CDOP2 Product Requirement Document - Version 1.0 - H-SAF

Product Requirement Document 

Doc. No: SAF/HSAF/CDOP2/PRD/1.0 

Issue: Version 1.0 

Date: 11/12/2012 

Page: 1/66 

EUMETSAT Satellite Application Facility 

on Support to Operational Hydrology 

and Water Management 

(H-SAF) 

CDOP-2 Product Requirement Document 

Reference Number: 

SAF/HSAF/CDOP2/PRD/1.0 

Issue/Revision Index: Issue 1.0 

Last Change: 11/12/2012




Date: 11/12/2012 

Page: 2/66 

DOCUMENT SIGNATURE TABLE 

Name Date Signature 

Prepared by : H-SAF Project Team 11/12/2012 

Approved by : 

H-SAF Project Manager 

DOCUMENT CHANGE RECORD 

Issue / Revision Date Description 

0.1 23/11/2012 Preliminary version prepared for CDOP2 

1.0 11/12/2012 Baseline version approved by Steering Group (CDOP2 SG2) on 11 

December 2012




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DISTRIBUTION LIST 

Country Organization Name Contact 

Austria TU-Wien Stefan Hasenauer sh@ipf.tuwien.ac.at 

Wolfgang Wagner 

ww@ipf.tuwien.ac.at 

ZAMG Alexander Jann alexander.jann@zamg.ac.at 

Barbara Zeiner 

b.zeiner@zamg.ac.at 

Belgium IRM Emmanuel Roulin emmanuel.roulin@oma.be 

Bulgary NIMH/BAS Gergana Kozinarova Gergana.Kozinarova@meteo.bg 

Finland FMI Jouni Pulliainen jouni.pulliainen@fmi.fi 

Ali Nadir Arslan 

Ali.nadir.arslan@fmi.fi 

Kari Luojus 

Kari.luojus.fmi.fi 

Kati Anttila 

kati.anttila@fmi.fi 

Matias Takala 

Matias.Takala@fmi.fi 

Niilo Siljamo 

niilo.siljamo@fmi.fi 

Panu Lahtinen 

panu.lahtinen@fmi.fi 

Terhikki Manninen 

terhikki.manninen@fmi.fi 

France Météo France Jean-Christophe Calvet jean-christophe.calvet@meteo.fr 

Germany BfG Peter Krahe krahe@bafg.de 

Claudia Rachimow 

rachimow@bafg.de 

Hungary OMSZ Judit Kerenyi kerenyi.j@met.hu 

International ECMWF Lars Isaksen lars.isaksen@ecmwf.int 

Patricia de Rosnay 

patricia.rosnay@ecmwf.int 

Clément Albergel 

Clement.Albergel@ecmwf.int 

International EUMETSAT Dominique Faucher dominique.faucher@eumetsat.int 

Frédéric Gasiglia 

frederic.gasiglia@eumetsat.int 

Jochen Grandell 

jochen.grandell@eumetsat.int 

Lorenzo Sarlo 

Lorenzo.Sarlo@eumetsat.int 

Lothar Schueller 

Lothar.Schueller@eumetsat.int 

Stefano Geraci 

stefano.geraci@eumetsat.int 

Volker Gaertner 

volker.gaertner@eumetsat.int 

Italy CNMCA Antonio Vocino a.vocino@meteoam.it 

Daniele Biron 

d.biron@meteoam.it 

Davide Melfi 

d.melfi@meteoam.it 

Francesco Zauli 

f.zauli@meteoam.it 

Leonardo Facciorusso l.facciorusso@meteoam.it 

USAM Luigi De Leonibus deleonibus@meteoam.it 

Paolo Rosci 

rosci@meteoam.it 

CNR-ISAC Alberto Mugnai a.mugnai@isac.cnr.it 

Giulia Panegrossi 

giulia.panegrossi@artov.isac.cnr.it




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Stefano Dietrich 

Vincenzo Levizzani 

Elsa Cattani 

s.dietrich@isac.cnr.it 

v.levizzani@isac.cnr.it 

e.cattani@isac.cnr.it 

Sante Laviola 

s.laviola@isac.cnr.it 

DPC Paola Pagliara paola.pagliara@protezionecivile.it 

Angelo Rinollo 

Silvia Puca 

angelo.rinollo@protezionecivile.it 

silvia.puca@protezionecivile.it 

Telespazio Emiliano Agosta emiliano.agosta@telespazio.com 

Flavio Gattari 

flavio.gattari@telespazio.com 

UniFerrara Federico Porcu' porcu@fe.infn.it 

Marco Petracca 

marco.petracca84@libero.it 

Poland IMWM Michal Kasina michal.kasina@imgw.pl 

Piotr Struzik 

piotr.struzik@imgw.pl 

Slovakia SHMÚ Ján Kaňák jan.kanak@shmu.sk 

Sweden SMHI Stefan Nilsson stefan.nilsson@smhi.se 

Turkey ITU Ahmet Öztopal oztopal@itu.edu.tr 

METU Zuhal Akyurek zakyurek@metu.edu.tr 

Serdar Surer 

Kenan Bolat 

serdarsurer@gmail.com 

kenan23@gmail.com 

TSMS Sezel Karayusufoglu skarayusufoglu@dmi.gov.tr 

Fatih Demýr 

fdemir@dmi.gov.tr 

AU Aynur Sensoy asensoy@anadolu.edu.tr 

OMU Ibrahim Sonmez isonmez@dmi.gov.tr




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TABLE OF CONTENTS 

1 INTRODUCTION .......................................................................................................... 7 

1.1 Purpose of the document ...................................................................................... 7 

1.2 Scope .................................................................................................................... 7 

2 H-SAF PRODUCTS...................................................................................................... 8 

2.1 Products list ........................................................................................................... 8 

2.2 General requirements .......................................................................................... 10 

3 PRODUCT REQUIREMENTS .................................................................................... 10 

3.1 Precipitation products requirements .................................................................... 10 

3.1.1 Precipitation Accuracy Values ...................................................................... 10 

3.1.1 Precipitation Products Requirements ........................................................... 13 

3.2 Soil Moisture products ......................................................................................... 39 

3.2.1 Soil Moisture Accuracy Values ..................................................................... 39 

3.2.2 Soil Moisture products requirements ............................................................ 41 

3.3 Snow products .................................................................................................... 46 

3.3.1 Snow Accuracy Values ................................................................................ 46 

3.3.1 Snow products requirements........................................................................ 47 

APPENDIX 1 GLOSSARY .............................................................................................. 57 

APPENDIX 2 REFERENCES ......................................................................................... 62 

2.1 Applicable documents ......................................................................................... 62 

2.2 Reference documents ......................................................................................... 62 

2.3 Scientific References ........................................................................................... 62 

APPENDIX 3 TBC/TBD LIST .......................................................................................... 65




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LIST OF TABLES 

Table 1 H-SAF products list ........................................................................................................... 10 

Table 2: RMSE% and standard deviation of interpolation algorithms for 3 different regular grids. 

(VS 11_P01 Evaluation on accuracy of precipitation data” ) ................................................... 11 

Table 3 RMSE% AND STANDARD DEVIATION OF INTERPOLATION ALGORITHMS FOR 3 

DIFFERENT IRREGULARLY SAMPLED DATA GRID. (VS 11_P01 Evaluation on accuracy of 

precipitation data” ) ................................................................................................................ 12 

Table 4 SUMMARY TABLE. RMSE MEAN VALUES % OBTAINED BY DIFFERENT 

INTERPOLATION METHODS AND STEPS FOR HOURLY IRREGULARLY SAMPLED DATA 

GRID. (VS 11_P01 Evaluation on accuracy of precipitation data” ) ........................................ 12




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1 Introduction 

1.1 Purpose of the document 

This document shows the Product Requirements of the Satellite Application Facility on 

Support to Operational Hydrology and Water Management (H-SAF). 

PRD document is released for the beginning of the CDOP-2 phase. 

1.2 Scope 

PRD includes the H-SAF products requirements in terms of: 

- General information: 

o Product acronym, name, identificator 

o Targeted applications and users 

o Characteristics and methods 

o Input satellite data 

o Validation method 

- Requirements on: 

o Generation frequency 

o Dissemination: format, means and type of dissemination 

o Accuracy: Threshold, Target and Optimal accuracy 

o Coverage, resolution and timeliness: Spatial coverage, spatial resolution, 

vertical resolution and timeliness. 

o Format 

References or comments are also included in each of the product requirement. 

The PRD documents the committed target for development and operations within the 

Second Continuous Development and Operations Phase (CDOP-2) based on the 

cooperation agreement between the Leading Entity (USAM) and EUMETSAT. It is the 

main reference document for all development related reviews and it provides the basis for 

information given to users, what can be expected from the H-SAF after completion of 

planned developments.




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2 H-SAF Products 

2.1 Products list 

Product 

identifier 


acronym 

Precipitation products 

Product name 

H01 PR-OBS-1 Precipitation rate at ground by MW conical scanners 

H02A PR-OBS-2A Precipitation rate at ground by MW cross-track scanners 

H02B PR-OBS-2B Precipitation rate at ground by MW cross-track scanners 

H03A PR-OBS-3A Precipitation rate at ground by GEO/IR supported by LEO/MW 

H03B PR-OBS-3B Precipitation rate at ground by GEO/IR supported by LEO/MW 

H40A PR-OBS-3-FCI-A Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG FCI 

H40B PR-OBS-3-FCI-B Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG FCI 

H04A PR-OBS-4A Precipitation rate at ground by LEO/MW supported by GEO/IR 

H04B PR-OBS-4B Precipitation rate at ground by LEO/MW supported by GEO/IR 

H41A PR-OBS-4-FCI-A Precipitation rate at ground by LEO/MW supported by GEO/IR 

H41B PR-OBS-4-FCI-B Precipitation rate at ground by LEO/MW supported by GEO/IR and MTG FCI 

H05A PR-OBS-5A Accumulated precipitation at ground by blended MW and IR 

H05B PR-OBS-5B Accumulated precipitation at ground by blended MW and IR 

H42A PR-OBS-5-FCI-A Accumulated precipitation at ground by blended MW and IR and MTG FCI 

H42B PR-OBS-5-FCI-B Accumulated precipitation at ground by blended MW and IR and MTG FCI 

H15A PR-OBS-6A Blended SEVIRI Convection area / LEO MW Convective Precipitation 

H15B PR-OBS-6B Blended SEVIRI Convection area / LEO MW Convective Precipitation 

H17 PR-OBS-1 ver2 Precipitation rate at ground by MW conical scanners ver. 2 

H18 PR-OBS-2 ver2 Precipitation rate at ground by MW cross-track scanners ver. 2




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identifier 

H19 

H20 

H21 


acronym 

PR-OBS-7 

PR-OBS-8 

PR-OBS-9 

Product name 

Rainfall intensity from GMI (Global Precipitation Measurement - Microwave 

Imager) [Bayesian algorithm] 

Rainfall intensity from GMI (Global Precipitation Measurement - Microwave 

Imager) [Neural Network algorithm] 

High frequency MW delineation of cloud areas with new development of 

hydrometeors 

H22 PR-OBS-10 Snowfall intensity 

H50 PR-OBS-11 Rainfall intensity from MTG LI 

Soil Moisture products 

H08 

H14 

SM-DIS-1 

(ex SM-OBS-2) 

SM-DAS-2 

(ex SM-ASS-2) 

Small-scale surface soil moisture by radar scatterometer [1 km, 

ASCAT/SAR] 

Soil Moisture Profile Index in the roots region retrieved by surface wetness 

scatterometer assimilation method 

H16 SM-OBS-3 Large-scale surface soil moisture by radar scatterometer (25 km, ASCAT) 

H25 SM-OBS-4 ASCAT Large-scale surface soil moisture(25 Km) 

H27 

SM-DAS-3 

(ex SM-ASS-3) 

Soil Wetness Index in the roots region by scatterometer assimilation in a 

NWP model 

Snow Parameter products 

H10 SN-OBS-1 Snow detection (snow mask) by VIS/IR radiometry 

H11 SN-OBS-2 Snow status (dry/wet) by MW radiometry 

H12 SN-OBS-3 Effective snow cover by VIS/IR radiometry 

H13 SN-OBS-4 Snow water equivalent by MW radiometry 

H31 

H32 

H33 

SN-OBS-0G 

SN-OBS-0P 

SN-OBS-0M 

Snow detection for flat land (snow mask) by VIS/NIR [current operational 

SEVIRI based LSA-SAF snow product] 

Snow detection for flat land (snow mask) by VIS/NIR [current preoperational 

Metop/AVHRR based LSA-SAF snow product] 

Merged MSG and EPS Snow Cover [current in-development Merged 

MSG/Seviri-Metop/AVHRR based LSA-SAF snow product] 

H34 SN-OBS-1G Snow detection (snow mask) by VIS/NIR of SEVIRI [From H10 + H31] 

H35 

SN-OBS-1P 

Snow detection (snow mask) and Effective snow cover by VIS/NIR of 

AVHRR [From H12 + H32] 

H43 SN-OBS-0G-FCI Snow detection (snow mask) by VIS/NIR of MTG FCI 

Suffix “A”: H-SAF area; Suffix “B”: area extended to Africa and Southern Atlantic




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Table 1 H-SAF products list 

2.2 General requirements 

UR.GE.01 - H-SAF shall generate and disseminate satellite-derived products according to 

the detailed product requirements presented in section 3. 

UR.GE.04 - The H-SAF products shall cover as a minimum all EUMETSAT member and 

cooperating States and associated costal zones. The nominal H-SAF area coverage 

stretches from latitude 25°N to 75°N, longitude 25°W to 45°E. 

UR.GE.05 - The distributed H-SAF products shall be associated with characterisation of 

their error structure so that users will be guided to appropriate utilisation. Guidance to 

utilisation will also be supported by education and training activities on the nature of the 

products and their applicability in hydrology and water management. 

UR.GE.06 - All products generated in H-SAF shall be collected in near-real-time in the 

central archive (real or virtual), and shall be made available to the user community through 

the EUMETSAT Data Centre. 

UR.GE.13 - In order to enable reconstruction of time series, or re-calibration and/or reprocessing 

by advanced algorithms, raw data shall be archived at the acquisition sites 

(either physically or virtually) and made accessible to the H-SAF central archive. 

UR.GE.14 - The system shall be designed to deal with emergency management such as 

recovering missed real time production. The options range from the generation of products 

at the closest possible time (though delayed), to highly-delayed recovery only for the 

purpose of reconstructing time series, to acceptance of a definitive gap if the recovery is 

impossible or not sufficiently cost-effective. 

UR.GE.15 - The H-SAF shall install and maintain a H-SAF web site and maintain a help 

desk. The web site will provide general public information on H-SAF, H-SAF products 

description, rolling information on the H-SAF implementation status, the publication of 

product images, and all related documentation. 

UR.GE.DOC.1 - The H-SAF shall make available updated user documentation related to 

its (pre-) operational products: ATBD, Product User Manual and Validation Reports. 

3 Product Requirements 

3.1 Precipitation products requirements 

3.1.1 Precipitation Accuracy Values 

Product requirements for accuracy are adopted on the basis of the principle that values be 

unified for each sub type of product family and by making use of the following criteria for 

the three values:




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OPTIMAL: About intensity precipitation products, the impact of the statistic 

characteristics of the parameter/phenomena onto the best available observations 

(raingauges) was referred as from the WMO report on “field intercomparison of rainfall 

intensity gauges”. That report shows that rain-gauges adopting the time sampling of 1 

minute give a percentage error from the reference raingauge of about 30% and only 

with specific laboratory tuning of the instruments it is possible to achieve the 5% error. 

These results are related to rainfall intensity of about 12mm/hr as inferred by the 

observation of mechanical raingauges (the greatest majority of the operational 

instruments). 

Considering what above the optimal accuracy requirements for precipitation intensity has 

been revised as following: 

10mm/hr 25% 

10mm/hr




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3.1.1 Precipitation Products Requirements 

H01 Precipitation rate at ground by MW conical scanners PR-OBS-1 

Type 

Application and users 

Characteristics and 

Methods 

Comments 

NRT Product 

Operational hydrological units 

National meteorological services 

Civil defense 

Research & development activities 

Instantaneous precipitation maps generated from MW images taken by conical scanners on 

operational satellites in sun-synchronous orbits processed soon after each satellite pass. 

The retrieval algorithm is based on physical retrieval supported by a pre-computed cloudradiation 

database built from meteorological situations simulated by a cloud resolving model 

followed by a radiative transfer model 

Precipitation rate from conical scanning instruments will be derived from SSMIS radiometers 

onboard DMSP satellites. 

Timeliness conditioned by limited access to DMSP (via NOAA and UKMO) 

Foreseen 1h timeliness as a long term requirement - SSM/I on DMSP up to 15 - SSMIS on 

DMSP from 16 onward 

Generation frequency 

Up to six passes/day in the intervals 06-12 and 18-24 UTC 

Observing cycle over Europe: ~ 10 h 

Input satellite data 

SSMI and SSMIS on DMSP (SSMI until Nov. 2011 – no longer available) 

Dissemination 

Format Means Type 

Values in grid points of specified coordinates in the orbital 

projection (BUFR) 

Accuracy 

FTP, EUMETCast 

NRT 

Threshold Target Optimal 

Changing with precipitation type: 

90% for > 10 mm/h, 

120% for 1-10 mm/h, 

240% for < 1 mm/h 

Validation method 

Coverage, resolution and timeliness 


80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 

Meteorological radar and rain gauge 


25% for > 10 mm/h, 

50% for 1-10 mm/h, 

90% for < 1 mm/h 

Spatial coverage Spatial resolution Timeliness 

H-SAF area (25°N to 75°N latitude, 25°W to 

45°E longitude 

Resolution changing with precipitation type: 30 

km in average 

Sampling: 16 km 

2.5 h




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H01 new 

rel. 

Type 

Precipitation rate at ground by MW conical scanners (new 

rel.) 


PR-OBS-1 new rel. 



Methods 

Comments 



Civil defense 


Instantaneous precipitation maps generated from passive MW images taken by conical scanners 

on operational satellites in sun-synchronous orbits processed soon after each satellite pass. 

The retrieval algorithm is based on physically-based Bayesian approach supported by a precomputed 

cloud/dynamic-radiation database (CDRD) built from meteorological situations 

simulated by a cloud resolving model followed by a radiative transfer model 

References: [RD 14, 15, 16] (Section 3) 

Timeliness conditioned by limited access to DMSP (via NOAA and UKMO); 

Foreseen 1h timeliness as a long term requirement - SSM/I on DMSP up to 15 - SSMIS on 

DMSP from 16 onward 


Up to six passes/day in the intervals 06-12 and 18-24 UTC 



SSMI and SSMIS on DMSP (SSMI until Nov. 2011 – no longer available) 

Dissemination 


Values in grid points of specified coordinates in the orbital 

projection (BUFR) 

Accuracy 

FTP, EUMETCast 

NRT 



90% for > 10 mm/h, 

120% for 1-10 mm/h, 

240% for < 1 mm/h 




80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 



25% for > 10 mm/h, 

50% for 1-10 mm/h, 

90% for < 1 mm/h 



45°E longitude) extended to Africa and southern 

Atlantic 

30 km until Dec. 2012 - 15 km since Jan. 2013 

Sampling: 12.5 km 

2.5 h




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H02A Precipitation rate at ground by MW cross-track scanners PR-OBS-2A 

Type 



Methods 




Civil defense 


Instantaneous precipitation maps generated from MW images taken by cross-track scanners 

on operational satellites in sun-synchronous orbits processed soon after each satellite pass. 

Before undertaking retrieval the AMSU-A resolution is enhanced by blending with AMSU- 

B/MHS. 

The retrieval algorithm is based on a neural network trained by means of a pre-computed 

cloud-radiation database built from meteorological situations simulated by a cloud resolving 

model followed by a radiative transfer model 

Comments 


Precipitation rate from cross-track scanning instruments will be derived from AMSU-A and 

MHS radiometers onboard NOAA and Metop operational satellites. Nevertheless, PR-OBS-2 

will keep exploiting AMSU-A/B (on NOAA-15 & -16) measurements until available 

Up to six passes/day with somewhat irregular distribution across the day. 



AMSU-A and AMSU/B on NOAA (up to NOAA-17) 

AMSU/A and MHS on Metop-A (and MetOp-B when available) and NOAA 18/19 

Dissemination 


Values in grid points of specified coordinates in the orbital projection (BUFR) FTP, EUMETCast NRT 

Accuracy 



90 % for > 10 mm/h, 

120 % for 1-10 mm/h, 

240 % for < 1 mm/h 




80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 



25% for > 10 mm/h, 

25% for 1-10 mm/h, 

90% for < 1 mm/h 


H-SAF area (25°N to 

75°N latitude, 25°W 

to 45°E longitude) 

Resolution changing with precipitation type: 40 km in average 


1 h




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H02A new 

rel 

Type 

Precipitation rate at ground by MW cross-track scanners (new rel.) 


PR-OBS-2A new rel. 



Methods 



Civil defense 


Instantaneous precipitation maps generated from passive MW images taken by cross-track 

scanners on operational satellites in sun-synchronous orbits processed soon after each 

satellite pass. Before undertaking retrieval the AMSU-A resolution is enhanced by blending 

with AMSU-B/MHS. 




References: [RD 12], (Section 3) 

Comments 


Timeliness refers to data in the acquisition range of Rome - Outside is ~ 1 h (EARS) 






Dissemination 



Accuracy 



90 % for > 10 mm/h, 

120 % for 1-10 mm/h, 

240 % for < 1 mm/h 




80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 



25% for > 10 mm/h, 

25% for 1-10 mm/h, 

90% for < 1 mm/h 


H-SAF area Resolution changing along the scan: varying from 16 x 16 km 2 / 

circular at nadir to 26 x 52 km 2 / oval at scan edge 

1 h 

Sampling: 16 km




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H02B Precipitation rate at ground by MW cross-track scanners PR-OBS-2B 

Type 



Methods 




Civil defense 


Instantaneous precipitation maps generated from passive MW images taken by cross-track 

scanners on operational satellites in sun-synchronous orbits processed soon after each 

satellite pass. Before undertaking retrieval the AMSU-A resolution is enhanced by blending 

with AMSU-B/MHS. 




References: [RD 12], (Section 3) 

Comments 


Timeliness refers to data in the acquisition range of Rome - Outside is ~ 1 h (EARS) 






Dissemination 



Accuracy 



50 % for > 10 mm/h, 

60 % for 1-10 mm/h, 

120 % for < 1 mm/h 

POD, FAR: TBD 




30% for > 10 mm/h, 

40% for 1-10 mm/h, 

80% for < 1 mm/h 



15% for > 10 mm/h, 

20% for 1-10 mm/h, 

40% for < 1 mm/h 


H-SAF area extended to 

Africa and southern Atlantic 

Resolution changing along the scan: varying from 16 x 16 km 2 / 

circular at nadir to 26 x 52 km 2 / oval at scan edge 

Sampling 16 km 

2.5 h




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H03A Precipitation rate at ground by GEO/IR supported by LEO/MW PR-OBS-3A 

Type 


Characteristics and Methods 



Operational oceanographic units 


Civil defense 


Instantaneous precipitation maps generated by IR images from operational 

geostationary satellites “calibrated” by precipitation measurements from PMW 

satellite sensors in sun-synchronous orbits, processed soon after each acquisition 

of a new image from GEO (“Rapid Update”). 

The calibrating lookup tables are updated after each new pass of a MW-equipped 

satellite 

References: [RD 11], (Section 4 pp.65-79) 

Comments 


Product mostly suitable for convective precipitation 

Every new SEVIRI image (at 15 min intervals) 

Observing cycle over Europe: 15 min 


SEVIRI on MSG (Meteosat-9) 

Dissemination 


Values in grid points of the Meteosat projection (GRIB-2) FTP, EUMETCast NRT 

Accuracy 



90% for > 10 mm/h, 

120% for 1-10 mm/h, 

240% for < 1 mm/h 




80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 



25% for > 10 mm/h, 

50% for 1-10 mm/h, 

90% for < 1 mm/h 


H-SAF area 

Resolution changing cross Europe: 8 km in average 

Sampling: 5 km in average 

15 min




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H03B Precipitation rate at ground by GEO/IR supported by LEO/MW PR-OBS-3B 

Type 



Methods 





Civil defense 


Instantaneous precipitation maps generated by IR images from operational geostationary 

satellites “calibrated” by precipitation measurements from PMW satellite sensors in sunsynchronous 

orbits, processed soon after each acquisition of a new image from GEO (“Rapid 

Update”). 

The calibrating lookup tables are updated after each new pass of a MW-equipped satellite 

References: [RD 11], (Section 4 pp.65-79) 

Comments 






SEVIRI on MSG (Meteosat-9) 

Dissemination 



Accuracy 



100% for > 10 mm/h, 

190% for 1-10 mm/h, 

N. A. for < 1 mm/h 

POD, FAR TBD 


40% for > 10 mm/h, 

80% for 1-10 mm/h, 

N. A. % for < 1 mm/h 


20% for > 10 mm/h, 

40% for 1-10 mm/h, 

N. A. % for < 1 mm/h 


Meteorological radar and rain gauge (TBC) 



H-SAF area extended to Africa and southern 

Atlantic 



15 min




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H40A 

Type 

Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG 

FCI 


PR-OBS-3-FCI-A 



Methods 




Civil defense 




orbits, processed soon after each acquisition of a new image from GEO 

(“Rapid Update”). 

The calibrating lookup tables are updated after each new pass of a MW-equipped 

satellite 

Comments 



It is assumed that the commissioning phase of MTG will start at the end of CDOP-2, 

however the prototype of product can be designed on the requirement of MTG service 

and simulated data can be used. If the simulated data or a simulator will be available, 

the H-SAF will produce a data set based on simulated data and the product will be 

tested. 

TBD 

FCI on MTG 

Dissemination 


Values in grid points of the Meteosat projection (GRIB-2) FTP - EUMETCast NRT 

Accuracy 



80 % for > 10 mm/h, 

160 % for 1-10 mm/h, 

N/A for < 1 mm/h 

POD, FAR: TBD 



40 % for > 10 mm/h, 

80 % for 1-10 mm/h, 


Meteorological radar, rain gauge 


20 % for > 10 mm/h, 

40 % for 1-10 mm/h, 




H-SAF Area 

Resolution dependent from IFOV of FCI 


15 min




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H40B 

Type 

Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG 

FCI 


PR-OBS-3-FCI-B 





Civil defense 



Methods 



orbits, processed soon after each acquisition of a new image from GEO 

(“Rapid Update”). 


Comments 



It is assumed that the commissioning phase of MTG will start at the end of CDOP-2, 

however the prototype of product can be designed on the requirement of MTG service and 

simulated data can be used. If the simulated data or a simulator will be available, the H- 

SAF will produce a data set based on simulated data and the product will be tested. 

TBD 

FCI on MTG 

Dissemination 


Values in grid points of the Meteosat projection (GRIB-2) FTP - EUMETCast NRT 

Accuracy 



100% for > 10 mm/h 

190 % for 1-10 mm/h 


POD, FAR: TBD 


40 % for > 10 mm/h 

80 % for 1-10 mm/h 



20 % for > 10 mm/h 

40 % for 1-10 mm/h 



Meteorological radar, rain gauge (TBC from validation team) 



To extend to Africa and southern Atlantic 

Resolution dependent from IFOV of FCI 


25 min




Date: 11/12/2012 

Page: 22/66 

H04A Precipitation rate at ground by LEO/MW supported by GEO/IR PR-OBS-4A 

Type 



Hydrology 

Climate monitoring 

Risk Management 

Meteorology 


Methods 

Instantaneous precipitation maps generated by PMW satellite sensors from operational 

satellites in sun-synchronous orbits, time-interpolated by exploiting the dynamical 

information observed on IR images from GEO. 

The algorithm performs the interpolation soon after the acquisition of a new image from 

LEO. This method (“Morphing”) is particularly suited for computing accumulated 

precipitation of use in hydrology. 

Comments 

Product primarily designed for climatology. 

Applicability in an operational framework to be assessed. 

Input data are merged into one product file 



12 times per day 

SEVIRI on MSG; 

H-SAF PR-OBS-01 


Dissemination 


Equidistant cylindrical or Plate Carree (GRIB-2) FTP, EUMETCast NRT 

Accuracy 



90% for > 10 mm/h, 

120% for 1-10 mm/h, 

240% for < 1 mm/h 


80% for > 10 mm/h, 

105% for 1-10 mm/h, 

145% for < 1 mm/h 


25% for > 10 mm/h, 

50% for 1-10 mm/h, 

90% for < 1 mm/h 


Meteorological radar and rain gauge (TBC from validation team) 



H-SAF area (25°N to 75°N latitude, 25°W to 45°E 

longitude) (degradation expected at very high 

latitudes) 

Resolution: 30 km in average 


4 hours




Date: 11/12/2012 

Page: 23/66 

H04B Precipitation rate at ground by LEO/MW supported by GEO/IR PR-OBS-4B 

Type 



Climatological community 

National meteorological services (to be assessed) 



Methods 







Comments 

Product primarily designed for climatology. 

Applicability in an operational framework to be assessed. 




SEVIRI on MSG 



Dissemination 



Accuracy 



50 % for > 10 mm/h 

60 % for 1-10 mm/h 

120 % for < 1 mm/h 

POD, FAR: TBD 


30 % for > 10 mm/h 

40 % for 1-10 mm/h 

80 % for < 1 mm/h 


15 % for > 10 mm/h 

20 % for 1-10 mm/h 

40 % for < 1 mm/h 








5 hours




Date: 11/12/2012 

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H41A 

Type 

Precipitation rate at ground by LEO/MW supported by GEO/IR and MTG 

FCI 


PR-OBS-4-FCI-A 






Methods 







Comments 

See the comments about the product retrieved by SEVIRI. 

We are assuming that the commissioning phase of MTG will start at the end of CDOP- 

2, however the prototype of product can be designed on the requirement of MTG 

service and simulated data can be used. If the simulated data or a simulator will be 

available, the H-SAF will produce a data set based on simulated data and the product 

will be tested. 




FCI on MTG 



Dissemination 



Accuracy 



50 % for > 10 mm/h 

60 % for 1-10 mm/h 

120 % for < 1 mm/h 

POD, FAR: TBD 


30 % for > 10 mm/h 

40 % for 1-10 mm/h 

80 % for < 1 mm/h 


15 % for > 10 mm/h 

20 % for 1-10 mm/h 

40 % for < 1 mm/h 






longitude) (degradation expected at very high latitudes) 

Resolution: ~ 30 km 

Sampling dependent of FCI IFOV 

4 hours




Date: 11/12/2012 

Page: 25/66 

H41B 

Type 

Precipitation rate at ground by LEO/MW supported by GEO/IR and MTG 

FCI 


PR-OBS-4-FCI-B 






Methods 


satellites in sun-synchronous orbits, time-interpolated by exploiting the dynamical information 

observed on IR images from GEO. 

The algorithm performs the interpolation soon after the acquisition of a new image from LEO. 

This method (“Morphing”) is particularly suited for computing accumulated precipitation of use 

in hydrology. 

Comments 


We are assuming that the commissioning phase of MTG will start at the end of CDOP-2, 


simulated data can be used. If the simulated data or a simulator will be available, the H-SAF 

will produce a data set based on simulated data and the product will be tested. 




FCI on MTG; 



Dissemination 



Accuracy 



50 % for > 10 mm/h 

60 % for 1-10 mm/h 

120 % for < 1 mm/h 

POD, FAR: TBD 


30 % for > 10 mm/h 

40 % for 1-10 mm/h 

80 % for < 1 mm/h 


25% for > 10 mm/h, 

50% for 1-10 mm/h, 

90% for < 1 mm/h 








5 hours




Date: 11/12/2012 

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H05A Accumulated precipitation at ground by blended MW+IR PR-OBS-5A 

Type 






Civil defense 



Methods 

Derived from precipitation maps generated by merging MW images from operational sunsynchronous 

satellites and IR images from geostationary satellites (i.e., products PR-OBS- 

3 and, later, PR-OBS-4). 

Integration is performed over 3, 6, 12 and 24 h. In order to reduce biases, the satellitederived 

field is forced to match raingauge observations and, in future, the accumulated 

precipitation field outputted from a NWP model 

Comments 

Accuracy improves (at the expense of timeliness) moving input from PR-OBS-3 to PR- 

OBS-4. 

Timeliness longer when input PR-OBS-4 


Four products (integrals over 3, 6, 12 and 24 h) every three hours (rolling) 

Observing cycle over Europe: 3 h 


SEVIRI on MSG 

Dissemination 



Accuracy 


Changing with integration interval: 

120% for 3-h accumulation, 

100% for 24-h accumulation 












longitude) (degradation expected at very high latitudes) 



3 h




Date: 11/12/2012 

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H05B Accumulated precipitation at ground by blended MW+IR PR-OBS-5B 

Type 






Civil defense 



Methods 







Comments 

Accuracy improves (at the expense of timeliness) moving input from PR-OBS-3 to PR- 

OBS-4. 

Timeliness longer when input PR-OBS-4 





SEVIRI on MSG 

Dissemination 



Accuracy 



150 % for 3-h accumulation 


POD, FAR: TBD 














25 min




Date: 11/12/2012 

Page: 28/66 

H42A Accumulated precipitation at ground by blended MW+IR and MTG FCI PR-OBS-5-FCI-A 

Type 






Civil defense 



Methods 







Comments 










FCI on MTG 

AMSU-A/B (NOAA 15/16) 

MHS (Metop, NOAA 18/19) 

Dissemination 



Accuracy 





POD, FAR: TBD 











H-SAF area (25°N to 75°N latitude, 25°W to 45°E longitude) 

(degradation expected at very high latitudes) 



15 min




Date: 11/12/2012 

Page: 29/66 

H42B Accumulated precipitation at ground by blended MW+IR and MTG FCI PR-OBS-5-FCI-B 

Type 






Civil defense 



Methods 


satellites and IR images from geostationary satellites (i.e., products PR-OBS-3 

and, later, PR-OBS-4). 




Comments 










FCI on MTG 

AMSU-A/B (NOAA 15/16) 

MHS (Metop, NOAA 18/19) 

Dissemination 



Accuracy 





POD, FAR: TBD 














25 min




Date: 11/12/2012 

Page: 30/66 

H15A Blended SEVIRI Convection area/LEO MW Convective Precipitation PR-OBS-6A 

Type 






Civil defense 



Methods 


satellites “calibrated” by precipitation measurements from MW images in sun-synchronous 

orbits, processed soon after each acquisition of a new image from GEO (“Rapid Update”). 


Comments 






SEVIRI on MSG 

Dissemination 



Accuracy 



80 % for > 10 mm/h 

160 % for 1-10 mm/h 


TBC 



40 % for > 10 mm/h 

80 % for 1-10 mm/h 


TBC 



20 % for > 10 mm/h 

40 % for 1-10 mm/h 


TBC 




45°E longitude) (degradation expected at very 

high latitudes) 



15 min




Date: 11/12/2012 

Page: 31/66 

H15B Blended SEVIRI Convection area/LEO MW Convective Precipitation PR-OBS-6B 

Type 






Civil defense 



Methods 


satellites “calibrated” by precipitation measurements from MW images in sun-synchronous 

orbits, processed soon after each acquisition of a new image from GEO (“Rapid Update”). 


Comments 






SEVIRI on MSG 

Dissemination 



Accuracy 



80 % for > 10 mm/h 

160 % for 1-10 mm/h 


TBC 



40 % for > 10 mm/h 

80 % for 1-10 mm/h 


TBC 



20 % for > 10 mm/h 

40 % for 1-10 mm/h 


TBC 






25 min




Date: 11/12/2012 

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H17 Precipitation rate at ground by MW conical scanners ver. 2 PR-OBS-1 ver2 

Type 





Civil defense 



Modified Bayesian retrieval PR-OBS-1 algorithm to make use of SSI 

(Statistical Significance Index) 

Comments 



TBD 

SSMIS on DMSP 

Dissemination 


Values in grid points of specified 

coordinates in the orbital projection 

(BUFR) 

Accuracy 

FTP - EUMETCast 

NRT 



40 % for > 10 mm/h 

60 % for 1-10 mm/h 

200 % for < 1 mm/h 

POD, FAR: TBD 


20 % for > 10 mm/h 

40 % for 1-10 mm/h 

100 % for < 1 mm/h 


10 % for > 10 mm/h 

20 % for 1-10 mm/h 

50 % for < 1 mm/h 




Spatial coverage Spatial resolution Vertical resolution Timeliness 


extended to Africa 

and southern 

Atlantic 



2.5 h




Date: 11/12/2012 

Page: 33/66 

H18 Precipitation rate at ground by MW cross-track scanners ver. 3 PR-OBS-2 ver3 

Type 





Civil defense 



Re-train the CDRD-based ANN network with additional SSI input 

Comments 



TBD 

AMSU-A and MHS on NOAA and EPS (MetOp) satellites 

Dissemination 




(BUFR) 

Accuracy 

FTP - EUMETCast 

NRT 



40 % for > 10 mm/h 

60 % for 1-10 mm/h 

200 % for < 1 mm/h 

POD, FAR: TBD 


20 % for > 10 mm/h 

40 % for 1-10 mm/h 

100 % for < 1 mm/h 


10 % for > 10 mm/h 

20 % for 1-10 mm/h 

50 % for < 1 mm/h 







and southern 

Atlantic 

Resolution changing along the 

scan: varying from 16 x 16 km 2 / 

circular at nadir to 26 x 52 km 2 / 

oval at scan edge 


2.5 h




Date: 11/12/2012 

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H19 Rainfall intensity from GMI (Global Precipitation Measurement - 

Microwave Imager) [Bayesian algorithm] 

PR-OBS-7 

Type 


Offline Product 




Civil defense 



Bayesian algorithm 

Comments 

Generation frequency N. A. 


GMI and DPR on GPM observatory 

Dissemination 




(BUFR) 

Accuracy 

FTP 

Offline 


TBD TBD TBD 





Global for low and 

middle latitudes up 

to 62° 

Resolution: 4.4 X 7.3 Km N. A.




Date: 11/12/2012 

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H20 Rainfall intensity from GMI (Global Precipitation Measurement - 

Microwave Imager) [Neural Network algorithm] 

PR-OBS-8 

Type 






Civil defense 



Neural Network algorithm 

Comments 



GMI and DPR on GPM observatory 

Dissemination 




(BUFR) 

Accuracy 

FTP 

Offline 


TBD TBD TBD 





Global for low and 

middle latitudes up 

to 62° 

Resolution: 4.4 X 7.3 Km N. A.




Date: 11/12/2012 

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H21 

Type 

High frequency MW delineation of cloud areas with new development of 

hydrometeors 


PR-OBS-9 




Civil defense 



Threshold method calibrated with mid-latitude radar dataset. 

Comments 



TBD 

AMSU-B and MHS on NOAA, EPS, and MetOp 

Dissemination 




(BUFR) 

Accuracy 

FTP 

NRT 



100 % for > 10 mm/h 

110 % for 1-10 mm/h 

170 % for < 1 mm/h 

POD, FAR: TBD 


30 % for > 10 mm/h 

40 % for 1-10 mm/h 

80 % for < 1 mm/h 


15 % for > 10 mm/h 

20 % for 1-10 mm/h 

40 % for < 1 mm/h 







and southern 

Atlantic 



2.5 h




Date: 11/12/2012 

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H22 Snowfall intensity PR-OBS-10 

Type 






Civil defense 



Threshold method calibrated with mid- and high-latitude radar dataset. 

Comments 



TBD 

AMSU-B and MHS on NOAA,EPS, and MetOp 

Dissemination 




(BUFR) 

Accuracy 

FTP 

NRT 


POD (≥ 1 mm/h) 0.3 

FAR (≥ 1 mm/h) 0.7 



POD (≥ 1 mm/h) 0.6 

FAR (≥ 1 mm/h) 0.4 


POD (≥ 1 mm/h) 0.8 

FAR (≥ 1 mm/h) 0.2 




and southern 

Atlantic 


Sampling: 16 km (at nadir) 

2.5 h




Date: 11/12/2012 

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H50 Rainfall intensity from MTG LI PR-OBS-11 

Type 






Civil defense 



nstantaneous precipitation maps generated by LI data maps from 

operational geostationary satellites “calibrated” by precipitation 

measurements. The preliminary activities will be done with simulated data 

from data of LAMPINET (the Italian network lightning). 

The methods is based on the Tapia concept and a initial calibration has to be 

performed. During the development phase will be evaluated the impact of 

First guess. 

The output will show the field of convective rainfall linked to lightning . 

Comments 



We are assuming that the commissioning phase of MTG will start at the end 

of CDOP-2, however the prototype of product can be designed on the 

requirement of MTG service and simulated data can be used. If the 

simulated data or a simulator will be available the H-SAF will produce a 

data set based on simulated LI data and the data set will tested with the 

validation procedure. A report will be presented. 

TBD 

LI on MTG 

Dissemination 


BUFR FTP NRT 

Accuracy 



40 % for > 10 mm/h 

60 % for 1-10 mm/h 

200 % for < 1 mm/h 


20 % for > 10 mm/h 

40 % for 1-10 mm/h 

100 % for < 1 mm/h 


10 % for > 10 mm/h 

20 % for 1-10 mm/h 

50 % for < 1 mm/h 





Europe >20Km 15 min.




Date: 11/12/2012 

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3.2 Soil Moisture products 

3.2.1 Soil Moisture Accuracy Values 

During Development Phase and CDOP1, Accuracy Requirements for products H14 SM- 

DAS-2 and H08 SM-OBS-2 have been given in volumetric unit (m3m-3) and the main 

score to be evaluated was the Root Mean Square Difference, supportive scores being: the 

Mean Error (or bias, ME), the Standard Deviation (SD) and the Correlation Coefficient 

(CC). 

The first definition of H-SAF soil moisture validation goals stems to a large extent from the 

efforts to build the SMOS and SMAP satellites that both aim to retrieve the absolute 

volumetric soil moisture content with an RMSD of 0.04 m3m-3. But considering the 

evolution of the literature on this topic over the last few years one can clearly see a shift in 

the way of how the validation of remotely sensed / modelled soil moisture data is being 

regarded. RMSD by itself is not sufficient, other measures such as CC are also important, 

and for some applications even more important than the RMSD (Entekhabi et al., 2010; 

Brocca et al., 2011). 

Several authors have demonstrated that local measurements could be used to validate 

model output as well as remotely-sensed soil moisture (SM) at a different scale (e.g. 

Albergel et al, 2009, 2010; Rüdiger et al., 2009; Brocca et al., 2010a; 2011). However, 

spatial variability of SM is very high and can vary from centimetres to metres. Precipitation, 

evapotranspiration, soil texture, topography, vegetation and land use could either enhance 

or reduce the spatial variability of soil moisture depending on how it is distributed and 

combined with other factors (Famiglietti et al., 2008; Brocca et al., 2010b, 2012). 

Differences in soil properties could imply important variations in the mean and variance of 

soil moisture, even over small distances. Each soil moisture data set is characterized by its 

specific mean value, variability and dynamical range. Saleem and Salvucci (2002) and 

Koster et al. (2009, 2011) suggested that the true information content of modelled soil 

moisture does not necessarily rely on their absolute magnitudes but instead on their time 

variation. The latter represents the time-integrated impacts of antecedent meteorological 

forcing on the hydrological state of the soil system within the model. 

The high spatial variability of in situ SM used for validation as well as SM data set specific 

characteristics suggest that the Correlation Coefficient (CC) should be the main score to 

be evaluated. On this basis the soil moisture products development and validation groups 

propose to change the main score to evaluate the "Product Requirements" for H08 and 

H14 products from the RMSD to the CC. The following values are proposed as accuracy 

thresholds: 

• Threshold accuracy: 0.50 

• Target accuracy 0.65




Date: 11/12/2012 

Page: 40/66 

• Optimal accuracy 0.80 

It is noted that a sufficiently long period of time is needed to calculate the scores (periods 

of at least 12 months are needed). 

For references on the matter, see Appendix 2, references from [RD 18] to [RD 29].




Date: 11/12/2012 

Page: 41/66 

3.2.2 Soil Moisture products requirements 

H08 Small–scale surface soil moisture by radar scatterometer SM-DIS-1 

(ex SM-OBS-2) 

Type 




Climatology 



Methods 

Comments 

Derived from the CAF Global ASCAT SM product limited to the H-SAF area. Maps of the 

soil moisture content in the surface layer (0-2 cm) generated from the Metop scatterometer 

(ASCAT) processed shortly after each satellite orbit completion. It is generated by 

disaggregating the large-scale product (25 km resolution), to 0.5-km sampling with 

downscaling parameters derived from ENVISAT ASAR (C-band). 

Processing implying heavy support from external data, including SAR imagery, for building 

the database. 

Generation frequency On completion of each orbit at 100 min intervals, through the intervals 07-11 and 17-23 

UTC 



ASCAT on Metop 

Dissemination 



Accuracy 


Correlation coefficient (CC): 0.50 Correlation coefficient (CC): 0.65 Correlation coefficient (CC): 0,80 


In-situ measurements (e.g. Time Domain Reflectometers (TDR)), Output of hydrometeorological 

models, Satellite data (e.g. SMOS) 



H-SAF area (25°N to 75°N latitude, 

25°W to 45°E longitude) 

Resolution resulting from disaggregation starting from 25 km 


130 min




Date: 11/12/2012 

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H14 

Type 

Soil Moisture Profile Index in the roots region retrieved by surface 

wetness scatterometer assimilation method 


SM-DAS-2 

(ex SM-ASS-2) 



Climatology 



Methods 

Analysed liquid soil moisture profile index for four different soil layers (covering the root 

zone from the surface to ~ 3 metres) generated by the ECMWF soil moisture assimilation 

system at 24 hour time steps. 

The analysed soil moisture fields are based on a modelled first guess, the screen-level 

temperature and humidity analyses, and the ASCAT-derived surface soil moisture. They 

are then re-scaled soil wetness index by normalising by the saturated soil moisture value as 

a function of soil type. 

The Global product is generated starting from the Global surface soil moisture product 

(CAF product, SM-OBS-3 when becomes available) 

Comments 


Product development initially based on ERS-1/2 AMI-SCAT. 

Model output at 24-h intervals 

Observing cycle ~ 24 h (NWP model assimilation / stabilisation process) 



Dissemination 


Values in grid points on a Gaussian grid FTP, EUMETCast NRT 

Accuracy 




Comparison with in situ measurements (e.g. Time Domain Reflectometers 

(TDR)). 

Comparison with SMOS 



global 

Horizontal resolution: 25km 

Vertical resolution: 4 layers in the range surface to2.89m: layer-1 (0-7cm), 

layer-2 (7-28cm), layer-3 (28-100cm) and layer-4 (100-289cm). 

24 to 36 h




Date: 11/12/2012 

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H16 Large-scale surface soil moisture by radar scatterometer SM-OBS-3 

Type 




Climatology 



Methods 

Comments 


It refers to the soil moisture content in the surface layer (0.5-2 cm) generated from the Metop 

scatterometer (ASCAT). It is a coarse-resolution product (25 km), controlled by the 

instrument IFOV. 

Existing ASCAT product developed in cooperation between EUMETSAT and TU Wien within 

CAF 

On completion of each orbit, at 100 min intervals, through the whole day 




Dissemination 



Accuracy 




In-situ measurements (e.g. Time Domain Reflectometers (TDR) 

Output of hydro-meteorological models 

Satellite data (e.g. SMOS, AMSU, SMAP) 



global 

Resolution: 25 km 


2 h




Date: 11/12/2012 

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H25 ASCAT Large-scale surface soil moisture(25 Km) SM-OBS-4 

Type 




Climatology 



Methods 

Comments 

Time series of ASCAT large-scale surface soil moisture 

Currently a TU WIen product 




Dissemination 


various scientific file formats FTP Offline 

Accuracy 




In-situ measurements (e.g. Time Domain Reflectometers (TDR)) 

Output of hydro-meteorological models 

Satellite data (e.g. SMOS) 



Global 

Resolution: 25 km 


N. A.




Date: 11/12/2012 

Page: 45/66 

H27 

Type 

Soil Wetness Index in the roots region by scatterometer assimilation in a 

NWP model 


SM-DAS-3 

(ex SM-ASS-3) 



Climatology 



Methods 

Re-analysed liquid soil moisture profile index for four different soil layers (covering the 

root zone from the surface to ~ 3 metres) generated by the ECMWF land surface reanalysis 

system at 24 hour time steps. H-27 provides a consistent time series of both 

surface and root zone soil moisture with a daily global coverage which is highly relevant 

for hydrological applications and water budget investigations. 

The analysed soil moisture fields are based on a modelled first guess, the screen-level 

temperature and humidity analyses, and the ASCAT-derived surface soil moisture. They 

are then re-scaled to soil wetness index by normalising by the saturated soil moisture 

value as a function of soil type. 

The Global product is generated using the re-analysed Global surface soil moisture 

product assimilated in the ECMWF land surface re-analysis suite. This product will be 

developed in CDOP-2 based on CDOP developments. . Data assimilation is indeed the 

only approach that enables to retrieve both surface and root zone soil moisture from 

satellite surface swath data. 

Comments 

Re-analysis of SM-ASS-2 using consistent production algorithm to provide long time 

series of the root zone soil wetness profile index 



Satellites used in NWP 


Dissemination 


Values in grid points on a Gaussian grid FTP Offline 

Accuracy 




Comparison with in situ measurements (e.g. Time Domain Reflectometers 

(TDR)). 

Comparison with SMOS 



Global Horizontal resolution: ~16 km N. A.




Date: 11/12/2012 

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3.3 Snow products 

3.3.1 Snow Accuracy Values 

Product requirements for accuracy were adopted taking in consideration the actual 

performances achievable as demonstrated by continuous validation and taking in 

consideration the baseline proposed requirement values have not been tested before. 

Especially in the mountainous areas, 5 km x 5 km spatial resolution of the product can not 

be represented by the distribution of the available ground data. During the validation 

analysis simple satellite-ground comparison is performed. When automatic snow 

observation stations (specially established at most proper sites for snow measurement in 

Turkey) which are used in the comparison at high altitudes (elevation >2000 m) 90% POD 

values are obtained. However between 750 m and 2000m due to morphology of snow 

changes rapidly and the distribution of the ground observations are synoptic, the results 

are decreasing due to the available limitations. 

In Remote Sensing Community the question of the acceptable level of accuracy is often 

answered by reference to the seminal work of Anderson et al. (1976) who outline the 

criteria for an effective land use and land cover classification scheme for use in 

conjunction with remotely sensed data. Specifically, Anderson et al. (1976, p. 5), citing the 

earlier work of Anderson (1971), state that “the minimum level of interpretation accuracy in 

the identification of land use and land cover categories from remote sensor data should be 

at least 85 percent”. Therefore, although an 85% accuracy target is widely accepted by the 

remote sensing community as a benchmark, as several recent examples indicate (Foody 

2002, Reese et al. 2002, Fuller et al. 2003, Tømmervik et al. 2003), its usefulness as a 

standard is unclear. Others have also questioned the validity of the 85% target (Laba et al. 

2002, p. 453). The accuracy assessments of several recently completed regional-scale 

land cover mapping projects indicate that producer's and user's accuracies are stabilizing 

in the50-70% range, independent of level of taxonomic detail or methodological 

approaches (Edwards et al. 1998, Ma et al. 2001, Zhu et al. 2000). Additional 

improvements in accuracy are not likely, and that only through the use of sensors with high 

spectral, spatial, and temporal resolution will map accuracies approach 80%. 

The appropriate accuracy assessment protocol often develops from consideration of the 

following question: Is the product sufficiently accurate for a specific application? The 

understanding is that not all applications require the same level of accuracy to be 

successfully accomplished, and therefore the same level of effort need not be expended to 

determine product accuracy for different possible applications. For hydrological 

applications 85% POD and 15% FAR would be ideal in using the snow cover maps for 

runoff generation. 

Furthermore, different threshold requirements for flat/forested areas and mountainous 

areas should be identified. Revised threshold values are 0.8 POD for flat and forested 

areas and 0.6 POD for mountainous areas. For the target values, the proposed 

requirement is 0.85 POD for the flat and forested areas, and 0.7 for the mountainous area. 

Regarding the FAR, the threshold is 0.2 for flat area and 0.3 for mountainous area, and for 

the target value: for 0.15 for flat area and 0.2 for mountainous area.




Date: 11/12/2012 

Page: 47/66 

With this respect, following table summarizes product requirements for Snow products. 

3.3.1 Snow products requirements 

H10 Snow detection (snow mask) by VIS/IR radiometry SN-OBS-1 

Type 



Methods 

Comments 




Civil defense 


Binary map of snow / no-snow situation. VIS/IR images from GEO are used. The product may 

be processed in different ways and have different quality depending on the surface being flat, 

forested or mountainous. The algorithm is based on thresholding of several channels of 

SEVIRI, the most important being those in short-wave, thus the product is generated in 

daylight. In order to search for cloud-free pixels, multi-temporal analysis is performed over all 

images available in 24 hours (in daylight) 

Different methods used for flat/forested and mountainous regions. 

Timeliness is intended as delay after acquisition of the last image utilised in the multi-temporal 

analysis 



Output result every 24 h 

SEVIRI on MSG 

Dissemination 


Values in grid points of the Meteosat projection (HDF5) FTP, EUMETCast NRT 

Accuracy 


Probability Of Detection (POD): 

Flat / Forested areas: 80 % 

Mountainous areas: 60% 

False Alarm Rate (FAR): 











Snow observing stations 

Probability Of Detection (POD): 99 % 

False Alarm Rate (FAR): 5 % 



longitude) (degradation expected at very high 

latitudes) 

SEVIRI pixel resolution and grid 

30 min




Date: 11/12/2012 

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H11 Snow status (dry/wet) by MW radiometry SN-OBS-2 

Type 



Methods 




Civil defense 


This product indicates the status of the snow mantle, whether it is wet or dry and, in time 

series, thawing or freezing. 

Multi-channel MW observations are used (middle frequencies), and the algorithm is based 

on thresholding. 

In order to remove ambiguity between wet snow and bare soil, use is made of product SN- 

OBS-1 for preventive snow recognition, and of exploitation of change detection 

Comments 

AMRS-E failed on 4 Oct 2011 : input data replaced with SSMIS 

Before failure: timeliness controlled by the delay in accessing AMSR-E data from NASA by 

FTP, intended as delay after acquisition of the last image utilised in the multi-temporal 

analysis. 



After each orbit, but then merging with daily SN-OBS-1 maps; therefore: output result every 

24 h 

SSMIS on DMSP 

Dissemination 


GRIB2 FTP, EUMETCast NRT 

Accuracy 


Hit Rate (HR): 60 % 












Sampling: 0.25 degrees 

6 h




Date: 11/12/2012 

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H12 Effective snow cover by VIS/IR radiometry SN-OBS-3 

Type 

Application and 

users 


Methods 




Civil defense 


The combined effect, within a product resolution element, of fractional snow cover and other 

reflective contributors is used to estimate the fractional cover at resolution element level. 

The product may be processed in different ways and have different quality depending on the 

surface being flat, forested or mountainous. 

The algorithm is based on multi-channel analysis of AVHRR, the most important being those in 

short-wave, thus the product is generated in daylight. 

The “deficit” of brightness in respect of the maximum one is correlated to the lack of snow in the 

product resolution element. In the case of forests, the signal attenuation due to forest canopy 

obstruction is taken in to account by application of transmissivity map assembled in advance using 

MODIS and GlobCover land cover data. 

In order to search for cloud-free pixels, multi-temporal analysis is performed over all images 

available in 24 hours (in daylight) 

Comments 



analysis 

Generation 

frequency 


After each AVHRR pass, then multi-temporal analysis for cloud-free pixels 


AVHRR (NOAA, Metop) 

Dissemination 



Accuracy 


45% (Overall accuracy) 65% (Overall Accuracy) 95% (Overall Accuracy) 



Better spatial resolution satellite data (Landsat) 

Snow courses 



H-SAF area (25°N to 75°N 

latitude, 25°W to 45°E longitude) 

Resolution: 1 - 2 km, 

Sampling:0.01 degrees 

30 min 

Timeliness is intended as delay after acquisition of the 

last image utilised in the multi-temporal analysis




Date: 11/12/2012 

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H13 Snow water equivalent by MW radiometry SN-OBS-4 

Type 

Application and 

users 


Methods 




Civil defense 


Maps of snow water equivalent derived from MW measurements sensitive to snow thickness and 

density. 



The algorithm is based on assimilating MW brightness temperatures of several channels at 

frequencies with different penetration in snow, into a first-guess field built by the (sparse) network 

of stations measuring snow depth for flat areas, for mountainous areas snow depth measured at 

stations is not used directly in the algorithm 

Comments 

AMRS-E failed on 4 Oct 2011 : input data replaced with SSMIS 

Before failure: timeliness controlled by the delay in accessing AMSR-E data from NASA by FTP, 

intended as delay after acquisition of the last image utilised in the multi-temporal analysis. 


Generation 

frequency 


Assimilation of SSMI/S brightness temperatures in a background field 


SSMI/S 

Dissemination 



Accuracy 


Flat / Forested areas: 40mm 

Mountainous areas: 45mm 











Sampling: 0.25 degrees 

6 h




Date: 11/12/2012 

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H31 Snow detection for flat land by VIS/NIR of SEVIRI SN-OBS-0G 

Type 



NWP 

Climate Monitoring 

Carbon Models 


Multichannel (VIS, NIR, IR) analysis 

Product generated for all land pixels, accuracy requirements for the flat 

land pixels of the product 

Comments 

LSA SAF Product LSA-13 until CDOP1 



SEVIRI on MSG 

Dissemination 


HDF5 FTP - EUMETCast NRT 

Accuracy 


False Alarm: 25% 

Hit Rate: 70%% 


Hit Rate: 80% 


Hit Rate: 90% 


SYNOP, other satellite snow products, such as NOAA/NESDIS IMS or 

MODIS 



MSG Disk SEVIRI pixel resolution and grid 3 hours




Date: 11/12/2012 

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H32 Snow detection by VIS/NIR of AVHRR SN-OBS-0P 

Type 



NWP 


Carbon Models 



Product generated for all land pixels, accuracy requirements for the flat 

land pixels of the product 

Comments 




AVHRR on Metop, and AVHRR on NOAA, if feasible 

Dissemination 



Accuracy 





Hit Rate: 80% 


Hit Rate: 90% 


SYNOP, other satellite snow products, such as NOAA/NESDIS IMS or 

MODIS 



Global 0.01° x 0.01° 3 hours




Date: 11/12/2012 

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H33 

Type 

Merged MSG and EPS Snow Cover [current in-development Merged 

MSG/Seviri-Metop/AVHRR based LSA-SAF snow product] 


SN-OBS-0M 


NWP 


Carbon Models 


Comments 



Multichannel (VIS, NIR, IR), multisensor analysis 


1 day 

Metop/AVHRR 

MSG/SEVIRI 

Dissemination 


HDF5 EUMETCast, HTTP NRT, offline 

Accuracy 





Hit Rate: 80% 


Hit Rate: 90% 


in situ observations, other satellite products (such as IMS, MODIS) 



Europe & High latitutes 0.05°x0.05° 3 h




Date: 11/12/2012 

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H34 Snow detection (snow mask) by VIS/NIR of SEVIRI SN-OBS-1G 

Type 



Methods 

Comments 




Civil defense 


Binary map of snow / no-snow situation. VIS/IR images from GEO are used. The product may 

be processed in different ways and have different quality depending on the surface being flat, 

forested or mountainous. The algorithm is based on thresholding of several channels of 

SEVIRI, the most important being those in short-wave, thus the product is generated in 

daylight. In order to search for cloud-free pixels, multi-temporal analysis is performed over all 

images available in 24 hours (in daylight) 



analysis 




SEVIRI on MSG 

Dissemination 


Values in grid points of the Meteosat projection (HDF5) FTP, EUMETCast NRT 

Accuracy 














Probability Of Detection (POD): 99 % 



Snow observing stations, other satellite snow products, such as NOAA/NESDIS 

IMS or MODIS 



MSG disk SEVIRI pixel resolution and grid 30 min




Date: 11/12/2012 

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H35 

Snow detection (snow mask) and Effective snow cover by VIS/NIR of 

AVHRR 

SN-OBS-1P 

Type 



Methods 




Civil defense 


The combined effect, within a product resolution element, of fractional snow cover and other 

reflective contributors is used to estimate the fractional cover at resolution element level. 



The algorithm is based on multi-channel analysis of AVHRR, the most important being those in 

short-wave, thus the product is generated in daylight. 

The “deficit” of brightness in respect of the maximum one is correlated to the lack of snow in the 

product resolution element. In the case of forests, the expected maximum brightness (or the 

“transmissivity”) is evaluated in advance by a high-resolution instrument (MODIS). 

In order to search for cloud-free pixels, multi-temporal analysis is performed over all images 

available in 24 hours (in daylight) 

Comments 

Derived from H12 and H32 



analysis 



AVHRR (NOAA, Metop) 

Dissemination 


Values in grid points of the equal-latitude/longitude projection (HDF5) FTP, EUMETCast NRT 

Accuracy 


45% (Overall accuracy) 65% (Overall Accuracy) 95% (Overall Accuracy) 


Snow observing stations, other satellite snow products, such as NOAA/NESDIS 

IMS or MODIS 



Global 


Sampling: ~ 5 km 

30 min 

Timeliness is intended as delay after acquisition of the last 

image utilised in the multi-temporal analysis




Date: 11/12/2012 

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H43 Snow detection (snow mask) by VIS/NIR of MTG FCI SN-OBS-0G-FCI 

Type 






Civil defense 



Comments 



TBD 

FCI on MTG 

Dissemination 



Accuracy 


TBD TBD TBD 


TBD 



MTG Disk TBD TBD




Date: 11/12/2012 

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Appendix 1 Glossary 

AAPP AVHRR and ATOVS Processing Package 

ADEOS Advanced Earth Observation Satellite (I and II) 

ALOS Advanced Land Observing Satellite 

AMIR Advanced Microwave Imaging Radiometer 

AMSR Advanced Microwave Scanning Radiometer (on ADEOS-II) 

AMSR-E Advanced Microwave Scanning Radiometer - E (on EOS-Aqua) 

AMSU-A Advanced Microwave Sounding Unit - A (on NOAA satellites and EOS-Aqua) 

AMSU-B Advanced Microwave Sounding Unit - B (on NOAA satellites up to NOAA-17) 

API 

Application Program(ming) Interface 

ASAR Advanced SAR (on ENVISAT) 

ASCAT Advanced Scatterometer (on MetOp) 

ASI 

Agenzia Spaziale Italiana 

ATDD Algorithms Theoretical Definition Document 

ATMS Advanced Technology Microwave Sounder (on NPP and NPOESS) 

ATOVS Advanced TIROS Operational Vertical Sounder (on NOAA and MetOp) 

AU 

Anatolian University 

AVHRR Advanced Very High Resolution Radiometer (on NOAA and MetOp) 

BAMPR Bayesian Algorithm for Microwave Precipitation Retrieval 

BfG 

Bundesanstalt für Gewässerkunde 

BRDF Bi-directional Reflectance Distribution Function 

BVA 

Boundary Value Analysis 

CASE Computer Aided System Engineering 

CDA 

Command and Data Acquisition (EUMETSAT station at Svalbard) 

CDD 

Component Design Document 

CDR 

Critical Design Review 

CESBIO Centre d'Etudes Spatiales de la BIOsphere (of CNRS) 

CETP Centre d’études des Environnements Terrestres et Planétaires (of CNRS) 

CI 

Configuration Item 

CMIS Conical-scanning Microwave Imager/Sounder (on NPOESS) 

CMP Configuration Management Plan 

CNMCA Centro Nazionale di Meteorologia e Climatologia Aeronautica 

CNR 

Consiglio Nazionale delle Ricerche 

CNRM Centre Nationale de la Recherche Météorologique (of Météo-France) 

CNRS Centre Nationale de la Recherche Scientifique 

COTS Commercial-off-the-shelf 

CPU 

Central Processing Unit 

CR 

Component Requirement




Date: 11/12/2012 

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CRD 

Component Requirement Document 

CVERF Component Verification File 

CVS 

Concurrent Versions System 

DCOM Distributed Component Object Model 

DEM Digital Elevation Model 

DFD 

Data Flow Diagram 

DMSP Defense Meteorological Satellite Program 

DOF 

Data Output Format 

DPC 

Dipartimento della Protezione Civile 

DWD Deutscher Wetterdienst 

E&T 

Education and Training 

EARS EUMETSAT Advanced Retransmission Service (station) 

ECMWF European Centre for Medium-range Weather Forecasts 

ECSS European Cooperation on Space Standardization 

EGPM European contribution to the GPM mission 

EOS 

Earth Observing System 

EPS 

EUMETSAT Polar System 

ERS European Remote-sensing Satellite (1 and 2) 

ESA 

European Space Agency 

EUR 

End-User Requirements 

FAR 

False Alarm Ratio 

FMI 

Finnish Meteorological Institute 

FOC 

Full Operational Chain 

FTP 

File Transfer Protocol 

GEO 

Geostationary Earth Orbit 

GIS 

Geographical Information System 

GMES Global Monitoring for Environment and Security 

GOMAS Geostationary Observatory for Microwave Atmospheric Sounding 

GOS 

Global Observing System 

GPM Global Precipitation Measurement mission 

GPROF Goddard Profiling algorithm 

GTS 

Global Telecommunication System 

HMS Hungarian Meteorological Service 

HRU Hydrological Response Unit 

H-SAF SAF on support to Operational Hydrology and Water Management 

HSB 

Humidity Sounder for Brazil (on EOS-Aqua) 

HTML Hyper Text Markup Language 

HTTP Hyper Text Transfer Protocol 

HUT/LST Helsinki University of Technology / Laboratory of Space Technology




Date: 11/12/2012 

Page: 59/66 

HV 

Hydrovalidation (referred to Hydro Validation Subsystem items, e.g.: reports, components etc.) 

HVR 

Hydrological Validation Review 

HYDRO Preliminary results of Hydrological validation 

HYDROS Hydrosphere State Mission 

HW 

Hardware 

ICD 

Interface Control Document 

IFS 

Integrated Forecast System 

INWM Institute of Meteorology and Water Management (of Poland) 

IPF 

Institut für Photogrammetrie und Fernerkundung 

ISAC Istituto di Scienze dell’Atmosfera e del Clima (of CNR) 

ISO 

International Standards Organization 

IT 

Information Technology 

ITU 

Istanbul Technical University 

JPS 

Joint Polar System (MetOp + NOAA/NPOESS) 

KOM Kick-Off Meeting 

LAI 

Leaf Area Index 

LEO 

Low Earth Orbit 

LIS 

Lightning Imaging Sensor (on TRMM) 

LST 

Solar Local Time (of a sun-synchronous satellite) 

MARS Meteorological Archive and Retrieval System 

MetOp Meteorological Operational satellite 

METU Middle East Technical University (of Turkey) 

MHS Microwave Humidity Sounder (on NOAA N/N’ and MetOp) 

MIMR Multi-frequency Imaging Microwave Radiometer 

MODIS Moderate-resolution Imaging Spectro-radiometer (on EOS Terra and Aqua) 

MSG Meteosat Second Generation 

MTBF Mean Time Between Failure 

MTG Meteosat Third Generation 

MTTR Mean Time To Repair 

MVIRI Meteosat Visible Infra-Red Imager (on Meteosat 1 to 7) 

N/A 

Not Available 

N.A. 

Not Applicable 

NASA National Aeronautics and Space Administration 

NATO North Atlantic Treaty Organisation 

NIMH National Institute for Meteorology and Hydrology of Bulgaria 

NMS National Meteorological Service 

NOAA National Oceanic and Atmospheric Organisation (intended as a satellite series) 

NPOESS National Polar-orbiting Operational Environmental Satellite System 

NPP 

NPOESS Preparatory Programme




Date: 11/12/2012 

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NRT 

Near-Real Time 

NWP Numerical Weather Prediction 

OFL 

Off-line 

OM 

Offline Monitoring (referred to Offline Monitoring Subsystem items, e.g.: components) 

OMG Object Management Group 

OO 

Object Oriented 

OP 

Proposal for H-SAF Operational phase 

OPS 

Operational Product Segment 

ORB 

Object Request Broker 

ORR 

Operations Readiness Review 

PAC 

Prototype Algorithm Code 

PALSAR Phased Array L-band Synthetic Aperture Radar (on ALOS) 

PAW Plant Available Water 

PDR 

Preliminary Design Review 

POD 

Probability of Detection 

PP 

Project Plan 

PR 

Precipitation (referred to Precipitation Subsystem items, e.g.: products, components etc.) 

QoS 

Quality of Service 

R&D Research and Development 

REP 

Report 

RMI 

Royal Meteorological Institute (of Belgium) 

RMSE Root Mean Square Error 

RR 

Requirements Review 

RT 

Real Time 

SAF 

Satellite Application Facility 

SAG 

Science Advisory Group 

SAR 

Synthetic Aperture Radar 

SCA 

Snow Covered Area 

SCAT Scatterometer (on ERS-1 and 2) 

SD 

Snow depth 

SDAS Surface Data Assimilation System 

SDD 

System Design Document 

SEVIRI Spinning Enhanced Visible Infra-Red Imager (on MSG) 

SHW State Hydraulic Works of Turkey 

SHFWG SAF Hydrology Framework Working Group 

SHMI Slovakian Hydrological and Meteorological Institute 

SIRR 

System Integration Readiness Review 

SIVVP System Integration, Verification & Validation Plan 

SLAs Service-Level Agreements




Date: 11/12/2012 

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SM 

SMART 

SMMR 

SMOS 

SN 

SP 

SR 

SRD 

SSM/I 

SSMIS 

SSVD 

STRR 

SVALF 

SVERF 

SVRR 

SW 

SWE 

SYKE 

TBC 

TBD 

TKK/LST 

TLE 

TMI 

TRMM 

TSMS 

TU Wien 

U-MARF 

UML 

UR 

URD 

VIIRS 

WMO 

WP 

WPD 

WS 

XMI 

ZAMG 

Soil Moisture (referred to Soil Moisture Subsystem items, e.g.: products, components etc.) 

Service Migration and Reuse Technique 

Scanning Multichannel Microwave Radiometer (on SeaSat and Nimbus VII) 

Soil Moisture and Ocean Salinity 

Snow Parameters (referred to Snow Parameters Subsystem products) 

Snow Parameters (referred to Snow Parameters Subsystem items, e.g.: components) 

System Requirement 

System Requirements Document 

Special Sensor Microwave / Imager (on DMSP up to F-15) 

Special Sensor Microwave Imager/Sounder (on DMSP starting with F-16) 

System/Software Version Document 

System Test Results Review 

System Validation File 

System Verification File 

System Validation Results Review 

Software 

Snow Water Equivalent 

Finnish Environment Institute 

To be confirmed 

To be defined 

Helsinki University of Technology / Laboratory of Space Technology 

Two-line-element (telemetry data format) 

TRMM Microwave Imager (on TRMM) 

Tropical Rainfall Measuring Mission 

Turkish State Meteorological Service 

Technische Universität Wien 

Unified Meteorological Archive and Retrieval Facility 

Unified Modelling Language 

User Requirement 

User Requirements Document 

Visible/Infrared Imager Radiometer Suite (on NPP and NPOESS) 

World Meteorological Organization 

Work Package 

Work Package Description 

Workshop 

XML (eXtensible Markup Language ) Metadata Interchange 

Zentral Anstalt für Meteorologie und Geodynamik




Date: 11/12/2012 

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Appendix 2 

References 

2.1 Applicable documents 

[AD 1] 

[AD 2] 

[AD 3] 

Cooperation Agreement between EUMETSAT and the NMS of Italy on the Continuous 

Development and Operations Phase of the Satellite Application Facility on Support to 

Hydrology and Operational Water Management (Ref.: EUM/C/70/DOC/10) 

H-SAF Project Plan (PP). Ref.:SAF/HSAF/PP/2.2 

Definition of Product Status Categories for the SAF Network Ref: 

EUM/PPS/TEN/07/0036 

2.2 Reference documents 

[RD 1] 

[RD 2] 

Soutter M, R. Caloz and A. Beney, 2001: “Potential Contribution of EUMETSAT Space 

Systems in the Fields of Hydrology and Water Management”. Final report to 

EUMETSAT dated 21 August 2001. 

Conclusions from the Working Group on a Potential SAF on Support to Operational 

Hydrology and Water Management - Annex 1 to EUM/C/53/03/DOC/48, 2002. 

[RD 3] Summary Report of the SAF Hydrology Framework Working Group - 

EUM/PPS/REP/04/0002. 

[RD 4] 

Proposal for the development of a “Satellite Application Facility on Support to 

Operational Hydrology and Water Management (H-SAF)”, submitted by the Italian 

Meteorological Service on behalf of the H-SAF Consortium - Issue 2.1 dated 15 May 

2005 

[RD 5] Definition of Product Status Categories for the SAF Network. EUM/PPS/TEN/07/0036 - 

Issue v1A dated 14 May 2007 

2.3 Scientific References 

[RD 6] 

[RD 7] 

[RD 8] 

[RD 9] 

Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., Wagner, W. (2009): An improved soil 

moisture retrieval algorithm for ERS and METOP scatterometer observations. IEEE 

Transactions on Geoscience and Remote Sensing, 47 (7), pp. 1999-2013 

Wagner, W., G. Lemoine, H. Rott (1999): A Method for Estimating Soil Moisture from 

ERS Scatterometer and Soil Data, Remote Sensing of Environment, Volume 70, Issue 

2, pp. 191-207 

Wagner, W., C. Pathe, M. Doubkova, D. Sabel, A. Bartsch, S. Hasenauer, G. Blöschl, 

K. Scipal, J. Martínez-Fernández, A. Löw (2008): Temporal stability of soil moisture 

and radar backscatter observed by the Advanced Synthetic Aperture Radar (ASAR), 

Sensors, Volume 8, pp. 1174-1197 

Mugnai, A., D. Casella, M. Formenton, P. Sanò, G.J. Tripoli, W.Y. Leung, E.A. Smith, 

and A. Mehta, 2009: Generation of an European Cloud-Radiation Database to be used




Date: 11/12/2012 

Page: 63/66 

for PR-OBS-1 (Precipitation Rate at Ground by MW Conical Scanners), H-SAF VS 310 

Activity Report, 39 pp 

[RD 10] 

Joyce, R.J., J.E. Janowiak, P.A. Arkin, and P. Xie, 2004: CMORPH: A method that 

produces global precipitation estimates from passive microwave and infrared data at 

high spatial and temporal resolution. J. Hydrometeor., 5, 487-503. 

[RD 11] Turk, F.J., G. Rohaly, J. Hawkins, E.A. Smith, F.S. Marzano, A. Mugnai, and V. 

Levizzani, 2000: Meteorological applications of precipitation estimation from combined 

SSM/I, TRMM and geostationary satellite data. In: Microwave Radiometry and Remote 

Sensing of the Earth's Surface and Atmosphere, P. Pampaloni and S. Paloscia Eds., 

VSP Int. Sci. Publisher, Utrecht (The Netherlands), 353-363. 

[RD 12] 

[RD 13] 

[RD 14] 

[RD 15] 

Surussavadee, C., and D.H. Staelin, 2006: Comparison of AMSU millimeterwave 

satellite observations, MM5/TBSCAT predicted radiances, and electromagnetic models 

for hydrometeors. IEEE Trans. Geosci. Remote Sens., 44, 2667-2678. 

H. Van de Vyver and E. Roulin: Scale-recursive estimation for merging precipitation 

data from radar and microwave cross-track scanners’. 

Sanò, P., Casella, D., Mugnai, A., Schiavon, G., Smith, E.A., and Tripoli, G.J.: 

Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive 

microwave measurements, Part 1: Algorithm description and testing, IEEE Trans. 

Geosci. Remote Sens., in press, 2012. 

Casella, D., Panegrossi, G., Sanò, P., Mugnai, A., Smith, E.A., Tripoli, G.J., Dietrich, 

S., Formenton, M., Di Paola, F., Leung, H. W.-Y., and Mehta, A.V.: Transitioning from 

CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave 

measurements, Part 2: Overcoming database profile selection ambiguity by 

consideration of meteorological control on microphysics, IEEE Trans. Geosci. Remote 

Sens., submitted, 2012. 

[RD 16] 

[RD 17] 

Mugnai, A., Casella, D., Cattani, E., Dietrich, S., Laviola, S., Levizzani, V., Panegrossi, 

G., Petracca, M., Sanò, P., Di Paola, F., Biron, D., De Leonibus, L., Melfi, D., Rosci, P., 

Vocino, A., Zauli, F., Puca, S., Rinollo, A., Milani, L., Porcù, F., and Gattari, F.: 

Precipitation products from the Hydrology SAF, Nat. Hazards Earth Syst. Sci., Special 

Issue on Plinius 13, submitted, 2012a. 

Mugnai, A., Smith, E.A., Tripoli, G.J., Bizzarri, D., Casella, D., Dietrich, S., Di Paola, F., 

Panegrossi, G., and Sanò, P.: CDRD and PNPR satellite passive microwave 

precipitation retrieval algorithms: EuroTRMM / EURAINSAT origins and H-SAF 

operations, Nat. Hazards Earth Syst. Sci., Special Issue on Plinius 13, submitted, 

2012b. 

[RD 18] Albergel, C., C. Rüdiger, D. Carrer, J.-C. Calvet, N. Fritz, V. Naeimi, Z. Bartalis, and S. 

Hasenauer, 2009: An evaluation of ASCAT surface soil moisture products with in situ 

observations in Southwestern France, Hydrol. Earth Syst. Sci., 13, 115–124, 

doi:10.5194/hess-13-115-2009.




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[RD 19] Albergel, C., J.-C. Calvet, P. de Rosnay, G. Balsamo, W. Wagner, S. Hasenauer, V. 

Naemi, E. Martin, E. Bazile F. Bouyssel, and Mahfouf, J.-F., 2010: Cross-evaluation of 

modelled and remotely sensed surface soil moisture with in situ data in southwestern 

France, Hydrol. Earth Syst. Sci., 14, 2177-2191, doi:10.5194/hess-14-2177-2010. 

[RD 20] 

[RD 21] 

[RD 22] 

[RD 23] 

[RD 24] 

[RD 25] 

[RD 26] 

[RD 27] 

[RD 28] 

[RD 29] 

Brocca, L., F. Melone, T. Moramarco, W. Wagner and S. Hasenauer, 2010a: ASCAT 

soil wetness index validation through in situ and modelled soil moisture data in central 

Italy. Remote Sens. Environ., 114(11), 2745-2755, doi:10.1016/j.rse.2010.06.009. 

Brocca, L., Melone, F., Moramarco, T., Morbidelli, R., 2010b: Spatial-temporal 

variability of soil moisture and its estimation across scales. Water Resour. Res., 46, 

W02516, doi:10.1029/2009WR008016. 

Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, 

W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M., 

2011: Soil moisture estimation through ASCAT and AMSR-E sensors: an 

intercomparison and validation study across Europe. Remote Sensing of Environment, 

115, 3390-3408, doi:10.1016/j.rse.2011.08.003. 

Brocca, L., Tullo, T., Melone, F., Moramarco, T., Morbidelli, R., 2012: Catchment scale 

soil moisture spatial-temporal variability. Journal of Hydrology, 422-423, 71-83, 

doi:10.1016/j.jhydrol.2011.12.039. 

Entekhabi, D., Reichle, R.H., Koster, R.D. And Crow, W.T., 2010: Performance metrics 

for soil moisture retrievals and application requirements. J. Hydrometeorol., 11 (3), 

832-840. 

Famiglietti, J.S., D. Ryu, A.A. Berg, M. Rodell and T.J. Jackson 2008: Field 

observations of soil moisture variability across scales, Water Resour. Res., 44, 

W01423, doi:10.1029/2006WR005804. 

Koster R.D., Z. Guo, R. Yang, P.A. Dirmeyer, K. Mitchell and M.J. Puma 2009: On the 

nature of soil moisture in Land Surface Model, Journal of Climate, 22, 4322-4334, 

doi:10.1175/2009JCLI2832.1. 

Koster, R.D, S. P. P. Mahanama, T. J. Yamada, G. Balsamo, A. A. Berg, M. Boisserie, 

P. A. Dirmeyer, F. J. Doblas-Reyes, G. Drewitt, C. T. Gordon, Z. Guo, J.-H. Jeong, W.- 

S. Lee, Z. Li, L. Luo, S. Malyshev, W. J. Merryfield, S. I. Seneviratne, T. Stanelle, B. J. 

J. M. van den Hurk, F. Vitart and E. F. Wood., 2011: The Second Phase of the Global 

Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal 

Forecast Skill. J. Hydrometeor. 12:5, 805-822 

Rüdiger, C., J.-C. Calvet, C. Gruhier, T. Holmes, R. De Jeu, and W. Wagner, 2009: An 

intercomparison of ERS-Scat and AMSR-E soil moisture observations with model 

simulations over France, J. Hydrometeorol., 10(2), 431–447, 

doi:10.1175/2008JHM997.1. 

Saleem, J.A., and G.D. Salvucci, 2002: Comparison of soil wetness indices for inducing 

functional similarity of hydrologic response across sites in Illinois. J. Hydrometeor., 3, 

80–




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Appendix 3 

TBC/TBD List 

Item 

Section/Paragraph Resolution date 

Accuracy and Timeliness 

characteristics for Precipitation 

product H15 

Accuracy POD and FAR for 

Precipitation product H02B, H03B, 

H04B, H41A, H41B, H05B, H42A, 

H42B 

Generation Frequency and Accuracy 

POD and FAR for Precipitation 

product H40A and H40B, H17, H18, 

H21, H22, H50 

Accuracy values for Precipitation 

product H19, H20, 

Generation Frequency, Accuracy 

values, Validation method, Spatial 

resolution and Timeliness for Snow 

product H43 

Section 3.1 

Section 3.1 

Section 3.1 

Section 3.1 

Section 3.3.1 

Within CDOP2 ORR 




Within CDOP2 ORR




Date: 11/12/2012 

Page: 66/66 

- END OF THE DOCUMENT -

CDOP2 Product Requirement Document - Version 1.0 - H-SAF

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