Improving moisture profile retrieval from broadband infrared radiances with an optimized first-guess scheme

Abstract

Variational retrieval of legacy atmospheric moisture profiles needs to begin with a first guess. An optimized first-guess scheme is developed for moisture profile retrieval from broadband infrared (IR) radiances. In this scheme, the non-exponential response of moisturemixingratio toIRradianceat high temperatures (>273 K) is considered. It is found that the first guess of low-level (below 550 hPa) moisture profiles is substantially improved after the new scheme. The data collected by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation are used for validation. This scheme provides an important optimization method for the next generation of Geostationary Operational Environmental Satellite (GOES)-R legacy profile retrieval algorithm because the Advanced Baseline Imager (ABI) onboard the GOES-R has very similar configurations to SEVIRI.