Laura G. Jairam

Neural Network Estimation of Atmospheric Profiles Using AIRS/IASI/AMSU Data in the Presence of Clouds

A novel statistical method for the retrieval of atmospheric temperature and water vapor profiles has been developed and evaluated with sounding data 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 on the EUMETSAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of the stochastic cloud clearing methodology together with neural network estimation. A stand-alone statistical algorithm will be presented that operates directly on cloud-impacted AIRS/AMSU and IASI/AMSU data, with no need for a physical cloud clearing process. The performance of this method was evaluated using global (ascending and descending) EOS-Aqua orbits collocated with ECMWF fields for a variety of days throughout 2003, 2004, 2005, and 2006. Over 1,000,000 fields of regard (3×3 arrays of footprints) over ocean and land were used in the study. The method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance. Retrieval accuracy will be evaluated using ECMWF atmospheric fields as ground truth. The accuracy of the neural network retrieval method will be compared to the accuracy of the AIRS Level 2 (Version 5) retrieval method.

Neural network estimation of atmospheric profiles using AIRS/IASI/AMSU data in the presence of clouds

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.

Neural network microwave precipitation retrievals and modeling results

We describe a simulation methodology used to develop and validate precipitation retrieval algorithms for current and future passive microwave sounders with emphasis on the NPOESS (National Polar-orbiting Operational Environmental Satellite System) sensors. Precipitation algorithms are currently being developed for ATMS, MIS, and NAST-M. ATMS, like AMSU, will have channels near the oxygen bands throughout 50-60 GHz, the water vapor resonance band at 183.31 GHz, as well as several window channels. ATMS will offer improvements in radiometric and spatial resolution over the AMSU-A/B and MHS sensors currently flying on NASA (Aqua), NOAA (POES) and EUMETSAT (MetOp) satellites. The similarity of ATMS to AMSU-A/B will allow the AMSU-A/B precipitation algorithm developed by Chen and Staelin to be adapted for ATMS, and the improvements of ATMS over AMSU-A/B suggest that a superior precipitation retrieval algorithm can be developed for ATMS. Like the Chen and Staelin algorithm for AMSU-A/B, the algorithm for ATMS to be presented will also be based a statisticsbased approach involving extensive signal processing and neural network estimation in contrast to traditional physics-based approaches. One potential advantage of a neural-network-based algorithm is computational speed. The main difference in applying the Chen-Staelin method to ATMS will consist of using the output of the most up-to-date simulation methodology instead of the ground-based weather radar and earlier versions of the simulation methodology. We also present recent progress on the millimeter-wave radiance simulation methodology that is used to derive simulated global ground-truth data sets for the development of precipitation retrieval algorithms suitable for use on a global scale by spaceborne millimeter-wave spectrometers. The methodology utilizes the MM5 Cloud Resolving Model (CRM), at 1-km resolution, to generate atmospheric thermodynamic quantities (for example, humidity and hydrometeor profiles). These data are then input into a Radiative Transfer Algorithm (RTA) to simulate at-sensor millimeter-wave radiances at a variety of viewing geometries. The simulated radiances are filtered and resampled to match the sensor resolution and orientation.

Porting and testing NPOESS CrIMSS EDR algorithms

As a part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and the NPOESS Preparatory Project (NPP), the instruments Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure profiles and calibrated radiances [1]. In preparation for the NPOESS/NPP launch, porting and testing of the CrIMSS Environmental Data Record (EDR) algorithms need to be performed.

Improved Simulation Methodology for Retrieval of Convective Precipitation from Spaceborne Passive Microwave Measurements

This manuscript focuses on recent efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (National Polar-orbiting Operational Environmental Satellite System) satellite program. Emphasis will be placed on the following three critical components: a methodology for simulating passive microwave observations, a technique for validating the methodology with aircraft measurements, and a statistics-based algorithm for estimating precipitation rate.

Neural network retrieval of precipitation using NPOESS microwave sensors

This paper will present efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (national polar-orbiting operational environmental satellite system) satellite program and the NPOESS preparatory project (NPP) prior to the launch of the first satellite in 2009. The advanced technology microwave sounder (ATMS) offers improvements including finer sampling and spatial resolution over heritage instruments such as the advanced microwave sounding unit instruments AMSU-A/B aboard the NOAA-15, NOAA-16, and NOAA-17, and similar instruments. The conical scanning microwave sounder (CSMS) is planned for the second and subsequent NPOESS satellites. A system for simulating ATMS and CSMS microwave observations from atmospheric data has been developed. This system has shown encouraging results when validated with observations from AMSU-B on NOAA-16. This system is flexible and can be used not only with cross-track scanning instruments but also with conically scanning instruments. A neural network was trained to estimate 5.2deg MM5 rain rates from simulated ATMS observations. Encouraging agreement was observed. However, this algorithm is only preliminary and many improvements are in progress.

Combined microwave and hyperspectral infrared retrievals of atmospheric profiles in the presence of clouds using nonlinear stochastic methods

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 sequential stages: 1) stochastic cloud clearing (SCC), 2) eigenvector radiance compression and denoising, and 3) neural network (NN) 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 is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, a feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. The performance of the algorithm (henceforth referred to as SCC/NN) was evaluated using global (ascending and descending) EOS-Aqua orbits co-located with ECMWF forecasts (generated every three hours on a 0.5-degree lat/lon grid) and radiosonde observations (RAOBs) for a variety of days throughout 2003 and 2004. Over 500,000 fields of regard (3times3 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 4) algorithm throughout most of the troposphere while achieving approximately four times the 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 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.

Radiometric Validation of Microwave Satellite Instruments Using the Npoess Aircraft Sounder Testbed-Microwave (NAST-M) Sensor

This paper outlines the results of two recent efforts to use the NPOESS Aircraft Sounder Testbed-Microwave (NAST-M) airborne sensor to directly validate the microwave radiometers on a number of operational satellites. Radiance differences between the NAST-M sensor and the Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sensor (MHS) were found to be less than 1 K for most channels. Comparison results for ocean underflights of the Aqua, NOAA, and MetOp-A satellites are shown.

Improved modeling and retrieval of convective precipitation from spaceborne passive microwave measurements

This manuscript focuses on recent efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (National Polar-Orbiting Operational Environmental Satellite System) satellite program. Emphasis will be placed on the following three critical components: a methodology for simulating passive microwave observations, a technique for validating the methodology with aircraft measurements, and a statistics-based algorithm for estimating precipitation rate.