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

A nonlinear stochastic method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU), and is presently being adapted for use with the NPOESS Cross-track Infrared Microwave Sounding Suite (CrIMSS) consisting of the hyperspectral Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). The algorithm is implemented in three stages, motivating the name, SCENE (Stochastic Cloud clearing,(1) followed by Eigenvector radiance compression and denoising, followed by Neural network Estimation). First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing of the infrared and microwave data. Second, a Projected Principal Components (PPC) transform(2) is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, an artificial feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. This paper has two major components. First, details of the SCENE algorithm are discussed, including both the architectural implementation and parameter selection and optimization. Second, the performance of the SCENE algorithm is compared with that of the AIRS Level 2 algorithm (version 4.0.9)(3) currently being used for the Aqua mission. The stochastic cloud-clearing algorithm estimates infrared radiances that would be observed in the absence of clouds. This algorithm examines 3x3 sets of nine AIRS fields of view, selects the clearest ones, and then in a series of simple linear-and non-linear operations on both the infrared and microwave channels estimates a single cloud-cleared infrared spectrum for the 3x3 set. The algorithm is both trained and tested using global numerical weather analyses within 60 degrees of the equator. The analyses were generated by the European Center for Medium-range Weather Forecasting (ECMWF), and were converted to radiances using the SARTA v1.04 radiative transfer package. The PPC compression technique was used to reduce the infrared radiance dimensionality by a factor of 100, while retaining over 99.99% of the radiance variance that is correlated to the geophysical profiles. A feedforward neural network (NN) with a single hidden layer of approximately 3000 degrees of freedom was then used to estimate the atmospheric moisture and temperature profiles at approximately 60 levels from the surface to 20 km. The performance of the SCENE algorithm was evaluated using global, ascending EOS-Aqua orbits colocated with ECMWF forecasts (generated every three hours on a 0.5-degree lat/lon grid) for a variety of days throughout 2002 and 2003. Over 300,000 fields of regard (3x3 arrays of footprints) over ocean were used in the study. The RMS temperature and moisture profile retrieval errors for the SCENE algorithm were compared to those of the AIRS Level 2 algorithm, and the performance of the SCENE algorithm exceeded that of the AIRS Level 2 algorithm throughout most of the troposphere. The SCENE algorithm requires significantly less computation than traditional variational retrieval methods while achieving comparable performance, thus the algorithm is particularly suitable for quick-look retrieval generation for post-launch CrIMSS performance validation.