HSAF: precipitation from space

Transcription

1 HSAF: precipitation from space Summer Alpine School Lt Col Francesco ZAULI 23 of June 2011 Valsavarenche SEMPER VIGILES Noi non dormiamo mai! 1

2 Precipitation Products from the Hydrology SAF D. Biron, D. Casella, E. Cattani, L. De Leonibus, S. Dietrich, F. Di Paola, M. Formenton, S. Laviola, V. Levizzani, D. Melfi, A. Mugnai, P. Sanò, F. Zauli and S. PUCA for the validation team

3 1. H-SAF in the context of EUMETSAT s SAF network 2. H-SAF Precipitation Algorithms 3. H-SAF Operational Products 4. H-SAF Precipitation Validation Activities 5. CDOP-1 and CDOP-2 Precipitation Activities 6. Programmatic Outlook

4 1. H-SAF in the context of EUMETSAT s SAF network - Mission of EUMETSAT; - Space and ground segments; - satellite data level: 0,1,2; - Saf concept; - support to users: products, work shop, documentation, HD;

5 EUMETSAT s Satellite Application Facilities Decentralized elements of the EUMETSAT Application Ground Segment Meteosat MetOp-EPS Other satellites (NOAA, TRMM, AQUA,...) NWP SAF Ocean & Ice SAF Nowcasting SAF Climate SAF EUMETSAT Central Facility Ozone SAF Land SAF GRAS Meteo SAF Hydrology SAF U S E R S

6 Satellite Application Facility on Support to Operational Hydrology and Water Management H-SAF The H-SAF objectives are: to provide new satellite-derived products from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology; identified products: o precipitation (liquid, solid, rate, accumulated); o soil moisture (at large-scale, at local-scale, at surface, in the roots region); o snow parameters (detection, cover, melting conditions, water equivalent); to perform independent validation of the usefulness of the new products for fighting against floods, landslides, avalanches, and evaluating water resources; the activity includes: o downscaling/upscaling modelling from observed/predicted fields to basin level; o fusion of satellite-derived measurements with data from radar and raingauge networks; o assimilation of satellite-derived products in hydrological models; o assessment of the impact of the new satellite-derived products on hydrological applications.

7 Composition of the H-SAF Consortium Country Units in the Country (responsible unit in bold) Role in the Project Austria - Zentral Anstalt für Meteorologie und Geodynamik - Technische Univ. Wien, Inst. Photogrammetrie & Fernerkundung Leader for soil moisture Belgium - Institut Royal Météorologique Bulgaria - National Institute of Meteorology and Hydrology ECMWF - European Centre for Medium-range Weather Forecasts Contributor for core soil moisture Finland - Finnish Meteorological Institute - Helsinki Technical University, Laboratory of Space Technology Leader for snow parameters - Finnish Environment Institute France - Météo-France - CNRS Centre d'etudes Spatiales de la BIOsphere - CNRS Centre d études des Environnem. Terrestres et Planétaires Germany - Bundesanstalt für Gewässerkunde Hungary - Hungarian Meteorological Service Italy - Servizio Meteorologico dell Aeronautica - Dipartimento Protezione Civile, Presidenza Consiglio Ministri - CNR Istituto di Scienze dell Atmosfera e del Clima Host + Leader for precipitation - Ferrara University, Department of Physics Poland - Institute of Meteorology and Water Management Leader for Hydrology Romania - National Meteorological Administration Slovakia - Slovenský Hydrometeorologický Ústav Turkey - Turkish State Meteorological Service - Middle East Technical University, Civil Engineering Department - Istanbul Technical University, Meteorological Department - Anadolu University Contributor for core snow parameters

8 H-SAF Precipitation Products Code Acronym H-01 PR-OBS-1 H-02 PR-OBS-2 H-03 PR-OBS-3 H-04 PR-OBS-4 H-05 PR-OBS-5 H-06 PR-ASS-1 Product name Precipitation rate at ground by MW conical scanners (with indication of phase) Precipitation rate at ground by MW cross-track scanners (with indication of phase) Precipitation rate at ground by GEO/IR supported by LEO/MW Precipitation rate at ground by LEO/MW supported by GEO/IR (with flag for phase) Accumulated precipitation at ground by blended MW and IR Instantaneous and accumulated precipitation at ground computed by a NWP model Responsible of the algorithm Italy, CNR-ISAC Italy, CNR-ISAC Italy, CNR-ISAC Italy, CNR-ISAC Italy, CNMCA Italy, CNMCA All precipitation products are operationally generated at the Centro Nazionale di Meteorologia e Climatologia Aeronautica (CNMCA) [Note: CNMCA also manages the Data service for all H-SAF products].

9 2. H-SAF Algorithms - Instruments and differences; - measurements; - Physics and limits; - reliability;

10 What does the satellite? Which is the difference from the raingauge? Fig Geometry of cross-track scanning for AMSU. 10

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12 Window regions Absorption bands 12

13 183 GHz to rr ~50 GHz to limb correction

14 Si: power density of incident plane wave Ps: power scattered by the particle Qs = Ps / Si Scattering cross sectional area ξs = Qs / π r 2 Efficiency of Mie scattering 14

15 SSM/I & SSMIS Precipitation Retrieval FORWARD PROBLEM Cloud Resolving Model Simulated Cloud profiles Simulated Dynamical Variables Radiative Transfer Model Simulated Brightness Temperatures Dynamical & Environmental Variables Multi-frequency MW Brightness Temperatures from Satellite Cloud Dynamics and Radiation Database Bayesian Retrieval Algorithm CDRD Algorithm INVERSE PROBLEM Retrieved Profiles Sanò, P., D. Casella, A. Mugnai, G. Schiavon, E.A. Smith and G.J. Tripoli, 2011: Bayesian estimation of precipitation from space using the Cloud Dynamics and Radiation Database approach. IEEE Trans. Geosci. Remote Sens. (to be submitted)

16 UW-NMS Simulations for European CDRD Generation n 60 NMS simulations (March 1, 2006 through February 28, 2007: 15 per season) selected over European rainy regions (as from NOAA GFS-ANL) so as to maximize the coverage n 3 Nested grids n Size: 4600 km, 920 km, and 504 km n Resolution: 50 km,10 km, and 2 km n Each simulation is run for hours n Microphysical profiles taken every hour (after the spin-up time: 12 hours) at 2 km grid n ~ 70 M profiles ~ 1 M rainy profiles n Dynamical tags taken every hour at 50 km grid NMS : University of Wisconsin Non-hydrostatic Modeling System Casella, D., M. Formenton, A. Mugnai, P. Sanò, E.A. Smith and G.J. Tripoli, 2011: An advanced cloudradiation database for precipitation retrieval from passive-microwave satellite observations over the European area. IEEE Trans. Geosci. Remote Sens. (to be submitted).

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18 European CDRD / SSMIS T B Comparison

19 Rome Case Study 85 GHz TB s for SSM/I Overpass on , 1630 UTC

20 Rome, Italy Case Study Radar Satellite Comparison C-Band Polarimetric Doppler Radar POLAR 55C CNR-ISAC Radarmeteology Group: E. Gorgucci, L.Baldini, V. Romaniello RR (mm h -1 ) RR (mm h -1 ) Results of the retrieval algorithm with the use of the two tags: comparison with radar measurements No tag Tag m fit sum B res sqr B res SSE Rsquare

21 AMSU MHS Observations & NNA Retrievals Messina (Sicily) Flood, 1-2 October 2009

22 SSM/I SSMIS Observations & CDRD Retrievals EUMETSAT Science Working Group Meeting, Darmstadt, March, 2011 Messina (Sicily) Flood, 1-2 October 2009

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24 AMSU MHS Neural Network Algorithm The CDRD approach would be too time-consuming for cross-track scanning radiometers. Thus, we have adopted the neural network approach trained with tested physical models, that has been proposed by Chen and Staelin (IEEE- TGRS, 41, , 2003) and Surussavadee and Staelin (IEEE Trans. Geosci. Remote Sens., 46, , 2008 and IEEE Trans. Geosci. Remote Sens., 46, , 2008). The estimates for surface precipitation rates and hydrometeor water-paths were trained using a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for ice hydrometeors (F(λ)). The MM5 model has been initialized with National Center for Atmospheric Research for 122 representative storms and their corresponding brightness temperatures simulated at AMSU frequencies. Only storms with simulated morphologies that match simultaneous AMSU observations near 183±7 GHz were used. The global nature of these storms used for training addresses the principal weakness in statistical methods trained with radar or other non-global data. The validity of these simulated storms is supported by their general agreement with histograms of concurrent AMSU observations

25 MW-IR Blended Technique Rapid Update (RU) The RU allows to compute instantaneous rain intensities at the ground at the geostationary time-space scale (Turk et al. 2000, Torricella et al. 2007). It is based on a blended MW-IR technique that correlates, by means of the statistical probability matching, brightness temperatures measured by the IR geostationary sensors and PMW-estimated precipitation rates at the ground. Main inputs to the RU procedure geolocated IR brightness temperatures at 10.8 µm from the MSG- SEVIRI; rain intensities from PMW data and algorithms. Required information for both input data sets detailed information about the observation acquisition time; data geolocation spatial resolution observation geometry (satellite zenith angle).

26 Rain intensity maps from PMW data How the RU algorithm works AT TIME t Extract space and time coincident locations from IR and MW data for each grid box Create dynamical geolocated statistical relationships RR-T b MSG- SEVIRI IR brightness temperatures at 10.8 µm Assign RR at every IR pixel Produce instantaneous rain intensity maps at the geostationary time/ space resolution The process is restarted for each IR slot in the study period

27 Rapid Update Rain Estimates 1-2 October 2009 RU input data: MSG-SEVIRI BT at 10.8 µm and PMW rain intensities Rapid Update rain intensities [mm h -1 ] MSG-SEVIRI BT at 10.8 µm [K]

28 LEO + GEO Satellite Merging TRANSPORT METHODS 2/3?? (microwave) (in time) 1/3 Observed CMORPH + + (in time) 1/3 2/3

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30 Morphing Rain Estimates 1-2 October 2009 Morphing input data: MSG-SEVIRI BT at 10.8 µm and PMW rain intensities

31 3. H-SAF Operational products - infrastructures; - data availability; - product and services; ftp.meteoam.it - contact the HD EUMETCAST

32 Organiza+onal scheme from DP to CDOP Organiza)onal scheme of CDOP: EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF) Central Area Products development and generation area User area WP 1000 Coordination and Central Functions WP 2000 Precipitation products WP 3000 Soil Moisture products WP 4000 Snow products WP 5000 Hydrological Programme WP 6000 Products Validation Coordination Observed Products Operations Surface Soil Moisture Product Operations Flat and Forest Areas Intermediate Products Operations Products/models interfacing Products validation and value assessment Operations Assimilated Product Operations Volumetric Soil Moisture Product Operations Mountaineous Intermediate Products Operations Impact study programme Products monitoring and NRT feedback User Support Products Continuous Development Products Continuous Development Products Operations Preparation of long term follow-on Provision of ground data User Communities Coordination Products Continuous Development

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34 H-SAF : Precipitation Products H-01: CDRD Bayesian Algorithm & SSM/I SSMIS H-02: Neural Network Algorithm & AMSU MHS

35 H-SAF : Precipitation Products H-03: NRL Blending Algorithm & MW (SSM/I SSMIS + AMSU MHS) + IR (SEVIRI)

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38 H-SAF : Precipitation Products H-04: Morphing Algorithm & MW (SSM/I SSMIS + AMSU MHS) + IR (SEVIRI)

39 4. H-SAF Precipitation Validation Cluster - Data available; - Ground data reference; - Common validation strategy; - Availability of results

40 Silvia Puca (Leader) Dipartimento Protezione Civile (DPC) Italy Emanuela Campione Dipartimento Protezione Civile (DPC) Italy Gianfranco Vulpiani Dipartimento Protezione Civile (DPC) Italy Luca Delli Passeri Dipartimento Protezione Civile (DPC) Italy Emmanuel Roulin Institut Royal Météorologique (IRM) Belgium Angelo Rinollo Institut Royal Météorologique (IRM) Belgium The PRECIPITATION PRODUCT VALIDATION GROUP is composed by 24 experts in hydrology, rain gauge data, radar data, and meteorology coming from 8 countries. Gergana Kozinarova NIMH-BAS Bulgaria Georgy Koshinchanov NIMH-BAS Bulgaria Claudia Rachimow Bundesanstalt für Gewässerkunde (BfG) Germany Peter Krahe Bundesanstalt für Gewässerkunde (BfG) Germany Eszter Lábó Hungarian Meteorological Service (OMSZ) Hungary Judit Kerenyi Hungarian Meteorological Service (OMSZ) Hungary Federico Porcu' Ferrara University, Department of Physics (UniFe) Italy Marco Petracca Ferrara University, Department of Physics (UniFe) Italy Bozena Lapeta Institute of Meteorology and Water Management (IMWM) Poland Monika Pajek Institute of Meteorology and Water Management (IMWM) Poland Rafal Iwanski Institute of Meteorology and Water Management (IMWM) Poland Ján Kaňák Slovenský Hydrometeorologický Ústav (SHMÚ) Slovakia Ľuboslav Okon Slovenský Hydrometeorologický Ústav (SHMÚ) Slovakia Mariàn Jurasek Slovenský Hydrometeorologický Ústav (SHMÚ) Slovakia Ahmet Öztopal Istanbul Technical University (ITU) Turkey Ibrahim Sonmez Turkish State Meteorological Service (TSMS) Turkey Aydin Gurol Erturk Istanbul Technical University (ITU) Turkey

41 PPV Raingauge network is composed by 4100 stations: Data Sources Raingauges Instrument characteristics Telemetric and mechanic Time domain (near real time/ case studies) Near real time, case studies Time resolution (15 min, 30 min) Spatial distribution (whole national territory/ limited area) Number of station (please attach a map) Operational/ for research only min (telemetric), 3 24 h (mechanic) Whole national territory ~390 mechanic (RMI) + 12 telemetric (RMI) telemetric (SETHY) Operational (RMI) + research (other networks) Data quality check Telemetric: automatically checked / mechanic: autom. + manually checked

42 PPV Radar network is composed by 40 C-band and 1 Ka-band: Data Sources Radars Instrument characteristics Beam width ~1, max range ~150 Km, 250m, C-band, single polarization, Doppler polarimetric Time domain Time resolution Spatial distribution Number of station Operational/ for research only Data quality check Near real time/ case studies 5 min, 15 min, 30 min, 1h, 24h Whole national territory 33 C band +1 Ka band Operational Permanent ground clutter removed; monitoring of electronic calibration

43 Precipitation products validation programme The Precipitation Products Validation programme started with a first workshop in Rome, June 2006, The first activity was to lay down the Validation plan, that was finalised as early as 30 September 2006, i.e. about one year after the start of the H-SAF Development Phase. After the first Workshop, other ones followed, at roughly yearly intervals, often joined with the Hydrological validation group. For all the products generated by the project, the product validation cluster is responsible: to monitor the progress in product quality as further development evaluating statistical scores and case study analysis on the base of comparison between satellite products and ground data (radar and rain gauge); to provide validation service to end-users publishing on the H-SAF web-page the statistical scores evaluated and the case studies analysed; to provide online quality control to end-users generating NRT quality maps; to monitor operational features of the products as actual arrival, timeliness, intelligibility, etc..; to provide ground data service inside the project for algorithm calibration and validation activities;

44 Validation strategy Off-line activity: product accuracy evaluation During the Development Phase the group, in collaboration with the product developers, started to investigate and define a common validation methodology in order to make the results obtained by several institutes comparable and to better understand their meanings. The common validation methodology is composed by two components: one based on large statistics (multi-categorical and continuous): one on selected case studies: each institute produces case studies analysis based on its own knowledge and experience. Both components were, and still are, considered complementary in assessing the accuracy of the implemented algorithms. Large statistics helps in identifying the existence of pathological behavior, selected case studies are useful in identifying the roots of such behavior, whenever they show up. NRT activity: quality map generation An index indicating in NRT the reliability of the precipitation estimation on the basis of the analysis of the sensitivity parameters of the product as input data will be associated to the satellite precipitation estimation. The index will support the real time use of precipitation products by human operators who need to take decisions relaying on these products. The generated maps will be published in NRT on the H-SAF web.

45 Validation strategy In order to make comparable the results obtained by several institutes and to better understand their meanings it was necessary: - Standardization of the up-scaling techniques of radar and raingauge data vs AMSU, SSM/I and SEVIRI data. The radar and raingauge data were upscaled taking into account the satellite scanning geometry and IFOV resolution of AMSU- B, SSMI and SEVIRI. - Introduction of quality filter. - Development and sharing of software packages. Two codes were developed by the validation group for upscaling ground data data vs AMSU-B and SSMI IFOV. All institutes involved in PP validation activity use these two codes developed in collaboration by the University of Ferrara and RMI with PP developers.

46 The PP VALIDATION is a pre-operational System BELGIUM -RMI BULGARIA GERMANY -BFG HUNGARY -HMS ITALY -Uni. Fe POLAND -IMWM SLOVAKIA SHMU TURKEY -ITU, TSMS comparison national radar and rain gauge data with precipitation products on satellite native grid evaluation of the monthly continuous scores and contingency tables for the precipitation classes evaluation of PDF producing numerical files called DIST files and plots numerical files called CS and MC files numerical files called DIST files and plots ITALY - DPC The PP validation leader collects all the validation files (MC, CS and DIST files), verifies the consistency of the results and evaluates the monthly common statistical results

47 5. H-SAF CDOP-1 and CDOP-2 Precipitation Activities

48 Present Status The H-SAF Development Phase ( ) was completed on August 31, All precipitation products are being generated routinely. Data are still used by the Product Validation teams and for impact studies in the Hydrological Validation Programme. All products can be accessed from the Italian Meteorological Service ftp site. Access is restricted to beta users (username and password needed), to contact the HD to receiver user and password The H-SAF web site is open: An H-SAF Continuous Development and Operations Phase (CDOP) ( ) was proposed by the Italian Meteorological Service in March 2010, during which algorithms and processing schemes will be improved and extended to new satellites in particular, to GPM and MTG.

49 Ac+vity Schedule DEVELOPMENT PHASE Product Development CDOP CDOP2 Product Genera+on and Dissemina+on Product Valida+on Con+nuous Development Consolidated/improved Products Con+nued Valida+on Product removal 49

50 Algorithms Improvements & Developments during CDOP-1 The first part of the H-SAF Continuous Development and Operations Phase (CDOP-1) ( ) started on September 1, 2010 and will last 18 months. During CDOP-1, the following activities on precipitation retrieval algorithms/products are carried out: H-01: Precipitation rate at ground by MW conical scanners The product will be upgraded by: Further development and expansion of the Cloud-Radiation Database (CRD) that is used by the Bayesian retrieval algorithm; Consequent categorization of the CRD so as to speed up the retrieval procedures; Use of further constraints in the retrieval [the so-called Cloud Dynamics and Radiation Database (CDRD) approach] so as to reduce the retrieval uncertainties; Improvement of the screening procedures over particularly unfavourable backgrounds (e.g., snow cover, coastlines, etc.); Preventive classification of clouds obtained within H-03 e.g., by fully exploiting the SEVIRI channels that are sensitive to cloud microphysical properties at the cloud top in conjunction with the scattering index that is derived from the water vapour channels at 183 GHz of AMSU-B, MHS and SSMIS.

51 Algorithms Improvements & Developments during CDOP-1 H-02 : Precipitation rate at ground by MW cross-track scanners The product will be upgraded by: Improvement of the screening procedures over particularly unfavourable backgrounds (e.g., snow cover, coastlines, etc.); Generation of a new version of the neural network algorithm, that will be trained by the same simulations and approach used for building the CRD of H-01 this is important because these simulations are more representative of the precipitation regimes over the European region w.r.t. those that have been used so far for H-02, and because of the need to harmonize the precipitation products by cross-track and conical MW scanners. Note that a particular effort will be devoted to the blending of the H-01 & H-02 products so as to provide the users with a single product and harmonize the input MW data to be entered into H-03 and H-04.

52 Algorithms Improvements & Developments during CDOP-1 H-03 : Precipitation rate at ground by GEO/IR supported by LEO/MW The product will be upgraded by: Merging of H-01 and H-02 input data with an account of different resolutions, error structures and other characteristics of the two MW products; Preventive classification of clouds into precipitating and non-precipitating. Two developments are envisaged: 1) Use of products from NWC-SAF (e.g., Cloud-type) and CNMCA (Nefodina), and statistical analysis of SEVIRI data, to distinguish convective and nonconvective, precipitating and non-precipitating, and cirrus clouds (to be removed). 2) Full exploitation of SEVIRI channels sensitive to cloud microphysical properties at the cloud top to derive precipitation indexes. In addition, for images taken at times of LEO passes, a scattering index derived from quasi-window channels of the 183-GHz band of AMSU-B, MHS, SSMIS can be extracted. The combination of indexes from SEVIRI and 183-GHz channels enables building a precipitation mask. Noteworthy, this precipitation mask will be used to support H-01 and H-02 processing as well, by limiting the search area and enabling bias mitigation.

53 Algorithms Improvements & Developments during CDOP-2 In CDOP-2 ( ) all products will be enlarged to Full Disk area All CDOP-1 products will be consolidated. In addition, the following products will be developed H-17 (H-01 v.2) : Precipitation rate at ground by MW conical scanners (with indication of phase) The database will be enlarged to Full Disk area and categorized by type of event. Use of Dual-frequency Precipitation Radar (DPR) to calibrate simulated profiles and to provide them with indexes of statistical significance (SSI) in Cloud Dynamics and Radiation Database (CDRD). Bayesian retrieval improvement to take into account the SSI in the retrieval phase. H-18 (H-02 v.2) : Precipitation rate at ground by MW cross-track scanners (with indication of phase) The H-18 retrieval algorithm is a new version w.r.t. H-02. As for H-17, it will be based on the CDRD approach and it will include the SSI in the neural network training.

54 Algorithms Improvements & Developments during CDOP-2 H-19 Rainfall intensity from GMI Bayesian algorithm Instantaneous precipitation maps generated from GPM Microwave Imager (GMI) on board the GPM Core Observatory satellite. It will represent a reference standard to uniformly calibrate data from a constellation of spacecraft with passive microwave sensors embarking the GMI instruments together with a Dual-frequency Precipitation Radar (DPR). H-17 retrieval strategy will be adapted to the characteristics of GMI in order to provide as output instantaneous Bayesian rainfall retrieval from GMI measurements. H-19 will be processed out-line and delays in data processing and distribution will depend on agreements on GPM data delivery policy. H-20 Rainfall intensity from GMI Neural Network algorithm Instantaneous precipitation maps generated from GMI on board the GPM Core Observatory satellite. As alternative approach with respect to H-19, the presence of DPR on GPM Core Observatory together with GMI allows to build a neural network algorithm trained using coincident GMI brightness temperatures measurements and DPR derived rainfall profiles. Only the central portions of the GMI swath will overlap the radar, so that at least 1 year of GPM observations is necessary to build up the product. H-20 will be processed offline and delays in data processing and distribution will depend on agreements on GPM data delivery policy.

55 Algorithms Improvements & Developments during CDOP-2 H-21 High frequency MW delineation of cloud areas with new development of hydrometeors A clouds and precipitation product will be produced with a new thresholding algorithm based on AMSU-B and MHS window and water vapour absorption bands. The algorithm is designed for an operational framework through the use of a sequence of brightness temperature thresholds that identify convective/stratiform precipitation, cloud areas with forming hydrometeors, and water vapour-loaded areas. The channel combinations and thresholds are set upon calibration with an appropriate radar data set. The product category is NRT as it runs upon acquisition of the satellitepassive microwave sensor swath. H-22 Snowfall intensity A high-frequency passive microwave algorithm will produce snowfall intensity maps in NRT via the application of channel combinations in the window and water vapour absorption bands of AMSU-B and MHS. The classification of the cloud scene along the satellite swath is done using thresholds in the brightness temperature and brightness temperature difference space. The algorithm is designed to produce its own snow cover map (wet and dry snow), but it can also use a snow map from the SAF product. The product will need extensive calibration exercises as it is very experimental.

56 Example: H-21 + H-22 1 Ingestion / Processing / Land-Sea discrimination 2 Module 183-WSLW: Small cloud droplets/snow cover filtering 3 Modules 183-WSLC/S: Rain classification 4 Module 183-WSL:: Rain rate estimation (mm h -1 ) Land: ΔT = (BT 89 -BT 150 ) < 3 K Sea: ΔT = (BT 89 -BT 150 ) < 0 K (for -20 K<ΔT< 0 K, LWP retrieval) Stratiform threshold: Convective threshold: ΔT > 10 K Sea: 0 < ΔT < 10 K Land: 3 < ΔT < 10 K

57 2011 March UTC 2011 March UTC 2011 March UTC 2011 March UTC H-21 + H WSL Rain and Snowfall Rate Retrievals

58 H-21 + H March March March WSL SNWC_MAP NESDIS SNWC_MAP

59 6. H-SAF Precipitation Outlook

60 Characteristics of the SAF H-SAF is the main European concerted effort on precipitation. Other SAFs produce precipitation products, but H-SAF focus is largely on precipitation products from GEO+LEO satellites using MW and IR bands. The SAF produces operational products for hydrological applications, but also valid per se. The SAF has adopted from its inception a validation strategy, which is now growing and reaching a critical mass necessary to validate operational and climate products. If all the review are positively passed, the SAF has several years ahead to plan for advanced products and work with the next generation of EUMETSAT satellites, i.e. Post-EPS and MTG.

61 InterSAF cooperation Precipitation is an important atmospheric variable for several SAFs, such as H-SAF, NWC-SAF and CM-SAF. A Precipitation Working Group is currently planned for ensuring an adequate level of coordination of the activities among the various SAFs. H-SAF will actively participate to it with its MW-IR based algorithms and new developments in view of applications in the other SAFs. Over the past few weeks a general agreement was reached with NWC- SAF on the use of cross-track products and algorithms from AMSU-B and MHS for nowcasting. Analogous collaborations is to be explored with CM-SAF concerning CDRs and reprocessing in general for climate applications.

62 Cooperation with GPM The H-SAF has a great interest in cooperating with the Global Precipitation Measurement (GPM) mission. Since Europe is not providing a direct mission support, the SAF is the only Europe-wide entity that can devise a long-term partnership involving a continental structure on precipitation from space. Individual European countries (e.g., France with Megha-Tropiques) do it on a national level, but the SAF is the only European body able to enter the partnership. The SAF will obviously not compete for algorithms that are already produced by the US for GMI and by Japan for the DPR. It will rather contribute its own algorithms for the constellation and will ensure support for the intercalibration of the GPM products. Moreover, the SAF validation structure will be an asset for the mission over the continent. Thus, the keywords are: Development of algorithms Intercalibration Validation.

63 However, let s be always very careful about rain intensity ftp://ftp.meteoam.it