Assimilating Earth System Observations at NASA: MERRA and Beyond Siegfried Schubert, Michael Bosilovich, Michele Rienecker, Max Suarez, Ron Gelaro, Randy Koster, Julio Bacmeister, Ricardo Todling, Larry Takacs, Emily Liu, Gi-Kong Kim, Man-Li Wu, Phil Pegion, Myong-In Lee, Junye Chen, Steve Bloom, Rolf Reichle, Steven Pawson, Ivanka Stajner, Arlindo da Silva, Christian Keppenne, Watson Gregg and many others in NASA’s Global Modeling and Assimilation Office Presentation at the 3rd WCRP Conference on Reanalysis Tokyo, Japan 28 January - 01 Feb, 2008
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Assimilating Earth System Observations at NASA: MERRA
and BeyondSiegfried Schubert, Michael Bosilovich, Michele Rienecker, Max Suarez,
Ron Gelaro, Randy Koster, Julio Bacmeister, Ricardo Todling, Larry Takacs, Emily Liu, Gi-Kong Kim, Man-Li Wu, Phil Pegion, Myong-In Lee, Junye Chen, Steve Bloom, Rolf Reichle, Steven Pawson, Ivanka
Stajner, Arlindo da Silva, Christian Keppenne, Watson Gregg
and many others in NASA’s Global Modeling and Assimilation Office
Presentation at the 3rd WCRP Conference on Reanalysis Tokyo, Japan
28 January - 01 Feb, 2008
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Overview
• MERRA– The GEOS-5 DAS and observations– Some early results
• Beyond MERRA– Building the components for an
Integrated Earth System Analysis Capability
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AGCMFinite-volume dynamical core (S.J. Lin)Moist physics (J. Bacmeister, S. Moorthi and M. Suarez)Physics integrated under the Earth System Modeling Framework (ESMF)Generalized vertical coord to 0.01 hPaCatchment land surface model (R. Koster)Prescribed aerosols (P. Colarco)Interactive ozonePrescribed SST, sea-ice
AnalysisGrid Point Statistical Interpolation (GSI from NCEP)Direct assimilation of satellite radiance data using
JCSDA Community Radiative Transfer Model (CRTM)
Variational bias correction for radiances
AssimilationApply Incremental Analysis
Increments (IAU) to reduce shock of data insertion (Bloom et al.)
IAU gradually forces the model integration throughout the 6 hour analysis period
GEOS-5 Atmospheric DAS for MERRA(Supported by NASA MAP Program)
MERRA cloud liquid water path*, compares well with SSMI estimates
January 2004
*Comparison with observations is complicated. SSMI LWP contains contributions from convective and precipitating liquid. “True”, i.e., radiatively active, model LWP (tql) does not contain convective (or precipitating) condensate. The convective contribution (qccu) to LWP can be estimated from MERRA output
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Jul 06
Jul 04
Jul 04EC OPS
qLT
CLOUDSATCloud Tops
Marine Stratus DeckOff Peru
Marine Stratus DeckOff Peru
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Regional Climate
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GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
GPCPGPCP
EC-OPSEC-OPS
CMAPCMAP
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
EC-OPSEC-OPS
Monthly Mean Precipitation over India July 2004 (mm/day)
Difference from GPCP
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Latitude-time Cross Section of Precipitation over India 2004 (72.5E-80E)
GPCP(Pentad)
TRMM (daily mean)
JRA(daily mean)
GEOS5(daily mean)
GEOS5(daily mean)
latit
ude
latit
ude
latit
ude
latit
ude
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GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
GPCPGPCP
EC-OPSEC-OPS
CMAPCMAP
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
EC-OPSEC-OPS
Monthly Mean Precipitation over Americas July 2004 (mm/day)
Difference from GPCP
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Precipitation (mm/d) and 925mb wind
NARR
GEOS-5
Seasonal evolution of North American monsoon (2004)
GEOS-5 reproduces the typical structure of the monsoon rainband. Seasonal march of the rainband is reasonable, with a peak in July.
Maximum rainfall region is located reasonably well in the windward slope of the mountains (the Sierra Madre Occidental).
Southwesterly flows in the Gulf of California and in the upslope of the mountains seem to be benefit from the high-resolution (1/2-degree) data assimilation.
Amplitude of Precipitation Diurnal Cycle (24-h harmonic)
mm/d
Larger diurnal variability over continents than oceans
GEOS-5 tends to overestimate the amplitude over continents and underestimate over oceans
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0-6Z 6-12Z 12-18Z 18-0Z
GEOS-5GEOS-5
JRAJRA
NCEP R1NCEP R1
NCEP R2NCEP R2
EC-OPSEC-OPS
TRMM
Diurnal variation in precipitation over the United States for July 2004 (mm/day). The July mean is removed.
TRMM
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Vertical Structure of LLJ: Jul/Aug 2004 v-wind at 35°N
NARR
GEOS-5
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MERRA FILE COLLECTIONS
• MERRA products are organized into 24 collections in HDF
• Data are produced on three horizontal grids:• Native ----------- (1/2 by 2/3 w/ FV conventions)• Reduced ------- (1 1/4 by 1 1/4 Dateline-edge, Pole-edge)• Reduced FV -- (1 by 1¼ w/ FV conventions)
• In the vertical, 3-D data are at:• 72 model layers• 42 pressure levels
• Temporal resolution:• 3D products are 3-hourly• 2D products are hourly and at native resolution
• Total online collections ~150TB
• Distributed through a modeling data portal at the Goddard DAAC(including GDS, ftp )
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Summary
• MERRA improves upon many features of existing reanalyses
• Biases generally smaller than climate signals
• Precipitation issues remain: trends; diurnal cycle, summer land
• Comprehensive output suite
• Spin-ups completed and MERRA processing has started
• Expect to complete processing by mid-2009
Next Steps
Developing Components of Future Integrated Earth System
Analysis, with consistent analyses across all
components
GMAO Data Assimilation Systems
Atmosphere Constituents Aerosols
Land Surface Ocean Biology Physical Ocean
Assimilation of AMSRAssimilation of AMSR--E soil moisture retrievalsE soil moisture retrievals
Assimilation of TOPEX/Jason Altimeter DataAssimilation of TOPEX/Jason Altimeter Data
Assimilation of AURA/MLS and OMI OzoneAssimilation of AURA/MLS and OMI OzoneJan 04 Precipitation inJan 04 Precipitation in MERRA MERRA Aerosol Transport Aerosol Transport
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Soil moisture [m3/m3]
Land data assimilation at the NASA GMAOLand data assimilation at the NASA GMAO
Land surface data products (incl. root zone soil moisture, evaporation)
Land surface temperature (MODIS,
AVHRR,GOES,… ”ISCCP”)
260 280 300
Snow cover fraction (MODIS)
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Ocean data assimilationOcean data assimilation in the GMAOin the GMAO
Temperature and salinity profiles from Argo floats
Surface chlorophyll(CZCS, SeaWiFS, MODIS)
Ensemble-based ocean data assimilation system
Ocean state estimates for climate analysis and for short-term climate forecasts
In situ temperature profiles (TAO/PIRATA moorings, XBTs)
Sea Level anomalies (TOPEX/JASON)
SST (AMSR-E; MODIS)
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AtmosphereMeteorological analysesChemistry constituents: ozone, coupled with meteorologyChemistry constituents: CO, CO2 NOx under developmentChemistry constituents: Aerosol Transport, with source distributions from
satelliteGEOS-5 AGCM, currently 3Dvar, 4Dvar in test phase
Land SurfaceSoil moisture, surface temperature and snowCatchment LSM with EnKF
OceanRetrospective Ocean analyses for seasonal forecastsMOM4: MvOI, EnKF Assimilation in the CGCM coupled to atmospheric
analysisOcean color analyses: ocean time series, removing cross-satellite biases
AtmosphereMeteorological analysesChemistry constituents: ozone, coupled with meteorologyChemistry constituents: CO, CO2 NOx under developmentChemistry constituents: Aerosol Transport, with source distributions from
satelliteGEOS-5 AGCM, currently 3Dvar, 4Dvar in test phase
Land SurfaceSoil moisture, surface temperature and snowCatchment LSM with EnKF
OceanRetrospective Ocean analyses for seasonal forecastsMOM4: MvOI, EnKF Assimilation in the CGCM coupled to atmospheric
analysisOcean color analyses: ocean time series, removing cross-satellite biases