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The Global Observing System Stephen English

The Global Observing System Stephen English With material kindly provided by Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen European Centre for Medium-Range Weather Forecasts. Role of observations.

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The Global Observing System Stephen English

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  1. The Global Observing System Stephen English With material kindly provided by Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen European Centre for Medium-Range Weather Forecasts

  2. Role of observations Observations limit error growth and make forecasting possible…. Every 12 hours we assimilate ~7,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere. We monitor an additional 12,000,000. SEVIRI 6.2 µm RMS error (m) Time (hours)

  3. The state space MASS (temperature, pressure…) Radiosondes, surface observations, satellite sounders, aircraft MOISTURE (humidity, clouds, precipitation…) Radiosondes, surface observations, satellite sounders and imagers, aircraft, radar, lidar DYNAMICS (wind, vorticity, convergence…) Radiosondes, surface observations, satellite imagers, satellite scatterometer/radar/lidar, aircraft COMPOSITION (ozone, aerosol…) Ozone sondes, surface observations, satellite sounders SURFACE (surface type, temperature, moisture, homogeneity…) Satellite active and passive systems, surface observations

  4. Composition Ozone sondes Air quality stations Mass Moisture Soil moisture Rain gauge Radiosonde Synop Ship Aircraft Buoys Profilers Wind

  5. Data sources: Conventional

  6. What types of satellites are used in NWP? Advantages Disadvantages GEO - Regional coverage No global coverage by single satellite - Temporal coverage LEO- Global coverage with single satellite

  7. Composition Ultraviolet sensors Sub-mm, and near IR plus Visible (e.g. Lidar) Polar IR + MW sounders Mass Radar and GPS total path delay Moisture Radio occultation Geo IR Sounder Geo IR and Polar MW Imagers Feature tracking in imagery (e.g. cloud track winds), scatterometers and doppler winds IR = InfraRed MW = MicroWave Wind

  8. Metop

  9. Metop

  10. Example of conventional data coverage Aircraft – AMDAR (note also have Airep and ACARs) Buoy Surface (synop) - ship Balloon profiles e.g. radiosondes

  11. Example of 6-hourly satellite data coverage LEO Sounders LEO Imagers Scatterometers GEO imagers GPS Radio Occultation Satellite Winds (AMVs) 30 March 2012 00 UTC

  12. Combined impact of all satellite data • EUCOS Observing System Experiments (OSEs): • 2007 ECMWF forecasting system, • winter & summer season, • different baseline systems: • no satellite data (NOSAT), • NOSAT + AMVs, • NOSAT + 1 AMSU-A, • general impact of satellites, • impact of individual systems, • all conventional observations. •  500 hPa geopotential height anomaly correlation 3/4 day 3 days

  13. User requirements and satellite data: OSCAR www.wmo-sat.info • Vision for the GOS in 2025 adopted June 2009 • GOS user guide WMO-No. 488 (2007) • Manual of the GOS WMO-No. 544 (2003) (updated for ET-SAT Geneva April 2012)

  14. Examples questions we use Data Assimilation techniques to study: • Would it be beneficial for the Chinese FY3 program to move to the “early morning orbit” with the Europeans occupying the “morning orbit” and the Americans the “afternoon orbit”? • Preparation for future instruments such as lidar and radar (EarthCARE). • Study using Ensemble of Data Assimilations to estimate the number of GPSRO soundings needed in future (discuss with Sean Healy if interested). Using DA to help design the GOS

  15. 2009 ExperimentsEnza Di Tomaso* and Niels Bormann MetOp-A AM Early AM PM NOAA-17 T i m e NOAA-16 NOAA-15 NOAA-18 NOAA-19 Aqua

  16. FY3 orbit: what is the optimal orbit configuration? “NOAA-15 experiment” *MetOp-A * NOAA-18 * NOAA-15 “two-satellite experiment” *MetOp-A * NOAA-18 “NOAA-19 experiment” *MetOp-A * NOAA-18 * NOAA-19

  17. 3.5 months 107 cases CY36R1 T511 “no-MW sounder experiment” GOOD “two-”, “three-”, “all-satellite experiment” GOOD Are 3 satellites better than 2? YES two-satellite RMSE – no-Mw sounder RMSE three-satellite RMSE – no-Mw sounder RMSE all-satellite RMSE – no-Mw sounder RMSE

  18. Do orbital positions matter? YES NOAA-19 experiment GOOD NOAA-15 experiment GOOD RMS difference forecast – analysis for NOAA-15 and NOAA-19 experiments

  19. Baseline 1: microwave only (NPP + METOP-A) • Baseline 2: microwave + infrared (NPP + METOP-A) 2012 experiments

  20. 3 months 90 cases CY38R1 T511 Microwave + infrared baseline Microwave only baseline pm better NH NH Early am better SH SH

  21. -24 – -21 -21 – -18 -18 – -16 -16 – -12 -12 – -9 -9 – -6 -6 – -3 -3 – 0 0 – 3 3 – 6 6 – 9 9 – 12 12 – 15 15 – 18 Preparing for future missions e.g. Aeolus and EarthCARE • 1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) Model First-Guess Observation Analysis 23

  22. Combining NWP with CTM models and data assimilation systems New requirements in GOS for atmospheric composition

  23. Monitoring of observations • Webpages • Automatic warnings • Collaboration between users and providers • J = ½(y-H(x))TR-1(y-H(x)) + Jb • At beginning and end of minimisation, with and without QC, plus bias corrections.

  24. Email-alert Email alert: Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier) Hard limits (fixed) Data monitoring – automated warnings http://www.ecmwf.int/products/forecasts/satellite_check/ Selected statistics are checked against an expected range. E.g., global mean bias correction for GOES-12 (in blue): (M. Dahoui & N. Bormann)

  25. Data monitoring – automated warnings

  26. Satellite data monitoring Data monitoring – automated warnings

  27. Global Observing System is essential to weather forecasting Technology driven….a more integrated approach now? Mass is well observed. Moisture – satellite observations are data rich but poorly exploited. Radar and lidar will become more important. Dynamics – even wind observations are scarce. Composition – NWP techniques have been successfully extended to environmental analysis and prediction but more observations are needed. Surface – DA for surface fields is being attempted.

  28. Thank you for your attention Thanks again to Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen

  29. Backup slides Detailed list of instruments for NWP and atmospheric composition (not shown but included for information)

  30. Sun-Synchronous Polar Satellites

  31. Sun-Synchronous Polar Satellites (2) Non Sun-Synchronous Observations

  32. Data sources: Geostationary Satellites

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