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A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements
Different aspects of the operational constraints of remote sensing inverse problems are thoroughly investigated by simulation studies, using a deterministic method, namely regularized total least squares (RTLS). For demonstration purposes, water vapor profiles retrievals from simulated Suomi NPP Cross-track Infrared Souder (CrIS) hyperspectral measurements are considered. Synthetic CrIS radiances are generated using a line-by-line radiative transfer model (GENSPECT) with ∼424 realistic radiosonde profiles and US 1976 standard atmosphere as inputs. These results are also compared with those from a prevalent stochastic method. Our findings show that the stochastic method, even with additional deterministic constraints (truncated singular value decomposition) applied on top of it, is often unable to produce useful retrieval results, i.e., posterior error is more than the a priori error. In contrast, RTLS is able to produce deterministically unique results according to the available information content in the measurements, which could result in a paradigm shift in operational satellite inversion.
A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements
Different aspects of the operational constraints of remote sensing inverse problems are thoroughly investigated by simulation studies, using a deterministic method, namely regularized total least squares (RTLS). For demonstration purposes, water vapor profiles retrievals from simulated Suomi NPP Cross-track Infrared Souder (CrIS) hyperspectral measurements are considered. Synthetic CrIS radiances are generated using a line-by-line radiative transfer model (GENSPECT) with ∼424 realistic radiosonde profiles and US 1976 standard atmosphere as inputs. These results are also compared with those from a prevalent stochastic method. Our findings show that the stochastic method, even with additional deterministic constraints (truncated singular value decomposition) applied on top of it, is often unable to produce useful retrieval results, i.e., posterior error is more than the a priori error. In contrast, RTLS is able to produce deterministically unique results according to the available information content in the measurements, which could result in a paradigm shift in operational satellite inversion.
A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements
Koner, Prabhat K (author) / Harris, Andrew R / Dash, Prasanjit
2016
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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