Presentation on theme: "The Global Observing System"— Presentation transcript:
1 The Global Observing System Peter Bauer and colleaguesEuropean Centre for Medium-Range Weather Forecasts
2 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
3 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
4 ECMWF forecasting systems SeasonalForecastsMedium-RangeForecasts(Deterministic and EPS)MonthlyForecastsAtmospheric modelAtmospheric modelWave modelWave modelOcean modelOcean data assimilation with 10-day window every 10 days. Forcing from ocean to atmosphere through SST+ocean currents (wave model), vice versa through P-E, wind stress, heat flux.Real Time Ocean Analysis ~8 hoursDelayed Ocean Analysis ~12 days
5 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 model’s 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 Satellite observing system Data types:Data volume:
7 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, windTEMP/TEMPSHIP/DROPSONDES:RadiosondesASAPs (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 profilesPROFILERS:UHF/VHF Doppler radars (Europe, US, Japan) wind profilesAircraft:AIREPS (manual reports from pilots)AMDARs, ACARs, etc. (automated readings) temperature, pressure, wind profiles
9 Data sources: Satellites Radiances ( brightness temperature = level 1):AMSU-A on NOAA-15/18/19, AQUA, MetopAMSU-B/MHS on NOAA-18/19, MetopSSM/I on F-15, AMSR-E on AquaHIRS on NOAA-17/19, MetopAIRS on AQUA, IASI on MetopMVIRI on Meteosat-7, SEVIRI on Meteosat-9, GOES-11/12, MTSAT-1R imagersBending angles ( bending angle = level 1):COSMIC (6 satellites), GRAS on MetopOzone ( total column ozone = level 2):Total column ozone from SBUV on NOAA-17/18, OMI on Aura, SCIAMACHY on EnvisatAtmospheric Motion Vectors ( wind speed = level 2):Meteosat-7/9, GOES-11/12, MTSAT-1R, MODIS on Terra/AquaSea surface parameters ( wind speed and wave height = level 2):Near-surface wind speed from ERS-2 scatterometer, ASCAT on MetopSignificant wave height from RA-2/ASAR on Envisat, Jason altimeters
10 Example of 6-hourly satellite data coverage LEO SoundersLEO ImagersScatterometersGEO imagersSatellite Winds (AMVs)GPS Radio Occultation9 April UTC
11 What types of satellites are used in NWP? Advantages DisadvantagesGEO - 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 Observation numbers per cycle EXP-HI EXP EXP-SV EXP-CLI EXP-RNDAverage radiance data count per analysis from period 08/12/ /02/2009:
13 Data Assimilation – Incremental 4D-Var T799L91T95L91T159L91T255L91T799L91(Trémolet 2004)
14 Transfer of information between radiances and control variables Data Assimilation – RadiancesTransfer of information between radiances and control variablesControl Variable /state vectorForecastmodelState attime iRadiativetransferRadianceobservationsWind and mass,humidityDynamics,moist physicsWind and mass,humidity,Clear, cloud and rain including scatteringClear, cloud and rainClear skyClear skyclouds and rain
15 What is the observation operator? Example 1: Radiosonde profile of T H = spatial interpolationExample 2: Clear-sky radiance observation H = spatial interpolation + clear-sky radiative transferExample 3: Cloud/rain radiance observation H = spatial interpolation + moist physical parameterizations+ multiple scattering radiative transferMVIRIModelSSM/IModel
16 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
17 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 correlation3/4 day3 days
18 Impact of microwave sounder data in NWP: Met Office OSEs N-15,-16 and -17 AMSUN-16 & N-17 HIRSAMVsScatterometer windsSSM/I ocean surface wind speedConventional observations2007 OSEs:N-16, N-18, MetOp-2 AMSUSSMISAIRS & IASI(W. Bell)
19 Sensitivity of analysis increments to observations 2007 GMAO/GSI system, 1.875o, 64 levels, 6-hour window;J from analysis increments; August 2004.temperature zonal windNorth-Pacific North PacificUS USsatelliteconventionaltotal(Zhu & Gelaro 2008)
20 Advanced diagnostics Data assimilation: Forecast sensitivity: State at max. 12 hoursState atinitial timeNWPmodelState attime iObservationoperatorObservationsimulationsState at analysis timeSensitivity of cost to change at initial timeAD of forecastmodelSensitivity of cost to change in state at time iAD of observationoperatorCost function JObservationsState atinitial timeNWPmodeltime iAD of forecastmax. 48 hoursSensitivity of cost to change at initial timeAnalysisCost function JForecast sensitivity:Sensitivity of cost to observations
21 Advanced diagnosticsRelative FC error reduction per systemThe 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.Relative FC error reduction per observation(C. Cardinali)
24 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
25 Data monitoring – time series Time evolution of statistics over predefined areas/surfaces/flags(M. Dahoui)
26 Data monitoring – overview plots Time evolution of statistics for several channelsUseful for quick and routine verificationsCan not be used for high spectral resolution soundersRTTOV version upgrade(M. Dahoui)
27 Data monitoring – automated warnings Selected statistics are checked against an expected range.E.g., global mean bias correction for GOES-12 (in blue):-alertSoft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier)Hard limits (fixed)alert:(M. Dahoui & N. Bormann)
28 Data monitoring – automated warnings (M. Dahoui & N. Bormann)
29 Data monitoring – automated warnings Satellite data monitoringData monitoring – automated warnings(M. Dahoui & N. Bormann)
30 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
31 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 soundingMore active instruments (wind, clouds, precipitation)
32 Cloudsat/CALIPSO data monitoring (J.-J. Morcrette)
33 ECMWF usage of SMOS data Global monitoring:Development of model forward operator (emissivity model)Data pre-processing (HDF2BUFR → ODB/IFS)Implementation of passive monitoring system, diagnostics, quality controlData assimilation study:Impact of SMOS constrained soil moisture on medium-range forecastsH-polH-polV-polH-pol22 January UTC; 1st background departure monitoring (no q/c)
34 FG departure in m3/m3 (January 2010) Soil moisture from ASCAT dataFG departure in m3/m3 (January 2010)FG departure bias vs ASCAT incidence angleHistograms of FG departures(P. de Rosnay)
35 Active instruments: ESA’s ADM ESA ADM AEOLUS Doppler Lidar for wind vector observationPressure (hPa)Control+ADMControlControl-sondesECMWF 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)
36 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
37 Areas of instability: Eady index Eady-index as a proxy for baroclinic instability in the atmospheredifference between seasons is rather strong;year-to-year variability has significant seasonal dependence as well.
38 Data coverage 14/12/2008 00 UTC data density AMSU-A channel 9 EXP-HI: EXP-SV:EXP-CLI:EXP-RND:01-07/01/2009 AverageSVRNDCLI
40 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
41 Observations used in ERA-Interim: ECMWF ReanalysisERA-Interim is current ECMWF reanalysis project following ERA-15 & 40.2006 model cycle, 4D-Var, variational bias-correction, more data (rain assimilation, GPSRO); period available, period finished, real-time in 2009.VTPRTOMS/ SBUVHIRS/ MSU/ SSUCloud motion windsBuoy dataSSM/IERS-1ERS-2AMSUMETEOSAT reprocessedcloud motion windsConventional surface and upper-air observationsNCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, …Aircraft data19572002197319791982198819871991199519981989The ERA-40 observing system:Observations used in ERA-Interim:ERA-40 observations until August 2002ECMWF operational data after August 2002Reprocessed altimeter wave-height data from ERSHumidity information from SSM/I rain-affected radiance dataReprocessed METEOSAT AMV wind dataReprocessed ozone profiles from GOMEReprocessed GPSRO data from CHAMPERA-Interim
42 Reanalysis as inter-calibration tool Global mean bias corrections produced in ERA-Interim (MSU Channel 2):Recorded warm-target temperatures, NOAA-14:(Grody et al. 2004)Variations in warm targetare due to orbital driftVarBC is able to correctthe resulting calibrationerrors(D. Dee)
43 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
44 Combining NWP with CTM models and data assimilation systems EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service
45 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.
46 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 Satellite data on aerosols for use in MACC Comment: Fire products (METEOSAT, MODIS, …) are a common requirement.
48 NWP, conventional and satellite observations General impact assessment of current observing systemData monitoringFuture observations and observation usageSpecial Applications: Climate & ChemistryConcluding remarks
49 Concluding remarksAt 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 Concluding remarksFuture 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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