TY - JOUR
AU - Grecu, Mircea
AB - THE GODDARD PROFILING ALGORITHM (GPROF): DESCRIPTION AND CURRENT APPLICATIONS 1 2 2 3 William S. Olson , Song Yang , John E. Stout , and Mircea Grecu Joint Center for Earth Systems Technology, University of Maryland, Baltimore, MD, USA George Mason University, Fairfax, VA, USA Goddard Earth Sciences and Technology Center and NASA/Goddard Space Flight Center, Greenbelt, MD, USA 1 INTRODUCTION The use of Bayesian estimation methods in passive microwave radiometry follows from a recognition that the total information content of radiometer observations is insufficient to determine a “unique” estimate of surface rain rate or precipitation vertical profile. In other words, for a given set of multi- frequency microwave observations at a given location, there exist several precipitation profiles that are radiatively consistent with the observations, and so iterative methods for seeking a unique solution (Smith et al. 1994) would not necessarily return a better estimate. Also, being non-iterative, Bayesian methods are relatively computationally efficient, since iterative forward radiance calculations are not required. Here, the Goddard Profiling Algorithm (GPROF) is described, and appli- cations of the most recent implementation (Version 6) of the algorithm are presented and critiqued. 2 ALGORITHM DESCRIPTION GPROF is based upon a Bayesian technique originally 
TI - Measuring Precipitation From Space: The Goddard Profiling Algorithm (GPROF): Description and Current Applications
DA - 2007-01-01
UR - https://www.deepdyve.com/lp/springer-journals/measuring-precipitation-from-space-the-goddard-profiling-algorithm-BdwfxEzURl
DP - DeepDyve
ER -