PROJECT TITLE :
Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation
This paper introduces a replacement Bayesian approach to the inverse downside of passive microwave rainfall retrieval. The proposed methodology [known as the shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP)] depends on a regularization technique and makes use of 2 joint dictionaries of coincident rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection–estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently sleek manifolds with analogous native geometry. The detection step employs a nearest neighbor classification rule, whereas the estimation scheme is provided with a constrained shrinkage estimator to confirm the stability of retrieval and some physical consistency. The algorithm is examined using coincident observations of the active precipitation radar and also the passive microwave imager onboard the TRMM satellite. We present promising results of instantaneous rainfall retrieval for a few tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We have a tendency to give proof that the algorithm is capable of properly capturing totally different storm morphologies as well as high-intensity rain cells and trailing light rainfall, significantly over land and coastal areas. The algorithm is additionally validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall merchandise of the TRMM satellite.
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