The Global Observing System Stephen English and colleagues (with special thanks to Peter Bauer )

1. The Global Observing System Stephen English and colleagues (with special thanks to Peter Bauer ) European Centre for Medium-Range Weather Forecasts

2. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

3. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

4. Role of observations Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere. 500hPa height, NH 1990 2000 SEVIRI 6.2 µm 2010 RMS error (m) Forecast lead time (days) Time (hours) From C Lupu and E.Kallen

5. From E. Kallen

6. Data sources: Conventional

7. Example of conventional data coverage Aircraft – AMDAR Buoy Synop - ship Temp

8. What types of satellites are used in NWP? Advantages Disadvantages GEO - large regional coverage - no global coverage by single satellite - very high temporal resolution - moderate spatial resolution (VIS/IR) > short-range forecasting/nowcasting > 5-10 km for VIS/IR > feature-tracking (motion vectors) > much worse for MW > tracking of diurnal cycle (convection) LEO- global coverage with single satellite - low temporal resolution - high spatial resolution >best for NWP! From P. Bauer

9. Sun-Synchronous Polar Satellites

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

11. ECMWF support to EUMETSAT – LEO constellation MetOp-A FY-3A DMSP F18 DMSP F16 T i m e Coriolis DMSP F17 NOAA-15 NOAA-18 FY-3B NOAA-19 + NPP Aqua • Characterise the benefit of having ATOVS data from three evenly-spaced orbits versus data from a less optimal coverage for NWP ECWMF/EUMETSAT Bilateral Meeting 03/2012 SE

12. Data sources: Geostationary Satellites

13. 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

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

15. Composition UV Sub-mm VIS+NIR Lidar Limb- sounders Polar IR + MW sounders Mass Radar GPS ZPD Moisture GPSRO Geo IR Sounder Geo IR and Polar MW Imagers AMVs Scatterometers Wind lidar Wind

16. Satellite data used by ECMWF

18. User requirements http://www.wmo-sat.info/db/ • Vision for the GOS in 2025 adopted June 2009 • GOS user guide WMO-No. 488 (2007) • Manual of the GOS WMO-No. 544 (2003) (Update of satellite section being prepared for ET-SAT Geneva April 2012)

19. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

20. 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 From P. Bauer

21. Impact of microwave sounder data in NWP: Met Office OSEs • 2003 OSEs: • N-15,-16 and -17 AMSU • N-16 & N-17 HIRS • AMVs • Scatterometer winds • SSM/I ocean surface wind speed • Conventional observations • 2007 OSEs: • N-16, N-18, MetOp-2 AMSU • SSMIS • AIRS & IASI • Scatterometer winds • AMVs • SSM/I ocean surface wind speed • Conventional observations (From W. Bell)

22. State at analysis time Forecast sensitivity: max. 48 hours State at time i State at initial time NWP model Sensitivity of cost to observations Sensitivity of cost to change at initial time AD of forecast model Cost function J Analysis Advanced diagnostics Data assimilation: max. 12 hours State at time i Observation operator Observation simulations State at initial time NWP model Sensitivity of cost to change in state at time i Sensitivity of cost to change at initial time AD of forecast model AD of observation operator Cost function J Observations From P. Bauer

23. Advanced diagnostics Relative FC error reduction per system The forecast sensitivity (Cardinali, 2009, QJRMS, 135, 239-250) denotes the sensitivity of a forecast error metric (dry energy norm at 24 or 48-hour range) to the observations. The forecast sensitivity is determined by the sensitivity of the forecast error to the initial state, the innovation vector, and the Kalman gain. Relative FC error reduction per observation (From C. Cardinali)

24. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

25. Data monitoring – time series Time evolution of statistics over predefined areas/surfaces/flags (From M. Dahoui)

26. 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)

27. Data monitoring – automated warnings (From M. Dahoui & N. Bormann)

28. Satellite data monitoring Data monitoring – automated warnings (From M. Dahoui & N. Bormann)

29. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

30. New data availabilities • Now • SMOS, Suomi-NPP • 2013-2017 • ADM (Doppler-lidar: Atmospheric wind vector) • SMAP (like SMOS but active + passive) • Earthcare (radar, lidar) • FY3 -> ATOVS quality • 2017-2020 • Meteosat 3rd Generation • FY3 -> Metop quality • 2020+ • EPS Second Generation • But don’t always focus on satellite data! RS90 radiosonde much better than older radiosondes....`advanced conventional observations’

31. -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 EarthCARE • 1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) Model First-Guess Observation Analysis From S Di Michele 31

32. EarthCARE Model First-Guess • 1D-Var Assimilation of Calipso lidar Backscatter Coefficients (km-1 sr-1) Observation Analysis From S Di Michele 32

33. SMOS monitoring results • Monthly-average geographical mean evolution of the First-guess departures • Period Nov-2010 - August-2011 • V-pol • H-pol • fg departures in H-pol well correlated with snow covered areas, • Significant sources of RFI are still easy to spot with fg-departures, • In V-pol, observations are mainly overestimated. From J. Munoz Sabater

34. Active instruments: ESA’s ADM ESA ADM AEOLUS Doppler Lidar for wind vector observation Pressure (hPa) Control+ADM Control Control-sondes ECMWF is responsible for the development of the level 2 processor and will exploit the data as soon as available. Simulated DWL data adds value at all altitudes and well into longer-range forecasts. Zonal wind forecast error (m/s) From P Bauer

35. Evolution of ECMWF forecast skill ~16km ~25km ~39km ~63km ~125km ~210km From E Kallen

36. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

37. 1957 2002 METEOSAT reprocessed cloud motion winds TOMS/ SBUV 1982 1988 Aircraft data 1979 1973 Conventional surface and upper-air observations NCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, … 1973 1979 VTPR 1987 HIRS/ MSU/ SSU Cloud motion winds Buoy data 1991 SSM/I 1995 ERS-1 ERS-2 1998 AMSU ECMWF Reanalysis Observations used in ERA-Interim: • ERA-Interim is current ECMWF reanalysis project following ERA-15 & 40. • 2006 model cycle, 4D-Var, variational bias-correction, more data (rain assimilation, GPSRO); 1989-1998 period available, 1998-2005 period finished, real-time in 2009. 1989 The ERA-40 observing system: • ERA-40 observations until August 2002 • ECMWF operational data after August 2002 • Reprocessed altimeter wave-height data from ERS • Humidity information from SSM/I rain-affected radiance data • Reprocessed METEOSAT AMV wind data • Reprocessed ozone profiles from GOME • Reprocessed GPSRO data from CHAMP ERA-Interim From P. Bauer

38. From E Kallen

39. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

40. Combining NWP with CTM models and data assimilation systems EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service From P Bauer

41. Satellite data on CO2 and CH4 for use in MACC Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer

42. Satellite data on reactive gases for use in MACC Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer

43. Satellite data on aerosols for use in MACC Comment: Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer

44. NWP, conventional and satellite observations General impact assessment of current observing system Data monitoring Future observations and observation usage Special Applications: Climate & Chemistry Concluding remarks

45. Concluding remarks • At ECMWF, 95% of the actively assimilated data originates from satellites (90% is assimilated as radiances and only 5% as derived products and 5% from conventional products). • Impact experiments demonstrate the crucial role of conventional observations! • Ingredients for successful data implementation: - pre-launch test data, well defined formats, testing of telecommunications, provision of detailed instrument information. • - early data access after launch and active “cal/val” role for NWP centres - near real-time data access to maximize operational use. optimal return of investment by global user community (e.g. Metop ATOVS was used operationally only 3 months after launch despite whole new ground segment!). • Currently most important NWP instruments at ECMWF:- high spectral resolution infrared sounders (temperature, moisture),- microwave sounders and imagers (temperature, moisture, clouds, precipitation),- GPS transmitters/receivers (temperature),- IR imagers/sounders in geostationary orbits (moisture, clouds, wind),- scatterometers (near surface wind speed, wave height) • - altimeters (height anomaly),- UV/VIS/IR spectrometers (trace gases, temperature).

46. Concluding remarks • Future upgrades to data monitoring:- Coordination with data providers, building on experience within Europe e.g. Collaboration with China over FY3.- more effective automated warning system. • Future challenges with respect to observations:- Active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour),- Advanced imagers – synthetic aperture radiometers (soil moisture). • - Geostationary high spectral resolution sounders • Future challenges with respect to design of the Global Observing System:- In the past over-reliance on US data. European data now very important. New partnerships (e.g. China) will become increasingly important • - Coordination of multi-agency programmes • - Prioritisation for high benefit : low cost missions versus “new science” missions • - Knowing which observations will be needed in 10-20 years time when NWP will have advanced considerably • - Balancing needs of NWP, Climate and nowcasting, alongside new requirements for environmental monitoring (composition and chemistry).