Osi Saf Cdop-2 Quarterly Operations Report

SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

OSI SAF CDOP-2 _

QUARTERLY OPERATIONS REPORT _

1st quarter 2012 _

Apr. 2013 _

version 1_2

Prepared by DMI, IFREMER, KNMI, Meteo-France and met.no

QR12-1 Page 1 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 Table of contents

1 Introduction ...... 4 1.1 Scope of the document ...... 4 1.2 Products characteristics ...... 4 1.3 Reference and applicable documents ...... 8 1.3.1 Applicable documents ...... 8 1.3.2 Reference documents ...... 8 1.4 Definitions, acronyms and abbreviations...... 8

2 OSI SAF products availability and timeliness...... 10 2.1 Availability on FTP servers...... 11 2.2 Availability via EUMETCast...... 14

3 Main anomalies, corrective and preventive measures...... 17 3.1 At SS1 ...... 17 3.2 At SS2 ...... 17 3.3 At SS3 ...... 17

4 Main events and modifications, maintenance activities ...... 18 4.1 At SS1 ...... 18 4.2 At SS2 ...... 18 4.3 At SS3 ...... 18

5 OSI SAF products quality ...... 19 5.1 SST quality...... 19 5.1.1 METEOSAT SST quality ...... 19 5.1.2 GOES-E SST quality...... 24 5.1.3 NAR SST quality ...... 28 5.1.4 GLB and MGR SST quality ...... 38 5.1.5 AHL SST quality...... 42 5.2 Radiative Fluxes quality ...... 44 5.2.1 DLI quality ...... 44 5.2.2 SSI quality...... 48 5.3 Sea Ice quality...... 53 5.3.1 Reference data...... 53 5.3.2 Validation requirements ...... 53 5.3.3 Validation against DMI ice charts...... 53 5.3.4 Validation results for Northern Hemisphere based on Greenland Ice charts 54 5.3.5 Multi-year variability ...... 58 5.3.6 Validation against NIC (National Ice Center) ice charts for Southern Hemisphere ...... 60 5.3.7 Multi-year variability, Southern Hemisphere ...... 64 5.3.8 Validation against met.no ice charts...... 65 5.3.9 Results from manual error registration ...... 66

QR12-1 Page 2 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.3.10 Statistics for confidence levels ...... 66 5.3.11 Sea Ice validation comments ...... 68 5.3.12 Validation of the Low Resolution Sea Ice Drift product ...... 68 5.4 Global Wind quality ...... 70 5.4.1 ASCAT Wind quality...... 71 5.4.2 Buoy validations...... 75

6 Service and Product usage ...... 77 6.1 Statistics on the Web site and help desk...... 77 6.1.1 Statistics on the central OSI SAF Web Site and help desk ...... 77 6.1.2 Statistics on the OSI SAF Sea Ice Web portal and help desk ...... 90 6.1.3 Statistics on the OSI SAF KNMI scatterometer web page and helpdesk 91 6.2 Statistics on the FTP sites use ...... 94 6.2.1 Statistics on the SS1 ftp sites use...... 94 6.2.2 Statistics on the SS2 ftp site use...... 97 6.2.3 Statistics on the SS3 ftp site use...... 99 6.3 Statistics of OSI SAF orders at EUMETSAT data centre ...... 99

7 Documentation update...... 100

QR12-1 Page 3 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 1 Introduction

1.1 Scope of the document

The present report covers 1st quarter 2012, i.e. from 01 JAN. to 31 MARCH 2012. The objective of this document is to provide EUMETSAT and users, in complement with the Web Site, www.osi-saf.org , with an overview on O&SI SAF products availability and quality, main anomalies and events, product usage, users’ feedback, and updated available documentation.

SS1 is the Production Sub-system 1, involving M-F/CMS, met.no and DMI, under M- F/CMS responsibility. It concerns SST and Radiative Fluxes products. SS2 is the Production Sub-system 2 which involves met.no and DMI, under met.no responsibility. It concerns the Sea Ice products. SS3 is KNMI. It concerns the Wind products.

This version of the quarterly report over the first quarter of 2012 is the final one. Compared to the preliminary version already delivered, the section 5.3 on Sea Ice quality has been completed.

1.2 Products characteristics

The characteristics of the current products are recalled in the following table, extracted from the Service Specification Document (SESP, [AD-1]).

QR12-1 Page 4 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

d d n a

o n t e

n h a a

m u e

t o y d i y q g s e

t c e n e a c e u i m r r a t l o t

f i r m

r e

l o s n l a u i s v c n s e r o c

s N a o i t o e

e t i c s e c r t t a t t

a t a d n l l c c c

S a i c a t l a r a o u u a i t i i f e r e e t t h d d i m u t a g r r a n a o o p m r e h e r r o i e p p a n V I F T s g s t P P C M wind speed (m/s) and BUFR via EUMETCAST, Better than 2 m/s in wind direction (degrees). on FTP server and EDC; component RMS with a Triple collocation ASCAT 25 km Sigma0's and swath NetCDF on FTP server, bias of less than 0.5 m/s with NWP and Winds ASCAT25 winds ASCAT EDC and NAIAD 2 h 45 Global Continuous 25 km in wind speed buoys wind speed (m/s) and BUFR via EUMETCAST, Better than 2 m/s in wind direction (degrees). on FTP server and EDC; component RMS with a Triple collocation ASCAT Sigma0's and swath NetCDF on FTP server bias of less than 0.5 m/s with NWP and 12.5 km Winds ASCAT12 winds ASCAT and EDC 2 h 45 Global Continuous 12.5 km in wind speed buoys wind speed (m/s) and Better than 2 m/s in wind direction (degrees). component RMS with a Triple collocation ASCAT coastal ASCAT12 Sigma0's and swath BUFR, bias of less than 0.5 m/s with NWP and Winds + winds ASCAT NetCDF 2 h 45 Global Continuous 12.5 km in wind speed buoys N..A. only Better than 2 m/s in wind archive component RMS with a Triple collocation SeaWinds Sigma0's and swath BUFR and NetCDF (Only availabl bias of less than 0.5 m/s with NWP and 100km Wind SeaW100 winds SeaWinds archive) e Global Continuous 100 km in wind speed buoys N..A. only Better than 2 m/s in wind archive component RMS with a Triple collocation SeaWinds Sigma0's and swath BUFR and NetCDF (Only availabl bias of less than 0.5 m/s with NWP and 25km Wind SeaW025 winds SeaWinds archive) e Global Continuous 25 km in wind speed buoys NetCDF on IFREMER NAR Sea NOAA –18 and FTP server. polar Surface multispectral then 19, Metop GRIB2 via EUMETCast European stereogr. monthly bias : 0,5°c, Sdt Comparison with Temperature NAR SST algorithm AVHRR and UMARF 4 h Seas 6 h 2 km Deviation : 0,8°c buoy observations underskin GRIB ed2 via EUMETCast GLB Metop temperature (°K). and EDC Sea Surface multispectral Metop/ NetCDF on IFREMER 0.05° lat- monthly bias : 0,5°c, Sdt Comparison with Temperature GLB SST algorithm AVHRR FTP server 6h global 12h lon Deviation : 0,8°c buoy observations

QR12-1 Page 5 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Full resolution Metop Sea underskin Surface temperature (°K). NetCDF Temperature multispectral Metop/ L2P on IFREMER FTP monthly bias : 0,5°c, Sdt Comparison with metagranules MGR SST algorithm AVHRR server and NAIAD 4 h Global continuous 1 km Deviation : 0,8°c buoy observations underskin AHL Sea temperature (°K). GRIB Atlantic Surface multispectral AVHRR, NPP/ NetCDF High monthly bias : 0,5°c, Sdt Comparison with Temperature AHL SST algorithm VIIRS HDF5 3 h 30 Latitude 12 h 5 km Deviation : 0,8°c buoy observations GRIB ed 2 and NetCDF underskin L2P through EUMETCast METEOSAT temperature (°K). and EDC. Sea Surface multispectral NetCDF L2Pon IFREMER 60S-60N 0,05°lat- Bias : 0,5°c, sdt Deviation Comparison with Temperature MET SST algorithm MET FTP server 3 h 60W-60E 1h lon : 1°c buoy observations GRIB ed 2 and NetCDF underskin L2P through EUMETCast GOES-E Sea temperature (°K). and EDC. 60S-60N Surface GOES-E multispectral NetCDF L2Pon IFREMER 135W- 0,05°lat- Bias : 0,5°c, sdt Deviation Comparison with Temperature SST algorithm GOES-E FTP server 3 h 15W 1h lon : 1°c buoy observations AHL NOAA/ Downward AVHRR, GRIB Atlantic monthly relative bias : 5%, Comparison with Longwave W/m². Bulk MetOp/ NetCDF North of monthly relative Std. Pyrgeometers Irradiance AHL DLI parameterization AVHRR HDF5 3 h 30 50N 12 h 5 km Deviation :10% measurement NOAA/ AHL Short- AVHRR, GRIB Atlantic monthly relative bias : Comparison with wave Solar W/m². physical MetOp/ NetCDF North of 10%, monthly relative Std. Pyranometers Irradiance AHL SSI parameterization AVHRR HDF5 3 h 30 50N 12 h 5 km Deviation :30% measurement METEOSAT Downward monthly (TBC) relative Comparison with Longwave W/m². Bulk GRIB 60S-60N 1 h + daily 0.05°lat- bias : 5%, monthly relative Pyrgeometers Irradiance MET DLI parameterization MET NetCDF 2 h 60W-60E integrated lon Std. Deviation :10% measurement GOES-E 0.05°lat Downward 60S-60N monthly (TBC) relative Comparison with Longwave GEOS-E W/m². Bulk GRIB 135W- 1 h + daily bias : 5%, monthly relative Pyrgeometers Irradiance DLI parameterization GOES-E NetCDF 2 h 15W integrated Std. Deviation :10% measurement METEOSAT 0.05°lat monthly (TBC) relative Short-wave bias : 10%, monthly Comparison with Solar W/m². physical GRIB 60S-60N 1 h + daily relative Std. Deviation Pyranometers Irradiance MET SSI parameterization MET NetCDF 2 h 60W-60E integrated :30% measurement GOES-E Short- 60S-60N monthly (TBC) relative Comparison with wave Solar GOES-E W/m². physical GRIB 135W- 1 h + daily 0.05°lat- bias : 10%, monthly Pyranometers Irradiance SSI parameterization GOES-E NetCDF 2 h 15W integrated lon relative Std. Deviation measurement

QR12-1 Page 6 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

:30%

EUMETCast: and EDC : Fractional ice cover in GRIB1 polar 10% for NH-product. 15% Comparison with GBL Sea Ice percentage. FTP: GRIB1, stereogr. for SH-product (yearly high resolution Concentration GBL SIC Multisensor analysis. SSM/I NetCDF3,HDF5 5 h Global 1 day 10km average) manual ice charts EUMETCast: and EDC : Discrimination Open GRIB1 polar Comparison with GBL Sea Ice ice/Closed ice/No ice. SSM/I, FTP: GRIB1, stereogr. high resolution Edge GBL SIE Multisensor analysis Metop/ASCAT NetCDF3,HDF5 5 h Global 1 day 10 km 20 km (yearly average) manual ice charts EUMETCast: and EDC: Discrimination First GRIB1 polar Comparison with GBL Sea Ice year, Multi year. SSM/I, FTP: GRIB1, stereogr. high resolution Type GBL SIT Multisensor analysis Metop/ASCAT NetCDF3,HDF5 5 h Global 1 day 10 km TBD manual ice charts km. Single and multi sensor analysis. polar 5 km yearly std deviation Low Resolution GBL LR Displacement after 48 SSM/I, stereogr. after 48 hours Collocation with Sea Ice Drift SID hours. Metop/ASCAT, NetCDF 6 h Global 1 day 10km displacement buoys N.A. Global Data set reprocessed Fractional ice cover in availabl 10% for NH-product. 15% Comparison with Sea Ice GBL REP percentage. Period e off for SH-product (yearly high resolution Concentration SIC 1978-2009 SMMR, SSM/I NetCDF on FTP server line. Global Daily 10km average) manual ice charts

table 1 : Products characteristics.

QR12-1 Page 7 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

1.3 Reference and applicable documents

1.3.1 Applicable documents

[AD-1] : Service Specification Document, SESP.

1.3.2 Reference documents

[RD-1] : LEO SST Product User Manual [RD-2] : GEO SST Product User Manual [RD-3] : AHL SST Product User Manual [RD-4] : GEO Radiative Fluxes Product User Manual [RD-5] : AHL Radiative Fluxes Product User Manual [RD-6] : Sea Ice Concentration Edge and Type Product User Manual [RD-7] : Sea Ice Drift Product User Manual [RD-8] : ASCAT Product User Manual

1.4 Definitions, acronyms and abbreviations

AHL Atlantic High Latitude AMS American Meteorological Society ASCAT Advanced scatterometer ATL Atlantic low and mid latitude AVHRR Advanced Very High Resolution Radiometer BUFR Binary Universal Format Representation CDOP Continuous Development and Operations Phase CMS Centre de Météorologie Spatiale DLI Downward Long wave Irradiance DMI Danish Meteorological Institute DMSP Defence Meteorological Satellite Program ECMWF European Centre for Medium range Weather Forecasts EPS European Polar System FAQ Frequently Asked Question FTP File Transfer Protocol GLB Global oceans GOES Geostationary Operational Environmental Satellite GOES-E GOES-East, nominal GOES at 75°W GRIB GRIdded Binary format GTS Global Transmission System HIRLAM High Resolution Limited Area Model HL High Latitude HRIT High Rate Information Transmission IFREMER Institut Français de Recherche pour l’Exploitation de la MER IOP Initial Operational Phase KNMI Koninklijk Nederlands Meteorologisch Instituut LEO Low Earth Orbiter LML Low and Mid Latitude MAP Merged Atlantic Product QR12-1 Page 8 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 MET Nominal Meteosat at 0°longitude Met.no Norwegian Meteorological Institute Metop METeorological OPerational Satellite M-F Météo-France MGR Metagranule MSG Meteosat Second Generation NAR Northern Atlantic and Regional NCEP National Centre for Environmental Prediction NESDIS National Environmental Satellite, Data and Information Service NetCDF Network Common Data Form NMS National Meteorological Service NOAA National Oceanic and Atmospheric Administration NPP NPOESS Preparatory Project NPOESS National Polar-orbiting Operationnal Environmental Satellite System NRT Near Real-Time NWP Numerical Weather Prediction OSI SAF Ocean and Sea Ice SAF QC Quality Control R&D Research and Development RMDCN Regional Meteorological Data Communication Network RMS Root-Mean-Squared SAF Satellite Application Facility Std Dev Standard deviation SEVIRI Spinning Enhanced Visible and Infra-Red Imager SMHI Swedish Meteorological and Hydrological Institute SSI Surface Short wave Irradiance SSMI Special Sensor Microwave Imager SSMIS Special Sensor Microwave Imager and Sounder SST Sea Surface Temperature TBC To Be Confirmed TBD To Be Defined UMARF Unified Meteorological Archive & Retrieval Facility WMO World Meteorological Organisation WWW World Wide Web table 2 : Definitions, acronyms and abbreviations.

QR12-1 Page 9 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 2 OSI SAF products availability and timeliness

As indicated in the table 1, extracted from the Service Specification Document [AD- 2], operational OSI SAF products are expected to be available for distribution within the specified time in more than 95% of the cases where input satellite data are available with the nominal level of quality, on monthly basis. In section 2.1 the above specifications are matched with the measured availability on the local FTP servers. In section 2.2 the above specifications are matched with the measured availability via EUMETCast. The dissemination of the OSI SAF products via EUMETCast implies an additional step, not under the strict OSI SAF responsibility, but general EUMETSAT’s one. The timeliness of the wind products on the KNMI FTP server is not measured separately and therefore the figures in table 2 are copied from table 3 for the wind products. Since the EUMETCast transmission is known to add only a very small delay to the timeliness, the availabilities on the KNMI FTP server are very close to or slightly better than the figures measured via EUMETCast.

QR12-1 Page 10 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

2.1 Availability on FTP servers

The following table indicates the percentage of the products that have been made available within the specified time on the local FTP servers.

Percentage of OSI SAF products available on the FTP servers within the specified time over 1st quarter 2012

e e I e

e o c c c i L T T T S d c I I I t

A A A

S D n

a i T

r T w a a

a T a S

S S I T I t

S E E E l o e e e S W e S L n - - - S S O O

O m T T T a S L S S S S S e

S S h S S S k D E E E

A A A

c s . e d d e R

E E E t

T T T L L L L 5 k R n L L L B I C C T C T f s n I a g n n p .

i i i E E G o E B B B O O B L O r e o L o A S d S S S S S H H H y 2 5

D R G C T G G S M G G E 1 D 2 A A M C S M n G M W A W G N M G S A A A JAN. 100 100 99,3 100 100 100 99,83 100 100 100 100 100 100 100 100 96,8 96,8 96,8 96,8 2012 FEB. 100 100 99,4 100 100 100 99,70 100 99,86 100 100 100 99,93 100 99,93 100 100 100 100 2012 MARCH 100 100 99,5 100 100 100 99,61 100 99,87 100 100 100 100 100 100 100 100 100 100 2012

table 3 : Percentage of OSI SAF products available on the FTP servers within the specified time over 1st quarter 2012.

Note : The timeliness of the wind products on the KNMI FTP server is not measured separately and therefore the figures in table 3 are copied from table 4 for the wind products. Since the EUMETCast transmission is known to add only a very small delay to the timeliness, the availabilities on the KNMI FTP server are very close to or slightly better than the figures measured via EUMETCast. Comment :

See anomaly details in section 3.

QR12-1 Page 11 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

The following graphs illustrate the evolution of the products availability over the past six months.

100

90 I I d d T T T T T I I L L I I n e e t T L S S S g p if in in S S S S S S D D D S S io y r S S S S S S S t d D w W L L T -E T E a E T l B R L R T -E H A - tr e e e m a L A H G A S H S S A S n c c Ic k t G N A S A A E S E e I I 5 s M O E O O O O c a a a , a O E E n e e e 2 o E T G G S S 1 C T G E T o S . E E C L L s T T M M M e B e A c B R A I G G C C a w S S e o A A S L

L L B B G G oct-11 nov-11 Dec-11 Jan-12 Feb-12 Mar-12

Figure 1 : Products availability on FTP servers for each product over the past six months.

QR12-1 Page 12 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

100

90 1 2 1 1 1 2 1 2 -1 - -1 -1 - -1 t v c b r c o e an e a o n D J F M

ASCAT 25 km Wind ASCAT 12,5 km wind ASCAT Coastal Wind GLB SST NAR SST AHL SST MGR SST METEOSAT SST GOES-E SST AHL DLI AHL SSI METEOSAT DLI GOES-E DLI METEOSAT SSI GOES-E SSI GBL Sea Ice Concentration

GBL Sea Ice Edge GBL Sea Ice Type GBL Low Res. Sea Ice Drift

Figure 2 : Products availability on FTP servers over the past six months.

QR12-1 Page 13 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

2.2 Availability via EUMETCast

The following table indicates the percentage of the products that have been delivered within the specified time :

Percentage of OSI SAF products available via EUMETCast within the specified time over 1st quarter 2012

e e I e

e o c c c i L T T T S d c I I I t

A A A

S D n

a i T

r T w a a

a T a S

S S I T I t

S E E E l o e e e S W e S L n - - - S S O O

O m T T T a S L S S S S S e

S S h S S S k D E E E

A A A

c s . e d d e R

E E E t

T T T L L L L 5 k R n L L L B I C C T C T f s n I a g n n p .

i i i E E G o E B B B O O B L O r e o L o A S d S S S S S H H H y 2 5

D R G C T G G S M G G E 1 D 2 A A M C S M n G M W A W G N M G S A A A JAN. 100 100 99,3 100 100 100 N.A. 99,87 100 100 100 100 99,87 100 100 100 100 100 100 2012 FEB. 100 100 99,4 100 100 100 N.A. 99,86 99,86 100 100 99,86 99,72 100 99,72 100 100 100 100 2012 MARCH 100 100 99,5 100 100 100 N.A. 99,87 99,87 100 100 99,87 99,87 99,87 99,87 100 100 100 100 2012

table 4 : Percentage of OSI SAF products delivered via EUMETCast within the specified time over 1st quarter 2012 . Note : the MGR SST product is not delivered through EUMETCAST.

Comments:

See details in section 3.

QR12-1 Page 14 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

The following graph illustrates the evolution of the products availability over the past six months.

100

90 I I d d T T T T T I I L L I I n e e t L S S S g p if in in S S S S S D D D S S io y r S S S S S S t d D w W L L T -E T E a E T l B R L T -E H A - tr e e e m a L A H A S H S S A S n c c Ic k t G N A S A A E S E e I I 5 s O E O O O O c a a a , a O E E n e e e 2 o E T G G S S 1 C T G E T o S . E E C L L s T T M M M e B e A c B R A I G G C C a w S S e o A A S L

L L B B G G oct-11 nov-11 Dec-11 Jan-12 Feb-12 Mar-12

Figure 3 : Products availability via EUMETCast for each product over the past six months.

QR12-1 Page 15 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

100

90 1 2 1 1 1 2 1 2 -1 - -1 -1 - -1 t v c n b r c o e a e a o n D J F M

ASCAT 25 km Wind ASCAT 12,5 km wind ASCAT Coastal Wind GLB SST

NAR SST AHL SST METEOSAT SST GOES-E SST AHL DLI AHL SSI METEOSAT DLI GOES-E DLI METEOSAT SSI GOES-E SSI GBL Sea Ice Concentration GBL Sea Ice Edge GBL Sea Ice Type GBL Low Res. Sea Ice Drift

Figure 4 : Products availability via EUMETCast over the past six months.

QR12-1 Page 16 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 3 Main anomalies, corrective and preventive measures

In case of anomaly (outage, degraded products…), correspondent service messages are made available in near-real time to the registered users through the Web site www.osi-saf.org.

3.1 At SS1

The access to IFREMER ftp server was down between 10 February at 18 UTC and 13 February at 0830 UTC, because all possible sessions were opened simultaneously by a user. Investigations are being made to prevent from this problem.

3.2 At SS2

03.02.2012 - OSI SAF Sea Ice: partly missing input data Due to outage of SSMI data from our data provider, OSI SAF Sea Ice Concentration, Edge and Type products from this day contained some missing data areas. Users were informed by service message and news on web portal.

02.01.2012 - Delayed sea ice products Due to a problem with the distribution of products to the SS2 FTP server, the sea ice products were 3 hours delayed at the OSI SAF FTP server on this day. Users were informed by service message and news on web portal.

3.3 At SS3

No anomalies or corrective and preventive measures have occurred during the reporting period.

QR12-1 Page 17 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 4 Main events and modifications, maintenance activities

In case of event or modification, corresponding service messages are made available in near-real time to the registered users through the Web site www.osi-saf.org.

4.1 At SS1

The MAP and LML SST and radiative fluxes products, already superseded by the METEOSAT, GOES-E and AHL ones, were still disseminated in parallel till 31 March 2012.

4.2 At SS2

19.01.2012 – AHL products dissemination started On this day the distribution of the OSI SAF Atlantic High Latitude SST, DLI and SSI products through EUMETCast and FTP started. The users were informed through EUMETCast notification and OSI SAF Service message.

4.3 At SS3

There have been no events or modifications during the reporting period.

QR12-1 Page 18 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5 OSI SAF products quality

5.1 SST quality

The comparison between SST products and Match up data bases (MDB) gathering in situ (buoy) measurements is performed on a routine basis for each METEOSAT and GOES-E satellite, currently METEOSAT-09 and GOES-12. Hourly SST values are required to have the following accuracy when compared to night time buoy measurements (see PRD) : • monthly bias (Bias Req in following tables)less than 0.5° C, • monthly difference standard deviation (Std Dev Req. in following tables) less than 1° C for the geostationary products (METEOSAT SST and GOES-E SST), and 0.8°C for the polar ones (MGR SST, GLB SST, NAR SST and AHL SST).

5.1.1 METEOSAT SST quality

The following maps indicate the locations of buoys for each month.

Figure 5 : Location of buoys for METEOSAT SST validation in JAN. 2012, for 3,4,5 quality indexes and by night.

QR12-1 Page 19 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Figure 6 : Location of buoys for METEOSAT SST validation in FEB. 2012, for 3,4,5 quality indexes and by night.

Figure 7 : Location of buoys for METEOSAT SST validation in MARCH 2012, for 3,4,5 quality indexes and by night.

QR12-1 Page 20 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 The following table provides the METEOSAT-derived SST quality results over the reporting period. METEOSAT SST quality results over 1st quarter 2012

METEOSAT SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 10298 -0,070 0,5 86,00 0,52 1,0 48,00 FEB. 2012 9907 -0,080 0,5 84,00 0,55 1,0 45,00 MARCH 2012 11411 -0,110 0,5 78,00 0,50 1,0 50,00 table 5 : METEOSAT SST quality results over 1st quarter 2012, for 3, 4, 5 quality indexes and by night.

(*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good and stable.

The following graphs illustrate the evolution of METEOSAT-derived SST quality results over the past 6 months.

Bias in °C Bias Margin (*) MET SST quality 1,0 0 100,00

0,50 50,00

0,00 0,00

-0,50 -50,00

-1,00 -1 00,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 8 : Left: METEOSAT SST Bias. Right METEOSAT SST Bias Margin

QR12-1 Page 21 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Std Dev in °C Std Dev margin (*) MET SST quality 2 100,00

1,5 50,00

1 0,00

0,5 -50,00

0 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 9 : Left: METEOSAT SST Standard deviation. Right METEOSAT SST Standard deviation Margin.

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Figure 10 : Complementary validation statistics on METEOSAT SST.

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5.1.2 GOES-E SST quality

The following maps indicate the location of buoys for each month.

Figure 11 : Location of buoys for GOES-E SST validation in JAN. 2012, for 3,4,5 quality indexes and by night.

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Figure 12 : Location of buoys for GOES-E SST validation in FEB. 2012, for 3,4,5 quality indexes and by night.

Figure 13 : Location of buoys for GOES-E ST validation in MARCH 2012, for 3,4,5 quality indexes and by night.

The following table provides the GOES-E-derived SST quality results over the reporting period.

GOES-E SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 10878 -0,130 0,5 74,00 0,47 1,0 53,00 FEB. 2012 9715 -0,140 0,5 72,00 0,53 1,0 47,00 MARCH 2012 13643 -0,170 0,5 66,00 0,50 1,0 50,00

table 6 : GOES-E SST quality results over 1st quarter 2012, for 3, 4, 5 quality indexes and by night.

Comments: Results are good and stable.

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The following graphs illustrate the evolution of GOES-E-derived SST quality results over the past 6 months.

Bias in °C Bias Margin (*) GOES-E SST quality GOES-E SST quality 1,00 100,00

0,50 50,00

0,00 0,00

-0,50 -50,00

-1,00 -100,00 1 1 1 2 2 2 1 1 1 1 1 1 t- - - - - r- 1 1 1 2 2 2 c v c n b a -1 -1 -1 -1 -1 -1 o o e a e t v c n b r n D J F M c o e a e a o n D J F M

Figure 14 : Left: Goes-E SST Bias. Right: Goes-E SST Bias Margin.

Std Dev in °C Std Dev margin (*) GOES-E SST quality GOES-E SST quality 2 100,00

1,5 50,00

1 0,00

0,5 -50,00

0 -100,00

1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 15 : Left: Goes-E SST Standard deviation. Right Goes-E SST Standard deviation Margin.

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Figure 16 : Complementary validation statistics on GOES-E SST.

QR12-1 Page 27 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.1.3 NAR SST quality

The operational NAR SST processing relies on two satellite data sources, Metop/AVHRR for the morning orbit and NOAA/AVHRR for afternoon orbit. Currently Metop-A and NOAA-19 are used. The comparison between NAR SST products and Match up data bases (MDB) gathering in situ (buoy) measurements is performed on a routine basis for each operational NOAA and Metop satellite. Compiled results are also provided in the first part of this section.

5.1.3.1 NAR Compiled SST quality

The following table provides NAR Metop-NOAA compiled SST quality results over the reporting period.

NAR compiled SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 1277 -0,14 0,5 72,00 0,35 0,8 56,25 FEB. 2012 1053 -0,15 0,5 70,00 0,38 0,8 52,50 MARCH 2012 1809 -0,14 0,5 72,00 0,4 0,8 50,00 table 7 : Quality results for NAR compiled SST over 1st quarter 2012, for 3, 4, 5 quality indexes and by night. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good and stable.

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The following graphs illustrate the evolution of NAR SST quality results over the past 6 months.

Bias in °C Bias Margin (*) NAR SST quality NAR SST quality 1 ,00 100,00

0 ,50 50,00

0 ,00 0,00

-0,50 -50,00

-1 ,00 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 17 : Left: NAR SST Bias. Right: NAR SST Bias Margin.

Std Dev in °C Std Dev margin (*) NAR SST quality NAR SST quality 2,00 100,00

1,50 50,00

1,00 0,00

0,50 -50,00

0,00 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 18 : Left: NAR SST Standard deviation. Right: NAR SST Standard deviation Margin.

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5.1.3.2 NOAA-19 NAR SST quality

The following maps indicate the locations of buoys for each month.

Figure 19 : Location of buoys for NOAA-19 NAR SST validation in JAN. 2012, for 3, 4, 5 quality indexes and by night.

Figure 20 : Location of buoys for NOAA-19 NAR SST validation in FEB. 2012, for 3, 4, 5 quality indexes and by night.

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Figure 21 : Location of buoys for NOAA-19 NAR SST validation in MARCH 2012, for 3, 4, 5 quality indexes and by night.

The following table provides the NOAA-19-derived SST quality results over the reporting period.

NOAA-19 NAR SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 305 -0,070 0,5 86 0,340 0,8 57,50 FEB. 2012 279 -0,090 0,5 82 0,410 0,8 48,75 MARCH 2012 560 -0,030 0,5 94 0,380 0,8 52,50

table 8 : Quality results for NOAA-19 NAR SST over 1st quarter 2012, for 3, 4, 5 quality indexes and by night. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good and stable.

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The following graphs illustrate the evolution of NOAA-19 NAR SST quality results over the past 6 months.

Bias in °C Bias Margin (*) NOAA-19 SST quality NOAA-19 SST quality 1,00 100

0,50 50

0,00 0

-0,50 -50

-1,00 -100

1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 22 : Left: NOAA-19 NAR SST Bias. Right NOAA-19 NAR SST Bias Margin.

Std Dev in °C Std Dev margin (*) NOAA-19 SST quality NOAA-19 SST quality 1,6 100 1,4 1,2 50 1,0 0,8 0 0,6 0,4 -50 0,2 0,0 -100

1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 23 : Left: NOAA-19 NAR SST Standard deviation. Right NOAA-19 NAR SST Standard deviation Margin.

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Figure 24 : Complementary validation statistics on NOAA-19 NAR SST.

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5.1.3.3 Metop NAR SST quality

The following maps indicate the locations of buoys for each month.

Figure 25 : Location of buoys for Metop-A NAR SST validation in JAN. 2012, for 3, 4, 5 quality indexes and by night.

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Figure 26 : Location of buoys for Metop-A NAR SST validation in FEB. 2012, for 3, 4, 5 quality indexes and by night.

Figure 27 : Location of buoys for Metop-A NAR SST validation in MARCH 2012, for 3, 4, 5 quality indexes and by night.

The following table provides Metop-A -derived SST quality results over the reporting period.

Metop-A NAR SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 660 -0,190 0,5 62,00 0,34 0,8 57,50 FEB. 2012 528 -0,180 0,5 64,00 0,37 0,8 53,75 MARCH 2012 762 -0,240 0,5 52,00 0,39 0,8 51,25

table 9 : Quality results for Metop-A NAR SST over 1st quarter 2012 , for 3, 4, 5 quality indexes and by night. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good.

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The following graphs illustrate the evolution of Metop-A NAR SST quality results over the past 6 months.

Bias in °C Bias Margin (*) METOP SST quality NAR METOP SST quality NAR 1,0 0 100,00

0,5 0 50,00

0,0 0 0,00

-0,50 -50,00

-1,0 0 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 28 : Left: Metop-A NAR SST Bias. Right: Metop-A NAR SST Bias Margin.

Std Dev in °C Std Dev margin (*) METOP SST quality NAR METOP SST quality NAR 2, 00 100,00

1, 50 50,00

1, 00 0,00

0, 50 -50,00

0,00 -100,00

1 1 1 2 2 2 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 t- - - - - r- t- - - - - r- c v c n b a c v c n b a o o e a e o o e a e n D J F M n D J F M

Figure 29 : Left: Metop-A NAR SST Standard deviation. Right: Metop-A NAR SST Standard deviation Margin.

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Figure 30 : Complementary validation statistics on Metop NAR SST.

QR12-1 Page 37 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.1.4 GLB and MGR SST quality

The OSI SAF SST products on global coverage (GLB SST and MGR SST) are based on Metop/AVHRR data, currently Metop-A.

Figure 31 : Location of buoys for global Metop-A SST validation in JAN. 2012, for 3, 4, 5 quality indexes and by night.

Figure 32 : Location of buoys for global Metop-A SST validation in FEB. 2012, for 3, 4, 5 quality indexes and by night.

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Figure 33 : Location of buoys for global Metop-A SST validation in MARCH 2012, for 3, 4, 5 quality indexes and by night.

The following table provides the METOP-derived SST quality results over the reporting period.

global Metop-A SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 4688 -0,080 0,5 84,00 0,47 0,8 41,25 FEB. 2012 4697 -0,050 0,5 90,00 0,42 0,8 47,50 MARCH 2012 4873 -0,100 0,5 80,00 0,45 0,8 43,75

table 10 : Quality results for global METOP SST over 1st quarter 2012, for 3,4,5 quality indexes and by night. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good and stable.

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The following graphs illustrate the evolution of global METOP SST quality results over the past 6 months.

Bias in °C Bias Margin (*) METOP SST quality METOP SST quality 1,00 100,00

0,50 50,00

0,00 0,00

-0,50 -50,00

-1,00 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 34 : Left: global Metop-A SST Bias. Right: global Metop-A SST Bias Margin.

Std Dev in °C Std Dev margin (*) METOP SST quality METOP SST quality 2 100,00

1,5 50,00

1 0,00

0,5 -50,00

0 -100,00 1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 35 : Left: global Metop-A SST Standard deviation. Right: global Metop-A SST Standard deviation Margin.

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Figure 36 : Complementary validation statistics on Metop GLB SST.

QR12-1 Page 41 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.1.5 AHL SST quality

The Atlantic High Latitude SST (AHL SST) is derived from polar satellites data, currently AVHRR on NOAA-18, NOAA-19 and METOP-A.

The following table provides the AVHRR-derived AHL SST quality results over the reporting period.

AHL AVHRR SST quality results over 1st quarter 2012 Month Number of Bias Bias Bias Std Std Dev Std Dev cases °C Req Margin Dev Req margin (*) °C (*) °C °C JAN. 2012 2382 -0.63 0.5 -26 0.74 0.8 7.5 FEB. 2012 3269 -0.52 0.5 -4 0.71 0.8 11 MARCH 2012 3478 -0.40 0.5 20 0.72 0.8 10 table 11 : Quality results for AHL AVHRR SST over 1st quarter 2012, for 3,4,5 quality indexes and by night. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments:

For January and February the AHL SST product does not fulfill the bias requirement. This has been further investigated. Quality index 4 and 5 data are within the requirement, while quality index 3 are below the limit. The negative bias seems to be linked with cloud and ice masking issues. We are working of further tuning of the additional cloud and ice masking steps that should improve this.

A figure with buoy location will be included, starting with the next Quarterly Report.

The following graphs illustrate the evolution of global AHL SST quality results over the past 6 months.

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Bias in °C Bias Margin (*) AHL SST quality AHL SST quality 1,00 100

0,50 50

0,00 0

-0,50 -50

-1,00 -100

2 2 2 2 2 2 2 2 2 2 2 2 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 t- n b r t- n b r c v c a e a c v c a e a O o e J O o e J N D F M N D F M

Figure 37 : Left: AHL SST Bias. Right: AHL SST Bias Margin.

Std Dev in °C Std Dev margin (*)

AHL SST quality AHL SST quality 2 100

1,5 50

1 0

0,5 -50

0 -100 2 2 2 2 2 2 2 2 2 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a O N D J F M O N D J F M

Figure 38 : Left: AHL SST Standard deviation. Right: AHL SST Standard deviation Margin.

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5.2.1 DLI quality

DLI products are constituted of the geostationary products (METEOSAT DLI and GOES-E DLI) and the polar ones (AHL DLI). DLI values are required to have the following accuracy when compared to land pyrgeometer measurements : • monthly relative bias less than 5%, • monthly difference standard deviation less than 10%.

The match-up data base the statistics are based on is continuously enriched, so that, for the same period, results may evolve depending on the date when the statistics were calculated

5.2.1.1 METEOSAT and GOES-E DLI quality

The list of pyrgeometer stations used for validating the geostationary DLI products is available on the OSI SAF Web Site from the following page: http://www.osisaf.org/production/cms/validation_dli_geo.php?safosi_session_id=2a5c e18676a34d0d8c1355612876a009

The following table provides the geostationary DLI quality results over the reporting period.

Geostationary METEOSAT & GOES-E DLI quality results over 1st quarter 2012 Month Number of Mean DLI Bias in Bias Bias Std Std Dev Std Dev cases % Req Marg in Dev Req margin (*) In % %(*) In % In % JAN. 2012 4604 265,25 -1,99 5 60,26 7,39 10 26,15 FEB. 2012 4932 264,79 -2,62 5 47,58 7,44 10 25,64 MARCH 2012 4235 303,06 -0,43 5 91,35 5,52 10 44,80 table 12 : Geostationary DLI quality results over 1st quarter 2012.

Comments: Results are good, with similar trend observed every year.

The following graphs illustrate the evolution of Geostationary DLI quality over the past 6 months.

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Bias in % Bias Margin (*) DLI quality DLI quality 10,0 100,00

5,0 50,00

0,0 0,00

-5,0 -50,00

-10,0 -100,00 1 1 1 2 2 2 1 1 1 1 1 1 2 2 2 -1 -1 -1 - - - -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 39 : Left: Geostationary DLI Bias. Right Geostationary DLI Bias Margin .

Std Dev in % Std Dev margin (*) DLI quality DLI quality 20,00 100,00

15,00 50,00

10,00 0,00

5,00 -50,00

0,00 -100,00

1 1 1 2 2 2 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 t v c n b r t v c n b r c o e a e a c o e a e a o n D J F M o n D J F M

Figure 40 : Left: Geostationary DLI Standard deviation. Right DLI Geostationary Standard deviation Margin.

QR12-1 Page 45 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.2.1.2 AHL DLI quality The following table provides the AHL DLI quality results over the reporting period. The pyrgeometer stations used for validation of the AHL DLI product are: • Ekofisk • Jan Mayen • Bjørnøya • Hopen

These stations are briefly described at http://dokipy.met.no/projects/iaoos- norway/radflux.html.

AHL DLI quality results over 1st quarter 2012 Month Number of Mean DLI Bias in Bias Bias Std Std Dev Std Dev cases % Req Marg in Dev Req margin (*) In % %(*) In % In % JAN. 2012 93 285.2 3.9 5.0 22 4.3 10.0 7 FEB. 2012 87 280.8 2.1 5.0 58 4.5 10.0 55 MARCH 2012 90 284.1 3.2 5.0 36 3.8 10.0 62 table 13 : AHL DLI quality results over 1st quarter 2012. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: The number of stations available for validation of DLI is very limited. Work is currently ongoing to increase the number of stations and stations operated by the Swedish Meteorological and Hydrological Institute is currently being examined and data streams prepared for inclusion. The validation results on the stations used is satisfying. The stations used have some problems when used for validation of SSI, but these problems are not relevant for the validation of DLI.

The following graphs illustrate the evolution of AHL DLI quality results over the past 6 months.

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Bias in % Bias Margin (*)

DLI quality AHL DLI quality 10,0 100,00

5,0 50,00 0,0 0,00 -5,0 -50,00 -10,0

1 1 1 2 2 2 -100,00 1 1 1 -1 -1 -1 - - - t v c n b r c o e a e a 2 o J 1 1 1 2 2 n D F M -1 -1 -1 -1 -1 -1 t v c n b r c o e a e a o n D J F M

Figure 41 : Left: AHL DLI Bias. Right AHL DLI Bias Margin .

Std Dev in % Std Dev margin (*) AHL DLI quality AHL DLI quality 20,00 100,00 15,00 50,00

10,00 0,00

5,00 -50,00

0,00 1 1 1 2 2 2 -100,00 -1 -1 -1 -1 -1 -1 t v c n b r c o e a e a o n J F M 1 1 1 2 2 2 D -1 -1 -1 -1 -1 -1 t v c n b r c o e a e a o n D J F M

Figure 42 : Left: AHL DLI Standard deviation. Right AHL DLI Standard deviation Margin.

QR12-1 Page 47 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 5.2.2 SSI quality

SSI products are constituted of the geostationary products (METEOSAT SSI and GOES-E SSI) and polar ones (AHL SSI). SSI values are required to have the following accuracy when compared to land pyranometer measurements : • monthly relative bias less than 10%, • monthly difference standard deviation less than 30%.

The match-up data base the statistics are based on is continuously enriched, so that, for the same period, results may evolve depending on the date when the statistics were calculated.

5.2.2.1 METEOSAT and GOES-E SSI quality

The list of pyranometer stations used for validating the geostationary SSI products is available on the OSI SAF Web Site from the following page: http://www.osisaf.org/production/cms/validation_ssi_geo.php?safosi_session_id=2a5c e18676a34d0d8c1355612876a009

The following table provides the geostationary SSI quality results over the reporting period.

Geostationary METEOSAT & GOES-E SSI quality results over 1st quarter 2012 Month Number of Mean SSI Bias in Bias Bias Std Std Dev Std Dev cases % Req Marg in Dev Req margin (*) In % %(*) In % In % JAN. 2012 5204 310,18 5,67 10 43,26 23,76 30 20,80 FEB. 2012 5608 352,65 4,29 10 57,07 22,62 30 24,59 MARCH 2012 6422 422,68 5,79 10 42,11 18,10 30 39,66 table 14 : Geostationary SSI quality results over 1st quarter 2012. (*)Bias Margin = 100 * (1-(|Bias / Bias Req|)) (*)Std Dev margin = 100 * (1-(Std Dev / Std Dev Req)) 100 refers then to a perfect product. 0 to a quality just as required. without margin. A negative result indicates that the product quality does not fulfill the requirement.

Comments: Results are good.

The following graphs illustrate the evolution of Geostationary SSI quality over the past 6 months.

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Bias in % Bias Margin (*)

SSI quality SSI quality 1 0,0 100,00

5,0 50,00

0,0 0,00

- 5,0 -50,00

-1 0,0 -100,00 2 2 2 1 1 1 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 1 1 1 t v c n b r -1 -1 -1 - - - c o e a e a t v c n b r o n J F M c o e a e a D o n D J F M

Figure 43 : Left: Geostationary SSI Bias. Right Geostationary SSI Bias Margin.

Std Dev in % Std Dev margin (*) SSI quality SSI quality 50,00 100,00

40,00 50,00 30,00 0,00 20,00

10,00 -50,00

0,00 -100,00 1 1 1 2 2 2 1 1 -1 -1 -1 -1 1 1 1 2 2 2 t- - n b r 1 1 -1 -1 -1 -1 c v c a e a t- - n b r o o e J c v c a e a n D F M o o e J n D F M

Figure 44 : Left: Geostationary SSI Standard deviation. Right Geostationary SSI Standard deviation Margin.

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The pyranometer stations used for validation of the AHL SSI product are: • Apelsvol • Landvik • Særheim • Fureneset • Tjøtta • Ekofisk • Jan Mayen • Bjørnøya • Hopen

Locations of these stations are provided in the illustration below. The map illustrates whether stations are used for SSI or DLI validation. As readily can be seen, the map contains more stations than actually used (see the list above). The reason for this is that some stations have characteristics which makes them unsuitable for validation of daily SSI due to e.g. shadow effects or other surrounding characteristics. Furthermore, some of the stations listed are briefly described at http://dokipy.met.no/projects/iaoos-norway/radflux.html. The stations used in this validation is owned and operated by the Norwegian Meteorological Institute, University of Bergen, Geophysical Institute and Bioforsk.

Figure 45 : Location of High Latitude stations used for AHL DLI and SSI validation.

QR12-1 Page 50 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 The following table provides the AHL SSI quality results over the reporting period.

AHL SSI quality results over 1st quarter 2012 Month Number of Mean SSI Bias in Bias Bias Std Std Dev Std Dev cases % Req Marg in Dev Req margin (*) In % %(*) In % In % JAN. 2012 1030 6.4 31.3 10.0 -213 50.8 30.0 -69 FEB. 2012 925 21.4 26.4 10.0 -164 31.7 30.0 -6 MARCH 2012 1050 64.2 11.2 10.0 -12 20 30.0 33 table 15 : AHLSSI quality results over 1st quarter 2012.

Comments: Validation of SSI during the Polar night is difficult for several reasons. The energy available is very small and most of the stations used for validation are located onshore. Some of the stations are situated in complex terrain with prominent shadow effects influencing the results. The number of stations used in this report is reduced compared to the validation report prepared in 2011. Work is currently ongoing to increase the number of stations and stations operated by the Swedish Meteorological and Hydrological Institute is currently being examined and data streams prepared for inclusion. Work is also ongoing to establish a number of reference stations on the Norwegian mainland (covering the latitudinal extent) and to have these maintained by the Norwegian Meteorological Institute. Validation results are not fulfilling the requirements in January, February and March. The reason for this is partly the Polar night with very little energy available and partly the snow covered surface at the validation stations. The methodology used is not handling snow cover well and the work undertaken to circumvent this is yet not successful. As one of the validation stations is a purely maritime station, the oil rig Ekofisk, validation results of this station is of particular interest. Although the station is heavily influenced by the surrounding infrastructure through shadows from towers, metal construction etc, the relative bias and standard deviation of this station were 8.8%, 4.9%, 3.5% and 34.6%, 25.7% and 12.3% for January, February and March respectively. Except for the relative requirement on standard deviation in January, requirements are met on this station. The reliability of this station is however irregular at times and the future perspectives are uncertain as the platform is being replaced. In general, the validation results are as expected for the time of year being examined, climatological conditions acknowledged.

The following graphs illustrate the evolution of AHL SSI quality results over the past 6 months.

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Bias in % Bias Margin (*) AHL SSI quality AHL SSI quality 100,00 10,0 50,00 5,0 0,00 0,0 -50,00 -5,0 -100,00 -10,0 1 1 1 2 2 2 -1 -1 -1 -1 -1 -1 t v c n b r 1 1 1 2 2 2 c o e a e a -1 -1 -1 -1 -1 -1 o n D J F M t v c n b r c o e a e a o n D J F M

Figure 46 : Left: AHLSSI Bias. Right AHL SSI Bias Margin.

Std Dev in % Std Dev margin (*)

AHL SSI quality AHL SSI quality 100,00 50, 00

40,00 50,00

30, 00 0,00 20, 00 -50,00 10, 00 -100,00 0, 00 1 1 1 2 2 2 2 1 1 -1 -1 -1 -1 1 1 1 2 2 t- - c n b r -1 -1 -1 -1 -1 -1 c v e a e a t v c n b r o o J c o e a e a n D F M o n D J F M

Figure 47 : Left: AHL SSI Standard deviation. Right AHL SSI Standard deviation Margin.

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5.3 Sea Ice quality

5.3.1 Reference data

At the current stage operational ice charts are believed to be the best independent source of reference data currently available. The OSISAF sea ice edge and concentration products are validated against navigational ice charts originating from the operational ice charting divisions at DMI, met.no and National Ice Center. The ice charts are primarily based on SAR (Radarsat and Envisat) data, together with AVHRR and MODIS data in several cases. A detailed interpretation of satellite imagery and a subsequent mapping procedure are carried out by skilled (experienced and trained) ice analysts. The ice charts are primarily used for strategic and tactical planning within the offshore and shipping community. Requirements are strict with demands for detailed high quality products for several areas.

5.3.2 Validation requirements

For the weekly validation at the Northern Hemisphere the concentration product is required to have a bias and standard deviation less than 10% ice concentration on an annual basis. For the weekly validation at the Southern Hemisphere the concentration product is required to have a bias and standard deviation less than 15% ice concentration on an annual basis. There is no requirement on the confidence level of the products, but statistics are shown as additional information.

5.3.3 Validation against DMI ice charts

The ice charting division at DMI (Greenland Ice Service) produces in average 3-5 charts per week. Most charts cover the Cape Farewell area, but also the east and west coast of Greenland are frequently covered. Besides the service related to navigational charts the Greenland Ice Service produces one weekly product covering all of Greenland and usually based on navigational charts, AVHRR and MODIS data.

The validation is carried out as a weekly validation by means of automatic comparison of SAF grid with navigational ice charts for ice edge and ice concentration. The weekly ice chart and the SAF product are gridded into a common projection and resolution. Following this a cell by cell comparison is carried out. Only cells based on Radarsat and/or AVHRR data are used. For each ice chart concentration level the deviation between ice chart concentration and OSISAF ice concentration is calculated. Afterwards deviations are grouped into categories, i.e. ±10% and ±20%. Furthermore the bias and standard deviation is calculated for each concentration level. The bias and standard deviation are reported for ice (> 0% ice concentration), for water (0% ice concentration) and for both ice and water as a total.

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5.3.4 Validation results for Northern Hemisphere based on Greenland Ice charts

Figure 48 : Comparison between the weekly DMI ice analysis and the SAF edge product. 'SAF underestimates' means grid points where the SAF product indicated water and the DMI ice analysis indicated ice and vice versa for the 'SAF overestimates' category.

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Figure 49 : Comparison between ice concentrations from the weekly DMI ice analysis and the SAF concentration product. 'Match +/- 10 %' corresponds to those grid points where concentration deviates within the range of +/-10 % and likewise for +/-20 %.

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Figure 50 : The bias of ice concentration for three categories: water, ice and total. When bias is below zero the SAF ice concentration tends to underestimate.

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Figure 51 : The standard deviation of ice concentration for threee categories: water, ice and total.

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5.3.5 Multi-year variability

Figure 52 : Quality of ice edge product for the validation period of 2002-2012.

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Figure 53 : Quality of ice concentration product for the validation period of 2002-2012.

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5.3.6 Validation against NIC (National Ice Center) ice charts for Southern Hemisphere

In the same way as for DMI ice charts collocations between OSISAF ice concentration/ice edge products and ice charts from National Ice Center are carried out using charts covering the Southern Hemisphere.

Figure 54 : Comparison between the bi-weekly NIC ice analysis and the SAF edge product. 'SAF underestimates' means grid points where the SAF product indicated water and the NIC ice analysis indicated ice and vice versa for the 'SAF overestimates' category.

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Figure 55 : Comparison between ice concentrations from the bi-weekly NIC ice analysis and the SAF concentration product. 'Match +/- 10 %' corresponds to those grid points where concentration deviates within the range of +/-10 % and likewise for +/-20 % .

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Figure 56 : The bias of ice concentration for three categories: water, ice and total. When bias is below zero the SAF ice concentration tends to underestimate.

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Figure 57 : The standard deviation of ice concentration for three categories: water, ice and total.

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5.3.7 Multi-year variability, Southern Hemisphere

Figure 58 : Quality of ice edge product for the validation period of 2005-2012

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Figure 59 : Quality of ice concentration product for the validation period of 2005-2012

5.3.8 Validation against met.no ice charts

The Sea Ice service at met.no produces daily ice charts covering the area from East Greenland to the Barents Sea with main emphasize on the areas around Svalbard. Areas where independent information (manual inspection of SAR, MODIS and AVHRR) are utilized are marked by the ice service. These areas are then collocated with the OSI SAF ice product. The focus is on areas close to the ice edge. The statistics is therefore not representative for the overall performance of the OSI SAF products. The validation results would generally be better if all areas where included.

Statistics for the performance of the OSI SAF sea ice concentration and edge products from JAN. to MARCH 2012 is given in the table below.

QR12-1 Page 65 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 Concentration product Edge product Year Month +/- +/- Bias Stdev Correct SAF lower SAF Mean Num 10% 20% (%) (%) higher edge obs (%) diff (km) 2012 JAN. 77.22 87.24 -4.91 12.57 95.84 2.88 1.28 17.59 126068 2012 FEB. 77.97 86.82 -3.9 13.46 94.74 2.89 2.37 21.2 132419 2012 MARCH 76.41 87.2 -3.61 12.45 96.75 2.12 1.13 12.95 121955

table 16 : Monthly validation results from comparing OSI SAF sea ice products to met.no ice service analysis, from JAN. 2012 to MARCH 2012. Mean edge diff is the mean difference in distance between the ice edges in the OSI SAF edge product and met.no ice chart.

5.3.9 Results from manual error registration

All sea ice products are evaluated by skilled ice analysts on a daily basis. A predefined set of error types are used as a reference for registering non-nominal cases of false ice or missing ice. This registration is used complementary to the automatic validation. Although the automatic validation provides an objective quality assessment it does not detect possible non-nominal cases of ice/no-ice presence. The manual error registration on the other hand, collects on a daily basis the possible errors or noise caused by anomalous situations with data or processing. The following error types are searched for in the registration:

Error code Type Description 1 Area missing data 2 point open water where ice was expected 3 Area false ice where open water was expected 4 point false ice induced from SSM/I processing errors 5 point other errors 6 point noisy false ice along coast table 17 : Error codes for the manual registration

The tables below summarize the statistics on registrations for last quarter according to the above mentioned error types. Daily and monthly graphs with all registrations can be viewed at:

http://saf.met.no/validation/icequal_monitor_daily.php http://saf.met.no/validation/icequal_monitor_monthly.php

5.3.10 Statistics for confidence levels

Based on the quality flags in the sea ice products, monthly statistics for the confidence levels are derived for each product type.

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Area Product Code=5 code=4 code=3 code=2 code=1 Unproces sed NH Conc 80.34 18.09 1.55 0.03 0.00 0.00 NH edge 93.96 1.47 2.36 1.75 0.46 0.00 NH type 89.82 1.49 7.59 0.97 0.13 0.00 SH conc 85.22 13.86 0.91 0.01 0.00 0.00 SH edge 92.28 1.54 1.77 2.55 1.87 0.00 SH type 89.42 0.42 9.43 0.39 0.35 0.00 table 18 : Statistics for confidence levels in JAN. 2012.

Area Product Code=5 code=4 code=3 code=2 code=1 Unproces sed NH Conc 79.30 18.61 2.04 0.04 0.00 0.00 NH edge 92.19 1.83 3.13 2.30 0.54 0.00 NH type 87.19 1.98 9.52 1.15 0.15 0.00 SH conc 91.03 8.57 0.40 0.01 0.00 0.00 SH edge 95.40 0.70 1.00 1.69 1.22 0.00 SH type 93.26 0.32 5.94 0.25 0.23 0.00 table 19 : Statistics for confidence levels in FEB. 2012.

Area Product Code=5 code=4 code=3 code=2 code=1 Unproces sed NH Conc 78.35 19.18 2.42 0.04 0.00 0.00 NH edge 90.42 2.43 3.66 2.77 0.72 0.00 NH type 87.92 0.93 10.31 0.70 0.14 0.00 SH conc 90.80 8.73 0.46 0.01 0.00 0.00 SH edge 94.57 0.85 1.61 2.07 0.90 0.00 SH type 91.71 0.26 7.66 0.20 0.17 0.00 table 20 : Statistics for confidence levels in MARCH 2012.

Explanation (see Product User Manual for more details): Code 1-5 is given as fraction of total processed data (total processed data=code 5+4+3+2+1=100%). 'Unprocessed' is given as fraction of total data (total data=total processed data + total unprocessed data).

Ice Concentration Ice Edge/Type Code Confidence (std dev of concentration) (% probability) 5 Excellent 0 – 1.5 99.0 – 100 4 Good 1.5 -2.5 95.0 - 98.9 3 Acceptable 2.5 – 3.5 75.0 - 94.9 2 Unreliable 3.5 -10.0 50.0 – 74.9 1 Erroneous >10.0 0.0 – 49.9 0 No data table 21 : Confidence levels explanation.

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5.3.11 Sea Ice validation comments

The usual seasonal pattern of good agreement during the Arctic winter and less agreement during the Antarctic summer between the OSI SAF products and the validation data can be observed.

5.3.12 Validation of the Low Resolution Sea Ice Drift product

As of December 2009, the Low Resolution Sea Ice Drift product (LRSID, OSI-405) is processed and distributed with ”pre-operational” status. Only the Northern Hemisphere is covered.

Validation dataset Validation is performed by collocation of the drift vectors with the trajectories of in situ drifters. Those drifting objects are generally buoys (e.g. the Ice Tethered Profilers) or ice camps (e.g. the Russian manned stations) that report their position at typically hourly intervals. Those trajectories are generally made available in near-real-time or at the end of the mission onto the ice. Position records are recorded either via the GPS (e.g. those of the ITPs) or the Argos Doppler-shift system (thos of the iABP). GPS positions are very precise (< 50 m) while those obtained by Argos have worse accuracy (approx. 350 m for ’high quality’ records). A nearest-neighbor approach is used for the collocation, and any collocation pair whose distance between the product and the buoy is larger than 30 km or the mismatch at start and stop time of the drift is more than 3 hours is discarded.

Reported statistics Due to the limited number of drifters in the Arctic and because some of them were not made available in near-real-time, it is not possible to report monthly statistics. Instead, quarterly statistics are mentionned in this report (Q1: JFM, Q2: AMJ, Q3: JAS and Q4: OND). Because of a denser atmopshere and surface melting, the OSI-405 production is limited to the autumn-winter-spring period each year. No ice drift vectors are retrieved from 1st May to 30th September. As a result, Q2 is only representative of the month of April and Q3 have no data. The Low Resolution Sea Ice Drift product comprises several single-sensor (e.g. AMSR-E, SSM/I F15 or ASCAT Metop-A) and a merged (or multi-sensor) products that are all processed and distributed on a daily basis. The validation and monitoring results are thus presented for the multi-sensor product (multi-oi) and a selection of the single-sensor ones.

Validation statistics In the following tables, validation statistics for the Northern Hemisphere (NH) products using multi-sensor (multi-oi) and AMSR-E only (amsr-aqua) are reported upon. In those tables, X (Y) are the X and Y components of the drift vectors. b() is the bias and σ() the standard deviation of the error ε(X) = Xprod – Xref. Columns α, β and ρ are respectively the slope and intercept of the regression line between Prod and Ref

QR12-1 Page 68 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 data pairs and the Pearson correlation coefficient. N is the number of collocation data pairs.

Year Month b(X) b(Y) σ(X) σ(Y) α β ρ Ν 2012 JAN. -0.287 +0.101 3.730 2.806 0.89 -0.19 0.93 89 2012 FEB. -0.113 -0.108 2.165 2.110 0.98 -0.11 0.93 75 2012 MARCH -0.519 -0.091 3.228 2.358 0.92 -0.25 0.81 84 table 22 : Validation results for the LRSID (multi-oi) product (NH) for for JAN-MAR 2012.

Year Month b(X) b(Y) σ(X) σ(Y) α β ρ Ν 2012 JAN. -0.501 -0.019 4.907 3.226 0.90 -0.29 0.88 63 2012 FEB. +0.023 -0.112 2.477 2.209 1.04 -0.03 0.92 69 2012 MARCH -0.854 -0.214 3.605 3.069 0.93 -0.47 0.76 73 table 23 : Validation results for the LRSID (SSMI-f15) product (NH) for for JAN- MAR 2012.

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5.4 Global Wind quality

The wind products are required to have an accuracy of better than 2.0 m/s in wind component RMS with a bias of less than 0.5 m/s in wind speed.

The scatterometer winds are monitored against forecast winds of the ECMWF global model. Forecasts of +3 to +15 hours are used and the model winds are interpolated with respect to time and location. The monitoring of relevant quality parameters as a function of time yields a sensitive method of detecting deviations of normal operation. However, one must be careful to regard the difference with reference background NWP model winds as the 'true' accuracy of the product, since both the NWP model winds and the scatterometer winds contain errors. Deviations in product quality usually appear as a step in one or more of the plots.

Note that the real model winds are converted to equivalent neutral winds by adding 0.2 m/s to the wind speed. In this way, a realistic comparison with the neutral scatterometer winds can be made.

The scatterometer winds are also compared to in situ equivalent neutral wind data from moored buoys, monthly averages are shown in section 5.7.2.

Seasonal weather variations imply differences in mean atmospheric stability, differences in dynamics, and differences in the distribution of wind speeds. These differences cause variations in the spatial representativeness errors associated with scatterometer wind validation and in the difference statistics. Such effects cause seasonal oscillations that appear mainly in the wind speed bias plots against both model winds and buoy winds. For more background information we refer to: Hans Hersbach (2010) Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF, J. Atmos. Oceanic Technol., 27, 721–736.

We have studied the scatterometer wind speed bias against buoy winds for the tropics and the Northern Hemisphere mid latitudes separately. It appears that the biases in the tropics are fairly constant throughout the year, whereas the wind speed biases in the NH are higher in the winter than in the summer. Hence the seasonal cycles are mainly caused by weather variations in the mid latitudes.

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5.4.1 ASCAT Wind quality

It is clear from the plots in this section, that the products do meet the accuracy requirements from the User Requirements Document (bias less than 0.5 m/s and RMS accuracy better than 2 m/s) when they are compared to ECMWF forecast winds.

Figure 60 : Average ASCAT 25-km wind speed difference (bias) with the reference ECMWF NWP forecast winds. Data are averaged over a one day period.

Figure 61 : Average ASCAT 12.5-km wind speed difference (bias) with the reference ECMWF NWP forecast winds. Data are averaged over a one day period.

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Figure 62 : Average ASCAT Coastal wind speed difference (bias) with the reference ECMWF NWP forecast winds. Data are averaged over a one day period.

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Figure 63 : ASCAT 25-km wind component (U direction: top and V direction: bottom) RMS differences of scatterometer winds versus the ECMWF forecast winds.

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Figure 64 : ASCAT 12.5-km wind component (U direction: top and V direction: bottom) RMS differences of scatterometer winds versus the ECMWF forecast winds.

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Figure 65 : ASCAT Coastal wind component (U direction: top and V direction: bottom) RMS differences of scatterometer winds versus the ECMWF forecast winds.

5.4.2 Buoy validations

We compare the scatterometer winds with wind data from moored buoys on a monthly basis. The buoy data of approximately 150 buoys spread over the oceans (most of them in the tropical oceans and near Europe and North America) are retrieved from the ECMWF MARS archive and collocated with scatterometer winds.

QR12-1 Page 75 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 The buoy winds are converted to 10-m neutral winds using the LKB model, see Liu, W.T., K.B. Katsaros, and J.A. Businger, Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints in the interface, J. Atmos. Sci., vol. 36, 1979.

The figure below shows the monthly results of November 2007 to February 2012. Note that the ASCAT winds before 20 November 2008 are real winds rather than neutral winds. Neutral scatterometer winds are known to be 0.2 m/s higher than real scatterometer winds.

0.20

0.00

) -0.20 s / m (

s -0.40 a i b

d -0.60 e

e ASCAT 25-km p

S -0.80 ASCAT 12.5-km SeaWinds 25-km SeaWinds 100-km -1.00 ASCAT Coastal OSCAT 50-km -1.20

8 8 8 9 9 9 0 0 0 1 1 1 2 7 8 0 0 8 8 9 0 0 9 9 0 1 1 0 0 1 1 1 1 1 -0 -0 -0 - l- -0 -0 -0 -0 - l- -0 -0 -1 -1 - l- -1 -1 -1 -1 - l- -1 -1 -1 v n r y u p v n r y u p v n r y u p v n r y u p v n o a a a J e o a a a J e o a a a J e o a a a J e o a N J M M S N J M M S N J M M S N J M M S N J

2.40

2.20

2.00 ) s / 1.80 m (

1.60 v e d

t 1.40

S ASCAT 25-km ASCAT 12.5-km 1.20 SeaWinds 25-km SeaWinds 100-km 1.00 ASCAT Coastal OSCAT 50-km 0.80

2.40

2.20

2.00 ) s

/ 1.80 m (

1.60 v e d t 1.40 ASCAT 25-km S ASCAT 12.5-km 1.20 SeaWinds 25-km SeaWinds 100-km 1.00 ASCAT Coastal OSCAT 50-km 0.80

Figure 66 : Comparison of scatterometer winds against buoy winds (monthly averages). For each product, the wind speed bias (top), wind u component standard deviation (middle) and wind v component standard deviation (bottom) are shown. OSCAT 50-km is an experimental OSI SAF product.

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6.1 Statistics on the Web site and help desk

The OSI SAF offers to the users a central Web Site, www.osi-saf.org , managed by M-F/CMS, a local page for SS2, http://saf.met.no, managed by Met.no, and dedicated to the Sea Ice, and a local page for SS3, http://www.knmi.nl/scatterometer/osisaf/, managed by KNMI and dedicated to the OSI SAF scatterometer winds.

Users are recommended to make requests preferably through the central Web site Help desk, with the guarantee that they demand will be acknowledged or answered to in time. However for requests concerning the Wind products they may get access to direct contact points at KNMI, and at Met.no for Sea Ice products.

6.1.1 Statistics on the central OSI SAF Web Site and help desk

The number of registered users increases regularly as shown in the following table.

Statistics on the central Web site use Month Registered users Sessions User requests JAN. 2012 646 4632 1 FEB. 2012 667 4559 3 MARCH 2012 684 4568 2 table 24 : Statistics on central OSI SAF Web site use over 1st quarter 2012.

User Requests status : reference subject status 120001 Request for archived ASCAT Wind data Closed 120002 information on FTP server access Closed 120003 user report on unavailability of data on IFREMET FTP server Closed 120004 user report on unavailability of data on IFREMET FTP server Closed 120005 Request for access right to IFREMER FTP server Closed 120006 request for information on Sea Ice data Closed table 25 : Statistics on central OSI SAF Web site use over 1st quarter 2012.

Status of requests forwarded from EUMETSAT HELP DESK : 300017177: need for help for opening Sea Ice data files. Closed.

QR12-1 Page 77 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 The following graph illustrates the evolution of external registered users on the central Web Site.

registered users

600

400

200

4 5 5 6 6 7 7 8 8 9 9 0 0 1 1 2 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 1 -1 1 -1 l n l n l n l n l n l n y n 0 n u a u a u a u a u a u a l a 2 a J J J J J J J J J J J J u J l. J J u J

Figure 67 : Evolution of external registered users on the central Web Site from April 2004 to MARCH 2012. Comment : The following table details the list of institutions or companies the registered users are from. Last registrations, made over the reporting period, are overlined in cyan blue..

AACountry AAInstitution, establishment or company AAAcronym Argentina AgriSatelital AgS Australia James Cook University University of Windsor Australia tidetech LTD tidetech Australia University Of New South Wales UNSW Australia- eMarine Information Infrastructure (eMII), Integrated Marine Observing System (IMOS) eMII Tasmanie Belgium signal and image center SIC Belgium Université catholique de Louvain UCL/TECLIM Brazil Admiral Paulo Moreira Marine Research Institute IEAPM Brazil Centro de Previsao de Tempo e Estudos Climáticos CPTEC/INPE Brazil Fugro Brasil FGB Brazil Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas UFAL/ICAT Brazil Instituto Nacional de Pesquisas Espaciais INPE Brazil Universidade de Brasília - Instituto de Geociências UNB-IG Brazil Universidade de são paulo USP Brazil Universidade Federal de Alagoas UFAL Brazil Universitade Federal do Rio de Janeiro LAMCE/COPPE/UFRJ Bulgaria National Institute of Meteorology and Hydrology NIMH Canada CIS Canadian Ice Service Canada Canadian Meterological Centre CMC Canada Centre for Earth Observation Science CEOS Data Assimilation and Satellite Meteorology, Meteorlogical Research Branch Environment Canada Canada ARMA/MRB Canada DFO/IML/MPO Fisheries and Oceans Canada Canada JASCO Research Ltd JASCO Canada Memorial University of Newfoundland MUN Canada University of Waterloo UW Canada University of Windsor

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Chile Centro i-mar, Universidad de Los Lagos I-MAR Chile Universidad catolica de la santisima concepcion UCSC China anhuigongyedaxue ahut China Chinese Academy of Sciences IOCAS China HK Observatory HKO China Institute of Oceanology, Chinese Academy of Sciences IOCAS China Institute of Remote Sensing Applications of Chinese Academy of Sciences IRSA/CAS China National Marine and Enviromental Forecasting Center China National Ocean Data Information Service NODIS China National Ocean Technology Center NOCT China National Satellite Meteorological Center NSMC China National Satellite Ocean Application Service NSOAS China Ocean Remote Sensing Institute ORSI China Ocean University of China China Second Institute of Oceanography SOI China South China Sea Institute of Oceanology, Chinese Academy of Sciences SCSIO, CAS China third institute oceanography TIO/SOA Croatia Rudjer Boskovic Institute IRB/ZIMO Denmark Aarhus University - Department of Bioscience BIOS Denmark Danish Meteorological Institute DMI Denmark Royal Danish Administration of Navigation and Hydrography RDANH Denmark Technical University of Denmark, Risø DTU Denmark University of Copenhagen UoC Estonia Estonian Meteorological and Hydrological Institute EMHI Estonia Tallinn University of Technology TUT Faroe Islands Faroe Marine Research Institute FAMRI Finland Finnish Institute of Marine Research FIMR Finland Finnish Meteorological Institute FMI Finland Valtion Teknillinen Tutkimuskeskus VTT Florida, USA ROFFS Roffer's Ocean Fishing Forecasting Service Florida, USA University of Miami RSMAS MPO France African Monitoring of the Environment for Sustainable Development AMESD France Centre de Localisation Satellite CLS France Centre de soutien meteorologique aux armées CISMF France CNRS-LOB Centre National de la Recherche Scientifique France CNRS Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales LEGOS/CNRS France Creocean Creocean France ecole nationale des telecommunication de bretagne ENSTB France Ecole Nationale Supérieure des Techniques Avancées de Bretagne ENSTA-Bretagne France Institut de Recherche pour le Développement IRD - US02

France Institut Français de Recherché pour l’Exploitation de la MER IFREMER France Institut National de la Recherche Agronomique INRA France Institut National de l'Energie Solaire INES France Institut universitaire européen de la mer IUEM France KiloWattsol KiloWattsol France Laboratoire de Physique des Océans, Université de Bretagne occidentale LPO France Laboratoire d'oceanographie et du climat: experimentation et approches numeriques LOCEAN France Mercator Ocean Mercator Ocean France Météo-France M-F France Météo-France / Centre National de la Recherche Météorologique M-F/CNRM France Observatoire français des Tornades et des Orages Violents KERAUNOS France Service hydrographique et océanographique de la marine SHOM QR12-1 Page 79 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

France TELECOM Bretagne TB France Université de Corse, UMR SPE CNRS 6134 UC Germany Alfred Wegener Institute for Polar and Marine Research AWI Germany Bundesamt für Seeschifffahrt und Hydrographie BSH Germany Center for Integrated Climate System Analsyis and Prediction CliSAP Germany DWD Deutscher Wetterdienst Germany Deutsches Luft- und Raumfahrtzentrum DLR / IPA Germany Deutsches Museum DM Germany Drift and Noise Polar Services Germany EUMETSAT EUMETSAT Germany FastOpt GmbH FastOpt Germany Flottenkommando Abt GeoInfoD Flottenkdo GeoInfoD Germany FUB Freie Universität Berlin Germany German Aerospace Center DLR Germany german aerospace center DLR Germany Institut of Physics - University of Oldenburg Uni OL Germany Institute for Atmospheric and Environmental Sciences IAU Germany Institute for Environmental Physics Uni. Heidelberg IUP-HD Germany Institute for environmental physics, University of Bremen IUP, Uni B Germany Leibniz Institut fur Meereswissenschaften IFM-GEOMAR Germany Max Planck Institute for Meteorology MPI-M Germany Max-Planck-Institute for Meteorology MPI-M Germany O.A.Sys - Ocean Atmosphere Systems GmbH OASYS Germany TU Dresden TU DD Greece Hellenic National Meteorological Service HNMS Greece National Observatory of Athens NOA Iceland Icelandic Meteorological Office IMO Iceland University of Iceland, Institute of Geosciences UofI India bharathiar university BU India CONSOLIDATED ENERGY CONSULTANTS LTD India India Meteorological Department IMD India Indian National Centre for Ocean Information INCOIS India Indian Navy IN India Indian Space Research Organization ISRO India Nansen Environmental Research Centre NERCI India National Institute of Ocean Technology NIOT India National Institute of Technology Karnataka NITK India Oceanic Sciences Divisions, MOG , Indian Space Applications Centre ISRO Indonesia vertex Mr Israel bar ilan university Italy Agenzia nazionale per le nuove tecnologie, l’energia e lo sviluppo economico sostenibile ENEA Italy Agenzia nazionale per le nuove tecnologie, l’energia e lo sviluppo economico sostenibile ENEA Italy Centro Nazionale di Meteorologia e Climatologia Aeronautic CNMCA Italy EC- Joint Research Centre EC-JRC Italy ESA ESA/ESRIN Italy fondazione imc - onlus , international marine centre IMC Italy Institute of Marine Science - CNR ISMAR-CNR Italy Istituto di BioMeteorologia - Consiglio Nazionale delle Ricerche IBIMET-CNR Italy Istituto Nazionale di Geofisica e Vulcanologia INGV Istituto Scienze dell'Atmosfera e del Clima - Consiglio Nazionale delle Ricerche ISAC - CNR Italy Italy Istituto Superiore per la ricerca e la protezione ambientale ISPRA Italy Italian Space Agency ASI

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Italy NATO Undersea Research Centre NURC Italy Politecnico di Torino DITIC POLITO Italy Universita degli Studi di Bari USB Italy university of bologna DISTA Japan Center for Atmospheric and Oceanic Studies CAOS Japan Hydrospheric Atmospheric Research Center HyARC Japan Japan Agency for Marine-Earth Science and Technology JAMSTEC Japan Meteorological Research Institute MRI Japan Tokai University Tokai U Japan weathernews WNI Kenya Jomo Kenyatta University of Agriculture and Technology JKUAT Korea Korea Meteorological Administration KMA

Lithuania Institute of Aerial Geodesy AGI Lithuania Lithuanian hydrometeorological service LHMS Lithuania University of Vilnius VU Marocco University Ibn Tofail UIT Mauritius Mauritius Oceanography Institute MOI Mexico Facultad de Ciencias Marinas, Universidad Autónoma de Baja California FCM/UABC Netherlands Bureau Waardenburg bv BuWa

Netherlands Delft University of Technology TU Delft Netherlands Deltares Deltares Netherlands National Aerospace Laboratory NLR Netherlands Nidera Nidera Netherlands Rijksinstituut voor Kust en Zee RIKZ Netherlands Royal Netherlands Meteorological Institute KNMI Niger African Centre of Meteorological Applications for Development ACMAD

Nigeria African Centre of Meteorological Applications for Development ACMAD Norway Institute of Marine Research IMR Norway MyOcean SIW TAC MyOcean SIW TAC Norway Nansen Environmental and Remote Sensing Center NERSC Norway Norge Handelshoyskole NHH Norway NP Norsk Polarinstitutt Norway Norvegian Defense Research Establishment FFI Norway Norvegian Meteorological Institute Met.no Norway The University Centre in Svalbard UNIS Peru Instituto del Mar del Peru IMARPE Peru Servicio Nacional de Meteorologia e Hidrologia SENAMHI Peru Universidad Nacional Mayor de San Marcos UNMSM Poland Institute of Meteorology and Water Management IMWM Poland Maritime Academy Gdynia AM/KN Poland Media Fm Media Fm Poland PRH BOBREK Korn Poland University of Gdansk, Institute of Oceanography UG/IO Portugal Centro de Estudos do Ambiente e do Mar - Univ Aveiro CESAM Portugal CESAM & Department of Physics; University of Aveiro CESAM Portugal IPIMAR Instituto de Investigação das Pescas e do Mar Portugal Instituto de Meteorologia IM Portugal Instituto Politécnico de Viana do Castelo IPVC Portugal Laboratório Nacional de Energia e Geologia LNEG Portugal Museu Nacional de Historia Natural MNHN Portugal National Remote Sensing Centre NRSC Portugal Universidade de Lisboa CGUL

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Portugal Universitade dos Acores UAC Republic of PKNU MF Korea Romania National Meteorological Administration NMA Romania University of Bucharest UB Russia V.I.Il`ichev Pacific Oceanological Institute Russia Atlantic Research institute of Marine fisheries and oceanography AtlantNIRO Russia Geophysical Center of Russian Academy of Sciences GC RAS Russia Hydrometcenter of Russia RHMC Russia Kaliningrad State Technical University KLGTU - KSTU Russia Murmansk Marine Biological Institute MMBI Nansen International Environmental and Remote Sensing Center Russia NIERSC Russia Shirshov Institute of Oceanology RAS SIO RAS Russia SRC PLANETA Roshydromet planeta Russia State research Center Planeta SRC Russia V.I.Il`ichev Pacific Oceanological Institute POI FEB RAS Scotland Edin-Univ University of Edinburgh Senegal Centre de Recherches Océanographiques de Dakar-Thiaroye CRODT Senegal Ecole Supérieure Polytechnique de Dakar ESP/UCAD Singapore Terra Weather Pte. Ltd. TERRAWX South Africa Kaytad Fishing Company KFC South Africa Marine and Coastal Management MCM South Africa South African Weather Service-Cape Town Regional Office SAWS Spain Basque Meteorology Agency EUSKALMET Spain CEAM Fundacion Centro de Estudios Ambientales del Mediterraneo Spain Institut Català de Ciències del Clima IC3 Institut d'Estudis Espacials de Catalunya Spain IEEC Instituto Canario de Ciencias Marinas ICCM Spain Spain Instituto Español Oceanography IEO Spain Instituto Español Oceanography IEO Instituto Mediterraneo de Estudios Avanzados Spain IMEDEA (CSIC-UIB) Spain Instituto Nacional de Meteorologia INM Spain Instituto Nacional de Pesquisas Espaciais INPE Spain Instituto Nacional de Tecnica Aeroespacial INTA Spain MeteoGalicia - Departamento de Climatología y Observación Meteogalicia Spain MINISTERIO DEFENSA - ARMADA ESPAÑOLA MDEF/ESP NAVY - IHM Spain Museo Nacional de Ciencias Naturales - Consejo Superior de Investigaciones Cientificas MNCN-CSIC Spain STARLAB BA starlab barcelona sl. Spain Universidad Autonoma de Madrid UAM Spain Universidad de Las Palmas de Gran Canaria ULPGC Spain Universidad de Oviedo UdO Spain Universidad Politécnica de Madrid UPM Spain Universitad de Valladolid LATUV Spain University of Jaén UJA Spain CACTI University of Vigo Sweden Swedish Meteorological and Hydrological Institute SMHI Switzerland Tecnavia S.A. Tecnavia S.A. Switzerland World Meteorological Organization WMO Taïwan Fisheries Research Institute FRI Taïwan Institute of Amos Physics, NCU ,Taiwan ATM/NCU Taïwan Taiwan Ocean Research Institute TORI Taiwan(R.O.C) National Central University NCU/TAIWAN

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Turkey Türkish State Meteorological Services TSMS United Kingdom Asgard Consulting Limited Asgard United Kingdom Department of Zoology, University of Oxford UOO United Kingdom ECMWF ECMWF United Kingdom Flasse Consulting Ltd FCL United Kingdom National Oceanography Centre, Southampton NOCS United Kingdom National Oceanography Centre, Southampton NOCS United Kingdom National Renewable Energy Centre NAREC United Kingdom Plymouth Marine Laboratory PML United Kingdom Terradat TDAT United Kingdom the scottish association for marine science SAMS United Kingdom UK Met Office UKMO United Kingdom University of East Anglia UEA United Kingdom University of East Anglia UEA United Kingdom University of Leicester UoL United Kingdom University of Plymouth UOP United Kingdom University of Southampton UoS United Kingdom Weatherquest Ltd weatherquest USA Alaska Deparment Of Fish and Game ADFG USA Applied Weather Technology AWT

USA Atmospheric and Environmental Research AER USA Berkeley Earth Surface Temperature BEST USA Center for Ocean-Atmosphere Prediction Studies COAPS USA Center for Ocean-Atmosphere Prediction Studies COAPS USA Clemson University CU USA Colorado State University CSU USA Darmouth College Dartmouth College USA SVC Dept. of Environmental Conservation , Skagit Valley College USA Earth & Space Research ESR USA Haskell Indian Nations University INU USA International Pacific Research Institute - Univ. of Hawaii IPRC USA Jet Propulsion Laboratory JPL USA Joint Typhoon Warning Center JTWC USA Locheed martin Corporation, LMCO NASA Langley Research Center, Affiliation Analytical Services and Materials, Inc. NASA LaRC USA USA NOAA/NESDIS National Oceanic and Atmospheric Administration USA Naval Postgraduate School NPS USA Scripps Institution of Oceanography SIO USA Scripps Institution of Oceanography SIO USA Starpath School of Navigation Starpath USA TCEQ Texas Commission on Environmental Quality USA United States Navy USN USA University at Albany-SUNY UAlbany

USA University of Maryland UMCP USA University of Miami RSMAS MPO USA University of South Florida USF USA Weather Routing Inc. WRI USA Woods Hole Oceanograhic Institution WHOI Vietnam National Center for Hydro-Meteorological Forecast NCHMF Vietnam

table 26 : List of Institutes registered on the central Web Site Note: Moreover are registered 165 individual users. QR12-1 Page 83 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 The following graph illustrates the evolution of sessions on the central OSI SAF Web Site. Sessions

75 000

50 000

25 000 0

4 5 5 6 6 7 7 8 8 9 9 0 0 1 1 2 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 1 -1 1 -1 l n l n l n l n l n l n y n 0 n u a u a u a u a u a u a l a 2 a J J J J J J J J J J J J u J l. J J u J

Figure 68 : Evolution of sessions on the central OSI SAF Web Site from April 2004 to MARCH 2012.

Figure 69 : Usage of the OSI SAF central Web Site by country in JAN. 2012.

QR12-1 Page 84 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Top 30 of 66 Total Locations

# Hits Files kB F kB In kB Out Location

1 20118 41.18% 18498 39.78% 118985 23.05% 0 0.00% 0 0.00% France 2 3820 7.82% 3684 7.92% 48870 9.47% 0 0.00% 0 0.00% Network 3 2684 5.49% 2658 5.72% 50886 9.86% 0 0.00% 0 0.00% Italy 4 2578 5.28% 2557 5.50% 33319 6.46% 0 0.00% 0 0.00% Unresolved/Unknown 5 2351 4.81% 2313 4.97% 23284 4.51% 0 0.00% 0 0.00% International (int) 6 1897 3.88% 1858 4.00% 48400 9.38% 0 0.00% 0 0.00% Germany 7 1350 2.76% 1282 2.76% 13930 2.70% 0 0.00% 0 0.00% Netherlands 8 1344 2.75% 1320 2.84% 27117 5.25% 0 0.00% 0 0.00% US Commercial 9 1080 2.21% 1079 2.32% 12846 2.49% 0 0.00% 0 0.00% Portugal 10 1022 2.09% 1010 2.17% 11129 2.16% 0 0.00% 0 0.00% United Kingdom 11 840 1.72% 834 1.79% 13619 2.64% 0 0.00% 0 0.00% Spain 12 831 1.70% 829 1.78% 9798 1.90% 0 0.00% 0 0.00% China 13 744 1.52% 744 1.60% 6603 1.28% 0 0.00% 0 0.00% Austria 14 727 1.49% 715 1.54% 15348 2.97% 0 0.00% 0 0.00% Denmark 15 656 1.34% 632 1.36% 5814 1.13% 0 0.00% 0 0.00% Norway 16 569 1.16% 569 1.22% 3678 0.71% 0 0.00% 0 0.00% Japan 17 468 0.96% 468 1.01% 4929 0.95% 0 0.00% 0 0.00% US Educational 18 409 0.84% 408 0.88% 10635 2.06% 0 0.00% 0 0.00% Poland 19 408 0.84% 340 0.73% 2994 0.58% 0 0.00% 0 0.00% Turkey 20 390 0.80% 390 0.84% 9332 1.81% 0 0.00% 0 0.00% Latvia 21 377 0.77% 377 0.81% 4404 0.85% 0 0.00% 0 0.00% US Government 22 377 0.77% 377 0.81% 3527 0.68% 0 0.00% 0 0.00% Canada 23 323 0.66% 323 0.69% 1732 0.34% 0 0.00% 0 0.00% Greece 24 265 0.54% 261 0.56% 2946 0.57% 0 0.00% 0 0.00% Russian Federation 25 265 0.54% 265 0.57% 3955 0.77% 0 0.00% 0 0.00% United States 26 227 0.46% 227 0.49% 1439 0.28% 0 0.00% 0 0.00% Switzerland 27 205 0.42% 203 0.44% 1639 0.32% 0 0.00% 0 0.00% Sweden 28 198 0.41% 198 0.43% 1685 0.33% 0 0.00% 0 0.00% US Military 29 186 0.38% 172 0.37% 1216 0.24% 0 0.00% 0 0.00% Australia 30 178 0.36% 151 0.32% 1614 0.31% 0 0.00% 0 0.00% Argentina

table 27 : Usage of the OSI SAF central Web Site by country in JAN. 2012

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Figure 70 : Usage of the OSI SAF central Web Site by country in FEB. 2012.

QR12-1 Page 86 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Top 30 of 66 Total Locations

# Hits Files kB F kB In kB Out Location

1 23662 41.96% 21085 40.07% 117582 22.25% 0 0.00% 0 0.00% France 2 4158 7.37% 4149 7.89% 70565 13.35% 0 0.00% 0 0.00% Network 3 3569 6.33% 3512 6.67% 26649 5.04% 0 0.00% 0 0.00% Unresolved/Unknown 4 1990 3.53% 1989 3.78% 56422 10.67% 0 0.00% 0 0.00% Italy 5 1686 2.99% 1622 3.08% 15219 2.88% 0 0.00% 0 0.00% US Commercial 6 1668 2.96% 1605 3.05% 16304 3.08% 0 0.00% 0 0.00% Greece 7 1578 2.80% 1451 2.76% 8540 1.62% 0 0.00% 0 0.00% Germany 8 1527 2.71% 1462 2.78% 19661 3.72% 0 0.00% 0 0.00% Netherlands 9 1471 2.61% 1378 2.62% 15875 3.00% 0 0.00% 0 0.00% China 10 1334 2.37% 1253 2.38% 6584 1.25% 0 0.00% 0 0.00% Russian Federation 11 1257 2.23% 1194 2.27% 17689 3.35% 0 0.00% 0 0.00% Romania 12 1080 1.92% 1051 2.00% 31695 6.00% 0 0.00% 0 0.00% International (int) 13 1015 1.80% 967 1.84% 7541 1.43% 0 0.00% 0 0.00% Norway 14 901 1.60% 895 1.70% 8914 1.69% 0 0.00% 0 0.00% Denmark 15 724 1.28% 724 1.38% 32459 6.14% 0 0.00% 0 0.00% Spain 16 637 1.13% 636 1.21% 2568 0.49% 0 0.00% 0 0.00% Japan 17 632 1.12% 632 1.20% 2791 0.53% 0 0.00% 0 0.00% US Military 18 536 0.95% 524 1.00% 1852 0.35% 0 0.00% 0 0.00% Australia 19 510 0.90% 495 0.94% 2370 0.45% 0 0.00% 0 0.00% Oman 20 470 0.83% 468 0.89% 5499 1.04% 0 0.00% 0 0.00% Austria 21 469 0.83% 442 0.84% 3662 0.69% 0 0.00% 0 0.00% Argentina 22 435 0.77% 433 0.82% 11666 2.21% 0 0.00% 0 0.00% US Government 23 412 0.73% 412 0.78% 3047 0.58% 0 0.00% 0 0.00% Canada 24 379 0.67% 352 0.67% 2291 0.43% 0 0.00% 0 0.00% United States 25 369 0.65% 364 0.69% 3847 0.73% 0 0.00% 0 0.00% Portugal 26 297 0.53% 293 0.56% 3938 0.75% 0 0.00% 0 0.00% Bulgaria 27 279 0.49% 279 0.53% 1701 0.32% 0 0.00% 0 0.00% US Educational 28 278 0.49% 278 0.53% 6049 1.14% 0 0.00% 0 0.00% Finland 29 254 0.45% 253 0.48% 1931 0.37% 0 0.00% 0 0.00% Poland 30 216 0.38% 216 0.41% 1406 0.27% 0 0.00% 0 0.00% Estonia

table 28 : Usage of the OSI SAF central Web Site by country in FEB. 2012

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Figure 71 : Usage of the OSI SAF central Web Site by country in MARCH 2012.

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Top 30 of 65 Total Locations

# Hits Files kB F kB In kB Out Location

1 25746 44.77% 24203 44.20% 236688 36.12% 0 0.00% 0 0.00% France 2 4371 7.60% 4320 7.89% 51656 7.88% 0 0.00% 0 0.00% Network 3 3180 5.53% 3169 5.79% 32819 5.01% 0 0.00% 0 0.00% Unresolved/Unknown 4 2426 4.22% 2421 4.42% 65482 9.99% 0 0.00% 0 0.00% Italy 5 2067 3.59% 1992 3.64% 16792 2.56% 0 0.00% 0 0.00% US Commercial 6 1855 3.23% 1758 3.21% 19761 3.02% 0 0.00% 0 0.00% Netherlands 7 1607 2.79% 1575 2.88% 31138 4.75% 0 0.00% 0 0.00% Germany 8 1430 2.49% 1317 2.40% 26679 4.07% 0 0.00% 0 0.00% International (int) 9 1111 1.93% 1086 1.98% 45507 6.94% 0 0.00% 0 0.00% United Kingdom 10 1102 1.92% 1089 1.99% 21538 3.29% 0 0.00% 0 0.00% China 11 861 1.50% 826 1.51% 8516 1.30% 0 0.00% 0 0.00% Russian Federation 12 786 1.37% 698 1.27% 4155 0.63% 0 0.00% 0 0.00% United States 13 718 1.25% 695 1.27% 5842 0.89% 0 0.00% 0 0.00% Australia 14 674 1.17% 674 1.23% 10433 1.59% 0 0.00% 0 0.00% Estonia 15 669 1.16% 545 1.00% 1317 0.20% 0 0.00% 0 0.00% Argentina 16 588 1.02% 588 1.07% 5106 0.78% 0 0.00% 0 0.00% Japan 17 577 1.00% 557 1.02% 3005 0.46% 0 0.00% 0 0.00% Spain 18 550 0.96% 506 0.92% 2525 0.39% 0 0.00% 0 0.00% US Educational 19 475 0.83% 451 0.82% 4460 0.68% 0 0.00% 0 0.00% Denmark 20 473 0.82% 461 0.84% 2710 0.41% 0 0.00% 0 0.00% Canada 21 458 0.80% 444 0.81% 2559 0.39% 0 0.00% 0 0.00% Norway 22 412 0.72% 372 0.68% 2753 0.42% 0 0.00% 0 0.00% Austria 23 388 0.67% 387 0.71% 4539 0.69% 0 0.00% 0 0.00% Portugal 24 379 0.66% 379 0.69% 3475 0.53% 0 0.00% 0 0.00% Peru 25 355 0.62% 354 0.65% 1508 0.23% 0 0.00% 0 0.00% Greece 26 319 0.55% 319 0.58% 2104 0.32% 0 0.00% 0 0.00% Switzerland 27 306 0.53% 262 0.48% 1316 0.20% 0 0.00% 0 0.00% Sweden 28 296 0.51% 296 0.54% 3132 0.48% 0 0.00% 0 0.00% Taiwan 29 276 0.48% 276 0.50% 1130 0.17% 0 0.00% 0 0.00% Iceland 30 272 0.47% 272 0.50% 2160 0.33% 0 0.00% 0 0.00% Poland

table 29 : Usage of the OSI SAF central Web Site by country in MARCH 2012

QR12-1 Page 89 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 6.1.2 Statistics on the OSI SAF Sea Ice Web portal and help desk

The following graphs illustrates the evolution of sessions and unique visitors on the HL OSI SAF Sea Ice portal (http://osisaf.met.no).

Figure 72 : Evolution of sessions (upper) and unique users (lower) on the HL OSI SAF Sea Ice portal from March 2011 to MARCH 2012 (http://osisaf.met.no).

Comments:

The number of sessions per month shows and increase after September 2011, which drops back in January 2012. In the same period the number of unique users is quite stable. After looking closer at the logs, the variation in number of sessions seems to be linked to search robots and our thredds servers, and not true users. New filters have been introduced to try to avoid too much traffic from such robots.

QR12-1 Page 90 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 6.1.3 Statistics on the OSI SAF KNMI scatterometer web page and helpdesk

The following graph illustrates the evolution of page views on the KNMI scatterometer web pages, which are partly devoted to the OSI SAF wind products, from August 2005 to MARCH 2012. Only external sessions (from outside KNMI) are counted.

100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0

5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 0 0 0 0 1 1 1 1 2 -0 0 -0 0 -0 0 -0 0 -0 0 -0 0 -0 0 -0 0 -0 0 -1 1 -1 1 -1 1 -1 1 -1 l t- n r- l t- n r- l t- n r- l t- n r- l t- n r- l t- n r- l t- n u c a p u c a p u c a p u c a p u c a p u c a p u c a J O J A J O J A J O J A J O J A J O J A J O J A J O J

Figure 73 : Number of page views on KNMI scatterometer website per month.

At [email protected], we received 93 Emails from 39 different addresses during the reporting period, requesting both wind data, processing software, and other support. This includes requests in the OSI SAF, the NWP SAF, and the EARS project. The total number of enquiries in this period was 40, and 22 of them were identified as OSI SAF enquiries. All requests were acknowledged or answered within three working days.

The following table gives the list of the registered wind users at KNMI.

Entity Shortened Country name Environment Canada Canada Koninklijk Nederlands Meteorologisch Instituut KNMI Netherlands Centre Mediterrani d'Investigacions Marines I Ambientals CMIMA-CSIC Spain Italian Air Force Weather Service Italy Norwegian Meteorological Institute Met.no Norway BMT Argoss Netherlands Danish Meteorological Institute DMI Denmark Jet Propulsion Laboratory JPL U.S.A. EUMETSAT Germany Institute of Meteorology and Water Management Poland IMGW Poland University of Concepcion CHILE Chile Turkish State Meteorological Services Turkey National Centre for Medium Range Weather Forecasting India India Nanjing University China Indian National Centre for Ocean Information Service INCOIS India

QR12-1 Page 91 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 Entity Shortened Country name Rudjer Boskovic Institute / Center for Marine Research Croatia Consiglio Nazionale delle Ricerche – ISAC Laboratorio Italy Ifremer France NOAA/NESDIS U.S.A. MetService New Zealand UAE Met. Department United Arab Erimates The Ohio State University, Dept. of Electrical Eng. U.S.A. University of Wisconsin-Madison U.S.A. BYU Center for Remote Sensing, Brigham Young U.S.A. University Woods Hole Oceanographic Institution U.S.A. Remote Sensing Systems U.S.A. Institute of Low Temperature Science, Hokkaido University Japan Center for Atmospheric and Oceanic Studies, Tohoku Japan University Naval Research Laboratory NRL U.S.A. ComSine Ltd U.K. Met Office U.K. Meteorology and Oceanography Group, Space Applications India Centre, ISRO Numerical Prediction Division, Japan Meteorological Japan Agency The First Institute of Oceanography FIO China PO.DAAC Data Engineering Team U.S.A. ECMWF U.K. Satellite Observing Systems U.K. Météo France M-F France School of Marine Science and Technology, Tokai University Japan Northwest Research Associates U.S.A. University of Washington U.S.A. Naval Hydrographic Service, Ministry of Defence Argentina Swedish Meteorological and Hydrological Institute SMHI Sweden Chalmers University of Technology Sweden Typhoon Research Department, Meteorological Research Japan Institute Gujarat University India Consiglio Nazionale delle Ricerche CNR Italy Oceanweather Inc. U.S.A. Ocean University of China China Nanjing University of China China Hydrometeorological Research Center of Russia Russia Meteorology Scientific Institution of ShanDong Province China VisioTerra France China Meteorological Administration China Institut de Recherche pour le Développement IRD France Weathernews Inc Japan NECTEC Thailand University of Ioannina Greece Bermuda Weather Service Bermuda Chinese Academy of Sciences China Naval Postgraduate School U.S.A. University of Hawaii U.S.A.

QR12-1 Page 92 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 Entity Shortened Country name Chinese Culture University Taiwan Federal University of Rio de Janeiro Brazil Flanders Marine Institute Belgium V. I. Il`ichev Pacific Oceanological Institute Russia Jet Propulsion Laboratory JPL U.S.A. NASA U.S.A. National Center for Atmospheric Research NCAR U.S.A. Chinese Academy of Meteorology Science China Weather Routing, Inc. WRI U.S.A. Instituto Oceanográfico de la Armada Equador Leibniz Institute for Baltic Sea Research Germany Nansen Environmental and Remote Sensing Center Norway UNMSM Peru Centro de Estudos do Ambiente e do Mar Portugal Andhra University, Visakhapatnam India Unidad de Tecnología Marina (UTM – CSIC) Spain MyOcean Sea Ice Wind TAC (Ifremer) France Jeju National University Korea Weather Data Marine Ltd. U.K. Admiral Paulo Moreira Marine Research Institute Brazil IMEDEA (UIB-CSIC) Spain Hong Kong Observatory Hong Kong Observatoire Midi-Pyrenees France Tidetech Australia Weatherguy.com U.S.A. Marine Data Literacy U.S.A. Hong Kong University of Science and Technology Hong Kong Environmental Agency of the Republic of Slovenia Slovenia Fisheries and Sea Research Institute Portugal National Meteorological Center China National Oceanography Centre, Southampton U.K. National Taiwan University Taiwan Florida State University U.S.A. Charles Sturt University, Wagga Wagga Australia Marine and Coastal Management South Africa Gent University Belgium Department of Meteorology Sri-Lanka Gwangju Institute of Science & Technology South Korea University of Hamburg Germany University of Las Palmas de Gran Canaria Spain The Third Institute of Oceanography China South China Sea Institute of Oceanology China Environmental Research Institute, University College Cork Ireland Shan dong meteorologic bureau China RPS MetOcean Pty Ltd Australia 14 independent users (not affiliated to an organization)

table 30 : List of registered Wind users at KNMI.

QR12-1 Page 93 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 6.2 Statistics on the FTP sites use

6.2.1 Statistics on the SS1 ftp sites use

SST and Fluxes products are available on both M-F and IFREMER FTP servers. Some of them are also available at the PODAAC.

6.2.1.1 Statistics on the IFREMER FTP server use

Number of OSI SAF products downloaded on IFREMER FTP server Over 1st quarter 2012 JAN. 2012 FEB. 2012 MARCH 2012 ATL SST (LML+MAP) 6228 6995 38525 ATL SSI (LML+MAP) 773 342 3177 ATL DLI (LML+MAP) 1047 1274 4646 NARSST 8388 3213 6706 MGR SST 153693 142948 189705 GBL SST 641 1811 715

table 31 : Number of OSI SAF products downloaded on IFREMER FTP server over 1st quarter 2012.

Number of files disseminated

250000

200000 GLB SST

s e

l 150000 MGR SST i f

f o

r e

b NAR SST 100000 m

u N ATL DLI (LML + MAP) 50000 ATL SSI (LML + MAP) 0

January ATL SST (LML + February MAP) March 2012

Figure 74 : Number of OSI SAF products downloaded on IFREMER FTP server over 1st quarter 2012.

QR12-1 Page 94 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 Volume of data downloaded by country (in Mb) JAN. 2012 FEB. 2012 MARCH 2012 France 4516 4915 3000 12431 Denmark 44134 36598 41216 121948 UK 0 0 0 0 USA 0 0 0 0 Portugal 0 0 0 0 Spain 1321 1208 1434 3963 Netherlands 1188 132 0 1320 Finland 0 0 0 Italy 7414 7506 9800 24720 Norway 0 0 0 0 Others 91453 53987 145909 291349 150026 104346 201359 table 32 : Volume of Data downloaded by country (in Mb) from IFREMER ftp server over 1st quarter 2012.

Volume of data (in Mb) downloaded per country

12431

France

121948 Denmark UK USA Portugal Spain Netherlands 3963 Finland 1320 291349 Italy 24720 Norway

Others

Figure 75 : Volume of Data downloaded by country (in Mb) from IFREMER ftp server over 1st quarter 2012.

QR12-1 Page 95 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 6.2.1.2 Statistics on the PODAAC FTP server use Currently, only NAR SST, GLB SST and MGR SST are archived at the PODAAC.

Statistics on the OSI SAF products downloaded on the PODAAC FTP server in January 2012 OSI SAF product Number Number GB of Users of files

MGR SST 170 82,3 61694 GLB SST 166 34,4 6531 NOAA-17 NAR SST 5 0.0 5 NOAA-18 NAR SST 75 13.1 1823 NOAA-19 NAR SST 101 12,1 7156 Metop-A NAR SST 155 9,3 5724 Total 672 138,1 82933 table 33 : Statistics of the OSI SAF products downloaded on the PODAAC FTP server in January 2012.

Statistics on the OSI SAF products downloaded on the PODAAC FTP server in February 2012 OSI SAF product Number Number GB of Users of files

MGR SST 115 43 29744 GLB SST 117 1,9 830 NOAA-17 NAR SST 13 0.0 15 NOAA-18 NAR SST 52 6.3 823 NOAA-19 NAR SST 70 0,7 746 Metop NAR SST 98 5,1 1625 Total 465 50,7 33783 table 34 : Statistics of the OSI SAF products downloaded on the PODAAC FTP server in February 2012.

Statistics on the OSI SAF products downloaded on the PODAAC FTP server in March 2012 OSI SAF product Number Number GB of Users of files

MGR SST 100 44,3 35043 GLB SST 100 0,2 4834 NOAA-17 NAR SST 5 0 7 NOAA-18 NAR SST 77 8,1 1796 NOAA-19 NAR SST 65 6,3 5832 Metop NAR SST 107 2,1 5319 Total 454 61 52831 table 35 : Statistics of the OSI SAF products downloaded on the PODAAC FTP server in March 2012.

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6.2.2 Statistics on the SS2 ftp site use

The number of downloads of Sea Ice products from the OSI SAF Sea Ice FTP server are given in table below. The numbers include the ice concentration, ice drift, ice edge and ice type product for each product area in GRIB,HDF5 and NetCDF format. The products have been split between the operational products and the reprocessed ice concentration data set.

Operational Reprocessed Month Ice Conc Ice Drift Ice Edge Ice Type Ice Conc JAN. 2012 10748 376 5098 3997 79494 FEB. 2012 9297 580 4237 6400 145220 MARCH 11318 2182 4473 5991 81662 2012 table 36 : Number of sea ice products downloaded from OSI SAF Sea Ice FTP server (ftp://osisaf.met.no) during this QR period

SST SSI DLI JAN. 2012 310 0 0 FEB. 2012 295 0 0 MARCH 2012 270 0 0 table 37 : Number of SST, SSI and DLI products downloaded from OSI SAF Sea Ice FTP server (ftp://osisaf.met.no) during this QR period.

Comment:

No users of the AHL SSI and DLI products from the FTP server have yet been registered. This is partly because this is a new product, and partly because SSI and DLI have fewer potential users compared to SST, ice and wind.

The next figure shows the downloads sorted on domains.

QR12-1 Page 97 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2

Figure 76 : FTP downloads of sea ice products (more than 5) sorted on domains for JAN to MAR 2012.

QR12-1 Page 98 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 6.2.3 Statistics on the SS3 ftp site use

KNMI keeps statistics of the retrieval of wind products of its FTP server. It appears that the 25-km and 12.5-km ASCAT products were retrieved routinely by approximately 35 users and the ASCAT coastal products by approximately 15 users. This includes both BUFR and NetCDF formats. Note that the BUFR products are also disseminated through EUMETCast.

We also receive statistics from PO.DAAC on the number of downloads of the ASCAT wind products in NetCDF format from their archive. During the 4th quarter of 2011: 424,827 ASCAT 25-km data files have been retrieved by 232 users. 416,185 ASCAT 12.5-km data files have been retrieved by 414 users. 118,407 ASCAT coastal data files have been retrieved by 170 users.

6.3 Statistics of OSI SAF orders at EUMETSAT data centre

The OSI SAF products are archived at the EUMETSAT data centre. The following table gives the number of related orders over the reporting period. t L5 Ç ah !{h t9 ah !{í h t9 ah !{h t9 ah !{í h t9 aa[h {{Ç!I[h t9 ah {{ÇD[.h t9 aa[h 5[L!I[h t9 aa[h {{L!I[h t9 b %h {{Çb !wh t9 C(h {L/h D.h t9 Dh 9{(h {L55[Lh t9 Dh 9{(h {L5{{Lh t9 Dh 9{(h {LI5[Lh t9 Dh 9{(h {LI{{Lh t9 Dh 9{(h {LI{{Çh t9 aa[h {L95D.h t9 aa[h {LÇòD.h t9 a{Dh {L5{{Lh t9 a{Dh {LI5[Lh t9 a{Dh {LI{{Lh t9 a{Dh {LI{{Çh t9 ah {{Çb !wh t9 a{Dh {L55[Lh t9 D + , table 38 : Orders at EUMETSAT Data centre for OSI SAF products

QR12-1 Page 99 of 102 T8.1 SAF/OSI/CDOP-2/M-F/TEC/RP/331 Quarterly Report OSI SAF CDOP-2 7 Documentation update

The following table provides the list of documents modified during the reporting period, as well as new documents made available to users. Last version of documents and new documents are available on the central Web Site (www.osi-saf.org).

Name of the Document OSI SAF reference Latest date versions ASCAT aircraft underflight SAF/OSI/CDOP/KNMI/TEC/TN/1 final November campaigns 87 2011 in hurricane conditions SAF contribution to NOAA campaigns Ice edge detection with dual VSA osi_as_11_01 N.A. February polarized SAR data 2012 Using Fine scale NWP model VSA osi_as_11_03 N.A. February outputs to improve the retrieval of 2012 SST from METOP AVHRR brightness temperatures ASCAT Product User Manual SAF/OSI/CDOP/KNMI/TEC/MA/1 1.11 March 2012 26 Quality Control of Ku-band SAF/OSI/CDOP2/KNMI/TEC/RP/ 1.0 March 2012 scatterometer winds 194 table 39 : Documentation updates.

Recent publications

P. Le Borgne, H. Roquet, C.J. Merchant, Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction, Remote Sensing of Environment, Volume 115, Issue 1, 17 January 2011, Pages 55-65, ISSN 0034-4257, DOI: 10.1016/j.rse.2010.08.004.

Eastwood, S., P. Le Borgne, S. Péré and D. Poulter, 2010, Diurnal variability in Sea Surface Temperature in the Arctic, in publication, Remote sensing of Environment

Merchant, C.J., A. R. Harris, H. Roquet, and P. Le Borgne, Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution, Radiometer Geophysical Research Letters, VOL. 36, L17604, doi:10.1029/2009GL039843, 2009.

Merchant C. J., P. Le Borgne, H. Roquet and A. Marsouin (2009), Sea surface temperature from a geostationary satellite by optimal estimation, Rem. Sens. Env., 113 (2), 445-457. DOI:10.1016/j.rse.2008.10.012 .

Clerici, M., Hoepffner, N., Diop, M., Ka, A., Kirugara, D. and Ndungu, J.(2009)'SST derivation from MSG for PUMA Pilot Projects in Fisheries', International Journal of Remote Sensing, 30:8,1941-1959.

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Ineichen, Pierre, Barroso, Carla Sofia, Geiger, Bernhard, Hollmann, Rainer, Marsouin, Anne and Mueller, Richard (2009) 'Satellite Application Facilities irradiance products: hourly time step comparison and validation over Europe', International Journal of Remote Sensing, 30: 21, 5549 5571.

Cailleau et al. 2010 A method of correction of radiative flux to force a regional forecasting system : application to IBI area, MERCATOR/ CORIOLIS conference Toulouse, November 2010.

F. Massonnet, T. Fichefet, H. Goosse, M. Vancoppenolle, P. Mathiot, C. K¨onig Beatty. On the influence of model physics on simulations of Arctic and Antarctic sea ice. The Cryosphere, 5, 687–699, published, 2011

Donlon, C.J., M. Martin, J. Stark, J. Roberts-Jones and E. Fiedler, “The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)”, accepted , Remote sensing of Environment

Rozman, P., Hölemann, J., Krumpen, T., Gerdes, R., Köberle, C., Lavergne, T., and Adams, S. "Validating Satellite Derived and Modeled Sea Ice Drift in the Laptev Sea with In-Situ Measurements of Winter 2007/08", Jounal of Polar Research, under review, 2011

Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A. "Sea ice motion from low resolution satellite sensors: an alternative method and its validation in the Arctic". J. Geophys. Res., doi:10.1029/2009JC005958, in press, 2010.

Tonboe, R. T. The simulated sea ice thermal microwave emission at window and sounding frequencies. Tellus 62A, 333-344, 2010.

Belmonte Rivas, M. and A. Stoffelen, New Bayesian algorithm for sea ice detection with QuikSCAT IEEE Transactions on Geoscience and Remote Sensing, I, 2011, 49, 6, 1894-1901, doi:10.1109/TGRS.2010.2101608.

Li Bi, James A. Jung, Michael C. Morgan, John F. Le Marshall, 2010, Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System, Monthly Weather Review, accepted after minor revision.

Verspeek, J.A., A. Stoffelen, M. Portabella, H. Bonekamp, C. Anderson and J. Figa, Validation and calibration of ASCAT using CMOD5.n IEEE Transactions on Geoscience and Remote Sensing, 2010, 48, 1, 386-395, doi:10.1109/TGRS.2009.2027896.

Portabella, M., A. Stoffelen, A. Turiel, A. Verhoef, J. Verspeek and J. Ballabrera, Rain effects on ASCAT retrieved winds: towards an improved Quality Control, submitted, IEEE Transactions on Geoscience and Remote Sensing, 2011.

Verhoef, A., M. Portabella and A. Stoffelen, High-resolution ASCAT scatterometer winds near the coast accepted, IEEE Transactions on Geoscience and Remote Sensing, 2012, doi:10.1109/TGRS.2011.2175001.

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Verspeek, J., A. Stoffelen, A. Verhoef, M. Portabella, Improved ASCAT wind retrieval using NWP ocean calibration, submitted, IEEE Transactions on Geoscience and Remote Sensing, 2011.

Vogelzang, J. and A. Stoffelen, Stucture functions for two-dimensional variational ambiguity removal, submitted, IEEE Transactions on Geoscience and Remote Sensing, 2011.

Anderson, C., J. Figa, H. Bonekamp, J. Wilson, J. Verspeek, A. Stoffelen and M. Portabella, Validation of Backscatter Measurements from the Advanced Scatterometer on MetOp-A J. Atm. Oceanic Technol., 2012, 29, 77-88.

Portabella, M., A. Stoffelen, A. Verhoef and J. Verspeek, A new method for improving ASCAT wind quality control accepted, IEEE Gosci. Remote Sensing Letters, 2012, 9, 4, doi:10.1109/LGRS.2011.2175435.

Vogelzang, J. and A. Stoffelen, NWP MODEL ERROR STRUCTURE FUNCTIONS OBTAINED FROM SCATTEROMETER WINDS IEEE Transactions on Geoscience and Remote Sensing, 2011, doi:10.1109/TGRS.2011.2168407.

Vogelzang, J., A. Stoffelen, A. Verhoef and J. Figa-Saldana, On the quality of high- resolution scatterometer winds J. Geophys. Res., 2011, 116, doi:10.1029/2010JC006640.

Vogelzang, J. and A. Stoffelen, Scatterometer wind vector products for application in meteorology and oceanography accepted, Journal of Sea Research, 2011.

Portabella, M. and A.C.M. Stoffelen, On Scatterometer Ocean Stress J. Atm. Oceanic Technol., 2009, 26, 2, 368-382, doi:10.1175/2008JTECHO578.1.

Vogelzang, J., A. Stoffelen, A. Verhoef, J. de Vries and H. Bonekamp, Validation of two-dimensional variational ambiguity removal on SeaWinds scatterometer data J. Atm. Oceanic Technol., 7, 2009, 26, 1229-1245, doi:10.1175/2008JTECHA1232.1.

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