The GMAO's assimilation research is focused on enhancing the use of NASA satellite data for weather and climate prediction and for climate analyses. Guidance on observing system issues is provided through Observing System Experiments (OSEs) and through an emerging Observing System Simulation Experiment (OSSE) capability.
Atmospheric Data Assimilation
The GMAO advances the assimilation of satellite data for weather prediction and for research-quality reanalyses. In addition to OSEs and OSSEs, adjoint tools have been developed to characterize the impacts of the different elements of the observing system on weather forecasts. Developments are undertaken in partnership with NOAA/NCEP/Environmental Modeling Center and NESDIS.
The GMAO also pursues innovative research in precipitation/cloud assimilation using the forecast model as a weak constraint to assimilate precipitation and cloud information from space-borne sensors to improve weather forecasting, global modeling, and climate analysis. The ultimate goal is to maximize the societal and scientific returns of current and future satellite precipitation missions (e.g., TRMM, AMSR, MODIS, CloudSat, CALIPSO, GPM) by exploring advanced assimilation techniques and conducting demonstration studies.
Details of our work can be found at our Atmospheric Data Assimilation site.
Ocean Data Assimilation
The ocean is the primary source of memory for the climate system. Initialization of the ocean state, particularly in the tropical oceans, is an important element of seasonal forecast systems. The GMAO ocean data assimilation focuses on advancing techniques such as the Ensemble Kalman Filter (EnKF) to take advantage of surface observations from space, to improve covariance modeling for ocean state estimation, and to initialize coupled models for seasonal prediction. Details of our work can be found at our Ocean Data Assimilation site.
Land Surface Data Assimilation
Land surface data assimilation contributes to integrated Earth system modeling and data assimilation in two ways. Firstly, the land surface provides memory in the climate system, particularly at sub-seasonal time scales (2 weeks to 2 months), and impacts precipitation predictability over some continental regions. Secondly, accurate land surface estimates are required to enable the assimilation of atmospheric observations that are sensitive to land surface conditions. The GMAO research on land surface data assimilation encompasses satellite observations for soil moisture, snow, land surface temperature, and terrestrial water storage.
Details of our work can be found at our Land Surface Data Assimilation site.
Atmospheric Constituent Assimilation
The atmospheric data assimilation system exploits various types of ozone data from operational and research satellites to advance our understanding of the ozone distribution in the atmosphere as well as to improve weather forecasts. A carbon data assimilation system is being developed to exploit NASA.s modeling capabilities and space-based data records to help understand the distribution and cycling of carbon species in the Earth System. Observations from instruments such as MOPITT, AIRS and eventually OCO are used to constrain atmospheric carbon species. Details of our work can be found at our Constituent Modeling and Assimilation site.
In addition to meteorological analyses and forecasts, the GEOS-5 system is used with the GOCART module of AeroChem to make real-time estimates and forecasts of aerosols, CO and CO2 tracers in support of NASA field campaigns. Details can be found at our Aerosol Transport and Assimilation site.