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Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range.

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Presentation on theme: "Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range."— Presentation transcript:

1 Slide 1 ECMWF Training Course - The Global Observing System - 05/2010 The Global Observing System Peter Bauer and colleagues European Centre for Medium-Range Weather Forecasts

2 Slide 2 ECMWF Training Course - The Global Observing System - 05/2010 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 Slide 3 ECMWF Training Course - The Global Observing System - 05/2010 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 Slide 4 ECMWF Training Course - The Global Observing System - 05/2010 Delayed Ocean Analysis ~12 days Real Time Ocean Analysis ~8 hours Medium-Range Forecasts (Deterministic and EPS) Medium-Range Forecasts (Deterministic and EPS) Seasonal Forecasts Seasonal Forecasts Monthly Forecasts Monthly Forecasts Atmospheric model Wave model Ocean model Atmospheric model Wave model ECMWF forecasting systems

5 Slide 5 ECMWF Training Course - The Global Observing System - 05/2010 Data assimilation system (4D-Var) The observations are used to correct errors in the short forecast from the previous analysis time. Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the models virtual atmosphere. This is done by a careful 4-dimensional interpolation in space and time of the available observations; this operation takes as much computer power as the 10-day forecast.

6 Slide 6 ECMWF Training Course - The Global Observing System - 05/2010 Satellite observing system Data types: Data volume:

7 Slide 7 ECMWF Training Course - The Global Observing System - 05/2010 Data sources: Conventional SYNOP/SHIP/METAR: Meteorological/aeronautical land surface weather stations (2m-temperature, dew-point temperature, 10m-wind) Ships temperature, dew-point temperature, wind (land: 2m, ships: 25m) BUOYS: Moored buoys (TAO, PIRATA) Drifters temperature, pressure, wind TEMP/TEMPSHIP/DROPSONDES: Radiosondes ASAPs (commercial ships replacing stationary weather ships) Dropsondes released from aircrafts (NOAA, Met Office, tropical cyclones, experimental field campaigns, e.g., FASTEX, NORPEX) temperature, humidity, pressure, wind profiles PROFILERS: UHF/VHF Doppler radars (Europe, US, Japan) wind profiles Aircraft: AIREPS (manual reports from pilots) AMDARs, ACARs, etc. (automated readings) temperature, pressure, wind profiles

8 Slide 8 ECMWF Training Course - The Global Observing System - 05/2010 Example of conventional data coverage

9 Slide 9 ECMWF Training Course - The Global Observing System - 05/2010 Radiances ( brightness temperature = level 1): AMSU-A on NOAA-15/18/19, AQUA, Metop AMSU-B/MHS on NOAA-18/19, Metop SSM/I on F-15, AMSR-E on Aqua HIRS on NOAA-17/19, Metop AIRS on AQUA, IASI on Metop MVIRI on Meteosat-7, SEVIRI on Meteosat-9, GOES-11/12, MTSAT-1R imagers Bending angles ( bending angle = level 1): COSMIC (6 satellites), GRAS on Metop Ozone ( total column ozone = level 2): Total column ozone from SBUV on NOAA-17/18, OMI on Aura, SCIAMACHY on Envisat Atmospheric Motion Vectors ( wind speed = level 2): Meteosat-7/9, GOES-11/12, MTSAT-1R, MODIS on Terra/Aqua Sea surface parameters ( wind speed and wave height = level 2): Near-surface wind speed from ERS-2 scatterometer, ASCAT on Metop Significant wave height from RA-2/ASAR on Envisat, Jason altimeters Data sources: Satellites

10 Slide 10 ECMWF Training Course - The Global Observing System - 05/2010 LEO SoundersLEO Imagers ScatterometersGEO imagers Satellite Winds (AMVs) GPS Radio Occultation Example of 6-hourly satellite data coverage 9 April UTC

11 Slide 11 ECMWF Training Course - The Global Observing System - 05/2010 What types of satellites are used in NWP? AdvantagesDisadvantages 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!

12 Slide 12 ECMWF Training Course - The Global Observing System - 05/2010 Observation numbers per cycle Average radiance data count per analysis from period 08/12/ /02/2009: EXP-HIEXPEXP-SVEXP-CLIEXP-RND

13 Slide 13 ECMWF Training Course - The Global Observing System - 05/2010 (Trémolet 2004) T799L91 T95L91 T159L91 T255L91 T799L91 Data Assimilation – Incremental 4D-Var

14 Slide 14 ECMWF Training Course - The Global Observing System - 05/2010 Control Variable / state vector Forecast model State at time i Radiative transfer Radiance observations Wind and mass, humidity Wind and mass, humidity, Clear sky Dynamics, moist physics clouds and rain Clear, cloud and rain including scattering Clear, cloud and rain Transfer of information between radiances and control variables Data Assimilation – Radiances

15 Slide 15 ECMWF Training Course - The Global Observing System - 05/2010 Example 1: Radiosonde profile of T H = spatial interpolation Example 2:Clear-sky radiance observation H = spatial interpolation + clear-sky radiative transfer Example 3:Cloud/rain radiance observation H = spatial interpolation + moist physical parameterizations + multiple scattering radiative transfer ModelSSM/I What is the observation operator? MVIRIModel

16 Slide 16 ECMWF Training Course - The Global Observing System - 05/2010 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

17 Slide 17 ECMWF Training Course - The Global Observing System - 05/2010 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

18 Slide 18 ECMWF Training Course - The Global Observing System - 05/2010 Impact of microwave sounder data in NWP: Met Office OSEs 2003 OSEs: N-15,-16 and -17 AMSU N-15,-16 and -17 AMSU N-16 & N-17 HIRS N-16 & N-17 HIRS AMVs AMVs Scatterometer winds Scatterometer winds SSM/I ocean surface wind speed SSM/I ocean surface wind speed Conventional observations Conventional observations 2007 OSEs: N-16, N-18, MetOp-2 AMSU N-16, N-18, MetOp-2 AMSU SSMIS SSMIS AIRS & IASI AIRS & IASI Scatterometer winds Scatterometer winds AMVs AMVs SSM/I ocean surface wind speed SSM/I ocean surface wind speed Conventional observations Conventional observations (W. Bell)

19 Slide 19 ECMWF Training Course - The Global Observing System - 05/2010 Sensitivity of analysis increments to observations 2007 GMAO/GSI system, o, 64 levels, 6-hour window; J from analysis increments; August temperaturezonal wind North-PacificNorth Pacific temperaturezonal windUS satellite conventional total (Zhu & Gelaro 2008)

20 Slide 20 ECMWF Training Course - The Global Observing System - 05/2010 State at initial time NWP model State at time i Observation operator Observation simulations Advanced diagnostics Observations AD of forecast model AD of observation operator Sensitivity of cost to change in state at time i Cost function J Sensitivity of cost to change at initial time max. 12 hours Data assimilation: State at initial time NWP model State at time i AD of forecast model max. 48 hours Sensitivity of cost to change at initial time Analysis Cost function J Forecast sensitivity: State at analysis time Sensitivity of cost to observations

21 Slide 21 ECMWF Training Course - The Global Observing System - 05/2010 Relative FC error reduction per system Relative FC error reduction per observation (C. Cardinali) Advanced diagnostics The forecast sensitivity (Cardinali, 2009, QJRMS, 135, ) 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.

22 Slide 22 ECMWF Training Course - The Global Observing System - 05/ AMSU-A, 2 MHSvs1 AMSU-A, 0 MHS (C. Cardinali) Advanced diagnostics – MW sounder denial Forecast error reduction [%]

23 Slide 23 ECMWF Training Course - The Global Observing System - 05/2010 Advanced diagnostics – MW imager denial (C. Cardinali) Forecast error reduction [%] No MW-imagers Control

24 Slide 24 ECMWF Training Course - The Global Observing System - 05/2010 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 Slide 25 ECMWF Training Course - The Global Observing System - 05/2010 Time evolution of statistics over predefined areas/surfaces/flags Data monitoring – time series (M. Dahoui)

26 Slide 26 ECMWF Training Course - The Global Observing System - 05/2010 Time evolution of statistics for several channels Useful for quick and routine verifications Can not be used for high spectral resolution sounders RTTOV version upgrade Data monitoring – overview plots (M. Dahoui)

27 Slide 27 ECMWF Training Course - The Global Observing System - 05/2010 Selected statistics are checked against an expected range. E.g., global mean bias correction for GOES-12 (in blue): Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier) Hard limits (fixed) -alert Data monitoring – automated warnings (M. Dahoui & N. Bormann) alert:

28 Slide 28 ECMWF Training Course - The Global Observing System - 05/2010 Data monitoring – automated warnings (M. Dahoui & N. Bormann)

29 Slide 29 ECMWF Training Course - The Global Observing System - 05/2010 Satellite data monitoring Data monitoring – automated warnings (M. Dahoui & N. Bormann)

30 Slide 30 ECMWF Training Course - The Global Observing System - 05/2010 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

31 Slide 31 ECMWF Training Course - The Global Observing System - 05/2010 New data availabilities 2010: Oceansat-2 (Scatterometer: surface wind vector) DMSP F-18 SSMIS (MW T:, q-sounding, clouds and precipitation) SMOS (MW: soil moisture) Megha Tropiques MADRAS/SAPHIR (MW: q-sounding, clouds and precipitation) FY-3A IRAS/MWTS/MWHS/MWRI (IR/MW: T, q-sounding, clouds and precipitation) GOSAT FTS (Advanced IR: T, q, trace gas sounding) 2011: NPP (Advanced IR: T, q-sounding) ADM (Doppler-lidar: Atmospheric wind vector) 2012 and beyond: More advanced IR sounders in polar (Metop, NPOESS) and geostationary orbits (MTG, GOES) for general sounding More active instruments (wind, clouds, precipitation)

32 Slide 32 ECMWF Training Course - The Global Observing System - 05/2010 Cloudsat/CALIPSO data monitoring (J.-J. Morcrette)

33 Slide 33 ECMWF Training Course - The Global Observing System - 05/2010 H-pol 22 January UTC; 1 st background departure monitoring (no q/c) Global monitoring: Development of model forward operator (emissivity model) Data pre-processing (HDF2BUFR ODB/IFS) Implementation of passive monitoring system, diagnostics, quality control Data assimilation study: Impact of SMOS constrained soil moisture on medium-range forecasts H-pol V-pol ECMWF usage of SMOS data

34 Slide 34 ECMWF Training Course - The Global Observing System - 05/2010 FG departure in m 3 /m 3 (January 2010) FG departure bias vs ASCAT incidence angle Histograms of FG departures (P. de Rosnay) Soil moisture from ASCAT data

35 Slide 35 ECMWF Training Course - The Global Observing System - 05/2010 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) Pressure (hPa) Control+ADM Control Control-sondes Active instruments: ESAs ADM ESA ADM AEOLUS Doppler Lidar for wind vector observation

36 Slide 36 ECMWF Training Course - The Global Observing System - 05/2010 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 Slide 37 ECMWF Training Course - The Global Observing System - 05/2010 Areas of instability: Eady index Eady-index as a proxy for baroclinic instability in the atmosphere difference between seasons is rather strong; year-to-year variability has significant seasonal dependence as well.

38 Slide 38 ECMWF Training Course - The Global Observing System - 05/2010 Data coverage 14/12/ UTC data density AMSU-A channel 9 EXP-HI: EXP: EXP-SV: EXP-CLI: EXP-RND: 01-07/01/2009 Average SV RND CLI

39 Slide 39 ECMWF Training Course - The Global Observing System - 05/2010 JAS08 D08JF09 Forecast impact: z500 – D08JF09

40 Slide 40 ECMWF Training Course - The Global Observing System - 05/2010 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

41 Slide 41 ECMWF Training Course - The Global Observing System - 05/2010 Observations used in ERA-Interim: The ERA-40 observing system: VTPR TOMS/ SBUV HIRS/ MSU/ SSU Cloud motion winds Buoy data SSM/I ERS-1 ERS-2 AMSU METEOSAT reprocessed cloud motion winds Conventional surface and upper-air observations NCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, … Aircraft data 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 1989 ECMWF Reanalysis ERA-Interim is current ECMWF reanalysis project following ERA-15 & model cycle, 4D-Var, variational bias-correction, more data (rain assimilation, GPSRO); period available, period finished, real-time in 2009.

42 Slide 42 ECMWF Training Course - The Global Observing System - 05/2010 Global mean bias corrections produced in ERA-Interim (MSU Channel 2): Recorded warm-target temperatures, NOAA-14: (Grody et al. 2004) Variations in warm target are due to orbital drift VarBC is able to correct the resulting calibration errors Reanalysis as inter-calibration tool (D. Dee)

43 Slide 43 ECMWF Training Course - The Global Observing System - 05/2010 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

44 Slide 44 ECMWF Training Course - The Global Observing System - 05/2010 Combining NWP with CTM models and data assimilation systems EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service

45 Slide 45 ECMWF Training Course - The Global Observing System - 05/2010 Satellite data on CO 2 and CH 4 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.

46 Slide 46 ECMWF Training Course - The Global Observing System - 05/2010 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.

47 Slide 47 ECMWF Training Course - The Global Observing System - 05/2010 Satellite data on aerosols for use in MACC Comment: Fire products (METEOSAT, MODIS, …) are a common requirement.

48 Slide 48 ECMWF Training Course - The Global Observing System - 05/2010 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

49 Slide 49 ECMWF Training Course - The Global Observing System - 05/2010 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: - early data access after launch: (1) fast monitoring of data quality – feedback to space agencies, (2) early testing of data impact in NWP data assimilation systems. - near real-time data access to maximize operational use. optimal return of investment by global user community (example: METOP). Currently most important NWP instruments at ECMWF: - advanced 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).

50 Slide 50 ECMWF Training Course - The Global Observing System - 05/2010 Concluding remarks Future challenges with respect to observations: - active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour), - advanced imagers – synthetic aperture radiometers (soil moisture). Future challenges with respect to data assimilation: - model resolution upgrades also affect data assimilation resolution, - more intelligent data thinning using ensemble methods (B) and forecast error growth metrics, - assimilation of cloud/precipitation-affected data will require revised control variable, background error statistics. Future upgrades to data monitoring: - more sophisticated data co-location tools to compare performance between data from different sensors, - more advanced automated warning system.


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