Product Requirement Document - Servizio Meteorologicohsaf.meteoam.it/documents/docs/20130100/SAF_HSAF... · Product Requirement Document Doc. No: ... Lothar Schueller Lothar.Schueller@eumetsat

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



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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 [email protected]

Wolfgang Wagner [email protected]

ZAMG Alexander Jann [email protected]

Barbara Zeiner [email protected]

Belgium IRM Emmanuel Roulin [email protected]

Bulgary NIMH/BAS Gergana Kozinarova [email protected]

Finland FMI Jouni Pulliainen [email protected]

Ali Nadir Arslan [email protected]

Kari Luojus Kari.luojus.fmi.fi

Kati Anttila [email protected]

Matias Takala [email protected]

Niilo Siljamo [email protected]

Panu Lahtinen [email protected]

Terhikki Manninen [email protected]

France Météo France Jean-Christophe Calvet [email protected]

Germany BfG Peter Krahe [email protected]

Claudia Rachimow [email protected]

Hungary OMSZ Judit Kerenyi [email protected]

International ECMWF Lars Isaksen [email protected]

Patricia de Rosnay [email protected]

Clément Albergel [email protected]

International EUMETSAT Dominique Faucher [email protected]

Frédéric Gasiglia [email protected]

Jochen Grandell [email protected]

Lorenzo Sarlo [email protected]

Lothar Schueller [email protected]

Stefano Geraci [email protected]

Volker Gaertner [email protected]

Italy CNMCA Antonio Vocino [email protected]

Daniele Biron [email protected]

Davide Melfi [email protected]

Francesco Zauli [email protected]

Leonardo Facciorusso [email protected]

USAM Luigi De Leonibus [email protected]

Paolo Rosci [email protected]

CNR-ISAC Alberto Mugnai [email protected]

Giulia Panegrossi [email protected]

mailto:[email protected]




















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Stefano Dietrich [email protected]

Vincenzo Levizzani [email protected]

Elsa Cattani [email protected]

Sante Laviola [email protected]

DPC Paola Pagliara [email protected]

Angelo Rinollo [email protected]

Silvia Puca [email protected]

Telespazio Emiliano Agosta [email protected]

Flavio Gattari [email protected]

UniFerrara Federico Porcu' [email protected]

Marco Petracca [email protected]

Poland IMWM Michal Kasina [email protected]

Piotr Struzik [email protected]

Slovakia SHMÚ Ján Kaňák [email protected]

Sweden SMHI Stefan Nilsson [email protected]

Turkey ITU Ahmet Öztopal [email protected]

METU Zuhal Akyurek [email protected]

Serdar Surer [email protected]

Kenan Bolat [email protected]

TSMS Sezel Karayusufoglu [email protected]

Fatih Demýr [email protected]

AU Aynur Sensoy [email protected]

OMU Ibrahim Sonmez [email protected]















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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

Product

acronym Product name

Precipitation products

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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Product

identifier

Product

acronym Product name

H19 PR-OBS-7 Rainfall intensity from GMI (Global Precipitation Measurement - Microwave

Imager) [Bayesian algorithm]

H20 PR-OBS-8 Rainfall intensity from GMI (Global Precipitation Measurement - Microwave

Imager) [Neural Network algorithm]

H21 PR-OBS-9 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 SM-DIS-1

(ex SM-OBS-2)

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

ASCAT/SAR]

H14 SM-DAS-2

(ex SM-ASS-2)

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 SN-OBS-0G Snow detection for flat land (snow mask) by VIS/NIR [current operational

SEVIRI based LSA-SAF snow product]

H32 SN-OBS-0P Snow detection for flat land (snow mask) by VIS/NIR [current pre-

operational Metop/AVHRR based LSA-SAF snow product]

H33 SN-OBS-0M 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 re-

processing 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<1mm/hr 50%

<1mm/hr 90% About cumulated precipitation product (H05) it was adopted the value of first class of precipitation intensity for both integration periods: 25% TARGET: The values for the three precipitation intensity classes were revised considering the error of the independent observation (raingauges) described above as the Optimal accuracy and the error introduced by the comparison techniques (interpolation, downscaling, upscaling, etc..). Considering that the comparison error varies from 30% to 90% as showed by the tables Table 2 andTable 3 below, the Target values were obtained adding 55% to the Optimal values. Values for the cumulated precipitation considered the reduction of comparison error due to the integration period which reduces the harmonics of the instantaneous field, the amount of this reduction is about 70-80% see table 3 below. In accordance to that and the WMO publication “Catalogue of national standard precipitation gauges” the target values is revised considering also the raingauges error due to the effects of wind and evaporation. The target values were revised adding 55% and 45% accordingly to the 3 hour and 24 hour cumulated precipitation classes. THRESHOLD: Values were revised considering the continuous actual performance of the products and the minimum information content required by end users.

Interpolation RMSE mean [%]

Step 2 Step 3 Step 4

Barnes 31.31 ± 10.18 47.82 ± 14.56 66.65 ± 43.38

Kriging 33.64 ± 10.33 53.55 ± 17.76 75.69 ± 64.32

NN 52.98 ± 15.75 73.66 ± 27.00 94.94 ± 48.67

IDS 73.51 ± 25.43 82.38 ± 28.91 90.76 ± 30.21

Table 2: RMSE% and standard deviation of interpolation algorithms for 3 different regular grids. (VS 11_P01 Evaluation on

accuracy of precipitation data” )



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Interpolation

RMSE mean [%]


Barnes 41.97 ± 13.54 63.72 ± 17.18 93.87 ± 39.55

Kriging 57.60 ± 21.55 189.91 ± 60.70 154.69 ± 61.71

NN 57.73 ± 17.45 79.05 ± 20.68 118.42 ± 50.29

IDS 84.09 ± 29.00 95.41 ± 35.12 102.84 ± 40.45

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” )

Interpolation RMSE mean [%]


Barnes 24.99 ± 7.51 36.55 ± 10.29 52.46 ± 15.76

Kriging 30.50 ± 10.34 99.89 ± 53.78 81.56 ± 42.18

NN 32.59 ± 8.83 46.75 ± 12.56 65.96 ± 18.11

IDS 55.47 ± 17.66 66.45 ± 20.82 74.53 ± 28.94

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” )

Bibliography

WMO td. 39 “ Catalogue of national standard precipitation gauges”

WMO td .99 “Field intercomparison of rainfall intensity gauges”

Interim Report VS11_P01 HSAF Marco Petracca:” Evaluation on accuracy of precipitation data” in progress



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3.1.1 Precipitation Products Requirements

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

Type NRT Product

Application and users Operational hydrological units

National meteorological services

Civil defense

Research & development activities

Characteristics and

Methods

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 cloud-

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

followed by a radiative transfer model

Comments 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)

FTP, EUMETCast NRT

Accuracy

Threshold Target Optimal

Changing with precipitation type:

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

Validation method Meteorological radar and rain gauge

Coverage, resolution and timeliness

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.

Precipitation rate at ground by MW conical scanners (new

rel.)

PR-OBS-1 new rel.

Type NRT Product



Civil defense


Characteristics and

Methods

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 pre-

computed 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)

Comments 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


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)

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





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 NRT Product



Civil defense


Characteristics and

Methods

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

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


Input satellite data 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

Format Means Type

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

Sampling: 16 km

1 h



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H02A new

rel

Precipitation rate at ground by MW cross-track scanners (new rel.) PR-OBS-2A new rel.

Type NRT Product



Civil defense


Characteristics and

Methods

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

Format Means Type


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 km2 /

circular at nadir to 26 x 52 km2 / oval at scan edge

Sampling: 16 km

1 h



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

Type NRT Product



Civil defense


Characteristics and

Methods

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

Format Means Type


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 km2 /

circular at nadir to 26 x 52 km2 / 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 NRT Product


Operational oceanographic units


Civil defense


Characteristics and Methods 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

Generation frequency Every new SEVIRI image (at 15 min intervals)

Observing cycle over Europe: 15 min

Input satellite data SEVIRI on MSG (Meteosat-9)

Dissemination

Format Means Type

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 NRT Product




Civil defense


Characteristics and

Methods

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)




Input satellite data SEVIRI on MSG (Meteosat-9)

Dissemination

Format Means Type


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

Validation method Meteorological radar and rain gauge (TBC)



H-SAF area extended to Africa and southern

Atlantic

Resolution changing cross Europe: 8 km in average


15 min



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H40A Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG

FCI

PR-OBS-3-FCI-A

Type NRT Product




Civil defense


Characteristics and

Methods



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

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.

Generation frequency TBD

Input satellite data FCI on MTG

Dissemination

Format Means Type

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,

N/A for < 1 mm/h


20 % for > 10 mm/h,

40 % for 1-10 mm/h,

N/A for < 1 mm/h

Validation method Meteorological radar, rain gauge



H-SAF Area Resolution dependent from IFOV of FCI


15 min



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H40B Precipitation rate at ground by GEO/IR supported by LEO/MW and MTG

FCI

PR-OBS-3-FCI-B

Type NRT Product




Civil defense


Characteristics and

Methods



synchronous 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.



Dissemination

Format Means Type

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

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

Validation method Meteorological radar, rain gauge (TBC from validation team)



To extend to Africa and southern Atlantic Resolution dependent from IFOV of FCI


25 min



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

Type NRT Product

Application and users Hydrology

Climate monitoring

Risk Management

Meteorology

Characteristics and

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

Generation frequency 12 times per day

Input satellite data SEVIRI on MSG;

H-SAF PR-OBS-01

H-SAF PR-OBS-02

Dissemination

Format Means Type

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

Validation method 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



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

Type NRT Product

Application and users Climatological community

National meteorological services (to be assessed)


Characteristics and

Methods







Comments Product primarily designed for climatology.

Applicability in an operational framework to be assessed.


Input satellite data SEVIRI on MSG

H-SAF PR-OBS-01

H-SAF PR-OBS-02

Dissemination

Format Means Type


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




To extend to Africa and southern Atlantic Resolution: 30 km in average


5 hours



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H41A Precipitation rate at ground by LEO/MW supported by GEO/IR and MTG

FCI

PR-OBS-4-FCI-A

Type NRT Product




Characteristics and

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.



H-SAF PR-OBS-01

H-SAF PR-OBS-02

Dissemination

Format Means Type


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



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H41B Precipitation rate at ground by LEO/MW supported by GEO/IR and MTG

FCI

PR-OBS-4-FCI-B

Type NRT Product




Characteristics and

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.


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.


Input satellite data FCI on MTG;

H-SAF PR-OBS-01

H-SAF PR-OBS-02

Dissemination

Format Means Type


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




To extend to Africa and southern Atlantic Resolution: ~ 30 km


5 hours



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

Type NRT Product




Civil defense


Characteristics and

Methods

Derived from precipitation maps generated by merging MW images from operational sun-

synchronous 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 satellite-

derived 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

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

Observing cycle over Europe: 3 h


Dissemination

Format Means Type


Accuracy


Changing with integration interval:

120% for 3-h accumulation,

100% for 24-h accumulation











longitude) (degradation expected at very high latitudes)

Resolution: ~ 30 km


3 h



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

Type NRT Product




Civil defense


Characteristics and

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




Dissemination

Format Means Type


Accuracy



150 % for 3-h accumulation


POD, FAR: TBD












25 min



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

Type NRT Product




Civil defense


Characteristics and

Methods















AMSU-A/B (NOAA 15/16)

MHS (Metop, NOAA 18/19)

Dissemination

Format Means Type


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)

Resolution: ~ 30 km


15 min



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

Type NRT Product




Civil defense


Characteristics and

Methods


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

and, later, PR-OBS-4).












AMSU-A/B (NOAA 15/16)

MHS (Metop, NOAA 18/19)

Dissemination

Format Means Type


Accuracy





POD, FAR: TBD












25 min



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

Type Product




Civil defense


Characteristics and

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”).






Dissemination

Format Means Type


Accuracy



80 % for > 10 mm/h

160 % for 1-10 mm/h

N/A for < 1 mm/h

TBC


40 % for > 10 mm/h

80 % for 1-10 mm/h

N/A for < 1 mm/h

TBC


20 % for > 10 mm/h

40 % for 1-10 mm/h

N/A for < 1 mm/h

TBC





45°E longitude) (degradation expected at very

high latitudes)

Resolution changing cross Europe: 8 km in average


15 min



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

Type Product




Civil defense


Characteristics and

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”).






Dissemination

Format Means Type


Accuracy



80 % for > 10 mm/h

160 % for 1-10 mm/h

N/A for < 1 mm/h

TBC


40 % for > 10 mm/h

80 % for 1-10 mm/h

N/A for < 1 mm/h

TBC


20 % for > 10 mm/h

40 % for 1-10 mm/h

N/A for < 1 mm/h

TBC




To extend to Africa and southern Atlantic Resolution changing cross Europe: 8 km in average


25 min



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

Type NRT Product



Civil defense


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

(Statistical Significance Index)

Comments


Input satellite data SSMIS on DMSP

Dissemination

Format Means Type

Values in grid points of specified

coordinates in the orbital projection

(BUFR)

FTP - EUMETCast NRT

Accuracy



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

H-SAF area

extended to Africa

and southern

Atlantic


Sampling: 12.5 km

2.5 h



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

Type NRT Product



Civil defense


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

Comments


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

Dissemination

Format Means Type



(BUFR)

FTP - EUMETCast NRT

Accuracy



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




H-SAF area

extended to Africa

and southern

Atlantic

Resolution changing along the

scan: varying from 16 x 16 km2 /

circular at nadir to 26 x 52 km2 /

oval at scan edge

Sampling: 16 km

2.5 h



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H19 Rainfall intensity from GMI (Global Precipitation Measurement -

Microwave Imager) [Bayesian algorithm]

PR-OBS-7

Type Offline Product




Civil defense


Characteristics and Methods Bayesian algorithm

Comments

Generation frequency N. A.

Input satellite data GMI and DPR on GPM observatory

Dissemination

Format Means Type



(BUFR)

FTP Offline

Accuracy


TBD TBD TBD




Global for low and

middle latitudes up

to 62°

Resolution: 4.4 X 7.3 Km N. A.



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H20 Rainfall intensity from GMI (Global Precipitation Measurement -

Microwave Imager) [Neural Network algorithm]

PR-OBS-8





Civil defense


Characteristics and Methods Neural Network algorithm

Comments


Input satellite data GMI and DPR on GPM observatory

Dissemination

Format Means Type



(BUFR)

FTP Offline

Accuracy


TBD TBD TBD




Global for low and

middle latitudes up

to 62°

Resolution: 4.4 X 7.3 Km N. A.



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H21 High frequency MW delineation of cloud areas with new development of

hydrometeors

PR-OBS-9

Type NRT Product



Civil defense


Characteristics and Methods Threshold method calibrated with mid-latitude radar dataset.

Comments


Input satellite data AMSU-B and MHS on NOAA, EPS, and MetOp

Dissemination

Format Means Type



(BUFR)

FTP NRT

Accuracy



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




H-SAF area

extended to Africa

and southern

Atlantic


Sampling: 16 km

2.5 h



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H22 Snowfall intensity PR-OBS-10

Type NRT Product




Civil defense


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

Comments


Input satellite data AMSU-B and MHS on NOAA,EPS, and MetOp

Dissemination

Format Means Type



(BUFR)

FTP NRT

Accuracy


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




H-SAF area

extended to Africa

and southern

Atlantic


Sampling: 16 km (at nadir)

2.5 h



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H50 Rainfall intensity from MTG LI PR-OBS-11

Type NRT Product




Civil defense


Characteristics and Methods 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.


Input satellite data LI on MTG

Dissemination

Format Means Type

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.



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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



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• 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].



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3.2.2 Soil Moisture products requirements

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

(ex SM-OBS-2)

Type NRT Product


Climatology


Characteristics and

Methods

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).

Comments 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


Input satellite data ASCAT on Metop

Dissemination

Format Means Type


Accuracy


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

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

meteorological 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

Sampling: 0.5 km

130 min



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H14 Soil Moisture Profile Index in the roots region retrieved by surface

wetness scatterometer assimilation method

SM-DAS-2

(ex SM-ASS-2)

Type NRT Product


Climatology


Characteristics and

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.

Generation frequency Model output at 24-h intervals

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


Dissemination

Format Means Type

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

Accuracy



Validation method 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



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

Type Product


Climatology


Characteristics and

Methods

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.

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

CAF

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



Dissemination

Format Means Type


Accuracy



Validation method 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

Sampling: 12.5 km

2 h



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



Climatology


Characteristics and

Methods

Time series of ASCAT large-scale surface soil moisture

Comments Currently a TU WIen product



Dissemination

Format Means Type

various scientific file formats FTP Offline

Accuracy



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

Output of hydro-meteorological models

Satellite data (e.g. SMOS)



Global Resolution: 25 km

Sampling: 12.5 km

N. A.



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H27 Soil Wetness Index in the roots region by scatterometer assimilation in a

NWP model

SM-DAS-3

(ex SM-ASS-3)



Climatology


Characteristics and

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 re-

analysis 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


Input satellite data Satellites used in NWP

ASCAT on Metop

Dissemination

Format Means Type

Values in grid points on a Gaussian grid FTP Offline

Accuracy



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

(TDR)).

Comparison with SMOS



Global Horizontal resolution: ~16 km N. A.



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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.



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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 NRT Product



Civil defense


Characteristics and

Methods

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)

Comments 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

Generation frequency Output result every 24 h


Dissemination

Format Means Type

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):









Probability Of Detection (POD): 99 %

False Alarm Rate (FAR): 5 %

Validation method Snow observing stations




longitude) (degradation expected at very high

latitudes)

SEVIRI pixel resolution and grid 30 min



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

Type NRT Product



Civil defense


Characteristics and

Methods

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.

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

24 h

Input satellite data SSMIS on DMSP

Dissemination

Format Means Type

GRIB2 FTP, EUMETCast NRT

Accuracy


Hit Rate (HR): 60 %


Hit Rate (HR): 80 %


Hit Rate (HR): 90 %





H-SAF area (25°N to 75°N latitude, 25°W to 45°E longitude) Resolution: ~ 20 km

Sampling: 0.25 degrees

6 h



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

Type NRT Product

Application and

users

Operational hydrological units


Civil defense


Characteristics and

Methods

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)



analysis

Generation

frequency

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

Output result every 24 h

Input satellite data AVHRR (NOAA, Metop)

Dissemination

Format Means Type


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



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

Type NRT Product

Application and

users

Operational hydrological units


Civil defense


Characteristics and

Methods

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.

Different methods used for flat/forested and mountainous regions.

Generation

frequency

Assimilation of SSMI/S brightness temperatures in a background field

Output result every 24 h

Input satellite data SSMI/S

Dissemination

Format Means Type


Accuracy


Flat / Forested areas: 40mm

Mountainous areas: 45mm








H-SAF area (25°N to 75°N latitude, 25°W to 45°E longitude) Resolution: ~ 20 km

Sampling: 0.25 degrees

6 h



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

Type NRT Product

Application and users NWP

Climate Monitoring

Carbon Models

Characteristics and Methods 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

Generation frequency


Dissemination

Format Means Type

HDF5 FTP - EUMETCast NRT

Accuracy


False Alarm: 25%

Hit Rate: 70%%

False Alarm: 15%

Hit Rate: 80%

False Alarm: 5%

Hit Rate: 90%

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

MODIS



MSG Disk SEVIRI pixel resolution and grid 3 hours



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

Type NRT Product


Climate Monitoring

Carbon Models


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

land pixels of the product



Input satellite data AVHRR on Metop, and AVHRR on NOAA, if feasible

Dissemination

Format Means Type


Accuracy


False Alarm: 25%

Hit Rate: 70%%

False Alarm: 15%

Hit Rate: 80%

False Alarm: 5%

Hit Rate: 90%

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

MODIS



Global 0.01° x 0.01° 3 hours



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H33 Merged MSG and EPS Snow Cover [current in-development Merged

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

SN-OBS-0M

Type NRT Product


Climate Monitoring

Carbon Models

Characteristics and Methods Multichannel (VIS, NIR, IR), multisensor analysis


Generation frequency 1 day

Input satellite data Metop/AVHRR

MSG/SEVIRI

Dissemination

Format Means Type

HDF5 EUMETCast, HTTP NRT, offline

Accuracy


False Alarm: 25%

Hit Rate: 70%%

False Alarm: 15%

Hit Rate: 80%

False Alarm: 5%

Hit Rate: 90%

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



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



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

Type NRT Product



Civil defense


Characteristics and

Methods

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

Generation frequency Output result every 24 h


Dissemination

Format Means Type

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

Accuracy














Probability Of Detection (POD): 99 %


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

IMS or MODIS



MSG disk SEVIRI pixel resolution and grid 30 min



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H35 Snow detection (snow mask) and Effective snow cover by VIS/NIR of

AVHRR

SN-OBS-1P

Type NRT Product



Civil defense


Characteristics and

Methods

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

Different methods used for flat/forested and mountainous regions.


analysis


Input satellite data AVHRR (NOAA, Metop)

Dissemination

Format Means Type

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

Accuracy


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

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

IMS or MODIS



Global Resolution: ~ 8 km

Sampling: ~ 5 km

30 min

Timeliness is intended as delay after acquisition of the last

image utilised in the multi-temporal analysis



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

Type NRT Product



Civil defense



Comments



Dissemination

Format Means Type


Accuracy


TBD TBD TBD

Validation method TBD



MTG Disk TBD TBD



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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



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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



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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



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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



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

SMART Service Migration and Reuse Technique

SMMR Scanning Multichannel Microwave Radiometer (on SeaSat and Nimbus VII)

SMOS Soil Moisture and Ocean Salinity

SN Snow Parameters (referred to Snow Parameters Subsystem products)

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

SR System Requirement

SRD System Requirements Document

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

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

SSVD System/Software Version Document

STRR System Test Results Review

SVALF System Validation File

SVERF System Verification File

SVRR System Validation Results Review

SW Software

SWE Snow Water Equivalent

SYKE Finnish Environment Institute

TBC To be confirmed

TBD To be defined

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

TLE Two-line-element (telemetry data format)

TMI TRMM Microwave Imager (on TRMM)

TRMM Tropical Rainfall Measuring Mission

TSMS Turkish State Meteorological Service

TU Wien Technische Universität Wien

U-MARF Unified Meteorological Archive and Retrieval Facility

UML Unified Modelling Language

UR User Requirement

URD User Requirements Document

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

WMO World Meteorological Organization

WP Work Package

WPD Work Package Description

WS Workshop

XMI XML (eXtensible Markup Language ) Metadata Interchange

ZAMG Zentral Anstalt für Meteorologie und Geodynamik



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Appendix 2 References

2.1 Applicable documents

[AD 1] 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)

[AD 2] H-SAF Project Plan (PP). Ref.:SAF/HSAF/PP/2.2

[AD 3] Definition of Product Status Categories for the SAF Network Ref:

EUM/PPS/TEN/07/0036

2.2 Reference documents

[RD 1] 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.

[RD 2] 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] 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

[RD 7] 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

[RD 8] 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

[RD 9] 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



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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] 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.

[RD 13] H. Van de Vyver and E. Roulin: Scale-recursive estimation for merging precipitation

data from radar and microwave cross-track scanners’.

[RD 14] 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.

[RD 15] 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] 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.

[RD 17] 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

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80–



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Appendix 3 TBC/TBD List

Item Section/Paragraph Resolution date

Accuracy and Timeliness

characteristics for Precipitation

product H15

Section 3.1 Within CDOP2 ORR

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 3.3.1 Within CDOP2 ORR



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- END OF THE DOCUMENT -

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Product Requirement Document - Servizio Meteorologicohsaf.meteoam.it/documents/docs/20130100/SAF_HSAF... · Product Requirement Document Doc. No: ... Lothar Schueller Lothar.Schueller@eumetsat