Calibration and Alignment

Abstract

Discrepancies between estimates of rainfall from ground-based radar and satellite observing systems can be attributed to either calibration differences or to geolocation and sampling differences. These latter include differences due to radar or satellite misregistration, differences in observation times, or variations in instrument and retrieval algorithm sensitivities. A new methodology has been developed and tested for integrating radar- and satellite-based estimates of precipitation using a feature calibration and alignment (FCA) technique. The parameters describing the calibration and alignment are found using a variational approach, and are composed of displacement and amplitude adjustments to the satellite rainfall retrievals, which minimize the differences with respect to the radar data and satisfy additional smoothness and magnitude constraints. In this approach the amplitude component represents a calibration of the satellite estimate to the radar, whereas the displacement components correct temporal and/or geolocation differences between the radar and satellite data. The method has been tested on a number of cases of the NASA WetNet PIP-2 dataset. These data consist of coincident estimates of rainfall by ground-based radar and the DMSP SSM/I. Sensitivity tests were conducted to tune the parameters of the algorithm. Results indicate the effectiveness of the technique in minimizing the discrepancies between radar and satellite observations of rainfall for a variety of rainfall events ranging from midlatitude frontal precipitation to heavy convection associated with a tropical cyclone (Hurricane Andrew). A remaining issue to be resolved is the incorporation of knowledge about location dependencies in the errors of the radar and microwave estimates. Once the satellite data have been adjusted to match the radar observations, the two independent estimates (radar and adjusted SSM/I rain rates) may be blended to improve the overall depiction of the rainfall event in a single analysis. The FCA technique also has potential applications in 1) the development of satellite rainfall retrieval algorithms that may be tuned to radar rain rates and 2) error assessment of rainfall predictions using radar or satellite rain rates as verification.