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Evolution of Modeling and Data Assimilation at NASA/GSFC

Evolution of Modeling and Data Assimilation at NASA/GSFC. Arlindo da Silva Global Modeling and Assimilation Office, NASA/GSFC Arlindo.daSilva@nasa.gov. CPTEC Workshop Cachoeira Paulista, Sao Paulo, Brazil 8-10 December 2008. Outline.

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Evolution of Modeling and Data Assimilation at NASA/GSFC

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  1. Evolution of Modeling and Data Assimilation at NASA/GSFC Arlindo da Silva Global Modeling and Assimilation Office, NASA/GSFC Arlindo.daSilva@nasa.gov CPTEC Workshop Cachoeira Paulista, Sao Paulo, Brazil 8-10 December 2008

  2. Outline • GEOS-5 Earth Modeling System for Prediction and Data Assimilation • Scope and architecture: system of systems • Historical overview and Roadmap • Quick overview of sub-systems • The case for a component based, open system • Rationale for frameworks: the ESMF • Other frameworks: WRF, PRISM • Concluding Remarks

  3. GEOS-5 Modeling Systems in support of NASA missions

  4. The Global Modeling and Assimilation Office (GMAO) is a component of the Earth Sciences Division at NASA's Goddard Space Flight Center. We contribute to NASA's Science Mission Directorate in the development and use of satellite observations through the integrating tools of models and assimilation systems. Office Head Michele Rienecker Strategic Management Team Max Suarez, Ron Gelaro, Steven Pawson, Siegfried Schubert, Man-Li Wu, Arlindo da Silva, Gi-Kong Kim Modeling Max Suarez Atmospheric Assimilation Ron Gelaro Constituents Chem: Steven Pawson Aero: Arlindo da Silva Operational Products Gi-Kong KIm SubSeasonal-Decadal Variability & Prediction Siegfried Schubert Civil Service Staff: 17 Contractor Staff: 52 University Research Staff: 21

  5. Global Modeling & Assimilation Office Contributing to NASA’s Mission • Contribute to Instrument Team products; advance the use of NASA data • AURA: MLS, HIRDLS, TES, OMI • AQUA: MODIS, AIRS • CERES, CALIPSO • Field Campaigns (INTEX, NAMMA, TC4, ARCTAS, …) • Science areas: • MERRA: reanalysis -hydrological cycle - NASA data in climate context • Prediction: weather, short-term climate, drought • Aerosol-weather connections • Weather-climate connections • Chemistry-climate interactions • Technical areas: • ESMF: improving extensibility of models through advanced software • GEOS-5 model: supports NASA’s MAP community • Future missions: • OCO, SMAP, Aquarius • NPP - Joint Center for Satellite Data Assimilation • Decadal Survey - Wind Lidar mission (with Code 613.1)

  6. GMAO Assimilation System(s) Atmosphere • Meteorological analyses (u,v,T, q): weather prediction, climate analyses • Chemistry constituents: ozone, coupled with meteorology • Chemistry constituents: CO, CO2 under development • Aerosols: Transport, with source distributions from satellite • GEOS-5 AGCM, currently 3Dvar, 4Dvar prototype in testing phase Land Surface • Soil moisture, surface temperature and snow • Catchment LSM with EnKF Ocean • Retrospective Ocean analyses (u, v, T, S) for seasonal forecasts • MOM4: OI, Assimilation in the CGCM coupled to atmospheric analysis • Poseidon: EnKF • Ocean color analyses: ocean time series, removing cross-satellite biases • Poseidon: SEIK filter Goal: Integrated Earth System Analysis, with consistent analyses across all components

  7. Evolution to GEOS-5 operational assimilation system MODEL ANALYSIS OBSERVATIONS GEOS-1 GEOS-2 GEOS-3 GEOS-4 (FVDAS) GEOS-5 Aries Dynamical Core Finite Volume Core “GSFC” physics NCAR physics hybrid physics 45 2 2.5 2 2.5 1 1 1 1.25 0.5 2/3 0.25 x 1/3 Optimal Interpolation Physical-Space Statistical Analysis System SSI (NCEP) GSI (NCEP/NASA) Conventional Observations (radiosondes, aircraft, …) NESDIS – Retrieved TOVS Temperature TOVS/AMSU/AIRS Radiance Scatterometer Total Precipitable Water Total Precipitable Water MODIS winds

  8. GEOS-6 2011 GEOS-5 Roadmap GEOS-5n (2008-10) GEOS-5 AO system GEOS-5 Atmosphere • Non-hydrostatic capable • Physics for hi-res • Chem assim • ADAS: 4D-Var weak constraint • AGCM w. hydrostatic • cubed sphere • ADAS: 4D-Var prototype • LSM with Dyn. Veg. • Carbon species assim. • Ocean biogeochem • Sea-ice • Coupled to LSM • ADAS + Adjoint tools • Replay for “coupling” • O3 assimilation • Coupled to GOCART • Coupled to GMI Combo • Ocean • ODAS • LDAS Weather - Climate coupling Chemistry-Climate coupling Chem-weather prediction ESM IESA Climate change More Science PIESA (weakly coupled, consistent analyses) Short-term climate predictions “Coupled” A-O analyses Science IESA with the ocean Products - instr. teams Field campaigns, NWP MERRA Science Beginning of IESA

  9. Brief overview of GEOS-5

  10. AGCM Finite-volume dynamical core Bacmeister moist physics Physics integrated under the Earth System Modeling Framework (ESMF) Generalized vertical coord to 0.01 hPa Catchment land surface model Prescribed aerosols Interactive ozone Prescribed SST, sea-ice 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z 03Z Total “observed change” Model predicted change Correction from DAS Initial States for Corrector Raw analysis (from GSI) Analysis Tendencies for Corrector Background (model forecast) Assimilated analysis (Application of IAU) Corrector Segment (1- and 3-hrly products) Analysis GEOS-5 Atmospheric Data Assimilation System Analysis • Grid Point Statistical Interpolation (GSI from NCEP) • Direct assimilation of satellite radiance data using JCSDA Community Radiative Transfer Model (CRTM) • Variational bias correction for radiances Assimilation • Apply Incremental Analysis Increments (IAU) to reduce shock of data insertion • IAU gradually forces the model integration throughout the 6 hour analysis period

  11. NCEP/EMC-GMAO Code Managementfor Atmospheric Data Assimilation Process: similar to ECMWF & Météo-France who have annual code mergers But,to promote collaboration and transitions, EMC and GMAO use same repository and mergers are more frequent (3 months) GSI & CRTM supported 3 months * * * * * * * * EMC 3 1 Accepted changes * *EMC, GMAO System change Repository change +Repository Merger (new tag) 2 + + Repository Protocols 1 – EMC, GMAO take (agreed-upon) merged code from repository to begin work 2 – EMC, GMAO incorporate developments into repository 3 – Code mergers, repository changes and timing are NCEP’s decision GMAO * * * * * * * Time

  12. GEOS-5 input data streams

  13. MERRA Modern Era Retrospective-analysis for Research and Applicationssupports NASA's Earth Science interests by • Utilizing the NASA global data assimilation system to produce a long-term (1979-present) synthesis that places the current suite of research satellite observations in a climate data context. • Providing the science and applications communities with state-of-the-art global analyses, with emphasis on improved estimates of the hydrological cycle on a broad range of weather and climate time scales

  14. MERRA http://gmao.gsfc.nasa.gov/merra/ Michael Bosilovich, Siegfried Schubert & Gi-Kong Kim MERRA System 1/2°  2/3°  72L to .01 mb 1979-present GSI Analysis with IAU Parallel AMIP run EMPHASIS ON WATER CYCLE • Global Precipitation, Evaporation, Land Hydrology, Cloud parameters and TPW GLOBAL HEAT AND WATER BUDGETS FOR ALL PROCESSES DIURNAL CYCLE FROM HOURLY 2-D FIELDS Consistent 1979-present 3D aerosol time series will also be produced

  15. Precipitation (mm/day) January 2004 July 2004 GEOS-5 GEOS-5 GPCP GPCP

  16. Precipitation - GPCP (mm/day): July 2004

  17. GSI Analysis System invisible Forecast Model invisible Input: Observations and Background Analyzed State Output: Forecast Adjoint GSI Analysis System Analyzed State Sensitivity Adjoint Forecast Model Output: Observation and Background Sensitivity Input: Forecast Observation Impact Adjoint Tools for Observation Impact Studies Ron Gelaro and Yanqiu Zhu The adjoint (transpose) of a data assimilation system allows accurate and efficient estimation of observation impact on analyses and forecasts • impacts of arbitrary subsets of observations (e.g., separate satellites, channels or locations) can be easily quantified • determined with respect to observational data, background fields or assimilation parameters, all computed simultaneously

  18. Evaluating AIRS Impact in GEOS-5 Traditional Data Impact Studies Emerging Adjoint-based Tools Data from most AIRS channels improve the GEOS-5 forecast NH degrade Channel Index Control Control + AIRS improve Some AIRS channels degrade the forecast SH 24-hr Forecast Error Reduction vs. Channel NH AIRS brings slightly positive impact on forecast skill in Northern Hemisphere; clear positive impact in Southern Hemisphere. Currently, forecast skills are increased when moisture channels from AIRS are not included… Control Control + AIRS without moisture channels Forecast Skill vs. Time

  19. Atmospheric Model/GCM Finite-volume dynamic core Bacmeister moist physics Physics integrated via ESMF Catchment land surface model Prescribed aerosols Interactive ozone o o o o o o o o o o Analysis Observations 21 UTC 00 UTC 03 UTC 6-hour assimilation window GEOS-5 4D-Var Atmospheric Data Assimilation System Atmospheric Analysis System • Gridpoint Statistical Interpolation (GSI) • TLM/Adjoint finite-volume dynamical core • Direct assimilation of satellite radiances • JCSDA Community Radiative Transfer Model (CRTM) • Variational bias correction for radiances

  20. 4D-Var Preliminary Results Single Observation Experiments Observation at the end of the 6-hr assimilation window

  21. GEOS-5: The NASA Catchment LSM 1. Use the hydrological catchment as the fundamental land surface unit. Don’t assume land surface element has a shape defined by the overlying atmospheric grid 2. Within each catchment, use hydrological models for dealing with subgrid-scale soil moisture distributions. TOPMODEL, with a special treatment of the unsaturated zone. (We employ many of the ideas introduced by Famiglietti and Wood, 1994.)

  22. GCM ATMOSPHERE climate chemistry landscape and veg structure Tsoil, Tcanopy snow albedo soil albedo, soil moisture conductance net SW SVAT: LAND SURFACE ENERGY & WATER BALANCE canopy energy balance soil energy balance soil moisture snow cover, snow albedo soil albedo ENT Dynamic Global Terrestrial Ecosystem Model (Kiang, Koster, Moorcroft, Ni-Meister, Rind) P, VP, CO2 Tair, Precip SW , PAR beam/diffuse Sensible & latent heat momentum Albedo, SW, CO2 fire aerosols VOCs DGTEM seasonal-decadal LANDSCAPE & VEG STRUCTURE patch (age distrib) cohort (density) individual plant functional type (pft) plant mass C&N:foliage, stem, root C&N: labile storage plant geometry LAI, SLA profile, dbh, height, root depth crown size (axes) hourly DISTURBANCE fire(above-ground biomass, dryness(soil moisture)) combustion products litter, new patches mixed canopies ED CANOPY RADIATIVE TRANSFER LAI & clumping profiles leaf albedo PAR profiles, sunlit/shaded net SW to soil patch albedo (canopy, soil, snow) update structure ALLOMETRY/ GROWTH/REPROD update plant geometry establish new seedlings density dependence mortality net CO2 uptake [layer] PAR[layer] sunlit/shaded daily carbon coupled C&N CANOPY BIOPHYSICS Ci Chl/N profile photosynthesis= Acan(leaf Chl, Ci, PAR, LAI,Tcan) conductance= gcan(moisture,Tcan,height,VPD, Acan) SOIL BGC labile C, labile N available N slow C, slow N soil respiration= (substrate, moisture, Tsoil) ALLOCATION/ PHENOLOGY budburst(Tgdd), cold/dry decid update individ C&N pools plant respiration N uptake, N fixation N deep soil layer litter u,v, P, VP Tair , LW Precip

  23. Global assimilation of AMSR-E soil moisture retrievals Assimilate retrievals of surface soil moisture from AMSR-E (2002-06) into NASA Catchment model (GEOS-5) Validate with USDA SCAN stations (only 23 of 103 suitable for validation) Assimilation product agrees better with ground data than satellite or model alone. Modest increase may be close to maximum possible with imperfect in situ data.

  24. NASA Ocean Biogeochemical Model (NOBM) Chlorophyll,Phytoplankton Groups Primary Production Nutrients DOC, DIC, pCO2 Spectral Irradiance/Radiance Outputs: Winds, ozone, relative humidity,pressure, precip. water, clouds (cover, τc), aerosols (τa, ωa, asym) Dust (Fe) Sea Ice Winds SST Radiative Model (OASIM) Ed(λ) Es(λ) Ed(λ) Es(λ) IOP Layer Depths Biogeochemical Processes Model Circulation Model (PoseidonV2) Temperature, Layer Depths Advection-diffusion

  25. Carbon Component pCO2 (air) Winds, Surface pressure pCO2 (water) Phyto- plankton Herbivores Dissolved Inorganic Carbon Dissolved Organic Carbon N/C Detritus Biogeochemical Processes Model Ecosystem Component Phytoplankton Nutrients Si Diatoms Silica Detritus NO3 Chloro- phytes Herbivores NH4 Cyano- bacteria Fe Cocco- lithophores N/C Detritus Iron Detritus

  26. Lars Nerger, “Assimilation of SeaWiFS Ocean Chlorophyll data with a simplified SEIK filter”

  27. Chemistry and Aerosols • The current GEOS-5 aerosol/chemistry capabilities evolved from several off-line CTM efforts at/through Code 613.3: • GOCART aerosols, CO/CO2 (Chin et al.) • CARMA aerosol microphysics (Toon et al., through Colarco) • StratChem (Douglas, Stolarski et al.) • GMI Tropospheric+Stratospheric (Combo) Chemistry • Which in turn derives from Harvard GEOS-Chem and StratChem

  28. Aerosol Modeling at GMAO • Aerosols transported on-line within GMAO’s Climate/Forecasting models • In climate mode: no data assimilation • In replay mode, using assimilated meteorology • Aerosols transported on-line within the GCM, without need for time interpolation of winds/diagnostics • Can be used for aerosol data assimilation • In full assimilation mode, combined meteorological/aerosol assimilation • Effective way of dealing with contamination of TOVS/AIRS radiances by aerosols

  29. Aerosol Processes by GEOS-5 • Advection: • Same Lin-Rood used my many off-line CTMs • Diffusion: • GEOS-5 has Lock type PBL parameterization • Convective transport: • Relaxed Arakawa-Schubert (RAS) parameterization • RAS provides convective transport as well as scavenging • Aerosol direct effects: • Chou et al. radiation package • Model transports dry aerosol mass; RH hygroscopic growth included during Mie calculation • Indirect effects (not yet integrated): • Nenes and Seinfeld parameterization for water clouds; additional ice clouds paramerization(Y.Sud)

  30. Collaborator: Mian Chin, Code 613.3

  31. Global 5-day chemical forecasts customized for each campaign O3, Aerosols, CO, CO2,.. Tag tracers Driven by real-time biomass emissions from MODIS Pre-mission System customization During-mission Web visualization, data delivery In-field forecasting support Comparison to aircraft data Post-mission: Gridded datasets available online for post mission analysis In depth evaluation, model tuning A truly GSFC wide effort: GMAO, ACDB, SIVO, NCCS GEOS-5/GOCART Forecasts CO Smoke SO4 O3

  32. Dust + Sea Salt GEOS-5/GOCART Forecast Valid at 3Z 20 July 2008 ECMWF

  33. Aerosol Data Assimilation at GMAO • Emphasis on estimation of • Global, 3D aerosol concentrations • Aerosol sources and model parameters • Observing System Simulation Experiments (OSSE) • Aerosol effects on climate, focus on hydrologic cycle • Aerosol forecasting capability in support of field campaigns

  34. MODIS Radiances • 1D-Var scheme using GOCART aerosol fields as background (Weaver et al 2005) • Ocean: draws to all 7 MODIS channels, drawing the tighest to 870nm • Land: draws only to 466 nm • Algorithm not integrated into GMAO’s realtime aerosol forecasting system

  35. OMI radiances • Next step: extension of 1D-Var scheme will for assimilation of OMI radiances • Combined assimilation of MODIS/OMI radiances • Built in adaptive bias correction for homo- genizing observing system

  36. CALIPSO • Simulation of attenuated backscatter from 3D aerosol distributions • CALIPSO aerosol 1D-Var at model vertical resolution • Adaptive tuning of GEOS-5 PBL • Joint assimilation of MODIS/OMI/CALIPSO measurements

  37. Biomass Emissions • Near real time estimates based on MODIS Fire products (AQUA/TERRA) • Used extensively during field campaigns • Currently developing next generation algorithm: • Based on fire radiative power • Determination of flaming/smoldering ratios and fire areas • Injection layer determined by Saulo’s Plume Rise parameterization • Focus on MODIS, eventually geostationary • Starting collaboration with NOAA and NRL

  38. Ozone in GEOS-5 DAS • Data: • SBUV and OMI ozone • TOVS and AIRS radiances • MLS retrieved stratospheric ozone profiles • Model: • Parameterized chemistry (production and loss rates) • Prognostic ozone used in: • Radiative heating computations in AGCM • Assimilation of IR radiances

  39. Assimilating AURA/MLS ozone NOAA 16 SBUV MLS Meta Sienkiewicz and Ivanka Stajner Zonal mean ozone 9/30/2004 00UTC SBUV daytime only – no data near South Pole due to high solar zenith angle MLS orbital limit ±82º SBUV/2 only MLS only Ozone partial pressure (mPa) Ozone hole develops in MLS assimilation

  40. GEOS-5 AGCM with Stratospheric chemistry module from GSFC/ACD • Simulations at 0.666° 0.5° with 72 layers • Year is defined only by boundary conditions (SST, ice, chemical emissions) • Example: April 1, “2004” - 70hPa near end of cold simulated Arctic winter High-resolution chemistry-climate model simulation with GEOS-5 Coherent filaments are peeled from the edge and interior of the polar vortex

  41. Carbon Data Assimilation - A Brief Overview Steven Pawson GSFC, CSU, ORNL, WHOI, HU • Research Goals: • Improved representation of processes • Model evaluation in context of carbon-cycle • Model-data combinations • Combining assimilation and inversion • Forward modeling and sampling for OSSEs (preparing for OCO) • Programmatic Goals: • GEOS-5: tool for carbon-cycle science • Earth-system modeling and assimilation • Products: • Assimilated CO2 concentrations based on existing (EOS) satellite data • 2002 onwards: based on AIRS, MODIS, … • 2008 onwards: based on OCO plus others • Flux estimates derived using inversion methods • Spatio-temporal resolution determined in project • Extensions to GEOS-5 for carbon-cycle science • GCM couplings to land and ocean • Extended assimilation capabilities

  42. Software Development and External Collaborations

  43. GMAO ocean biology LANL sea ice model Modern models integrate components from different sources ESMF accelerates development cycle ESMF NASA AGCM for climate and weather GMI chemistry GFDL Dynamics GMAO Physics GFDL Ocean GMAO Land GOCART aerosol GMAO Ocean Biology Add in the assimilation components and the satellite data  science + future mission design NSF NCAR / NASA GSFC / DOE LANL ANL / NOAA NCEP GFDL / MIT / U MICH

  44. GEOS-5 Component Architecture

  45. GEOS-5 AGCM at a Glance The Aerosol/Chemistry component must provide the following to radiation: Ox, O3, CH4, N2O, CFC11, CFC12,CFC22 and aerosols (dust, sea Salt, SO4 and carbonaceous)

  46. The AeroChem Component At runtime one selects one or more packages to run, and in case of ambiguity,which package provides a specific input to radiation

  47. System of systems • From such a collection of components several systems can be developed • Atmospheric data assimilation system • Oceanic data assimilation system • Seasonal forecasting system • Coupled climate-chemistry system • The development of each subsystem require careful validation for the applications at hand. • Computational resources dictate the particular combination of complexity/resolution that can be exercised.

  48. ESMF A High-Performance Framework for Earth Science Modeling & Data Assimilation Pilot Project: 2002-2005 Principal Investigators: Core ESMF: Tim Killeen (NCAR) Modeling: John Marshall (MIT)Data Assimilation: Arlindo da Silva (NASA) NASA/GSFC

  49. Technological Trends In climate research and NWP...increased emphasis on detailed representation of individual physical processes; requires many teams of specialists to contribute components to an overall coupled system In computing technology...increase in hardware and software complexity in high-performance computing, as we shift toward the use of scalable computing architectures

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