1. GEWEX GRP10/2011Ⓒ ECMWF Role of products:Imperatives 1, 2, 5 Coherent, consistent, data products (closure) Long time series Order: reprocessed / recalibrated L1-products first (to be coordinated with other int’l efforts), then L2 products GRP’s role for defining product error metrics (error modelling templates) Role of simulators:Imperatives 3, 6 Support comparison in observation space (using fact that L1-observations are accurate and do not use a priori constraints) Bridge to data assimilation Needs more education of users (implementation, interpretation) Needs decision on how deeply involved GRP becomes w/r/t RT-models Role of models: Imperatives 2, 3, 4, 5, 6 Define priorities for model evaluation and parameterization development Support physical consistency (and support advanced diagnostics) See role of models/data assimilation systems as valuable diagnostic and tool for defining priorities! SSG comments – post Seattle

2. GEWEX GRP10/2011Ⓒ ECMWF SSG comments Response to SSG AIs and JSC comments is being produced – role of GRP needs to be understood by SSG+ Interaction with other WMO bodies should be coordinated by administration Will approach WGNE for their view Level of maturity of most products very high / substantial level of 3 rd party funding been spent should be appreciated: the objective of creating long-term datasets leads to compromises (in observational as well as modelled products - bias correction), requirement to start with consistent L1 products is fundamental Role of GRP in defining assessment ingredients/metrics important (tbd) Flux data sets particularly interesting from NWP perspective Flux evaluations also produced very informative co-assessment of observations along with models and sensitivity studies (e.g. same forcing, different flux param.) Strategy for advanced diagnostics? GRP role w/r/t RT-models, simulators?

3. GEWEX GRP10/2011Ⓒ ECMWF Model verification → improved parameterizations Observational requirements: moisture profiles (UTLS) moisture convergence (particularly over land) ice clouds mixed-phase clouds boundary layer clouds diurnal cycle of convection aerosols soil moisture (profile) land surface fluxes (turbulent, radiation) snow cover, water equivalent, albedo Observation types for model development: high vertical resolution (water vapour, clouds) good spatial coverage (everything) uncertainty specification if derived product → Global NWP model physics (/dynamics) need to perform well: for medium-range forecast within data assimilation system for Ensemble Prediction System (EPS) extended to monthly forecasts (with ocean) for Seasonal Prediction System for longer scales (climate) also ocean, aerosols, stratosphere etc.

4. GEWEX GRP10/2011Ⓒ ECMWF Satellite data based climatologies Water vapour:SSM/I, TMI, MLS Clouds:SSM/I, TMI, Cloudsat/Calipso, (ISCCP) Precipitation:GPCP, TRMM Snow:AVHRR/SSM/I, MODIS Soil moisture:SMOS, ASCAT Radiation/energy:CERES, COADS Satellite orbit data Clouds:Cloudsat/Calipso (, all observations used in DA) Site observations ARM, operational networks, field campaigns, other sites NWP (re)analyses ERA-40, ERA-Interim:NWP-analyses incl. data used in DA system → evaluate mean model state (climate*) → improve physical parameterizations ⇒ better parameterizations of model state do not necessarily mean better forecast skill, but are crucial for improving skill consistently * http://www.ecmwf.int/products/forecasts/d/inspect/catalog/research/physics_clim/climate/clim2000 Datasets used for model verification

5. GEWEX GRP10/2011Ⓒ ECMWF Slide 5 Parameters to constrain: temperature wind water vapour snow surface properties (albedo, vegetation) soil moisture cloud precipitation Observation types for data assimilation: satellite radiometer radiances satellite radar/lidar reflectivities/backscatter x-sections → most radiance data is available from operational instruments → radar/lidar data is only available from few experimental missions → Requirements: continuity of existing system high-vertical resolution observations of water vapour (limb, active), over land wind observations (with accuracy better than 1 m/s) soil moisture data not yet optimal (ASCAT, SMOS) but promising Keeping in mind that data must be available in near-real-time (~3-hour delay for global NWP) Data assimilation→ improved initial conditions

6. GEWEX GRP10/2011Ⓒ ECMWF Data Assimilation: Satellite orbit data Temperature:AMSU-A, IASI, AIRS, HIRS, GPSRO Water vapour:AMSU-B/MHS, SSM/I, TMI, AMSR-E, AIRS, IASI, HIRS Wind:GEO/LEO-AMV Clouds:SSM/I, TMI, AMSR-E, AIRS, IASI Precipitation:SSM/I, TMI, AMSR-E Snow:AVHRR/SSM/I Soil moisture:ASCAT/SMOS Conventional Temperature:Radisondes, dropsondes, aircraft Water vapour:Radiosondes, dropsondes Wind:Radiosondes, profilers → produce physically consistent analyses to initialize forecast model ⇒ hydrological parameters are not the drivers for forecast performance ⇒ more complex processes (clouds/precipitation) require good parameterizations to translate observational information into better forecast skill ⇒ 95% of data is assimilated as level-1 product (errors, biases, efficiency, compatibility) Model verification/development requirements are different from data assimilation requirements! Datasets used for data assimilation

7. GEWEX GRP10/2011Ⓒ ECMWF AN FC 9h FC 48h FC 96h Observation – minus – Model: Temperature Metop-A AMSU-A NH std. dev. R/S T NH std. dev.bias R/S T Tr std. dev.bias COSMIC-1 φ NH std. dev.bias COSMIC-1 φ Tr std. dev.bias

8. GEWEX GRP10/2011Ⓒ ECMWF Metop-A MHS SH std. dev. DMSP F-14 SSM/I SH std. dev. R/S q TR std. dev.bias R/S q NH std. dev.bias R/S RH NH std. dev.bias Observation – minus – Model: Moisture AN FC 9h FC 48h FC 96h

9. GEWEX GRP10/2011Ⓒ ECMWF 12-year climatology (ECMWF vs TRMM) Rain intensityLST of rain maximum 3-hour difference of convective maximum over tropical land surfaces Too intense monsoon ModelTRMM (Data courtesy Y. Takayabu) Examples where modelling/assimilation needs GRP: Precipitation

10. GEWEX GRP10/2011Ⓒ ECMWF Comparison of monthly averaged rainfall with combined rain gauge and satellite products (GPCP) Reanalysis estimates of rainfall over ocean are still problematic Results over land are much better Examples where modelling/assimilation needs GRP: Precipitation

11. GEWEX GRP10/2011Ⓒ ECMWF Trenberth et al. 2011 – water cycle

12. GEWEX GRP10/2011Ⓒ ECMWF Trenberth et al. 2011 – energy cycle

13. GEWEX GRP10/2011Ⓒ ECMWF WATER VAPOUR CLOUD Liquid/Ice CLOUD Liquid/Ice PRECIP Rain/Snow Evaporation Autoconversion Evaporation Condensation CLOUD FRACTION Old Cloud Scheme New Cloud Scheme (since 11/2010) 2 prognostic cloud variables + w.v. Ice/water diagnostic fn(temperature) Diagnostic precipitation 5 prognostic cloud variables + water vapour Ice and water now independent More physically based, greater realism Significant change to degrees of freedom Change to water cycle balances in the model More than double the lines of “cloud” code! Examples where modelling/assimilation needs GRP: Clouds/radiation

14. GEWEX GRP10/2011Ⓒ ECMWF Temperature Ice Water Content (g m -3 ) -80 -60 -40 -20 0 10 6 10 5 10 4 10 3 10 2 10 1 10 0 -80 -60 -40 -20 0 -80 -60 -40 -20 0 10 6 10 5 10 4 10 3 10 2 10 1 10 0 Ice Water Content (g m -3 ) CloudSat/CALIPSO observations ECMWF old scheme without snow ECMWF new scheme with snow New scheme with prognostic ice and snow allows much higher ice water contents (seen by the radiation scheme) Relative frequency of occurrence of ice/snow for NH mid-latitudes in June 2006: ECMWF model vs. Cloudsat/Calipso retrievals Examples where modelling/assimilation needs GRP: Clouds/radiation

15. GEWEX GRP10/2011Ⓒ ECMWF Wind Surface Precipitation Orography July 2007 case study (36 hour accumulation) 5 mm/36hr 50 mm/36hr Surface Precip. Difference 1 year average “Prognostic snow” minus “Diagnostic snow” 5 mm/36hr - 5 mm/36hr Surface Precip Difference “Prognostic snow” minus “Diagnostic snow” - 5 mm/36hr 5 mm/36hr Examples where modelling/assimilation needs GRP: Clouds/radiation

16. GEWEX GRP10/2011Ⓒ ECMWF Side effect No. 1 RTTOV-9µφµφ Time series of fit between upper tropospheric MHS and model radiances Systematic difference between radiosonde and model specific humidity (kg/kg; NH 01/2011) Next model Current model

17. GEWEX GRP10/2011Ⓒ ECMWF Old Cloud Scheme New Cloud Scheme T2m TCLW Side effect No. 2

18. GEWEX GRP10/2011Ⓒ ECMWF Ceilometer observations Sodankyla/Finland: Old Cloud SchemeNew Cloud schemeRevised Cloud Scheme Side effect No. 2

19. GEWEX GRP10/2011Ⓒ ECMWF xxxx Observation: Grassland Model: Crops Examples where modelling/assimilation needs GRP: Precipitation

20. GEWEX GRP10/2011Ⓒ ECMWF Observation: Evergreen needle leaf Model: 70% crops, 30% Interrupted forest Examples where modelling/assimilation needs GRP: Precipitation

21. GEWEX GRP10/2011Ⓒ ECMWF Observation: Woody savannas Model: 30% tall grass, 70% interrupted forest Examples where modelling/assimilation needs GRP: Precipitation

22. GEWEX GRP10/2011Ⓒ ECMWF Aerosol optical depth Accumulated rainfall Merapi eruption (Indonesia, Nov. 2010) Relative change of LCC Relative change of total precipitation Impact of precipitation on aerosols Impact of aerosols on clouds and precipitation Examples where modelling/assimilation needs GRP: Aerosols/precipitation

23. GEWEX GRP10/2011Ⓒ ECMWF ERA sampled as CRUTEM3 (Brohan et al., 2006) ERA over land, not sampled 12m running averages for globe Examples where GRP needs modelling/assimilation: T 2m anomalies

24. GEWEX GRP10/2011Ⓒ ECMWF TRMM 3B42 CMORPH NRLBLND PERSIANN ECMWF 2005-10 times series of mean rainfall over Southern England 2005-10 mean product-radar rainfall correlation 0.00.20.40.60.81.0 (Courtesy C. Kidd) Examples where GRP needs modelling/assimilation: Precipitation

25. GEWEX GRP10/2011Ⓒ ECMWF Ch 2 Ch 3 Ch 4 Recorded on-board warm target temperature changes due to orbital drift for NOAA-14 (Grody et al. 2004) Examples where GRP needs modelling/assimilation: L1 biases

26. GEWEX GRP10/2011Ⓒ ECMWF Atmospheric reanalysis: ERA-Interim ECMWF forecasts: 1980 – 2010 Changes in skill are due to: improvements in modelling and data assimilation evolution of the observing system atmospheric predictability ERA-Interim: 1979 – 2010 uses a 2006 forecast system ERA-40 used a 2001 system re-forecasts more uniform quality improvements in modelling and data assimilation outweigh improvements in the observing system

27. GEWEX GRP10/2011Ⓒ ECMWF Observations used in ERA-Interim: Instruments Radiances from satellites Ozone from satellites Backscatter, GPSRO, AMVs from satellites Sondes, profilers, stations, ships, buoys, aircraft

28. GEWEX GRP10/2011Ⓒ ECMWF How accurate are trend estimates from reanalysis? Global mean temperatures, for MSU-equivalent vertical averages: ERA-Interim Radiosondes only (corrected) MSU only, from RSS

29. GEWEX GRP10/2011Ⓒ ECMWF Observation Counts in ERA-Interim