Recent Changes in the Derivation of Geostationary AMVs at EUMETSAT. Manuel Carranza Régis Borde Marie Doutriaux-Boucher
|
|
|
- Betty Stephens
- 5 years ago
- Views:
Transcription
1 Recent Changes in the Derivation of Geostationary AMVs at EUMETSAT Manuel Carranza Régis Borde Marie Doutriaux-Boucher
2 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
3 Introduction to EUMETSAT s geostationary AMVs Meteosat satellites currently in operation Meteosat-10: launched on 5 July 2012 as MSG-3, located at 0 East. It supports the Meteosat Prime Service (Full Earth Scanning, FES). Meteosat-9: launched on 21 December 2005 as MSG-2, located at 9.5 East. It supports the Rapid Scanning Service (RSS). Meteosat-8: launched on 28 August 2002 as MSG-1, located at 3.5 East. It is a backup for Meteosat-10 and, with lower priority, Meteosat-9. Meteosat-7: launched on 2 September 1997, located at 57.5 East. It supports the Indian Ocean Data Coverage (IODC) service.
4 Introduction to EUMETSAT s geostationary AMVs MSG SEVIRI channels
5 Introduction to EUMETSAT s geostationary AMVs AMV derivation process 1. Tracking Target area, 24x24 pixels (32x32 HRVIS) Search area, 80x80 pixels (96x96 HRVIS) T T + 15 min 2. Height assignment? 3. Quality control
6 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
7 Recent changes Latest MSG MPEF releases Release 1.5.3, September 2012 Introduction of CCC method for the AMV height assignment à Statistics of AMVs improved at high and mid levels, degraded at low levels Patch for low-level winds, February 2013 à Statistics of AMVs slightly better at low levels Release 1.5.4, September 2013 AMVs extracted at low levels in WV channels set to a poor QI Introduction of the best-fit pressure calculation Introduction of OCA product (2 layers, hourly), not yet used for AMVs
8 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
9 Cross-Correlation Contribution (CCC) method Mathematical formulation
10 Cross-Correlation Contribution (CCC) method Histograms of height frequency per channel VIS0.8 WV6.2 WV7.3 IR10.8 CCC method used for AMV height assignment Old method used for AMV height assignment
11 Cross-Correlation Contribution (CCC) method Low-level cloud problem AMV height (pre-ccc) 1050 AMV height (CCC) (hpa) 525 (hpa) Normalised RMS difference between CCC and control experiment for 48- hour wind forecast at 850 hpa Negative impact
12 Cross-Correlation Contribution (CCC) method Speed bias per channel for June 2012 Region Algorithm IR10.8 VIS0.8 WV6.2 WV7.3 ALL HGH MID LOW LOW HGH HGH MID GLO OPE CCC NH OPE CCC SH OPE CCC TRO OPE CCC
13 Cross-Correlation Contribution (CCC) method Forecast profile comparison
14 Cross-Correlation Contribution (CCC) method Low-level cloud solution: re-introduction of an inversion correction algorithm 1. Each pixel has a height provided by the CLA/CTH product 2. CCC computes an average height for each target area (based on the pixels that contribute the most to the tracking) 3. In case of temperature inversion, the height is set to 1/3 of the inversion strength above the inversion layer bottom 4. If the EBBT pressure is larger than the inversion corrected pressure, then the EBBT pressure is used instead
15 Cross-Correlation Contribution (CCC) method Average CCC height (left) and height difference (right), Feb (QI > 85) VIS0.8 IR10.8
16 Cross-Correlation Contribution (CCC) method Impact on QI of VIS0.8 low-level winds, Feb (QI > 85)
17 Cross-Correlation Contribution (CCC) method Long-term statistics (I) Number of AMVs, channel 02 (VIS 0.8 µm)
18 Cross-Correlation Contribution (CCC) method Long-term statistics (II) Spatial vector consistency, channel 02 (VIS 0.8 µm)
19 Cross-Correlation Contribution (CCC) method Long-term statistics (III) Forecast consistency, channel 02 (VIS 0.8 µm)
20 Cross-Correlation Contribution (CCC) method Long-term statistics (IV) Number of AMVs, channel 05 (WV 6.2 µm)
21 Cross-Correlation Contribution (CCC) method Long-term statistics (V) Number of AMVs, channel 06 (WV 7.3 µm)
22 Cross-Correlation Contribution (CCC) method Long-term statistics (VI) Number of AMVs, channel 09 (IR 10.8 µm)
23 Cross-Correlation Contribution (CCC) method Long-term statistics (VII) QI including forecast consistency, channel 09 (IR 10.8 µm)
24 Cross-Correlation Contribution (CCC) method Long-term statistics (VIII) Number of AMVs, channel 12 (HRVIS)
25 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
26 Best-fit pressure Recommendation IWW Definition and computation (from MET Office) The best-fit pressure is the height where the vector difference between the observed wind and the model forecast wind is the smallest. 1. The U and V wind components are extracted from the forecast profile. 2. The minimum vector difference w.r.t. the model forecast is computed. 3. A parabolic fit is used in order to find the best-fit pressure. 4. The best-fit U and V components are computed by linear interpolation. 5. The best-fit pressure is valid only if: The minimum vector difference is smaller than 4 m/s. The vector difference is larger than the minimum plus 2 m/s outside the band +/- 100 hpa from the best-fit pressure.
27 Best-fit pressure CCC best-fit height, 5 September 2013 (I) VIS0.8 0 Wind too high Wind too low Pressure (hpa) Wind too low Wind too high Best-fit bias (hpa) Number of AMVs
28 Best-fit pressure CCC best-fit height, 5 September 2013 (II) WV6.2 0 Wind too high Wind too low Pressure (hpa) Wind too low Wind too high Best-fit bias (hpa) Number of AMVs
29 Best-fit pressure CCC best-fit height, 5 September 2013 (III) WV7.3 0 Wind too high Wind too low Pressure (hpa) Wind too low Wind too high Best-fit bias (hpa) Number of AMVs
30 Best-fit pressure CCC best-fit height, 5 September 2013 (IV) IR Wind too high Wind too low Pressure (hpa) Wind too low Wind too high Best-fit bias (hpa) Number of AMVs
31 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
32 Optimal Cloud Analysis (OCA) Introduction to OCA - OCA: Optimal Cloud Analysis algorithm developed at EUMETSAT. - Latest version uses two layers. Cloud-top height is computed for both. - Microphysics parameters are computed: cloud phase, cloud effective radius, cloud optical thickness, etc. CLA/CTH OCA/CTH
33 Optimal Cloud Analysis (OCA) Cloud phase: CLA vs. OCA, 26 November 2013
34 Optimal Cloud Analysis (OCA) Cloud-top pressure: CLA vs. OCA, 26 November th International Winds Workshop
35 Optimal Cloud Analysis (OCA) AMV Intermediate Product: CLA vs. OCA, 26 November th International Winds Workshop
36 Optimal Cloud Analysis (OCA) AMV statistics: CLA vs. OCA, 26 November Winds with vs. without applied inversion (whole globe) CLA OCA DIFF Mean overall QI (inversion points) Mean forecast QI (inversion points) Mean overall QI (other points) Mean forecast QI (other points) Central inversion area (lat. [-35º,5º], lon. [-20º,20º]) CLA OCA DIFF Mean overall QI Mean forecast QI Jet area (lat. [5º,30º], lon. [-10º,30º]) CLA OCA DIFF Mean overall QI Mean forecast QI
37 Optimal Cloud Analysis (OCA) Use of microphysics parameters to set the AMV height The cloud geometrical depth can be estimated from OCA parameters: Meerkötter and Bugliaro: Δz cloud = 1.11 τ reff 5, Cw = T C w cloud Cloud depth Alternatively: Liquid water path: ( 5/ 9) τ eff LWP = ρ r CCC weighted average optical thickness, τ
38 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work
39 Nested tracking Algorithm principles (image courtesy of Bresky et al.) Small box nested within larger box Vector displacements
40 Nested tracking Preliminary results: CCC vs. Nested Tracking (tracking case)
41 Nested tracking Preliminary results: CCC vs. Nested Tracking (nominal case)
42 Nested tracking Comparison between NESDIS and EUMETSAT winds, 1 to 7 November 2013 VIS10.8 All winds VIS10.8 All winds NESDIS (Nested tracking) 19x19 box (HH-15, HH, HH +15) NESDIS (Nested tracking) 23x23 box (HH, HH+15, HH+30) No accel. / gross error checks EUMETSAT (CCC) 24x24 box (HH, HH+15, HH+30, HH+45) Vector RMSE (m/s) Standard Deviation (m/s) Speed Bias (m/s) Speed (m/s) Average pressure (hpa) Sample EUMETSAT (CCC) 24x24 box (HH, HH+15, HH+30, HH+45) Vector RMSE (m/s) Standard Deviation (m/s) Speed Bias (m/s) Speed (m/s) Average pressure (hpa) Sample
43 Summary Introduction to EUMETSAT s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments Optimal Cloud Analysis (OCA) Nested tracking Future work
44 Future work MFG Introduction of CCC method for the AMV height assignment (December 2015) Divergence product (December 2015) MSG Use OCA to set AMV height (dependant on OCA availability every 15 min.) Change WV AMV height assignment in clear-sky conditions (December 2015) Continue investigation on nested tracking scheme MTG MTG FCI: prototyping activities using proxy data MTG IRS: revisit the potential of optical flow methods applied to humidity fields (IASI data and/or proxy data). External study should start in 2014
45 Thank you!
46 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 05 (WV 6.2 µm)
47 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 05 (WV 6.2 µm)
48 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 06 (WV 7.3 µm)
49 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 06 (WV 7.3 µm)
50 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 09 (IR 10.8 µm)
51 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 09 (IR 10.8 µm)
52 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 12 (HRVIS)
53 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 12 (HRVIS)
GENERATION OF HIMAWARI-8 AMVs USING THE FUTURE MTG AMV PROCESSOR
GENERATION OF HIMAWARI-8 AMVs USING THE FUTURE MTG AMV PROCESSOR Manuel Carranza 1, Régis Borde 2, Masahiro Hayashi 3 1 GMV Aerospace and Defence S.A. at EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt,
OPERATIONAL RETRIEVAL OF MSG AMVS USING THE NEW CCC METHOD FOR HEIGHT ASSIGNMENT.
OPERATIONAL RETRIEVAL OF MSG AMVS USING THE NEW CCC METHOD FOR HEIGHT ASSIGNMENT. M. Doutriaux-Boucher, M. Carranza, A. de Smet, G. Dew, and R. Borde Eumetsat, Darmstadt, Germany. Present AMV retrieval
AN OVERVIEW OF 10 YEARS OF RESEARCH ACTIVITIES ON AMVs AT EUMETSAT
AN OVERVIEW OF 10 YEARS OF RESEARCH ACTIVITIES ON AMVs AT EUMETSAT R. Borde and J. Schmetz, K. Holmlund, A. Arriaga, A. De Smet, G. Dew, J.B. Gustafson, M. Doutriaux-Boucher, M. Carranza, O. Hautecoeur,
AVHRR Polar Winds Derivation at EUMETSAT: Current Status and Future Developments
AVHRR Polar Winds Derivation at EUMETSAT: Current Status and Future Developments Gregory Dew and Régis Borde With assistance from: Marie Doutriaux-Boucher, Manuel Carranza Contents 1. Background 2. Status
AMVs in the operational ECMWF system
AMVs in the operational ECMWF system Kirsti Salonen and Niels Bormann Slide 1 AMV sample coverage: monitored GOES-15 GOES-13 MET-10 MET-7 MTSAT-2 NOAA-15 NOAA-18 NOAA-19 FY-2D FY-2E AQUA TERRA METOP-A
Atmospheric Motion Vectors: Product Guide
Atmospheric Motion Vectors: Product Guide Doc.No. Issue : : EUM/TSS/MAN/14/786435 v1a EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 9 April 2015
ATMOSPHERIC MOTION VECTORS DERIVED FROM MSG RAPID SCANNING SERVICE DATA AT EUMETSAT
ATMOSPHERIC MOTION VECTORS DERIVED FROM MSG RAPID SCANNING SERVICE DATA AT EUMETSAT Manuel Carranza 1, Arthur de Smet 2, Jörgen Gustafsson 2 1 GMV Aerospace and Defence S.A. at EUMETSAT, Eumetsat-Allee
IMPROVEMENTS TO EUMETSAT AMVS. Régis Borde, Greg Dew, Arthur de Smet and Jörgen Bertil Gustaffson
IMPROVEMENTS TO EUMETSAT AMVS Régis Borde, Greg Dew, Arthur de Smet and Jörgen Bertil Gustaffson BACKGROUND Create a direct link between feature tracked and Height Assignment of AMV. References: Borde
AMVs in the ECMWF system:
AMVs in the ECMWF system: Overview of the recent operational and research activities Kirsti Salonen and Niels Bormann Slide 1 AMV sample coverage: monitored GOES-15 GOES-13 MET-10 MET-7 MTSAT-2 NOAA-15
NWCSAF/High Resolution Winds AMV Software evolution between 2012 and 2014
NWCSAF/High Resolution Winds AMV Software evolution between 2012 and 2014 16th June 2014 Twelfth International Winds Workshop Copenhagen, Denmark Javier García-Pereda (jgarciap@aemet.es) Twelfth International
MSG Indian Ocean Data Coverage (IODC) Jochen Grandell & Sauli Joro
MSG Indian Ocean Data Coverage (IODC) Jochen Grandell & Sauli Joro 1 EUM/STG-SWG/42/17/VWG/03 v1, 7 8 Mach 2017 Topics Introduction MSG-IODC Overall Project Schedule Status Product validation Products
THE ATMOSPHERIC MOTION VECTOR RETRIEVAL SCHEME FOR METEOSAT SECOND GENERATION. Kenneth Holmlund. EUMETSAT Am Kavalleriesand Darmstadt Germany
THE ATMOSPHERIC MOTION VECTOR RETRIEVAL SCHEME FOR METEOSAT SECOND GENERATION Kenneth Holmlund EUMETSAT Am Kavalleriesand 31 64293 Darmstadt Germany ABSTRACT The advent of the Meteosat Second Generation
Use of satellite winds at Deutscher Wetterdienst (DWD)
Use of satellite winds at Deutscher Wetterdienst (DWD) Alexander Cress Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach am Main, Germany alexander.cress@dwd.de Ø Introduction Ø Atmospheric
CURRENT STATUS OF THE EUMETSAT METEOSAT-8 ATMOSPHERIC MOTION VECTOR QUALITY CONTROL SYSTEM
CURRENT STATUS OF THE EUMETSAT METEOSAT-8 ATMOSPHERIC MOTION VECTOR QUALITY CONTROL SYSTEM Jörgen Gustafsson Meteorological Operations Division, EUMETSAT, Am Kavalleriesand 31, D-64295 Darmstadt, Germany
An Update on EUMETSAT Programmes and Plans. Dieter Klaes on behalf of EUMETSAT teams
An Update on EUMETSAT Programmes and Plans Dieter Klaes on behalf of EUMETSAT teams 1 21 st International TOVS Study Conference (ITSC), Darmstadt, Germany, 29 November 2017 5 December 2017 Current EUMETSAT
AMVs in the ECMWF system:
AMVs in the ECMWF system: Highlights of the operational and research activities Kirsti Salonen and Niels Bormann Slide 1 Number of used AMVs Look back: how the use of AMVs has evolved NOAA-15,-16,-18,-19
TOWARDS IMPROVED HEIGHT ASSIGNMENT AND QUALITY CONTROL OF AMVS IN MET OFFICE NWP
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA TOWARDS IMPROVED HEIGHT ASSIGNMENT AND QUALITY CONTROL OF AMVS IN MET OFFICE NWP James Cotton, Mary
A New Atmospheric Motion Vector Intercomparison Study
A New Atmospheric Motion Vector Intercomparison Study David Santek 1, Javier García-Pereda 2, Chris Velden 1, Iliana Genkova 3, Dave Stettner 1, Steve Wanzong 1, Sharon Nebuda 1, Régis Borde 4, Manuel
NWP SAF AMV monitoring: the 8th Analysis Report (AR8)
Document NWPSAF-MO-TR-035 Version 1.0 08/03/18 NWP SAF AMV monitoring: the 8th Analysis Report (AR8) Francis Warrick, James Cotton Met Office, UK NWP SAF AMV monitoring: the 8th Analysis Report (AR8) Francis
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS)
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne Bresky, Hongming Qi JCSDA 11th Workshop
Maryland 20746, U.S.A. Engineering Center (SSEC), University of Wisconsin Madison, Wisconsin 53706, U.S.A ABSTRACT
NESDIS Atmospheric Motion Vector (AMV) Nested Tracking Algorithm: Exploring its Performance Jaime Daniels 1, Wayne Bresky 2, Steven Wanzong 3, Andrew Bailey 2, and Chris Velden 3 1 NOAA/NESDIS Office of
EVALUATION OF TWO AMV POLAR WINDS RETRIEVAL ALGORITHMS USING FIVE YEARS OF REPROCESSED DATA
EVALUATION OF TWO AMV POLAR WINDS RETRIEVAL ALGORITHMS USING FIVE YEARS OF REPROCESSED DATA Roger Huckle, Marie Doutriaux-Boucher, Rob Roebeling, and Jörg Schulz EUMETSAT, Eumetsat-Allee 1, Darmstadt,
NESDIS Atmospheric Motion Vector (AMV) Nested Tracking Algorithm: Exploring its Performance
NESDIS Atmospheric Motion Vector (AMV) Nested Tracking Algorithm: Exploring its Performance Jaime Daniels NOAA/NESDIS, Center for Satellite Applications and Research Wayne Bresky & Andrew Bailey IM Systems
Introducing Atmospheric Motion Vectors Derived from the GOES-16 Advanced Baseline Imager (ABI)
Introducing Atmospheric Motion Vectors Derived from the GOES-16 Advanced Baseline Imager (ABI) Jaime Daniels NOAA/NESDIS, Center for Satellite Applications and Research Wayne Bresky, Andrew Bailey, Americo
STATUS AND DEVELOPMENT OF SATELLITE WIND MONITORING BY THE NWP SAF
STATUS AND DEVELOPMENT OF SATELLITE WIND MONITORING BY THE NWP SAF Mary Forsythe (1), Antonio Garcia-Mendez (2), Howard Berger (1,3), Bryan Conway (4), Sarah Watkin (1) (1) Met Office, Fitzroy Road, Exeter,
INTRODUCTION OF THE RECURSIVE FILTER FUNCTION IN MSG MPEF ENVIRONMENT
INTRODUCTION OF THE RECURSIVE FILTER FUNCTION IN MSG MPEF ENVIRONMENT Gregory Dew EUMETSAT Abstract EUMETSAT currently uses its own Quality Index (QI) scheme applied to wind vectors derived from the Meteosat-8
WHAT CAN WE LEARN FROM THE NWP SAF ATMOSPHERIC MOTION VECTOR MONITORING?
WHAT CAN WE LEARN FROM THE NWP SAF ATMOSPHERIC MOTION VECTOR MONITORING? Mary Forsythe 1, James Cotton 1, Antonio Garcia-Mendez 2, Bryan Conway 1 (1) Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
GOES-16 AMV data evaluation and algorithm assessment
GOES-16 AMV data evaluation and algorithm assessment Katie Lean and Niels Bormann IWW14, Jeju Island, South Korea, 23-27 th April 2018 katie.lean@ecmwf.int ECMWF May 3, 2018 Outline Introduction Changes
Wind tracing from SEVIRI clear and overcast radiance assimilation
Wind tracing from SEVIRI clear and overcast radiance assimilation Cristina Lupu and Tony McNally ECMWF, Reading, UK Slide 1 Outline Motivation & Objective Analysis impact of SEVIRI radiances and cloudy
Assimilation of Geostationary WV Radiances within the 4DVAR at ECMWF
Assimilation of Geostationary WV Radiances within the 4DVAR at ECMWF Christina Köpken Graeme Kelly, Jean-Noël Thépaut ECMWF EUMETSAT Fellowship ITSC-XII Lorne, Australia, 27 February - 5 March 2002 Assimilation
NWP SAF AMV monitoring: the 7th Analysis Report (AR7)
Document NWPSAF-MO-TR-032 Version 1.0 24/05/16 NWP SAF AMV monitoring: the 7th Analysis Report (AR7) Francis Warrick Met Office, UK NWP SAF AMV monitoring: the 7th Analysis Report (AR7) Francis Warrick
Reprocessed Satellite Data Products for Assimilation and Validation
Reprocessed Satellite Data Products for Assimilation and Validation Leo van de Berg, Bertrand Theodore EUMETSAT Page 1 Historical Presentation Contents Results of Eumetsat ERA-40 Activities Lessons Learned
AVHRR Global Winds Product: Validation Report
AVHRR Global Winds Product: Validation Report Doc.No. : EUM/TSS/REP/14/751801 Issue : v1d Date : 25 February 2015 WBS : EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49
IMPACT STUDIES OF HIGHER RESOLUTION COMS AMV IN THE KMA NWP SYSTEM
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA IMPACT STUDIES OF HIGHER RESOLUTION COMS AMV IN THE KMA NWP SYSTEM Jung-Rim Lee, Hyun-Cheol Shin,
MSG system over view
MSG system over view 1 Introduction METEOSAT SECOND GENERATION Overview 2 MSG Missions and Services 3 The SEVIRI Instrument 4 The MSG Ground Segment 5 SAF Network 6 Conclusions METEOSAT SECOND GENERATION
Cloud Top Height Product: Product Guide
Cloud Top Height Product: Product Guide Doc.No. : Issue : v1c e-signed Date : 26 April 2017 WBS : EUM/TSS/MAN/14/786420 EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49
Andrew Bailey (NOAA, Steve Wanzong (CIMSS/SSEC,
SURVEY ON THE DIFFERENT AMV PRODUCTS NWCSAF KMA 0. CONTACTS FOR EACH CENTRE Régis Borde (, regis.borde@eumetsat.int) Manuel Carranza (GMV INSYEN, manuel.carranza@eumetsat.int) Javier García Pereda (NWCSAF/AEMET,
Derivation of AMVs from single-level retrieved MTG-IRS moisture fields
Derivation of AMVs from single-level retrieved MTG-IRS moisture fields Laura Stewart MetOffice Reading, Meteorology Building, University of Reading, Reading, RG6 6BB Abstract The potential to derive AMVs
PRECONVECTIVE SOUNDING ANALYSIS USING IASI AND MSG- SEVIRI
PRECONVECTIVE SOUNDING ANALYSIS USING IASI AND MSG- SEVIRI Marianne König, Dieter Klaes EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany Abstract EUMETSAT operationally generates the Global Instability
OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery
OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery L. Garand 1 Y. Rochon 1, S. Heilliette 1, J. Feng 1, A.P. Trishchenko 2 1 Environment Canada, 2 Canada Center for
VALIDATION OF DUAL-MODE METOP AMVS
VALIDATION OF DUAL-MODE METOP AMVS Ákos Horváth 1, Régis Borde 2, and Hartwig Deneke 1 1 Leibniz Institute for Tropospheric Research, Permoserstrasse 15, Leipzig, Germany 2 EUMETSAT, Eumetsat Allee 1,
Which AMVs for which model? Chairs: Mary Forsythe, Roger Randriamampianina IWW14, 24 April 2018
Which AMVs for which model? Chairs: Mary Forsythe, Roger Randriamampianina IWW14, 24 April 2018 Thanks to Regis Borde, EUMETSAT James Cotton, Met Office Javier Garcia-Pereda, NWC SAF Graeme Kelly, Met
STATUS AND DEVELOPMENT OF OPERATIONAL METEOSAT WIND PRODUCTS. Mikael Rattenborg. EUMETSAT, Am Kavalleriesand 31, D Darmstadt, Germany ABSTRACT
STATUS AND DEVELOPMENT OF OPERATIONAL METEOSAT WIND PRODUCTS Mikael Rattenborg EUMETSAT, Am Kavalleriesand 31, D-64295 Darmstadt, Germany ABSTRACT The Operational Meteosat Meteorological Products are produced
STATUS OF THE OPERATIONAL SATELLITE WINDS PRODUCTION AT CPTEC/INPE AND IT USAGE IN REGIONAL DATA ASSIMILATION OVER SOUTH AMERICA
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA STATUS OF THE OPERATIONAL SATELLITE WINDS PRODUCTION AT CPTEC/INPE AND IT USAGE IN REGIONAL DATA
RECENT UPGRADES OF AND ACTIVITIES FOR ATMOSPHERIC MOTION VECTORS AT JMA/MSC
1 th International Winds Workshop, Tokyo, Japan, - ruary 1 RECENT UPGRADES OF AND ACTIVITIES FOR ATMOSPHERIC MOTION VECTORS AT JMA/MSC Ryo OYAMA Meteorological Satellite Center of Japan Meteorological
UPDATES IN THE ASSIMILATION OF GEOSTATIONARY RADIANCES AT ECMWF
UPDATES IN THE ASSIMILATION OF GEOSTATIONARY RADIANCES AT ECMWF Carole Peubey, Tony McNally, Jean-Noël Thépaut, Sakari Uppala and Dick Dee ECMWF, UK Abstract Currently, ECMWF assimilates clear sky radiances
GLOBAL ATMOSPHERIC MOTION VECTOR INTER-COMPARISON STUDY
GLOBAL ATMOSPHERIC MOTION VECTOR INTER-COMPARISON STUDY Iliana Genkova (1), Regis Borde (2), Johannes Schmetz (2), Chris Velden (3), Ken Holmlund (2), Mary Forsythe (4), Jamie Daniels (5), Niels Bormann
CLOUD TOP, CLOUD CENTRE, CLOUD LAYER WHERE TO PLACE AMVs?
CLOUD TOP, CLOUD CENTRE, CLOUD LAYER WHERE TO PLACE AMVs? A. Hernandez (curr. aff. AEMET) N. Bormann (ECMWF) IWW12, Copenhagen, June 2014 Slide 1 Outline SimulAMV2 study - introduction Model clouds and
COMPARISON OF AMV CLOUD TOP PRESSURE DERIVED FROM MSG WITH SPACE BASED LIDAR OBSERVATIONS (CALIPSO)
COMPARISON OF AMV CLOUD TOP PRESSURE DERIVED FROM MSG WITH SPACE BASED LIDAR OBSERVATIONS (CALIPSO) Geneviève Sèze, Stéphane Marchand, Jacques Pelon and Régis Borde 1LMD/IPSL, Paris, France 2 EUMETSAT,
ACCOUNTING FOR THE SITUATION-DEPENDENCE OF THE AMV OBSERVATION ERROR IN THE ECMWF SYSTEM
ACCOUNTING FOR THE SITUATION-DEPENDENCE OF THE AMV OBSERVATION ERROR IN THE ECMWF SYSTEM Kirsti Salonen and Niels Bormann ECMWF, Shinfield Park, Reading RG2 9AX, United Kingdom Abstract This article reports
22nd-26th February th International Wind Workshop Tokyo, Japan
New developments in the High Resolution Winds Product (HRW), at the Satellite Application Facility on support to Nowcasting and Very short range forecasting (NWCSAF) 22nd-26th February 2010 10th International
STATUS OF JAPANESE METEOROLOGICAL SATELLITES AND RECENT ACTIVITIES OF MSC
STATUS OF JAPANESE METEOROLOGICAL SATELLITES AND RECENT ACTIVITIES OF MSC Daisaku Uesawa Meteorological Satellite Center, Japan Meteorological Agency Abstract MTSAT-1R is the current operational Japanese
Cloud Top Height Product: Product Guide
Cloud Top Height Product: Product Guide Doc.No. Issue : : EUM/TSS/MAN/14/786420 v1a EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 21 August 2015
Impact Studies of Higher Resolution COMS AMV in the Operational KMA NWP System. Jung-Rim Lee 1
13 th International Winds Workshop (Monterey, CA, 30 June 2016) Impact Studies of Higher Resolution COMS AMV in the Operational KMA NWP System Jung-Rim Lee 1 Hyun Cheol Shin 1, Sangwon Joo 1, YoonJae Kim
Rosemary Munro*, Graeme Kelly, Michael Rohn* and Roger Saunders
ASSIMILATION OF METEOSAT RADIANCE DATA WITHIN THE 4DVAR SYSTEM AT ECMWF Rosemary Munro*, Graeme Kelly, Michael Rohn* and Roger Saunders European Centre for Medium Range Weather Forecasts Shinfield Park,
SEVIRI - Cloud Top Height Factsheet
Doc.No. : EUM/OPS/DOC/11/4199 Issue : v2a Date : 28 December 2011 WBS : EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 http://www.eumetsat.int EUMETSAT
Assimilation of Himawari-8 Atmospheric Motion Vectors into the Numerical Weather Prediction Systems of Japan Meteorological Agency
Assimilation of Himawari-8 Atmospheric Motion Vectors into the Numerical Weather Prediction Systems of Japan Meteorological Agency Koji Yamashita Japan Meteorological Agency kobo.yamashita@met.kishou.go.jp,
DIAGNOSING OBSERVATION ERROR STATISTICS FOR NUMERICAL WEATHER PREDICTION
DIAGNOSING OBSERVATION ERROR STATISTICS FOR NUMERICAL WEATHER PREDICTION J. Waller, S. Dance, N. Nichols (University of Reading) D. Simonin, S. Ballard, G. Kelly (Met Office) 1 AIMS 2 OBSERVATION ERRORS
Instrument Calibration Issues: Geostationary Platforms
Instrument Calibration Issues: Geostationary Platforms Ken Holmlund EUMETSAT kenneth.holmlund@eumetsat.int Abstract The main products derived from geostationary satellite data and used in Numerical Weather
CURRENT STATUS OF EUMETSAT OPERATIONAL WINDS. Ken Holmlund, R. Borde, M. Carranza, and O. Hautecoeur
CURRENT STATUS OF EUMETSAT OPERATIONAL WINDS Ken Holmlund, R. Borde, M. Carranza, and O. Hautecoeur Slide: 1 IWW12, Copenhagen, Denmark, 16-20 June 2014 Content ü Geostationary Satellite status MTP/MSG
QUALITY OF MPEF DIVERGENCE PRODUCT AS A TOOL FOR VERY SHORT RANGE FORECASTING OF CONVECTION
QUALITY OF MPEF DIVERGENCE PRODUCT AS A TOOL FOR VERY SHORT RANGE FORECASTING OF CONVECTION C.G. Georgiev 1, P. Santurette 2 1 National Institute of Meteorology and Hydrology, Bulgarian Academy of Sciences
Current Status of COMS AMV in NMSC/KMA
Current Status of COMS AMV in NMSC/KMA Eunha Sohn, Sung-Rae Chung, Jong-Seo Park Satellite Analysis Division, NMSC/KMA soneh0431@korea.kr COMS AMV of KMA/NMSC has been produced hourly since April 1, 2011.
EUMETSAT PLANS. Dr. K. Dieter Klaes EUMETSAT Am Kavalleriesand 31 D Darmstadt Germany
EUMETSAT PLANS Dr. K. Dieter Klaes EUMETSAT Am Kavalleriesand 31 D-64295 Darmstadt Germany Page 1 EUMETSAT SATELLITE PROGRAMMES 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 METEOSAT
Introduction to the NWC SAF
Introduction to the NWC SAF NWC SAF Event Week EUMeTrain course 18-22 November, online P. Fernández 1 Where are you listening from? Contents SAF Network scope NWC SAF Overview Products Next versions 2014
ERA-CLIM2-WP3: Satellite data reprocessing and inter-calibration
ERA-CLIM2-WP3: Satellite data reprocessing and inter-calibration Rob Roebeling, Jörg Schulz, Marie Doutriaux- Boucher, Alessio Lattanzio, Viju John, and Andrey Bogdanov Thanks to: Christian Marquardt and
COMPARISON OF THE OPTIMAL CLOUD ANALYSIS PRODUCT (OCA) AND THE GOES-R ABI CLOUD HEIGHT ALGORITHM (ACHA) CLOUD TOP PRESSURES FOR AMVS
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA COMPARISON OF THE OPTIMAL CLOUD ANALYSIS PRODUCT (OCA) AND THE GOES-R ABI CLOUD HEIGHT ALGORITHM
AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM
AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM Koji Yamashita Japan Meteorological Agency / Numerical Prediction Division 1-3-4, Otemachi, Chiyoda-ku,
Nowcasting of Severe Weather from Satellite Images (for Southern
Nowcasting of Severe Weather from Satellite Images (for Southern Europe) Petra Mikuš Jurković Forecasting/ nowcasting of convective storms NWP models cannot well predict the exact location and intesity
OBJECTIVE USE OF HIGH RESOLUTION WINDS PRODUCT FROM HRV MSG CHANNEL FOR NOWCASTING PURPOSES
OBJECTIVE USE OF HIGH RESOLUTION WINDS PRODUCT FROM HRV MSG CHANNEL FOR NOWCASTING PURPOSES José Miguel Fernández Serdán, Javier García Pereda Servicio de Técnicas de Análisis y Predicción, Servicio de
RECENT ADVANCES TO EXPERIMENTAL GMS ATMOSPHERIC MOTION VECTOR PROCESSING SYSTEM AT MSC/JMA
RECENT ADVANCES TO EXPERIMENTAL GMS ATMOSPHERIC MOTION VECTOR PROCESSING SYSTEM AT MSC/JMA Ryoji Kumabe 1, Yoshiki Kajino 1 and Masami Tokuno 2 1 Meteorological Satellite Center, Japan Meteorological Agency
EUMETSAT products and services for monitoring storms - New missions, more data and more meteorological products
EUMETSAT products and services for monitoring storms - New missions, more data and more meteorological products Jochen Grandell 1 EUM/RSP/VWG/17/921460 Outline Overview of EUMETSAT missions Current...and
High Latitude Satellite Derived Winds from Combined Geostationary and Polar Orbiting Satellite Data
High Latitude Satellite Derived Winds from Combined Geostationary and Polar Orbiting Satellite Data Brett Hoover Dave Santek, Matt Lazzara, Rich Dworak, Jeff Key, Chris Velden, and Nick Bearson 11 th International
Masahiro Kazumori, Takashi Kadowaki Numerical Prediction Division Japan Meteorological Agency
Development of an all-sky assimilation of microwave imager and sounder radiances for the Japan Meteorological Agency global numerical weather prediction system Masahiro Kazumori, Takashi Kadowaki Numerical
Assimilation of Himawari-8 data into JMA s NWP systems
Assimilation of Himawari-8 data into JMA s NWP systems Masahiro Kazumori, Koji Yamashita and Yuki Honda Numerical Prediction Division, Japan Meteorological Agency 1. Introduction The new-generation Himawari-8
EUMETSAT current and future plans on product generation and dissemination
EUMETSAT current and future plans on product generation and dissemination Ken Holmlund holmlund@eumetsat.de EUMETSAT SATELLITE PROGRAMMES METEOSAT 0 Service IODC Rapid Scan MSG MSG-1 MSG-2 MSG-3 MSG-4
Status report on current and future satellite systems by EUMETSAT Presented to CGMS-44, Plenary session, agenda item D.1
Status report on current and future satellite systems by EUMETSAT Presented to CGMS-44, Plenary session, agenda item D.1 CGMS-44-EUMETSAT-WP-19.ppt, version 1 (# 859110), 8 June 2016 MISSION PLANNING YEAR...
REPROCESSING OF ATMOSPHERIC MOTION VECTORS FROM METEOSAT IMAGE DATA
REPROCESSING OF ATMOSPHERIC MOTION VECTORS FROM METEOSAT IMAGE DATA Jörgen Gustafsson, Leo van de Berg, Fausto Roveda, Ahmet Yildirim Meteorological Operations Division, EUMETSAT Am Kavalleriesand 31,
RGB Experts and Developers Workshop - Introduction Tokyo, Japan 7-9 Nov 2017
RGB Experts and Developers Workshop - Introduction Tokyo, Japan 7-9 Nov 2017 Workshop Objectives Review of existing RGB standards Reconfirm and extend existing standards (new multi-spectral imagers) Stimulate
CONSTRUCTION OF CLOUD TRAJECTORIES AND MOTION OF CIRRUS CLOUDS AND WATER VAPOUR STRUCTURES
CONSTRUCTION OF CLOUD TRAJECTORIES AND MOTION OF CIRRUS CLOUDS AND WATER VAPOUR STRUCTURES André SZANTAI +, Michel DESBOIS +, Laurence PICON +, Henri LAURENT *, Françoise DESALMAND + + Laboratoire de Météorologie
Impact of hyperspectral IR radiances on wind analyses
Impact of hyperspectral IR radiances on wind analyses Kirsti Salonen and Anthony McNally Kirsti.Salonen@ecmwf.int ECMWF November 30, 2017 Motivation The upcoming hyper-spectral IR instruments on geostationary
USE, QUALITY CONTROL AND MONITORING OF SATELLITE WINDS AT UKMO. Pauline Butterworth. Meteorological Office, London Rd, Bracknell RG12 2SZ, UK ABSTRACT
USE, QUALITY CONTROL AND MONITORING OF SATELLITE WINDS AT UKMO Pauline Butterworth Meteorological Office, London Rd, Bracknell RG12 2SZ, UK ABSTRACT Satellite wind fields derived from geostationary imagery
Assessment of new AMV data in the ECMWF system: First year report
EUMETSAT/ECMWF Fellowship Programme Research Report No. 43 Assessment of new AMV data in the ECMWF system: First year report K. Lean, N. Bormann and K. Salonen January 2017 Series: EUMETSAT/ECMWF Fellowship
Atmospheric Motion Vectors: Past, Present and Future
Atmospheric Motion Vectors: Past, Present and Future Mary Forsythe Met Office, Exeter, United Kingdom ABSTRACT Atmospheric motion vectors (AMVs) are derived by tracking clouds or areas of water vapour
AMVS: PAST PROGRESS, FUTURE CHALLENGES
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA AMVS: PAST PROGRESS, FUTURE CHALLENGES Mary Forsythe, James Cotton, Francis Warrick Met Office, FitzRoy
DIAGNOSING OBSERVATION ERROR STATISTICS FOR NUMERICAL WEATHER PREDICTION
DIAGNOSING OBSERVATION ERROR STATISTICS FOR NUMERICAL WEATHER PREDICTION J. Waller, S. Dance, N. Nichols (University of Reading) D. Simonin, S. Ballard, G. Kelly (Met Office) EMS Annual Meeting: European
COMPARISON OF AMV HEIGHT ASSIGNMENT BIAS ESTIMATES FROM MODEL BEST-FIT PRESSURE AND LIDAR CORRECTIONS
Proceedings for the 13th International Winds Workshop 27 June - 1 July 216, Monterey, California, USA COMPARISON OF AMV HEIGHT ASSIGNMENT BIAS ESTIMATES FROM MODEL BEST-FIT PRESSURE AND LIDAR CORRECTIONS
EUMETSAT GPRC Report. Tim Hewison, Marianne Koenig, Sebastien Wagner, Rob Roebeling, Peter Miu, Jörg Schulz, Harald Rothfuss EUMETSAT
EUMETSAT GPRC Report Tim Hewison, Marianne Koenig, Sebastien Wagner, Rob Roebeling, Peter Miu, Jörg Schulz, Harald Rothfuss EUMETSAT Slide: 1 Overview Satellite Status GEO LEO IR Products for current Meteosats
IMPACTS OF SPATIAL OBSERVATION ERROR CORRELATION IN ATMOSPHERIC MOTION VECTORS ON DATA ASSIMILATION
Proceedings for the 13 th International Winds Workshop 27 June - 1 July 2016, Monterey, California, USA IMPACTS OF SPATIAL OBSERVATION ERROR CORRELATION IN ATMOSPHERIC MOTION VECTORS ON DATA ASSIMILATION
Applications of multi-spectral satellite data
Applications of multi-spectral satellite data Jochen Kerkmann EUMETSAT, Satellite Meteorologist, Training Officer Adjusted by E de Coning South African Weather Service Content 1. Why should we use RGBs?
HIGH SPATIAL AND TEMPORAL RESOLUTION ATMOSPHERIC MOTION VECTORS GENERATION, ERROR CHARACTERIZATION AND ASSIMILATION
HIGH SPATIAL AND TEMPORAL RESOLUTION ATMOSPHERIC MOTION VECTORS GENERATION, ERROR CHARACTERIZATION AND ASSIMILATION John Le Marshall Director, JCSDA 2004-2007 CAWCR 2007-2010 John Le Marshall 1,2, Rolf
Tracer quality identifiers for accurate Cloud Motion Wind
Abstract for the Workshop on Wind extraction from operational Meteorological Satellite data Washington DC, 17-19 September 1991 Tracer quality identifiers for accurate Cloud Motion Wind estimates Kenneth
OPERATIONAL USE OF METEOSAT-8 SEVIRI DATA AND DERIVED NOWCASTING PRODUCTS. Nataša Strelec Mahović
OPERATIONAL USE OF METEOSAT-8 SEVIRI DATA AND DERIVED NOWCASTING PRODUCTS Nataša Strelec Mahović Meteorological and Hydrological Service Grič 3, 10 000 Zagreb, Croatia strelec@cirus.dhz.hr ABSTRACT Meteosat-8
DESCRIPTION AND VALIDATION RESULTS OF THE HIGH RESOLUTION WIND PRODUCT FROM HRVIS MSG CHANNEL, AT EUMETSAT NOWCASTING SAF (SAFNWC)
DESCRIPTION AND VALIDATION RESULTS OF THE HIGH RESOLUTION WIND PRODUCT FROM HRVIS MSG CHANNEL, AT EUMETSAT NOWCASTING SAF (SAFNWC) Javier García Pereda, José Miguel Fernández Serdán Servicio de Teledetección,
Arctic Weather Every 10 Minutes: Design & Operation of ABI for PCW
Arctic Weather Every 10 Minutes: Design and Operation of ABI for PCW Dr. Paul C. Griffith and Sue Wirth 31st Space Symposium, Technical Track, Colorado Springs, Colorado This document is not subject to
Operational Generation And Assimilation Of Himawari-8. Atmospheric Motion Vectors
Operational Generation And Assimilation Of Himawari-8 Atmospheric Motion Vectors John Le Marshall 1, David Howard 1, Xingbao Wang 1, Yi Xiao 1, Jim Jung 2, Jaime Daniels 4,Steve Wanzong 2, Joan Fernon
Simulated MSG SEVIRI Imagery from HARMONIE-AROME
Simulated MSG SEVIRI Imagery from HARMONIE-AROME Angeles Hernandez, AEMET Joint 28th ALADIN Workshop & HIRLAM All-Staff Meeting 2018 - Toulouse, 16-20 Apr 2018 Outline 1. Simulated Satellite Images (SSI)
EUMETSAT Satellite Status
EUMETSAT Satellite Status Dr. K. Dieter Klaes EUMETSAT 1 ET-SAT Meeting 4-6 April 2017, WMO, Geneva, Switzerland EUMETSAT is an intergovernmental organisation with 30 Member States and 1 Cooperating State
THE EUMETSAT MULTI-SENSOR PRECIPITATION ESTIMATE (MPE)
THE EUMETSAT MULTI-SENSOR PRECIPITATION ESTIMATE (MPE) Thomas Heinemann, Alessio Lattanzio and Fausto Roveda EUMETSAT Am Kavalleriesand 31, 64295 Darmstadt, Germany ABSTRACT The combination of measurements
EUMETSAT GPRC Report. Jo Schmetz Tim Hewison, Sebastien Wagner, Rob Roebeling, Peter Miu, Simon Elliot, Harald Rothfuss, Bartolomeo Viticchie EUMETSAT
EUMETSAT GPRC Report Jo Schmetz Tim Hewison, Sebastien Wagner, Rob Roebeling, Peter Miu, Simon Elliot, Harald Rothfuss, Bartolomeo Viticchie EUMETSAT Slide: 1 Overview Satellite Status Calibration Support
ASSESSING THE QUALITY OF HISTORICAL AVHRR POLAR WIND HEIGHT ASSIGNMENT
ASSESSING THE QUALITY OF HISTORICAL AVHRR POLAR WIND HEIGHT ASSIGNMENT Richard Dworak + and Jeff Key* + Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin Madison,