DocumentCode :
1203258
Title :
A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data
Author :
Blackwell, William J.
Author_Institution :
Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA
Volume :
43
Issue :
11
fYear :
2005
Firstpage :
2535
Lastpage :
2546
Abstract :
A novel statistical method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with simulated clear-air and observed partially cloudy sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). The algorithm is implemented in two stages. First, a projected principal components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Second, a multilayer feedforward neural network (NN) is used to estimate the desired geophysical parameters from the PPCs. For the first time, NN temperature and moisture retrievals are presented using actual microwave and hyperspectral infrared observations of cloudy atmospheres, over both ocean and land (with variable terrain elevation), and at all sensor scan angles. The performance of the NN retrieval method (henceforth referred to as the PPC/NN method) was evaluated using global Earth Observing System Aqua orbits colocated with European Center for Medium-range Weather Forecasting fields for seven days throughout 2002 and 2003. Over 350,000 partially cloudy footprints were used in the study, and retrieval performance was compared with the AIRS Science Team Level-2 retrieval algorithm (version 3). Performance compares favorably with that obtained with simulated clear-air observations from the NOAA88b radiosonde set of approximately 7500 profiles. The PPC/NN method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance.
Keywords :
atmospheric humidity; atmospheric techniques; atmospheric temperature; feedforward neural nets; microwave measurement; moisture measurement; remote sensing; AD 2002 to 2003; AIRS; AMSU; Advanced Microwave Sounding Unit; Advanced Technology Microwave Sounder; Aqua; Atmospheric Infrared Sounder; Crosstrack Infrared Sounder; Earth Observing System; European Center for Medium-range Weather Forecasting; Infrared Atmospheric Sounding Interferometer; atmospheric temperature; canonical correlations; cloudy atmospheres; feedforward neural network; humidity; hyperspectral infrared observation; microwave observation; moisture profiles; projected principal components transform; sounding data; Hyperspectral sensors; Information retrieval; Infrared sensors; Moisture; Multi-layer neural network; Neural networks; Ocean temperature; Statistical analysis; Temperature sensors; Terrestrial atmosphere; Advanced Microwave Sounding Unit (AMSU); Advanced Technology Microwave Sounder (ATMS); Atmospheric InfraRed Sounder (AIRS); Crosstrack Infrared Sounder (CrIS); Infrared Atmospheric Sounding Interferometer (IASI); canonical correlations; humidity; hyperspectral; infrared; inversion; microwave; moisture; neural networks (NNs); principal components; retrieval; sounding; temperature;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2005.855071
Filename :
1522614
Link To Document :
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