Product Requirement Document
Doc. No: SAF/HSAF/PRD/1.2
Issue: Version 1.2
Date: 10/01/2012
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EUMETSAT Satellite Application Facility
on Support to Operational Hydrology
and Water Management
(H-SAF)
Product Requirement Document
Reference Number: SAF/HSAF/PRD/1.2
Issue/Revision Index: Issue 1.2
Last Change: 10/01/2012
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Issue: Version 1.2
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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]
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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 [%]
Step 2 Step 3 Step 4
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 [%]
Step 2 Step 3 Step 4
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
Changing with precipitation type:
80% for > 10 mm/h,
105% for 1-10 mm/h,
145% for < 1 mm/h
Changing with precipitation type:
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
Application and users Hydrology
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
Threshold Target Optimal
Changing with precipitation type:
90% for > 10 mm/h,
120% for 1-10 mm/h,
240% for < 1 mm/h
Changing with precipitation type:
80% for > 10 mm/h,
105% for 1-10 mm/h,
145% for < 1 mm/h
Changing with precipitation type:
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: 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
Application and users Hydrology
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).
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
Threshold Target Optimal
Changing with precipitation type:
90% for > 10 mm/h,
120% for 1-10 mm/h,
240% for < 1 mm/h
Changing with precipitation type:
80% for > 10 mm/h,
105% for 1-10 mm/h,
145% for < 1 mm/h
Changing with precipitation type:
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)
(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
Application and users Hydrology
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.
References: [RD 10], (Section 4 pp.65-79)
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
Values in grid points of the
Meteosat projection (GRIB-2)
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
Changing with precipitation type:
80% for > 10 mm/h,
105% for 1-10 mm/h,
145% for < 1 mm/h
Changing with precipitation type:
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)
(degradation
expected at very
high latitudes)
Resolution: 30 km in average
Sampling: 5 km in average
4 hours (<4: extrapolation)
H-05 Accumulated precipitation at ground by blended MW+IR PR-OBS-5
Type NRT Product
Application and users Hydrology
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
Values in grid points of the
Meteosat projection (GRIB-2)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
Changing with integration interval:
120% for 3-h accumulation,
100% for 24-h accumulation
Changing with integration interval:
80% for 3-h accumulation,
70% for 24-h accumulation
Changing with integration interval:
25% for 3-h accumulation,
25% for 24-h accumulation
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)
(degradation
expected at very
high latitudes)
Resolution: ~ 30 km
Sampling: 5 km in average
3 h
H-06 Instantaneous and accumulated precipitation at ground computed by a
NWP model
PR-ASS-1
Type NRT Product
Application and users Hydrology
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.
Product Requirement Document
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Issue: Version 1.2
Date: 10/01/2012
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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
Threshold Target Optimal
Precipitation rate: 100 % - 24-h
Accumulated precipitation: 200 %
Precipitation rate: 50 % - 24-h
Accumulated precipitation: 100 %
Precipitation rate: 25 % -
24-h
Accumulated precipitation:
50 %
Verification method Meteorological radar and rain gauge
Coverage, resolution and timeless
Spatial coverage Spatial resolution Vertical resolution Timeliness
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
Application and users Hydrology
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
Input satellite data SEVIRI on MSG Meteosat-9
SSMI, SSMIS on DMSP
AMSU-A/B on NOAA
Dissemination
Format Means Type
Values in grid points of the
Meteosat projection (GRIB-2)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
Changing with precipitation type:
80 % for > 10 mm/h, 160 % for 1-10
mm/h, N/A for < 1 mm/h
TBC
Changing with precipitation type:
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
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)
(degradation
expected at very
high latitudes)
Resolution changing cross
Europe: 8 km in average
Sampling: 5 km in average
15 min
3.2 Soil Moisture products requirements
H-08 Small–scale surface soil moisture by radar scatterometer SM-OBS-2
Type NRT Product
Application and users Hydrology
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
Observing cycle over Europe: 36 h
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
coordinates in the orbital projection
(BUFR)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
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)
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 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
Application and users Hydrology
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)
Input satellite data ASCAT on Metop
(via CAF Global ASCAT Soil moisture)
Dissemination
Format Means Type
Values in grid points of the NWP
model (GRIB-1)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
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
Coverage, resolution and timeless
Spatial coverage Spatial resolution Vertical resolution Timeliness
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
Application and users Hydrology
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
Observing cycle over Europe: 36 h
Input satellite data ASCAT on Metop
Dissemination
Format Means Type
Values in grid points of specified FTP, EUMETCast NRT
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coordinates in the orbital projection
(BUFR)
Accuracy
Threshold Target Optimal
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)
Coverage, resolution and timeless
Spatial coverage Spatial resolution Vertical resolution Timeliness
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
Application and users Hydrology
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
Input satellite data SEVIRI on MSG Meteosat-9
Dissemination
Format Means Type
Values in grid points of the
Meteosat projection (HDF5)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
Probability Of Detection (POD):
Flat / Forested areas: 80 %
Mountainous areas: 60%
Probability Of Detection (POD):
Flat / Forested areas: 85 %
Mountainous areas: 70%
Probability Of Detection
(POD): 99 %
False Alarm Rate (FAR): 5 %
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False Alarm Rate (FAR):
Flat / Forested areas: 20 %
Mountainous areas: 30%
False Alarm Rate (FAR):
Flat / Forested areas: 15 %
Mountainous areas: 20%
Verification method Snow observing stations
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)
(degradation
expected at very
high latitudes)
Resolution changing cross
Europe: 8 km in average
Sampling: 5 km in average
N/A 30 min
H-11 Snow status (dry/wet) by MW radiometry SN-OBS-2
Type NRT Product
Application and users Hydrology
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
Threshold Target Optimal
Hit Rate (HR): 60 %
False Alarm Rate (FAR): 20 %
Hit Rate (HR): 80 %
False Alarm Rate (FAR): 10 %
Hit Rate (HR): 90 %
False Alarm Rate (FAR): 5 %
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Verification method Snow observing stations
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: ~ 20 km
Sampling: 20 km
N/A 6 h
H-12 Effective snow cover by VIS/IR radiometry SN-OBS-3
Type NRT Product
Application and users Hydrology
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
Values in grid points of the equal-
latitude/longitude projection (HDF5)
FTP, EUMETCast NRT
Accuracy
Threshold Target Optimal
45% (Overall accuracy) 65% (Overall Accuracy) 95% (Overall Accuracy)
Verification method Snow observing stations
Coverage, resolution and timeless
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Spatial coverage Spatial resolution Vertical resolution Timeliness
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
Application and users Hydrology
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
Values in grid points of the equal-
latitude/longitude projection (HDF5)
FTP, EUMETCast NRT, Offline
Accuracy
Threshold Target Optimal
Flat / Forested areas: 40mm
Mountainous areas: 45mm
Flat / Forested areas: 20mm
Mountainous areas: 25mm
Flat / Forested areas: 10mm
Mountainous areas: 15mm
Verification method Snow observing stations
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: ~ 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
Product Requirement Document
Doc. No: SAF/HSAF/PRD/1.2
Issue: Version 1.2
Date: 10/01/2012
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