Product Requirement Document - Servizio Meteorologicohsaf.meteoam.it/docs/PRD/SAF_HSAF_PRD_1_2.pdf · Wolfgang Wagner [email protected] ... Lothar Schueller [email protected]

Product Requirement Document

Doc. No: SAF/HSAF/PRD/1.2

Issue: Version 1.2

Date: 10/01/2012

Page: 1/34

EUMETSAT Satellite Application Facility

on Support to Operational Hydrology

and Water Management

(H-SAF)


Reference Number: SAF/HSAF/PRD/1.2

Issue/Revision Index: Issue 1.2

Last Change: 10/01/2012



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Date: 10/01/2012

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DOCUMENT SIGNATURE TABLE

Name Date Signature

Prepared by : H-SAF Project Team 15/09/2010

Approved by : H-SAF Project Manager

DOCUMENT CHANGE RECORD

Issue / Revision Date Description

0.1 15/09/2010 Draft version prepared for CDOP

0.2 07/03/2011 Update version acknowledging decisions and recommendations of CDOP SG1.

0.3 25/04/2011

Update version prepared for the ORR-1, for EUMETSAT revision before the delivery as baseline. Main modifications applied:

Introductory descriptions and scope improved and corrected where suggested

Product list: explicited NEW product in CDOP

Product requirements made more coincise and precise

Removed planning elements from requirements

Corrections applied in the product specifications

Distribution list updated

1.0 16/05/2011

Preparation of baselineto be submitted to the ORR-1. Main modifications:

Deletion of elements referred to planning items instead of requirements (elimination of sections regarding product requirement drivers)

Insertion of TBD/TBC for those products characteristics still open to revision

General revision of the document

1.1 06/06/2011

Modifications for ORR-1 baseline:

Accuracy values for products: H01 (PR-OBS-1), H02 (PR-OBS-2), H03 (PR-OBS-3), H04 (PR-OBS-4), H05 (PR-OBS.5), H10 (SN-OBS-1)

1.2 10/01/2012

Modifications on H14 requirements as outcome of the PCR;

Modifications on H12 and H13 requirements as per Project Team meeting (PT3) decision

Removal of TBC in accuracy requirements of H04 and H05



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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]

Dobri Dimitrov [email protected]

Snezhanka Balabanova [email protected]

Eram Artinyan [email protected]

Georgy Koshinchanov [email protected]

Valery Spiridonov [email protected]

Kamelia Kroumova [email protected]

Zornitza Krasteva [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]

SYKE Sari Metsämäki [email protected]

TKK Juha-Petri Kärnä [email protected]

Sampsa Koponen [email protected]

France Météo France Jean-Christophe Calvet [email protected]

Germany BfG Peter Krahe [email protected]

Claudia Rachimow [email protected]

Hungary OMSZ Eszter Lábó [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]

mailto:[email protected]


























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Volker Gaertner [email protected]

Italy CNMCA Adriano Raspanti [email protected]

Antonio Vocino [email protected]

Daniele Biron [email protected]

Davide Melfi [email protected]

Francesco Zauli [email protected]

Leonardo Facciorusso [email protected]

Lucio Torrisi [email protected]

Luigi De Leonibus [email protected]

CNR-ISAC Alberto Mugnai [email protected]

Daniele Casella [email protected]

Francesco Di Paola [email protected]

Sante Laviola [email protected]

Stefano Dietrich [email protected]

Vincenzo Levizzani [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]

USAM Alessandro Galliani [email protected]

Paolo Rosci [email protected]

Poland IMWM Bozena Lapeta [email protected]

Jaga Niedbala [email protected]

Jakub Walawender [email protected]

Jerzy Niedbala [email protected]

Monika Pajek [email protected]

Pawel Przeniczny [email protected]

Piotr Struzik [email protected]

Rafal Iwanski [email protected]

Slovakia SHMÚ Dagmar Kotláriková [email protected]

Daniela Kyselová [email protected]

Ján Kaňák [email protected]

Ľuboslav Okon [email protected]

Marcel Zvolenský [email protected]

Marián Jurašek [email protected]

Sweden SMHI Stefan Nilsson [email protected]

Turkey ITU Ahmet Öztopal [email protected]

Arda Sorman [email protected]

Aynur Sensoy [email protected]

Sevinç Sırdaş [email protected]






















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Zekai Şen [email protected]

METU Ali Unal Sorman [email protected]

Orhan Gokdemir [email protected]

Ozgur Beser [email protected]

Serdar Surer [email protected]

Zuhal Akyurek [email protected]

TSMS Ali Umran Komuscu [email protected]

Aydın Gürol Ertürk [email protected]

Erdem Erdi [email protected]

Fatih Demýr [email protected]

Ibrahim Sonmez [email protected]











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TABLE OF CONTENTS

1 INTRODUCTION .......................................................................................................... 8

1.1 Purpose of the document ...................................................................................... 8

1.2 Scope .................................................................................................................... 8

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

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

2.2 General requirements ............................................................................................ 9

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

3.1 Precipitation products requirements .................................................................... 12

3.2 Soil Moisture products requirements ................................................................... 19

3.3 Snow products requirements ............................................................................... 22

APPENDIX 1 GLOSSARY .............................................................................................. 27

APPENDIX 2 REFERENCES ......................................................................................... 32

2.1 Applicable documents ......................................................................................... 32

2.2 Reference documents ......................................................................................... 32

2.3 Scientific References ........................................................................................... 32

APPENDIX 3 TBC/TBD LIST .......................................................................................... 33



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LIST OF TABLES

Table 1 H-SAF products list ............................................................................................................. 9

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 first time at the beginning of the CDOP 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

- 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.

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

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

Continuous Development and Operations Phase (CDOP) 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.

2 H-SAF Products

2.1 Products list

Product

identifier Product acronym Product name



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Product

identifier Product acronym Product name

Precipitation products

H-01 PR-OBS-1 Precipitation rate at ground by MW conical scanners

(with indication of phase)

H-02 PR-OBS-2 Precipitation rate at ground by MW cross-track scanners

(with indication of phase)

H-03 PR-OBS-3 Precipitation rate at ground by GEO/IR supported by LEO/MW

H-04 PR-OBS-4 Precipitation rate at ground by LEO/MW supported by GEO/IR

(with flag for phase)

H-05 PR-OBS-5 Accumulated precipitation at ground by blended MW+IR

H-06 PR-ASS-1 Instantaneous and accumulated precipitation at ground computed by a

NWP model

H-15 PR-OBS-6 Blended SEVIRI Convection area/ LEO MW Convective Precipitation

(NEW in CDOP)

Soil Moisture products

H-08 SM-OBS-2 Small-scale surface soil moisture by radar scatterometer

H-14 SM-DAS-2 Soil Moisture Index in the roots region retrieved by scatterometer

assimilation in NWP model

H-16 SM-OBS-3 Large-scale surface soil moisture by radar scatterometer

Snow Parameter products

H-10 SN-OBS-1 Snow detection (snow mask) by VIS/IR radiometry

H-11 SN-OBS-2 Snow status (dry/wet) by MW radiometry

H-12 SN-OBS-3 Effective snow cover by VIS/IR radiometry

H-13 SN-OBS-4 Snow water equivalent by MW radiometry

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



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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, an area for

collecting/updating information on the status of satellites and instruments used in H-SAF,

an area to collect E&T material, an area for “forums” (on algorithms, on validation

campaigns, etc.), indication of useful links (specifically with other SAF’s), and an area for

“Frequently Asked Questions” (FAQ) to alleviate the load on the Help desk.

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

Precipitation Accuracy Revised Values Product requirements for accuracy were revised by adopting 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: OPTIMAL: About intensity precipitation products (H01, H02, H03 and H04), 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).



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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 was 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..) adopted for the comparison derived by the comparison algorithms. 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” )

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



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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

3.1 Precipitation products requirements

H-01 Precipitation rate at ground by MW conical scanners PR-OBS-1

Type NRT Product

Application and users Hydrology

Climate Monitoring

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

The algorithm is the result of a combined effort between CNR-ISAC, the

NASA/Goddard Space Flight Center (Greenbelt, Maryland, USA) and the

University of Wisconsin (Madison, Wisconsin, USA).

References: [RD 9]

Comments Precipitation rate from conical scanning instruments will be derived from



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SSMIS radiometers onboard DMSP satellites. Nevertheless, PR-OBS-1 will

take advantage of SSM/I (on DMSP-15) and AMSR-E (on EOS-Aqua)

measurements if and until available

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 on DMSP

AMSR-E on EOS-Aqua

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

Verification method Meteorological radar and rain gauge

Coverage, resolution and timeless

Spatial coverage Spatial resolution Vertical 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

N/A 2.5 h

H-02 Precipitation rate at ground by MW cross-track scanners PR-OBS-2

Type NRT Product


Climate Monitoring

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

The algorithm was originally developed at the Massachusetts Institute of



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Technology (Cambridge, Massachusetts, USA) and successively modified

by CNR-ISAC.

References: [RD 12], (Section 3 pp.39-64)

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.

Observing cycle over Europe: ~ 5 h

Input satellite data AMSU-A (NOAA 15/16), MHS (Metop, NOAA 18/19) .

Input data are merged into one product file.

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




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

1h

H-03 Precipitation rate at ground by GEO/IR supported by LEO/MW PR-OBS-3

Type NRT Product


Climate Monitoring

Risk Management

Characteristics and Methods Instantaneous precipitation maps generated by IR images from operational

geostationary 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”).

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



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equipped satellite

The algorithm is the result of a combined effort between CNR-ISAC and the

Naval Research Laboratory (Monterey, California, USA).


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 (25°N

to 75°N latitude,

25°W to 45°E

longitude)

(degradation

expected at very

high latitudes)

Resolution changing cross

Europe: 8 km in average

Sampling: 5 km in average

15 min

H-04 Precipitation rate at ground by LEO/MW supported by GEO/IR PR-OBS-4

Type NRT Product


Climate monitoring

Risk Management

Meteorology

Characteristics and Methods Instantaneous precipitation maps generated by MW images 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



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computing accumulated precipitation of use in hydrology. It is also possible

to extrapolate the precipitation field for a few steps ahead, with reduced

accuracy but improved value for nowcasting

The algorithm was originally developed by the Climate Prediction Center of

NOAA-NESDIS (Camp Springs, Maryland, USA) and successively modified

by CNR-ISAC.


The information from different satellite data are merged into one product file

Comments Product primarily designed for climatology.

Applicability in an operational framework to be assessed.

Generation frequency 12 times per day

Input satellite data SEVIRI on MSG Meteosat-9; AMSU-A/B (NOAA 15/16); MHS (Metop,

NOAA 18/19).

Dissemination

Format Means Type



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 (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 (<4: extrapolation)

H-05 Accumulated precipitation at ground by blended MW+IR PR-OBS-5

Type NRT Product


Climate Monitoring

Risk Management

Meteorology



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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.

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

Observing cycle over Europe: 3 h

Input satellite data SEVIRI on MSG Meteosat-9. Not direct: through PR-OBS-3 and PR-OBS-4

Dissemination

Format Means Type



FTP, EUMETCast NRT

Accuracy


Changing with integration interval:

120% for 3-h accumulation,

100% for 24-h accumulation










H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

(degradation

expected at very

high latitudes)

Resolution: ~ 30 km


3 h

H-06 Instantaneous and accumulated precipitation at ground computed by a

NWP model

PR-ASS-1

Type NRT Product


Climate Monitoring

Meteorology

Characteristics and Methods Fields of precipitation rate and accumulated precipitation generated by a

non-hydrostatic operational NWP model (COSMO-ME) to provide spatial-

temporal continuity to the observed fields otherwise affected by temporal and

spatial gaps due to insufficient and inhomogeneous satellite cover.



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The accumulated precipitation is integrated over 3, 6, 12 and 24 h

Comments Forecast products with space-time regularity but error structure biased by

the model scale characteristics.

Timeliness includes cut off for data collection, assimilation, initialisation,

processing and stabilisation of the output

Generation frequency Five products (rain rate and integrals over the preceding 3, 6, 12 and 24 h)

every three hours (rolling)

Observing cycle 6 h (NWP model run four times/day)

Input satellite data Not direct

Dissemination

Format Means Type

Values in grid points of the NWP

model (GRIB-1)

FTP, EUMETCast NRT, Offline

Accuracy


Precipitation rate: 100 % - 24-h

Accumulated precipitation: 200 %

Precipitation rate: 50 % - 24-h

Accumulated precipitation: 100 %

Precipitation rate: 25 % -

24-h

Accumulated precipitation:

50 %




Being progressively

extended to the H-

SAF area (25°N to

75°N latitude, 25°W

to 45°E longitude)

Resolution: ~ 30 km

Sampling: 7 km (grid mesh of

COSMO-ME)

4 h

H-15 Blended SEVIRI Convection area/LEO MW Convective Precipitation PR-OBS-6

Type Product


Climate Monitoring

Risk Management

Meteorology

Characteristics and Methods Instantaneous precipitation maps generated by IR images from operational

geostationary 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”).

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

equipped satellite



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Comments Product mostly suitable for convective precipitation

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

Observing cycle over Europe: 15 min


SSMI, SSMIS on DMSP

AMSU-A/B on NOAA

Dissemination

Format Means Type



FTP, EUMETCast NRT

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

Changing with precipitation

type:

20 % for > 10 mm/h, 40 %

for 1-10 mm/h, N/A for < 1

mm/h

TBC




H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

(degradation

expected at very

high latitudes)




15 min

3.2 Soil Moisture products requirements

H-08 Small–scale surface soil moisture by radar scatterometer SM-OBS-2

Type NRT Product


Climate Monitoring

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).



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This product is scheduled to be released in ORR-2

References: [RD 8],

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

(via CAF Global ASCAT Soil moisture and SM-OBS-3 in CDOP-2)

Dissemination

Format Means Type

Values in grid points of specified


(BUFR)

FTP, EUMETCast NRT

Accuracy


0.10 m3· m-3 0.05 m3· m-3 0.04 m3· m-3

Verification 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

H-14 Soil Wetness Index in the roots region by scatterometer assimilation in

NWP model

SM-DAS-2

Type NRT Product


Climate Monitoring

Characteristics and Methods Analysed soil moisture 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 retrieved soil moisture fields are based on a modelled first guess, the

screen-level temperature and humidity analyses, and the ASCAT-derived

surface soil moisture. The H-14 product is expressed in a soil moisture

index by accounting for soil texture and soil hydraulic properties in the

Land Surface Model.



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The Global product is generated starting from the Global surface soil

moisture product (CAF product).

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)


(via CAF Global ASCAT Soil moisture)

Dissemination

Format Means Type

Values in grid points of the NWP

model (GRIB-1)

FTP, EUMETCast NRT

Accuracy


0.1 m3.m

-3 0.06 m

3.m

-3 0.04 m

3·m

-3

Verification method 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 h

H-16 Large-scale surface soil moisture by radar scatterometer SM-OBS-3

Type Product


Climate Monitoring

Characteristics and Methods It refers to the soil moisture content in the surface layer (0.5-2 cm) generated

from the Metop scatterometer (ASCAT).

References: [RD 6], [RD 8]

Comments

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



Dissemination

Format Means Type

Values in grid points of specified FTP, EUMETCast NRT



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(BUFR)

Accuracy


0.10 m3· m-3 0.05 m3· m-3 0.04 m3· m-3

Verification 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 2 h

3.3 Snow products requirements

Snow Accuracy Revised Values

Product requirements for accuracy were revised 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%.



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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.

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

H-10 Snow detection (snow mask) by VIS/IR radiometry SN-OBS-1

Type NRT Product


Climate Monitoring

Characteristics and Methods Binary map of snow / no-snow situation. VIS/IR images from GEO are used.

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 are used for flat/forested and mountainous regions.

Comments

Generation frequency Output result every 24 h


Dissemination

Format Means Type


Meteosat projection (HDF5)

FTP, EUMETCast NRT

Accuracy


Probability Of Detection (POD):

Flat / Forested areas: 80 %

Mountainous areas: 60%

Probability Of Detection (POD):



Probability Of Detection

(POD): 99 %

False Alarm Rate (FAR): 5 %



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False Alarm Rate (FAR):



False Alarm Rate (FAR):



Verification method Snow observing stations



H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

(degradation

expected at very

high latitudes)




N/A 30 min

H-11 Snow status (dry/wet) by MW radiometry SN-OBS-2

Type NRT Product


Climate Monitoring

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 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 EOS-Aqua orbit, but then merging with daily SN-OBS-1 maps;

therefore: output result every 24 h

Input satellite data AMSR-E on EOS-Aqua

Dissemination

Format Means Type

Values in grid points of the equal-

latitude/longitude projection (HDF5)

FTP, EUMETCast NRT

Accuracy


Hit Rate (HR): 60 %


Hit Rate (HR): 80 %


Hit Rate (HR): 90 %




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H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

Resolution: ~ 20 km

Sampling: 20 km

N/A 6 h

H-12 Effective snow cover by VIS/IR radiometry SN-OBS-3

Type NRT Product


Climate Monitoring

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 is 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

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

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)

MODIS <??>

Dissemination

Format Means Type



FTP, EUMETCast NRT

Accuracy


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





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H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

Resolution: ~ 8 km

Sampling: ~ 5 km

N/A 30 min

Timeliness is intended as

delay after acquisition of the

last image utilised in the

multi-temporal analysis

H-13 Snow water equivalent by MW radiometry SN-OBS-4

Type NRT Product


Climate Monitoring

Characteristics and Methods Maps of snow water equivalent derived from MW measurements

sensitive to snow thickness and density.

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 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

Comments

Generation frequency Assimilation of AMSR-E brightness temperatures in a background field

Output result every 24 h

Input satellite data AMSR-E on EOS-Aqua

Dissemination

Format Means Type



FTP, EUMETCast NRT, Offline

Accuracy


Flat / Forested areas: 40mm

Mountainous areas: 45mm







Spatial coverage Spatial resolution Vertical

resolution

Timeliness

H-SAF area (25°N

to 75°N latitude,

25°W to 45°E

longitude)

Resolution: ~ 25 km

Sampling: ~ 25 km

N/A 6 h



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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



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CR Component Requirement

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



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

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 Hungary)

NMS National Meteorological Service

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

NPOESS National Polar-orbiting Operational Environmental Satellite System



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NPP NPOESS Preparatory Programme

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



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SLAs Service-Level Agreements

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 (EUM/C/70/DOC/10)H-SAF Project

Plan (PP). Ref.: SAF/HSAF/PP/2.2

[AD 2] Definition of Product Status Categories for the SAF Network Ref:

EUM/PPS/TEN/07/0036H-SAF Configuration Management Plan (CMP). Ref.:

SAF/HSAF/CMP/1.0

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



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Page: 33/34

[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

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’.

Appendix 3 TBC/TBD List

Item Section/Paragraph Resolution date

Accuracy and Timeliness

characteristics for Precipitation

product H15

Section 3.1 Within CDOP2 ORR



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- END OF THE DOCUMENT -

Documents

Product Requirement Document - Servizio Meteorologicohsaf.meteoam.it/docs/PRD/SAF_HSAF_PRD_1_2.pdf · Wolfgang Wagner [email protected] ... Lothar Schueller [email protected]