The algorithm for the current Geostationary Operational Environmental Satellite (GOES) Sounders has been adapted
to produce atmospheric temperature and moisture legacy profiles from simulated infrared radiances of the Advanced
Baseline Imager (ABI) onboard the next generation GOES-R. The Spinning Enhanced Visible and InfraRed Imager
(SEVIRI) onboard the Meteosat Second Generation (MSG) Meteosat-8/9 is used as proxy to test the algorithm
because it has many of the same spectral and spatial features as ABI. The impact of radiative transfer model on the
algorithm is evaluated by comparing two models: the PFAAST and the RTTOV9.1. It is found that RTTOV9.1 is
better than PFAAST. The selection of numerical forecast profiles as first guess in the retrieval is another key factor.
We compared the retrievals by using a global model (ECMWF 12H forecast) and a regional (RAM-3H forecast) as
first guess, respectively. It is found that the retrieval of low-level water vapor by regional model is better than global
model because of the higher spatial/temporal resolution of regional model.
An improved atmospheric profile retrieval system for the current Geostationary Operational Environmental Satellite
(GOES) Sounder data process has been developed. This algorithm can also be applied to process Advanced Baseline
Imager (ABI) on the next generation GOES-R to continue the current GOES class Sounder legacy products. The
Spinning Enhanced Visible and Infrared Imager (SEVIRI) data from the Meteosat Second Generation (MSG) satellite is
employed as proxy to test and evaluate the algorithm for ABI legacy product. Since there is only a few sounding spectral
bands in SEVIRI, a first guess from forecast is needed in legacy profile retrieval. The results show that if a set of
temperature/humidity profiles from weather forecast is applied as first guess, the accuracy of temperature/humidity
profiles can be achieved with that from the current GOES Sounder. Considering that there is only one temperaturesensitive
spectral band in SEVIRI, temperature information is limited; however, the improvement on humidity profile
retrieval over forecast is noticeable because there are two water vapor absorption spectral bands in SEVIRI. The results
of total precitable water (TPW) and lift index (LI) from combined SEVIRI and forecast are presented as well.
Algorithm has been developed for retrieving atmospheric temperature and moisture profiles from hyperspectral infrared
(IR) sounder radiances under both clear and cloudy skies. Focus has been on handling surface emissivity and clouds in
IR only sounding retrieval.
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