Intercomparison and Evaluation Experiment

Transcription

1 The SnowPEx Satellite Satellite Snow Product Intercomparison Snow and Evaluation Experiment Product (6/2014 5/2016) Intercomparison and Evaluation Experiment Report to PSTG WG Meeting # 5 DLR, 5 Oct 2015 Thomas Nagler ENVEO

2 Trend of June (minimum) NH Snow Extent From snow cover from NOAA snow chart CDR derived (Derksen & Brown 2012) Rate of June-SE : -21.5% / decade

3 Spatial Difference of Snow Products (versus maximum snow extent) JASMES MDS10C Binary, 5 km March 2008 MOD10_C5 Fractional, 0.5 km

4 SnowPEx Objectives The primary objectives are Intercompare and evaluate global / hemispheric (pre) operational snow products derived from different EO sensors and generated by means of different algorithms, assessing the product quality by objective means. Evaluate and intercompare 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 Global Cryosphere Watch Initiative of the WMO and help to improve the snow cover data base for climate monitoring, as addressed by the WCRP-CliC programme.

5 Main Tasks within SnowPEx Review of Algorithms and products focusing on Snow Extent (SE) and Snow Water Equivalent (SWE) Definition of protocols and methods for validation and intercomparison of SE / SWE products Definition and compilation of reference data sets for quality assessment of SE and SWE products Intercomparison of SE / SWE products from various institutions and the quality assessment against reference data base Analysis of Hemispheric/Global SE and Snow Mass Trends and its uncertainty Study the synergy of SE and SWE products Conclusion and recommendations for satellite snow monitoring and publication of project results

6 Study Logic - Status Review of SE / SWE Algorithms and Products Identification of reference data ISSPI WS-1 July 2014 Methods and Protocols for Validation & Intercomparison of SE / SWE Products P R O G R E S S Compilation of Reference Data Set Validation and Intercomparison of Products (1st results) ISSPI WS-2 Sep 2015 Trend Analysis of SE / SWE products (1st results) Validation and Intercomparison of Products (Final Results) Trend Analysis (Final) SE & SWE Synergy Products Publication of Project Results, Conclusions, Recommendations for satellite snow monitoring

7 ISSPI-2 Workshop Sept 2015 Boulder Colorado ~40 participants snowpex/isspi-2nd-workshop

8 SnowPEx Participating Organisations SnowPEx Project Team SnowPEx International Partners

9 SnowPEx Snow Extent Products SnowPEx PROD. ID Product Name Thematic Parameter Frequency Period Pixel Sp. Contact ASNOW Autosnow Binary, Global daily 2006 present 4 km P. Romanov / NESDIS CRCLIM CryoClim Binary, Global daily 1982 present 5km R. Solberg / NR CRYOL CryoLand Fractional, PanEU daily 2000 present 0.5 km T. Nagler / ENVEO EURAC EURACSnow Binary, Alps daily 2002 present 0.25 km C. Notarnicola / EURAC GLSSE GlobSnow v2.1 Fractional, NH daily - monthly km S. Metsämäki / SYKE HSAF10 HSAF H10 Binary, PanEU daily 2009 present 5 km M. Takala / FMI IMS01 IMS Binary, NH daily 2014 present 1 km S. Helfrich / NOAA IMS04 NOAA IMS Binary, NH daily 2004 present 4 km S. Helfrich / NOAA IMS24 NOAA IMS Binary, NH daily km S. Helfrich / NOAA JXAM5 JASMES GHRM5C Binary, Global daily, weekly half-monthly km M. Hori / JAXA JXM10 JASMES MDS10C Binary, Global daily, weekly half-monthly km M. Hori / JAXA M10C05 MOD10_C5 Fractional, Global daily 2000 present 0.5 km D. Hall, G. Riggs / NASA MEASU MEaSUREs Binary, Global daily km D. Hall / NASA D. Robinson / U. Rudgers PATHF AVHRR Pathfinder Fractional, NH daily km R. Fernandes / NRCAN SCAG SCAG Fractional, NH daily km T. Painter / NASA

10 Collect products from providers for selected intercomparison periods Period #1: Period #4: Period #2: Period #5: Period #3:

11 2 Concepts for Intercomparison for SE Products by ENVEO and CCRS Data stack of SE products Transformation to common projection (EASE-GRID 2.0) Generate maximum SE map from all products Generate valid pixel mask by applying common mask of non-valid areas (e.g. sea) Aggregate SE to intercomparison grid sizes 5 km & 25 km Deviation of SE products from maximum SE map Intercomparison of valid pixels of SE products - Pixel by pixel - spatial difference maps Spatial Difference Map Number of snow pixels & intercompared pixels RMSE, Bias, Mean Absolute Difference Correlation Coefficient Fraction of total SE Time series of parameters Separate statistic analysis for forest / non-forest / plain areas / mountains / SCF classes

12 MEaSUREs JASMES MDS10C CC, PN JASMES GHRM5C CC, PN AVHRR Pathfinder CryoClim NOAA IMS AutoSnow GlobSnow, CC, PN MOD10_C5 CC, PN SCAG, CC, PN? CC cloud cover PN polar night

13 Product vs product (fractional): Mean 3-monthly RMSE for different surface types (2007/2008)

14 Mean 3-monthly RMSE and Bias from products intercomparisons 2003 / 2004 TOTAL OND 2003 BIAS \ RMSE CRCLIM IMS24 JXAM5 JXM10 M10C05 MEASU PATHF CRCLIM 23,23 21,42 18,31 24,29 26,46 28,79 IMS24-3,14 25,59 22,05 28,45 24,74 27,53 JXAM5 3,44 6,57 16,02 16,41 28,88 30,16 JXM10-1,63 1,51-5,06 19,70 23,73 24,94 M10C05 10,75 13,89 7,74 12,68 32,57 32,02 MEASU -7,61-4,50-11,05-5,99-18,70 28,85 PATHF -9,30-6,12-12,74-7,67-20,13-1,69

15 Mean 3-monthly RMSE and Bias from products intercomparisons 2003 / 2004

16 Snow Difference Maps: max SE SE products, March 2008, total area CryoClim Binary, 5 km GlobSnow Fractional, 1 km NOAA IMS Binary, 24 km JASMES GHRM5C Binary, 5 km JASMES MDS10C Binary, 5 km MOD10_C5 Fractional, 0.5 km MEaSUREs Binary, 25 km AutoSnow Binary, 4 km

17 Intercomparison of Seasonal Trends 2007/08 Monthly average snow areas Spreading of the products is significant and varies during the year; Spreading of products is useful, and should be provided to product producers for intercomparing new algorithms, etc.

18 Validation with high resolution snow products Landsat Data Set 459 Landsat scenes from Landsat-5 (188), Landsat-7 SLCon (255) and Landsat 8 (16) over the Northern Hemisphere ; cover different surface types, snow zones, topography, surface classes; ISSPI-2: a few Landsat scenes to be added (Hudson bay, Tibetanean plateau, time series of scenes of same path/row but different years) Landsat Data are free available; could be complemented in the future with Sentinel-2 Intersection with snow extent products ASNOW CRCLIM CRYOL EURAC GLSSE HSAF IMS01 IMS04 JXAM5 JXM10 M10C05 PATHF SCAG ~100

19 Processing Line for Generation of LS Reference Data North Dakota/US 6 Feb 2003 FalseColor Klein Dozier Salomonson Agregation to 1 km and 5 km Klein et al. (1998) BINARY NDSI, NDVI, NIR & Green band thresholds Dozier & Painter (2004) BINARY NDSI & NIR threshold Salomonson & Appel (2006) FRACTIONAL NDSI based, linear model Painter et al. (2009) TMSCAG FRACTIONAL MS unmixing TMSCAG

20 Validation Results LS validation of fractional & binary SE products 6 Feb 2003 LS7 SLCon P030/R028, North Dakota/US (flat/unforested) SE PRODUCT NUI LS ALG CORR Unbiased BIAS COEFF RMSE D 0,10-14,01 23,50 CRCLIM 5 km 1320 K 0,10-13,23 23,32 S 0,11-17,19 18, T 0,12-23,35 12,21 D 0,49-5,00 10,41 JXAM5 5 km 1163 K 0,51-4,31 9,90 S 0,39-9,37 10, T 0,27-18,52 9,69 D 0,73-4,74 9,83 JXM10 5 km 1033 K 0,74-4,07 9,52 S 0,69-9,34 9, T 0,67-18,84 9,35 D 0,79 5,19 8,88 M10C m K 0,78 5,80 8,78 S 0,91 0,06 5, T 0,86-9,64 7,14 Max-Min RMSE 11,29 0,9 0,49 3,28 D...Dozier K Klein S Salomonson T TMSCAG M10C05 vs Dozier AT ISSPI-2: Validation Protocol: agreed by the community at ISSPI-2; Identified the need to work on LS/S2 snow algorithms M10C05 vs LS TMSCAG

21 In-situ data coverage for validation Insitu measurements cover differrent snow zone, environments, includes data sets from various providers: Point data: ECMWF, FMI, SMHI, RIHMI, ECA&D, NVE, Env.Can, Univ. Saskatchewan, Alaska, Snow Course data: SYKE; Hydro Quebec, WSL, ZAMG/SnowGRID, RIHMI

22 First validation results using the Extended ECMWF insitu data on Snow Depth The three products providing direct SCF are evaluated: GLSSE, MOD10C05 and PATHF. Analysis made for temporally and sptatially intersecting cases ( )

23 In-situ dataset and validation protocol Spatial and temporal availability of in-situ data is sufficient for validation. In-situ data set availability: Most of In-situ data are available on request at data provider check the possibility to compile an open in-situ data set for SnowPEx periods, include metadata Separate validation in-situ station located in forests and open land, to avoid issues of products providing viewable / snow on ground. Protocol: Standard validation (threshold for SD) will be the major method applied. CCRS method is limited to Finland where transects (daily SD, and monthly transects) are operationally measured.

24 SnowPEx SWE Datasets Dataset Method Ancillary/ Forcing Data GlobSnow NASA AMSR-E standard NASA AMSR-E prototype ERAint-Land MERRA Crocus GLDAS-2 Passive microwave + in situ Standalone passive microwave Microwave + ground station climatology HTESSEL land surface model Catchment land surface model ISBA land surface + Crocus snow model Noah 3.3 land surface model Weather station snow depth measurements Resolution Time Series Reference 25 km Takala et al (2011) none 25 km Kelly (2009) Weather station snow depth climatology 25 km TBD ERA-interim 0.75 x Balsamo et al (2013) MERRA 0.5 x Rienecker et al (2011) ERA-interim 1 x Brun et al (2013) Princeton Met. 1 x Rodell et al (2004)

25 GlobSnow-2 SWE vs. RIHMI WDC ( ) Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! Blended product = combines satellite and ground-based WS-data

26 NASA Standard SWE vs. RIHMI WDC Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! Pure satellite product = utilizes only satellite data for retrieval

27 NASA Prototype SWE vs. RIHMI WDC Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! Pure satellite product = utilizes only satellite data for retrieval

28 Northern Hemisphere Snow Mass

29 Regular SnowPEx Validation / Intercomperison Dataset Current SnowPex Periods: include all Snow products, In-situ data, LS data sets Discussed at ISSPI-2: - regular compilation of an open SnowPEX validation / intercomparison data set (every 3-5 years) including all Snow products, in-situ data, high resolution snow maps (LS, S2) - Support of product intercomparison (new sensors, sensor drifts), development of new algorithms, etc

30 SNOWPEX ISSPI-3 Workshop Q3/Q4 2016? Updated Info at Datasets: