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The Future of Meteosat

The Future of Meteosat. Johannes Schmetz EUMETSAT Darmstadt, Germany. Content:. EUMETSAT Overview Current Meteosat system - products and rapid scan service Meteosat Second Generation (MSG) - The satellite - Performance and capabilities

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The Future of Meteosat

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  1. The Future of Meteosat Johannes Schmetz EUMETSAT Darmstadt, Germany

  2. Content: • EUMETSAT Overview • Current Meteosat system - products and rapid scan service • Meteosat Second Generation (MSG) - The satellite - Performance and capabilities - Applications and products - Satellite Application Facilities (SAF) • Toward the Post-MSG era

  3. EUMETSAT Member States 17 Member States 3 Cooperating States Hungary, Poland and Slovakia are Cooperating States of EUMETSAT

  4. EUMETSAT OBJECTIVES THE INITIAL CONVENTION: "The primary objective ... is to establish, maintain and exploit European systems of operational meteorological satellites...." THE NEW CONVENTION: "A further objective ... is to contribute to the operational monitoring of the climate and the detection of global climate change.."

  5. 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 0° Service M-5 M-6 M-7 Approved Programme/Service 0° Standby M-6 M-5 M-6 Expected Lifetime IODC M-5 Planned Rapid Scan M-6 MSG 0° Service MSG-1 MSG-2 MSG-3 MSG-4 EPS Polar Orbit Service Metop-1 Metop-2 Metop-3

  6. The current Meteosat: • Satellite • Products • Rapid scan service

  7. Meteosat Imager Definition of the imager channels

  8. Water Vapour Image

  9. Meteosat Meteorological Products Operational products available in near real-time • Clear Sky Radiances • Clear Sky Water Vapour Winds • Climate Data Set • Cloud Analysis • Cloud Motion Winds • Cloud Top Height • High Resolution Visible Winds • Sea Surface Temperatures • Upper Tropospheric Humidity All of the above are generated between 1 and 48 times each day on an operational basis. The Climate Data Set is stored for research use. The other products are distributed to users immediately after processing.

  10. Meteosat Climate Products ISCCP & GPCP • International Satellite Cloud Climatology Project • Clouds described by 80 parameters • Each 3 hours, in 2.5° latitude/longitude intervals • Global record since 1983 • Global Precipitation Climatology Project • Estimates of monthly precipitation totals • In 1° latitude/longitude intervals • Global record since 1986

  11. Performance of Meteosat-7 Winds: ECMWF Monitoring

  12. EUMETSAT's Satellite Coverage and Indian Ocean Data Coverage 60 N 0 Meteosat-7 Prime Position 0° Longitude Meteosat-5 IODC Position 63°E 60 S 80 160 140 120 100 80 60 40 20 0 20 40 60 100 120 140 160 180

  13. EUMETSAT’s Rapid Scan Service • Resulting from at request to support the Mesoscale Alpine Project (MAP) in September 1999 the backup spacecraft Meteosat-6 was configured to conduct a series of rapid scan operations. • Initially the rapid scan area was covering the Alpine region in 5 minute intervals. • In 2000 the scanned area was increased significantly and the repeat cycle fixed to 10 minute intervals to allow for the start of the operational Rapid Scan Service by middle of 2001.

  14. Examples from the pre-operational Phase • Eclipse 1999 • MAP

  15. Total Eclipse over Europe 11 August 1999 (10-Minute Scans by Meteosat-6)

  16. METEOSAT-6 RAPID SCANNING AT 5 MIN INTERVALS MAP - Area / 18 June 1999

  17. Rapid Scan Area during the operational Phase Europe Format for GIF files

  18. Meteosat Second Generation (MSG) • MSG System • Satellite performance • Products and examples • Satellite Application Facilities (SAF) • Post MSG User Consultation Process

  19. Meteosat versus MSG • Meteosat First • Generation (MOP/MTP) • 3-channel Imaging • Radiometer • 100 RPM Spin-stabilised • Body • Solid Apogee Boost • Motor • 5 years Station Keeping • 200 Watts Power • Demand • 720 kg in GTO orbit • Flight qualified with • Delta 2914, Ariane 1, 3, 4 • Meteosat Second • Generation (MSG) • 12-channel Enhanced • Imaging Radiometer • 100 RPM Spin-stabilised • Body • Bi-propellant Unified • Propulsion System • > 7 years Station Keeping • 600 Watts Power • Demand • 2000 kg in GTO orbit • Design compatibility with • Ariane 4 and 5, Atlas 1

  20. E xternal S upport G round S Stations PGS Station ( ) SAF ( ) BGS Station ( ) MSG System (from 2003) Operational Standby MSG MSG Processed Images meteorological satellites Data from other and other data Monitoring Raw & Processed Images and other data HRIT Satellite Control Data Collection Satellite Control (Back-up) System Reports LRIT Satellite Applications High Rate Primary Ground Facilities User Station Back-up Low Rate (HRUS) Ground User Station Data Collection (LRUS) Platforms (DCP) EUMETSAT Control & Processing Centre Darmstadt

  21. Scanning for MSG from South to North

  22. MSG sampling distance on ground 3.1 km 4 km 5 km 6 km 8 km 10 km 12 km > 12 km

  23. SEVIRI INSTRUMENT

  24. MSG-1 SEVIRI Calibration Performance

  25. SEVIRI Channels Weighting Functions

  26. MSG Coverage For HRV For all channels except HRV MSG MPEF products within 65° angle around subsatellite point

  27. Meteorological Product Extraction Facility (MPEF) • MPEF is part of Application Ground Segment (AGS) • other part are Satellite Application Facilities (SAF) • Generally MPEF products at synoptic scale (better than 100 km) • Important for MPEF design: • Evolution of the MPEF algorithms and products • Flexibility to add new algorithms and products (“plug-in approach”)

  28. Products generated by MPEF (1) • Atmospheric Motion Vectors (AMV) • Calibration support/monitoring (CLM) • Clear Sky Radiances (CSR) • Cloud Analysis (CLA) • Cloud Top Height (CTH) • Cloud mask (archived)

  29. Products generated the MPEF (2) • Tropospheric Humidity (UTH, MTH) • Climate Data Set (CDS) • ISCCP Data Set (IDS) • High Resolution Precipitation Index (HPI) • Global Instability Index (GII) - experimental - • Total ozone - experimental -

  30. Cloud processing in the MSG MPEF: • divided into two parts: 1) Scenes Analysis (SCE, derives a pure cloud mask), 2) Cloud Analysis (CLA, derives cloud parameters) • based on known threshold techniques (Lutz, 2000) • SCE and CLA are derived on pixel basis and for each repeat cycle

  31. Cloud processingCloud Analysis (CLA) - Description Cloud parameter Derives on a pixel basis: - the cloud phase (unknown, water, ice, mixed) - the cloud top height information (cloud top pressure, cloud top temperature, effective cloud amount) - the semi-transparency flag - the cloud type (10 different categories) In the near future it is foreseen to include other cloud parameters (e.g. cloud optical thickness)

  32. MSG MPEF cloud processing consists of two steps: Scenes Analysis (SCE) and Cloud Analysis (CLA) SCE is based on a multispectral threshold technique (Saunders and Kriebel, 1988) with 34 tests CLA provides: top pressure, top temperature, effective cloud amount, phase, type (fog, cirrus, St type, Cu type, flag for semitransparency) MSG MPEF cloud coverage from Cloud Analysis

  33. 215 220 225 230 235 240 245 250 255 260 265 K Water vapour clear-sky radiance product

  34. UTH based on clear-sky WV radiances Mean layer relative humidity between about 600 and 300/250 hPa for areas of about 100 km x 100 km Physical retrieval based on radiative model (Schmetz and Turpeinen, 1988) Local regression: log(UTH/cos Θ) = a + b TWV (Soden and Bretherton, 1993) MSG MPEF Upper Tropospheric Humidity (UTH)

  35. Global Instability Index (GII) based on two Methods • Statistical Retrieval: uses a neural network and radiosonde training dataset • Physical Retrieval: tries to retrieve an actual temperature and humidity profile • Both methods are based on the SEVIRI brightness temperatures in 6 channels (6.2 m, 7.3 m, 8.7 m, 10.8 m, 12.0 m, 13.4 m) • In prototyping with GOES-8 data 8.7 µm is replaced by 7.0 µm)

  36. Advantages and Disadvantages Statistical Method Physical Method • computationally fast • easy to implement • new indices cannot be added without retraining • training confined to a certain region and satellite • method fails to reproduce extreme cases of instability • Sound physical foundation • inclusion of further indices is straightforward • applicable to any geographic region and any satellite • computationally slow (factor of ~50) • not easy to implement

  37. Lifted Index • MSG Prototyping using GOES data • Upper: Physical retrieval • Lower: Neural network retrieval

  38. Total Ozone Product: left: Optimum estimation (R. Engelen, 2000) right: MSG prototype regression algorithm (Karcher, 1998) GOES-8 data were used (R. Engelen, 2000) Regression noisy due to the use of the very noisy channels 1 and 2 (stratospheric and upper-tropospheric temperatures).

  39. Atmospheric Motion VectorRetrieval • Tracking channels • IR10.8, WV6.2, WV7.3, VIS0.6, VIS0.8 • OZ9.7, IR3.9, HRVIS • Resolution • 50 km, every 15 min., rapid scans • Height Assignment • IR EBBT, IR/WV semitr.-corr.,CO2-ratioing, cloud base

  40. Final AMV product • Automatic Quality Control • Normalised Quality Indicators • Combination of n previous intermediate fields • linear average (speed, direction, location) • linear average of corrected height • Dissemination • Hourly • ’All’ vectors

  41. The SAF Concept Theoverall objectiveof a SAF is theprovision of operational services, in the context of a cost-effective and synergetic balance between the central and distributed services. The SAF services will be an integral part of the overall EUMETSAT operational services.

  42. Consortia for SAF Development SAF Host Institute Partners NWC&VSRFINM Météo France, SMHI, ZAMG O&SIMétéo France KNMI, IFREMER, DMI, DNMI, SMHI O3MFMI KNMI, DLR, DMI, MF, LAP, HNMS, RMIB, DWD CLMDWD RMIB, KNMI, SMHI, BSH, GKSS, FMI, VUB NWPMet. Office ECMWF, KNMI, Météo France GRASDMI UKMO, IEEC LSAIM RMIB, MF, SMHI, IMK, BfG, IATA, FMA, ICAT, UE, UV, UB, UA

  43. SAF Deliverables Type ADistribution of user software packages for operational applications or local data processing. Type BOff line product services, including off line production, archiving and distribution Type CReal Time product services.

  44. GERB (Geostationary Earth Radiation Budget) Instrument • WAVEBANDS: 0.32 µm - 4.0 µm, 0.32 µm - 30 µm By subtraction: 4.0 µm - 30 µm • RADIOMETRY: Shortwave absolute accuracy: < 2.4 Wm-2 ster-1 (i.e. <1%) Longwave absolute accuracy: < 0.4 Wm-2 ster-1 (ie <0.5%) • PIXEL SIZE: 44.6 km x 39.3 km (NS x EW) at nadir • CYCLE TIME: Full Earth disc, both channels in 5 min • CO-REGISTRATION: Spatial: 3 km wrt SEVIRI at satellite sub-point

  45. Toward the next generation of geo satellites: Post-MSG User Consultation 2001 - 2003 • Two Application Expert groups: a) Numerical Weather prediction (NWP) b) Nowcasting and Very Short-range Forecasting • Two phases: 1) ESTABLISHMENT/ENDORSEMENT OF USER REQUIREMENTS (technology free) 2) SELECTION OF A LIMITED NUMBER OF MISSION CONCEPTS FOR PHASE 0/A

  46. GLOBAL NWP and REGIONAL NWP requirements up to 2025 as horizon • both addressing: • Improvements in Products from NWP • Improvements in NWP Systems • Contribution of Satellite Observing Systems to Meeting Future • Observational Requirements • Contribution of Geostationary Satellites • Nowcasting and Very Short Forecasting Requirements up to 2025 as horizon • (address convective and non-convective conditions) • Start from SERVICE REQUIREMENTS (evolution up to 2025) • Identify PHENOMENA involved (where appropriate) • Identify related OBSERVABLES • REQUIREMENTS (x,y,z,t, timeliness) related to breakthrough level • Determine whether required as input to NWP • Identify CANDIDATE OBSERVING METHODS

  47. Conclusions: • The near future of the current Meteosat: An operational rapid scan service • Meteosat Second Generation (MSG) launch in mid 2002 • MSG provides continuity for current Meteosat users • Advanced capabilities and new products from MSG • Broad basis for full utilisation through distributed Applications Ground Segment with currently seven Satellite Application Facilities • MSG is significant upgrade of space component of Global Observing System

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