Mesoscale Atmospheric Processes Research Seminar: Efi Foufoula-Georgiou and Mohammad Ebtehaj

University of Minnesota

Past decades have witnessed remarkable emergence of new sources of multiscale multi-sensor precipitation data including global spaceborne active and passive sensors, regional ground based weather surveillance radars and local rain-gauges. Optimal integration of these multi-sensor data promises a posteriori estimates of precipitation fluxes with increased accuracy and resolution to be used in hydrologic applications.

In this context, a new framework is proposed for multiscale multi-sensor precipitation data fusion which capitalizes on two main observations: (1) non-Gaussian statistics of precipitation images which are concisely parameterized in the wavelet domain via a class of Gaussian Scale Mixtures, and (2) the conditionally Gaussian and weakly correlated local representation of precipitation reflectivity images in the wavelet domain, which allows exploiting the efficient linear estimation methodologies, while capturing the non-Gaussian data structure of rainfall. The proposed methodology is demonstrated using a dataset of coincidental observations of precipitation reflectivity images by the spaceborne precipitation radar (PR) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite and ground- based NEXRAD weather surveillance Doppler radars.