Dr. Ricardo Todling is a Research Meteorologist working
for NASA at the Global Modeling and Assimilation Office (GMAO; former Data Assimilation Office, DAO). He started at NASA/DAO under a National Research Council fellowship in 1992, followed by a fellowship from the Universities Space Research Association in 1994, and subsequently joined Science Applications International Corporation as a U.S. government contractor. Dr. Todling formally joined NASA as a civil servant in 2009.
In 1996 Dr. Todling became directly involved in the core data assimilation development at DAO. He was a key developer of the first DAO near-real-time data assimilation system (DAS) built in support of NASA's Earth Observing System (EOS) program in the late 1990's. Since then, he has been leading the atmospheric analysis group and the maintenance, development and testing of the Goddard Earth Observing System (GEOS) Atmospheric DAS. In his capacity, Dr. Todling provides support to NASA Instrument Teams for validation and calibration of their products; he has also contributed significantly to the development and deployment of the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its follow up MERRA-2.
Over the years, Dr. Todling has served as liaison between NASA/GMAO and NOAA's National Centers for Environmental Prediction (NCEP), particularly contributing to the development of the Gridpoint Statistical Interpolation (GSI) system: a software component of the assimilation systems of GMAO, NCEP, and various partners of the Joint Center for Satellite Data Assimilation (JCSDA). Additionally, Dr. Todling has led the development of the GEOS Hybrid four-dimensional Ensemble-Variational Data Assimilation System, and its
adjoint-based tool for assessment of the impact of observations in short-range forecasts.
Currently, Dr. Todling's primary focus is in the transition of the GSI-based system to a JEDI-based system. Beyond its core duties, Dr. Todling has ongoing collaborations and research to: (i) investigate the effect of volcanic aerosols in reanalysis; (ii) evaluate the potential of a proactive quality control strategy to improve GEOS forecasts; (iii) extend GEOS to the mesosphere and lower-thermosphere and generate a reanalysis going up to 150 km; and (iv) explore machine learning methodologies to improve the treatment of sea surface temperature and sea ice in GEOS forecasts, as well as to replace the GEOS ensemble used in the hybrid system with a surrogate perturbation model..