Hyperspectral IR sounders such as AIRS onboard NASA's EOS Aqua platform
and IASI onboard the Europe's METOP-A satellite provide unprecedented global
atmospheric temperature and moisture soundings with high accuracy and vertical
resolution. The AIRS and IASI radiance measurements have been used in the global
Numerical Weather Prediction (NWP) models with positive impact on weather forecasts.
We also applied the full spatial resolution soundings retrieved with CIMSS Hyperspectral
IR Sounding Retrieval (CHISR) algorithm from AIRS to tropical cyclone track and
intensity forecasts within a regional model - WRF. We found that through assimilating
the AIRS full spatial resolution temperature and moisture soundings, the tropical cyclone
track and intensity forecasts are significantly improved, the water vapor information
plays more important role in the forecast than the temperature information.
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.
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.
Surface emissivity plays an important role in the retrievals of surface and atmospheric parameters from satellite IR
measurements. In this research, a physical algorithm has been developed to retrieve hyperspectral IR emissivity
spectrum simultaneously with temperature and moisture profiles as well as surface skin temperature. To retrieve the
hyperspectral IR emissivity, the emissivity spectrum is represented by the eigenvectors, derived from the laboratory
measured hyperspectral emissivity database, in the retrieval process. Simulations are carried out with profiles over
different land surface properties and results show that simultaneous retrieval of emissivity spectrum can improve the
surface skin temperature as well as temperature and moisture profiles retrievals, particularly for the boundary layer
moisture. The algorithm has further been applied to the Atmospheric Infrared Sounder (AIRS) radiance measurements,
which covers a diversity of land surface types. The retrievals have then been compared with the ECMWF analyses and
radiosonde observations, and shown a very good agreement.
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