Synergetic atmospheric remote sensing under cloudy sky using single field of view IR/MW observations

Abstract

Space borne atmospheric remote sensing missions using combined passive microwave (MW) and infrared (IR) sensors take the advantage of high spatial and vertical resolution and the broad spectral sensitivity (to a variety of geophysical parameters) of the modern infrared sounders, and the superior ability of microwaves to penetrate many cloud types. The cloud-clearing algorithm, a commonly used algorithm to process the observation data collected by such kind of hyper-spectral sounding instruments under cloudy sky conditions, requires multiple footprints measurements as inputs to ‘clear’ the cloud scattering signature in the radiance spectra in order to simplify the forward and inverse calculation. It scarifies spatial gradient information, and requires supplemental processing steps to retrieve cloud height and cloud microphysical properties, which adds computational burden. We have developed a Physical retrieval algorithm with the simulation for cloud scattering properties included in the forward calculation. The Principal Component Based Radiative Transfer Model (PCRTM) is used for the IR forward simulation under cloudy sky conditions. And we use the Community Radiative Transfer Model (CRTM) for the MW part. This maximum-likelihood estimation based retrieval algorithm has been demonstrated to be able to obtain cloud properties along with atmospheric variables and surface properties simultaneously from single field of view (FOV) measurements. The algorithm's ability to process large volumes of asynoptic data from satellites at an ultra-fast pace, while retaining high-resolution features demonstrates its value of application in a real time objective ‘nowcasting’ system.