Research Publications


Research Areas

    Rainfall Algorithm Development: The Goddard PRofiling Algorithm (GPROF) was developed to retrieve both the instantaneous rainfall as well as the rainfall vertical structure from satellite passive microwave observations. It uses a Bayesian approach to match the observed brightness temperatures to hydrometeor profiles derived from cloud resolving models (CRMs). Research efforts to improve a number of aspects of the retrieval algorithm are ongoing. These include incorporating information from the TRMM precipitation radar into the a-priori database, extending the current scheme to include land backgrounds, and developing a parametric approach for use with a large constellation of radiometers with diverse characteristics.
    Rainfall Validation and Uncertainty Estimation: In situ rainfall measurements are limited over oceans due to the extreme paucity of measurements. As a result, estimates of uncertainties must depend on comprehensive error modeling. Related research efforts encompass a variety of aspects including the developing methods to propagate uncertainties through the retrieval algorithm, analysis of observational errors, and identifying biases associated with sensor differences and/or meteorlogical regimes.
    Climate Studies: Long-term global rainfall estimates now extend over well over two decades with high-quality satellite radiometer estimates approaching 20 years. Unfortunately the applicability of these datasets to studies of changes in the global hydrologic cycle are limited by regional and time-dependent biases that are often larger than the climate signals of interest. These biases are due to a combination of factors including changes in precipitation systems between meteorological regimes, sensor calibration errors, and sampling issues. Related research efforts are focused on identifying these biases, quantifying their impact, and developing methods to eliminate or reduce their influence.
    Modeling Studies: Continuing advances in Cloud Resolving Models now allow the prediction of explicit cloud microphysics that can in many cases be verified at regional scales. While observations can be used to validate these model results, the precipitation structure observations can also be used in a synergistic approach to examine model processes and improve these in preparation for more comprehensive data assimilation efforts at these scales.

Currently Funded Research Projects

Student Grants

    Center for the Earth Atmosphere Studies (CEAS) Fellowship
    Student: Sarah Ringerud
    Sponsor: NASA
    Period of Support: September 2011 - August 2012