Empirical-based hydrometeor identification algorithm using GMI brightness temperatures and WSR-88D derived hydrometeor classification
Date of Award
Master of Science (MS)
Atmospheric and Earth Science
Freezing precipitation--Remote sensing, Hail, Microwaves, Radiometers
This research uses passive microwave brightness temperatures (TB) from the Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI), and hydrometeor identification (HID) data from the GPM Ground Validation Network (GVN). An empirical relationship is developed between the observed TB and hydrometeors within the profile, using four years of co-located TB and GVN HIDs for training and testing purposes. The empirical relationship is used to create an HID algorithm, which predicts the probabilities of hydrometeor classifications, given a combination of TB values, with an average accuracy within 6.6 percent of observa- tions, using mean absolute error. The HID algorithm is applied to GMI data from April 2014 to March 2020 to create global climate maps of each HID. The climate maps are used for studying global, seasonal, and regional precipitation characteristics. The climate maps highlight hail and graupel occurrence in regions known for intense convection such as Argentina and the Central United States.
Solomon, Michael B., "Empirical-based hydrometeor identification algorithm using GMI brightness temperatures and WSR-88D derived hydrometeor classification" (2023). Theses. 453.