Neural network retrieval of atmospheric temperature and moisture profiles from AIRS/AMSU data in the presence of clouds

Author(s):  
William J. Blackwell ◽  
Frederick W. Chen
2008 ◽  
Vol 8 (6) ◽  
pp. 21001-21035
Author(s):  
D. K. Zhou ◽  
W. L. Smith ◽  
A. M. Larar ◽  
X. Liu ◽  
J. P. Taylor ◽  
...  

Abstract. Atmospheric thermodynamic parameters, such as atmospheric temperature and moisture profiles, cloud optical/microphysical properties, and surface properties are basic meteorological variables for weather forecasting. In addition, they are critical parameters in tropospheric chemistry studies. A physical, geophysical parameter retrieval scheme dealing with cloudy and cloud-free radiances observed with satellite ultraspectral infrared sounders has been developed to determine simultaneously surface, atmospheric thermodynamic, and cloud microphysical parameters. A one-dimensional variational (1-D Var.) multivariable inverse solution of the radiative transfer equation is used to iteratively improve a background state defined by eigenvector regression. This algorithm has been applied to data from the Infrared Atmospheric Sounding Interferometer (IASI) on the EUMETSAT Metop-A satellite. The IASI retrieved parameters presented herein are from radiance data gathered during the Joint Airborne IASI Validation Experiment (JAIVEx). JAIVEx provided intensive aircraft observations obtained from airborne Fourier Transform Spectrometer (FTS) systems, such as the NPOESS Airborne Sounder Testbed – Interferometer (NAST-I), in-situ measurements, and dedicated dropsonde and radiosonde measurements for the validation of the IASI products. Here, IASI atmospheric profile retrievals are compared with those obtained from dedicated dropsondes, radiosondes, and the airborne FTS system. The IASI examples presented here demonstrate the ability to retrieve fine-scale horizontal features with high vertical resolution from satellite ultraspectral sounder radiance spectra.


2008 ◽  
Vol 47 (1) ◽  
pp. 108-123 ◽  
Author(s):  
Suzanne W. Seemann ◽  
Eva E. Borbas ◽  
Robert O. Knuteson ◽  
Gordon R. Stephenson ◽  
Hung-Lung Huang

Abstract A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.


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