Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation

TL;DR

This paper presents a Bayesian method using local geometry and dictionary-based regularization for passive microwave rainfall retrieval, demonstrating improved accuracy over diverse tropical storm conditions.

Contribution

Introduces a novel Bayesian retrieval algorithm combining local manifold geometry and shrinkage estimation for passive microwave rainfall measurement.

Findings

Effective in capturing storm morphologies including high intensity rain-cells.

Performs well over land, ocean, and coastal zones.

Validated against TRMM satellite data for 2013.

Abstract

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on a regularization technique and makes use of two joint dictionaries of coincidental 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 smooth manifolds with analogous local geometry. The detection step employs a nearest neighborhood classification rule, while the estimation scheme is equipped with a constrained shrinkage estimator to ensure stability of retrieval and some physical consistency. The algorithm is examined using coincidental observations of the active precipitation radar (PR) and passive microwave imager (TMI) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high intensity rain-cells and trailing light rainfall, especially over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite.