Slide 1 ECMWF Training Course - The Global Observing System - 04/2012 The Global Observing System Stephen English and colleagues (with special thanks to Peter Bauer ) European Centre for Medium-Range Weather Forecasts

Slide 2 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 3 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 4 ECMWF Training Course - The Global Observing System - 04/2012 Role of observations Forecast lead time (days) RMS error (m) From C Lupu and E.Kallen Time (hours) 500hPa height, NH SEVIRI 6.2 µm Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the models virtual atmosphere.

Slide 5 ECMWF Training Course - The Global Observing System - 04/2012 From E. Kallen

Slide 6 ECMWF Training Course - The Global Observing System - 04/2012 Data sources: Conventional InstrumentParametersHeight SYNOP SHIP METAR temperature, dew-point temperature, wind Land: 2m, ships: 25m BUOYStemperature, pressure, wind2m TEMP TEMPSHIP DROPSONDES temperature, humidity, pressure, wind Profiles PROFILERSwindProfiles Aircraft temperature, pressure wind Profiles Flight level data

Slide 7 ECMWF Training Course - The Global Observing System - 04/2012 Example of conventional data coverage Aircraft – AMDAR Synop - ship Buoy Temp

Slide 8 ECMWF Training Course - The Global Observing System - 04/2012 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! From P. Bauer

Slide 9 ECMWF Training Course - The Global Observing System - 04/2012 Sun-Synchronous Polar Satellites InstrumentEarly morning orbit Morning orbitAfternoon orbit High spectral resolution IR sounder IASIAqua AIRS NPP CrIS Microwave T sounder F16, 17 SSMISMetop AMSU-A FY3A MWTS DMSP F18 SSMIS Meteor-M N1 MTVZA NOAA-15, 18, 19 AMSU-A Aqua AMSU-A FY3B MWTS, NPP ATMS Microwave Q sounder + imagers F16, 17 SSMISMetop MHS DMSP F18 SSMIS FY3A MWHS NOAA-18, 19 MHS FY3B MWHS, NPP ATMS Broadband IR sounder Metop HIRS FY3A IRAS NOAA-19 HIRS FY3B IRAS IR ImagersMetop AVHRR Meteor-M N1 MSU-MR Aqua+Terra MODIS NOAA-15, 16, 18, 19 AVHRR Composition (ozone etc). NOAA-17 SBUVNOAA-18, 19 SBUV ENVISAT GOMOS AURA OMI, MLS ENVISAT SCIAMACHY GOSAT

Slide 10 ECMWF Training Course - The Global Observing System - 04/2012 InstrumentHigh inclination (> 60°)Low inclination (<60°) Radio occultation GRAS, GRACE-A, COSMIC, TerraSarX C-NOFS, (SAC-C), ROSA MW ImagersTRMM TMI Meghatropics SAFIRE MADRAS Radar AltimeterENVISAT RA JASON Cryosat Sun-Synchronous Polar Satellites (2) InstrumentEarly morning orbit Morning orbitAfternoon orbit ScatterometerMetop ASCAT Coriolis Windsat Oceansat OSCAT RadarCloudSat LidarCalipso Visible reflectance Parasol L-band imagery SMOS SAC-D/Aquarius Non Sun-Synchronous Observations

Slide 11 ECMWF Training Course - The Global Observing System - 04/2012 Characterise the benefit of having ATOVS data from three evenly-spaced orbits versus data from a less optimal coverage for NWP MetOp-A NOAA-18 NOAA-19 + NPP Aqua NOAA-15 TimeTime ECMWF support to EUMETSAT – LEO constellation DMSP F16 DMSP F17 DMSP F18 FY-3B FY-3A ECWMF/EUMETSAT Bilateral Meeting 03/2012 SE11 Coriolis

Slide 12 ECMWF Training Course - The Global Observing System - 04/2012 ProductStatus SEVIRI Clear sky radianceAssimilated SEVIRI All sky radianceBeing tested for overcast radiances, and cloud-free radiances in the ASR dataset SEVIRI total column ozoneMonitored SEVIRI AMVsIR, Vis, WV-cloudy AMVs assimilated GOESAMVs MTSATAMVs Data sources: Geostationary Satellites

Slide 13 ECMWF Training Course - The Global Observing System - 04/2012 LEO SoundersLEO Imagers ScatterometersGEO imagers Satellite Winds (AMVs) GPS Radio Occultation Example of 6-hourly satellite data coverage 30 March UTC

Slide 14 ECMWF Training Course - The Global Observing System - 04/2012 Profilers Radiosonde Synop Ship Aircraft Buoys Moisture Mass Wind Composition Ozone sondes Air quality stations Soil moisture Rain gauge

Slide 15 ECMWF Training Course - The Global Observing System - 04/2012 GPSRO Geo IR and Polar MW Imagers AMVs Scatterometers Wind lidar Geo IR Sounder Radar GPS ZPD Polar IR + MW sounders Moisture Mass Wind Composition UV Sub-mm VIS+NIR Lidar Limb- sounders

Slide 16 ECMWF Training Course - The Global Observing System - 04/2012 Satellite data used by ECMWF

Slide 17 ECMWF Training Course - The Global Observing System - 04/2012

Slide 18 ECMWF Training Course - The Global Observing System - 04/2012 User requirements 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)

Slide 19 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 20 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 21 ECMWF Training Course - The Global Observing System - 04/2012 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 (From W. Bell)

Slide 22 ECMWF Training Course - The Global Observing System - 04/2012 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 From P. Bauer

Slide 23 ECMWF Training Course - The Global Observing System - 04/2012 Relative FC error reduction per system Relative FC error reduction per observation (From 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.

Slide 24 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 25 ECMWF Training Course - The Global Observing System - 04/2012 Time evolution of statistics over predefined areas/surfaces/flags Data monitoring – time series (From M. Dahoui)

Slide 26 ECMWF Training Course - The Global Observing System - 04/2012 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:

Slide 27 ECMWF Training Course - The Global Observing System - 04/2012 Data monitoring – automated warnings (From M. Dahoui & N. Bormann)

Slide 28 ECMWF Training Course - The Global Observing System - 04/2012 Satellite data monitoring Data monitoring – automated warnings (From M. Dahoui & N. Bormann)

Slide 29 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 30 ECMWF Training Course - The Global Observing System - 04/2012 New data availabilities Now SMOS, Suomi-NPP ADM (Doppler-lidar: Atmospheric wind vector) SMAP (like SMOS but active + passive) Earthcare (radar, lidar) FY3 -> ATOVS quality Meteosat 3 rd Generation FY3 -> Metop quality EPS Second Generation But dont always focus on satellite data! RS90 radiosonde much better than older radiosondes....`advanced conventional observations

Slide 31 ECMWF Training Course - The Global Observing System - 04/2012 Observation 15 – – – – – – – – – 0 0 – 3 3 – 6 6 – 9 9 – – 15 Model First-Guess Analysis 1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) EarthCARE 31 From S Di Michele

Slide 32 ECMWF Training Course - The Global Observing System - 04/2012 EarthCARE 1D-Var Assimilation of Calipso lidar Backscatter Coefficients (km -1 sr -1 ) Observation Model First-Guess Analysis 32 From S Di Michele

Slide 33 ECMWF Training Course - The Global Observing System - 04/2012 SMOS monitoring results H-pol V-pol Monthly-average geographical mean evolution of the First-guess departures Period Nov August-2011 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

Slide 34 ECMWF Training Course - The Global Observing System - 04/2012 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 From P Bauer

Slide 35 ECMWF Training Course - The Global Observing System - 04/2012 ~210km ~125km ~63km ~39km ~25km ~16km Evolution of ECMWF forecast skill From E Kallen

Slide 36 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 37 ECMWF Training Course - The Global Observing System - 04/2012 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 From P. Bauer

Slide 38 ECMWF Training Course - The Global Observing System - 04/2012 From E Kallen

Slide 39 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 40 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 41 ECMWF Training Course - The Global Observing System - 04/2012 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. From P Bauer

Slide 42 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 43 ECMWF Training Course - The Global Observing System - 04/2012 Satellite data on aerosols for use in MACC Comment: Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer

Slide 44 ECMWF Training Course - The Global Observing System - 04/2012 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

Slide 45 ECMWF Training Course - The Global Observing System - 04/2012 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).

Slide 46 ECMWF Training Course - The Global Observing System - 04/2012 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 years time when NWP will have advanced considerably - Balancing needs of NWP, Climate and nowcasting, alongside new requirements for environmental monitoring (composition and chemistry).