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As the forthcoming launch of the NPOESS Preparatory Project (NPP) nears, pre-launch predictions of onorbit
performance are of critical importance to illuminate possible emphasis areas for the intensive calibration/
validation (cal/val) period to follow launch. During this period of intensive cal/val (ICV), quick-look
performance assessment tools that can analyze global data over a variety of observing conditions will also play
an important role in verifying and potentially improving environmental data record (EDR) quality. In this paper,
we present recent work on a fast and accurate sounding algorithm based on neural networks for use with the
Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) to be flown on
the NPP satellite. The algorithm is being used to assess pre-launch sounding performance using proxy data
(where observations from current satellite sensors are transformed spectrally and spatially to resemble CrIS and
ATMS) from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU)
on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU/MHS
(Microwave Humidity Sounder) on the EUMETSAT MetOp-A satellite. The algorithm is also being developed
to provide a highly-accurate quick-look capability during the NPP ICV period. The present work focuses on
the cloud impact on the infrared (AIRS/IASI/CrIS) radiances and explores the use of stochastic cloud clearing
(SCC) mechanisms together with neural network (NN) estimation. A stand-alone statistical algorithm will be
presented that operates directly on cloud-impacted AIRS/AMSU, IASI/AMSU, and CrIS/ATMS (collectively
CrIMSS) data, with no need for a physical cloud clearing process. The algorithm is implemented in three stages.
First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing
of the infrared and microwave data using the SCC approach. The cloud clearing of the infrared radiances was
performed using principal components analysis of infrared brightness temperature contrasts in adjacent fields of
view and microwave-derived estimates of the infrared clear-column radiances to estimate and correct the radiance
contamination introduced by clouds. Second, 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. Third, an articial feedforward neural network (NN) is used to estimate the desired geophysical
parameters from the projected principal components. The performance of the method was evaluated using global
(ascending and descending) EOS-Aqua and MetOp-A orbits co-located with ECMWF forecasts (generated every
three hours on a 0.5-degree lat/lon grid) for a variety of days throughout 2003, 2004, 2005, and 2007. Over
1,000,000 fields of regard (3 × 3/2 × 2 arrays of footprints) over ocean and land were used in the study. The
performance of the SCC/NN algorithm exceeded that of the AIRS Level 2 (Version 5) algorithm throughout
most of the troposphere while achieving approximately 25-50 percent greater yield. Furthermore, the SCC/NN
performance in the lowest 1 km of the atmosphere greatly exceeds that of the AIRS Level 2 algorithm as the
level of cloudiness increases. The SCC/NN algorithm requires signicantly 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.
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