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Role of products: Imperatives 1, 2, 5 Coherent, consistent, data products (closure)

SSG comments – post Seattle. 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

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Role of products: Imperatives 1, 2, 5 Coherent, consistent, data products (closure)

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  1. SSG comments – post Seattle • 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!

  2. 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 3rd 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. 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. Datasets used for model verification • 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

  5. Data assimilation→ improved initial conditions • 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/lidarreflectivities/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)

  6. Datasets used for data assimilation • 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!

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

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

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

  10. Examples where modelling/assimilation needs GRP: Precipitation • 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

  11. Trenberth et al. 2011 – water cycle

  12. Trenberth et al. 2011 – energy cycle

  13. WATER VAPOUR Evaporation Condensation CLOUD FRACTION CLOUD Liquid/Ice Evaporation Autoconversion PRECIP Rain/Snow Examples where modelling/assimilation needs GRP: Clouds/radiation New Cloud Scheme (since 11/2010) Old Cloud Scheme CLOUD FRACTION • 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!

  14. Examples where modelling/assimilation needs GRP: Clouds/radiation Relative frequency of occurrence of ice/snow for NH mid-latitudes in June 2006: ECMWF model vs. Cloudsat/Calipso retrievals ECMWF old scheme without snow ECMWF new scheme with snow CloudSat/CALIPSO observations -80 -60 -40 -20 0 -80 -60 -40 -20 0 -80 -60 -40 -20 0 Temperature 106 105 104 103 102 101 100 106 105 104 103 102 101 100 106 105 104 103 102 101 100 Ice Water Content (g m-3) Ice Water Content (g m-3) Ice Water Content (g m-3) New scheme with prognostic ice and snow allows much higher ice water contents (seen by the radiation scheme)

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

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

  17. Side effect No. 2 New Cloud Scheme Old Cloud Scheme T2m TCLW

  18. Side effect No. 2 Ceilometer observations Sodankyla/Finland: Old Cloud Scheme New Cloud scheme Revised Cloud Scheme

  19. Examples where modelling/assimilation needs GRP: Precipitation Observation: Grassland Model: Crops xxxx

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

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

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

  23. Examples where GRP needs modelling/assimilation: T2m anomalies ERA sampled as CRUTEM3 (Brohan et al., 2006) ERA over land, not sampled 12m running averages for globe 12m running averages for globe

  24. Examples where GRP needs modelling/assimilation: Precipitation 2005-10 times series of mean rainfall over Southern England 2005-10 mean product-radar rainfall correlation TRMM 3B42 CMORPH NRLBLND PERSIANN ECMWF (Courtesy C. Kidd) 0.0 0.2 0.4 0.6 0.8 1.0

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

  26. 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. Observations used in ERA-Interim: Instruments Radiances from satellites Backscatter, GPSRO, AMVs from satellites Ozone from satellites Sondes, profilers, stations, ships, buoys, aircraft

  28. 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. Observation Counts in ERA-Interim

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