1. GMAO Global Modeling and Assimilation Office NASA/GSFC
NSIPP & DAO merger - a new focus activity for NASA’s global modeling and data assimilation
The Office will be a core resource for NASA’s Earth Science Enterprise in the development and use of satellite observations. Our main thrust will be to use comprehensive global models and data assimilation to maximize the impact of satellite observations in climate and weather prediction.
Models and assimilation systems are integrating tools, essential to realizing the value of satellite technologies.

2. Use models and assimilation tools to
GOALS
Use models and assimilation tools to
advance understanding of climate variability and change
help interpret and extrapolate the information from satellite observations
bring information in satellite observations to improve models
help define and design future satellite missions
Support ESE science goals through provision of targeted products
Maximize the impact of satellite observations in short term climate, weather and air quality predictions and other national applications
Transition satellite assimilation capabilities to operations
Contribute to the science of observing systems: evaluate existing and potential new data types - sensitivity of products and forecasts to different data types
Advance chemical constituent/aerosol modeling & assimilation
Advance subseasonal-to-decadal climate prediction - the ultimate test of models used for climate change
Agency Partnerships: JCSDA, ESMF, CLIVAR CPTs, NCEP, NCAR, GFDL, NOAA/OGP/CDEP

3. Atmospheric Modeling
New approach: unified modeling capability - weather capable climate model and climate-reliable weather model
Single Goddard modeling system
FVcore + evolving physics: GSFC developments with NCAR, GFDL collaborations
Include GISS modelE under a common Goddard model “toolkit” (with Code 930)
Model for weather and climate:
Improve moist processes in AGCMs - for climate and for assimilating cloud and precipitation data - preparation for GPM - modeling of hydrological cycle
Next generation model:
Embedded CRM - assimilation of high-resolution imagery data, clouds and precipitation data - use of Geostationary data; preparation for GPM,NPP, GIFTS
Non-hydrostatic model
Development and validation in collaboration with other centers and general community
Climate Process Modeling Teams (NCAR, GFDL, GSFC and their collaborators)
GMAO Atmosphere Working Group (GISS, GSFC, NASA Centers, GMAO science team)

4. A unified atmospheric model for climate and weather prediction
Enabling Technology: Earth System Modeling Framework  interoperability
ESMF development
ESMF maintenance, enhancement, optimization
NSIPP/Goddard Physics
Prognostic Clouds
(NSIPP, GISS, Sud)
NCAR GWD
Stratosphere and Mesosphere Physics:
bring assimilation to GWD, physics development
CLIVAR CPTs
to improve parameterizations
Finite Volume Dynamical Core
Embedded Cloud Resolving Model
nonhydrostatic model
FvCore
under ESMF
3Q 03
GMAO
unified model
4Q 03
unified model in
DAS
3Q 04
Cloud & precip assim
Improved
Convection
physics
4Q 05
Improved
physics
2006
Improved model
for cloud assimilation
2008
Weather in
climate model
2010
Improved assim
& simulation for
Aura
GOCART aerosol
Microphysics
stratospheric
chemistry
Fully coupled
Trop & strat
chemistry
Shared components possible
GISS modelE
Updated 1Q 04
NCCS & GMAO:
Parallelized & ESMF-compliant GISS ModelE

7. Using TRMM storm top and rain rate statistics to evaluate and improve the GMAO Convective Parameterization (RAS)
Monthly-Mean Convective Storm Top Frequency for RAS in the NSIPP-2 Model vs. TRMM as a Function of Rain Rate
Height (km)
Mon Mean Rain Rate (mm h-1)
Y3amip01
TRMM
Warm rain
Missing shallower cloud tops
30o N/S Ocean Only
Vertical Rain Rate Profiles
Y3amip01_Tok land
Y3amip01_Tok ocean
TRMM ocean
TRMM land
Monthly Area-Mean Rain Rate (mm h-1)
Sigma
model is missing shallower convective cloud tops present during heavier rain rates
has a larger percentage of warm rain than TRMM
model has reasonable vertical rain rate structure
rain rate too high
Robertson & Bacmeister

8. Unification of LAND SURFACE MODELs
Dynamic vegetation
module: species
succession
SiB2, SiB3, CLM,
outside collaborations
Dynamic vegetation
module: phenology
SiB2, SiB3, CLM, GISSLSM
outside collaborations
Coupling Physics,
Boundary Layer Physics
Mosaic, CLM, PLACE
Transpiration Physics:
stomatal conductance,
photosynthesis physics
SiB2, SiB3, CLM
Other evaporation:
interception loss,
bare soil evaporation
All models
Snow Physics
Catchment, SSib
Land Surface Hydrology:
runoff generation,
soil layering,
subgrid variability
Catchment,
TOPLATS, PLACE
River Routing
In-house,
outside collaboration
Albedo,
thermal
emissivity
All models
Carbon,
geochemical tracers
SiB2, SiB3, CLM, outside
collaborations
LSM unification:
Water balance
Energy balance
Carbon balance
Evolution of surface and subsurface states
Subsurface Thermodynamics
Catchment, CLM
Vegetation Physics
Other Physical Processes

9. CGCMv1 NSIPP1 AGCM 2 x 2.5 x 34L Poseidon v4 OGCM 1/3 x 5/8 x 27L
Mosaic LSM

10. CGCM: 2 = 0.48
Obs: 2 = 0.82

11. Predictability of Extreme weather events:
High resolution models
Large ensembles to resolve the tail of the pdf
Large-Scale Conditions Favoring Extreme Winter Weather in the SE U.S.
(note strong ENSO signal)
Probability Density Function of Extreme Winter Storms in SE U.S.
(DJF )
300mb Height
Observations
Red - El Nino winters
Blue - La Nina winters
Dotted - observations
Solid - model (9 ensemble members)
Frequency
Higher probability of extreme weather
NSIPP Model Simulations
Strength
The NSIPP model is able to mimic nature.
Extreme Winter storms are
more likely to occur during El Nino winters
Schubert, Suarez
GSFC/GMAO (NSIPP)

12. Schubert et al.

13. Next GMAO Assimilation system (GEOS5)
Joint Analysis System with NCEP - Accelerate the utilization and operational implementation of new satellite data types
NASA Development
NOAA Development
ESE Science Interests with NWP constraints on quality & performance
Primarily driven by operational NWP
Input Observations
Common Data
Processing/QC
NASA
metrics
NOAA
metrics
ModelAnalysis
Interface (ESMF)
Analysis Algorithm
Input Model State
Flexible background error
formulation in grid-space
Advanced observation
operators (radiances, winds)
Increased observation count
(~106/6-hr cycle)
Run-time choices for
NASA/NOAA applications
Interoperability
NCEP AGCM
GMAO AGCM
AnalysisModel
Interface (ESMF)

14. Land surface models and assimilation
Predictability of JJA precipitation
associated with SST
associated with SST & soil wetness
Theoretical estimates of prediction skill
Large ensembles used to assess potential data impacts
INITIALIZED
NOT INITIALIZED
OBSERVATIONS
Test of land initialized by observed forcing
Precipitation anomalies: the 1988 drought 10 5 3 2 1
0.7
0.5
0.3
0.2
0.1
0.0
- 0.1
- 0.2
- 0.3
- 0.5
- 0.7
- 1
- 2
- 3
- 5
mm/day
Forecast experiments with simple land initialization to test simulation results
NSIPP also places much emphasis on soil moisture assimilation. Early predictability studies showed the potential for increased skill in the summertime precipitation forecasts over the central US if information on surface soil wetness is available.
While waiting for soil moisture data from AMSR-E on Aqua, Randy Koster has conducted some initial experiments of initializing the LSM soil moisture by running the LSM offline with prescribed forcing. The initial tests seem to confirm the theoretical predictability results. Here the -ve numbers are colored blue, and indicate a deficit in precipitation. Whereas the forecasts with the LSM initialization does not reproduce the observations, the improvement in the drought forecast over the central US is clear. One can expect that, just as for the ocean, the initialization of the land surface by assimilating observations of soil moisture will improve upon these results.
Improvements in areas consistent with theoretical results
Koster & Suarez
NASA/GMAO

15. Anomaly correlation of forecast SSH with TOPEX altimetry
May starts
Altimeter data not used in initialization
Altimeter data used in initialization
Forecast
Lead
1 month
3 month
6 month
9 month
Kurkowski, Keppenne, Kovach

16. GMAO Interactions with CCSM and GFDL
CLIVAR CPTs - atmospheric physics
ESMF partnership
Finite volume dynamical core
Data assimilation input to climate model development
WACCM - chemistry-climate coupling (GMI)
chemistry assimilation