, 2 months, 3 months, and 6 months mosaics. 73

82 8.2 MODIS AND SENTINEL-3 ANALYSES In order to provide a comparison to the analyses demonstrated in the previous chapter a data from medium spatial resolution satellites (MODIS and Sentinel-3) were used as an input in a similar analysis. Because Sentinel-2 data does not provide a classification of snow cover yet, this analysis was performed to show results in the case data is classified into three classes: Snow, NoSnow, and Cloud, to have a better understanding of the situation. Also this analysis provides an example of situation when data from a single satellite is used as an input (MODIS, Sentinel-3) compared to the case when a combination of two satellites is used. Table 23 shows the results calculated for mosaics of different length (1 day, 3 days, 7 days, 10 days, and 14 days) from a data obtained during 2016/2017 winter season from MODIS and Sentinel-3 data over Europe. Table 23: Overview of a percentage of Snow, NoSnow and Cloud-covered pixels over Europe for winter season calculated for composites of different time length. The table compares results for data retrieved from different satellites: MODIS, Sentinel-3 and the combination of MODIS & Sentinel-3 data. MODIS Sentinel-3 MODIS & Sentinel-3 Snow NoSnow Cloud Snow NoSnow Cloud Snow NoSnow Cloud 1 day 9.07% 35.25% 55.68% 6.17% 35.65% 58.18% 13.84% 42.70% 43.46% 3 days 18.29% 56.06% 25.65% 9.77% 59.38% 30.85% 26.90% 59.04% 14.06% 7 days 27.74% 65.47% 6.79% 16.12% 74.23% 9.65% 39.16% 58.85% 1.99% 10 days 31.66% 65.82% 2.52% 19.40% 75.77% 4.83% 44.31% 54.88% 0.81% 14 days 36.16% 62.63% 1.21% 23.18% 75.03% 1.79% 50.08% 49.65% 0.27% Based on the table above it is obvious that the proportion of valid data (i.e. Snow and NoSnow) vs cloud-covered data (i.e. Cloud) changes considerably if the combination of the data from the two satellites is used. The following figures display graphs based on the numbers from the above table. Figure 9 shows the data calculated for n-day mosaics based on MODIS data, blue signifying Snow data, green displaying NoSnow data, and grey showing the Cloud proportion. Figure 10 displays the same for the n-days mosaics calculated from a single input of Snetinel-3 data. Figure 11 shows the result for the same length n-days mosaics based on the data calculated from the combination of MODIS and Sentinel-3 satellites. 74

83 Figure 9: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the MODIS data. Figure 10: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the Sentinel-3 data. Figure 11: Graph showing the % of Snow, NoSnow and Cloud-covered area over Europe for composites of different time length calculated from the data of winter season based on the combination of MODIS & Sentinel-3 data. 75

84 Figure 12 shows an example Snow (blue), NoSnow (green), and Cloud (grey) data coverage over the whole of Europe based on the data from a combination of two spaceborne data sources: MODIS and Sentinel-3. Each figure displays the situation for a mosaic of different time length: from top to bottom, and from left to right: 1 day mosaic, 3 days mosaic, 7 days mosaic, 10 days mosaic, and 14 days mosaic. Please note that the subsequent pictures on Figure 12 do not cover the same time window (do not build on each other). They were selected for the demonstration purpose of average Snow/ NoSnow/ Cloud coverage over Europe for selected mosaic time periods. 76

85 Figure 12: Example maps displaying the average Snow (blue), NoSnow (green), and Cloud (grey) coverage over Europe when different time length mosaic is created based on the combination of MODIS and Sentinel- 3 data. From left to right, and top to bottom: 1 day, 3 days, 7 days, 10 days, 14 days mosaic. 77