William J. Blackwell

A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data

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.

Hyperspectral Microwave Atmospheric Sounding

We introduce a new hyperspectral microwave remote sensing modality for atmospheric sounding, driven by recent advances in microwave device technology that now permit receiver arrays that can multiplex multiple broad frequency bands into more than 100 spectral channels, thus improving both the vertical and horizontal resolutions of the retrieved atmospheric profile. Global simulation studies over ocean and land in clear and cloudy atmospheres using three different atmospheric profile databases are presented that assess the temperature, moisture, and precipitation sounding capability of several notional hyperspectral systems with channels sampled near the 50-60-, 118.75-, and 183.31-GHz absorption lines. These analyses demonstrate that hyperspectral microwave operation using frequency multiplexing techniques substantially improves temperature and moisture profiling accuracy, particularly in atmospheres that challenge conventional nonhyperspectral microwave sounding systems because of high water vapor and cloud liquid water content. Retrieval performance studies are also included that compare hyperspectral microwave sounding performance to conventional microwave and hyperspectral infrared approaches, both in a geostationary and a low-Earth-orbit context, and a path forward to a new generation of high-performance all-weather sounding is discussed.

Foreword to the Special Issue on Hyperspectral Image and Signal Processing

The 24 papers in this special issue are grouped into the following categories: spectral unmixing; classification and segmentation; compression; information extraction, fusion, and simulation; band selection; high performance computing; and monitoring of the environment.

A Single-Transceiver Compressive Reflector Antenna for High-Sensing-Capacity Imaging

This letter presents the simulated design and signal processing algorithms of a novel single-transceiver compressive reflector antenna for high-sensing-capacity imaging. The compressive reflector antenna (CRA) generates a spatial code in the imaging region, which is dynamically changed by using a mechanical rotation of the reflector. The scattered data measured by the single transceiver is processed using compressive sensing techniques in order to perform a 3-D reconstruction of the object under test. Preliminary results show that the CRA outperforms traditional reflector antennas in terms of sensing capacity and reconstruction accuracy.

Neural network Jacobian analysis for high-resolution profiling of the atmosphere

Neural networks have been widely used to provide retrievals of geophysical parameters from spectral radiance measurements made remotely by air-, ground-, and space-based sensors. The advantages of retrievals based on neural networks include speed of execution, simplicity of the trained algorithm, and ease of error analysis, and the proliferation of high quality training data sets derived from models and/or operational measurements has further facilitated their use. In this article, we provide examples of geophysical retrieval algorithms based on neural networks with a focus on Jacobian analysis. We examine a hypothetical 80-channel hyperspectral microwave atmospheric sounder (HyMAS) and construct examples comparing neural network water vapor retrieval performance with simple regressions. Jacobians (derivatives of the outputs with respect to the network weights and with respect to the inputs) are also presented and discussed. Finally, a discussion of the Jacobian operating points is provided.

S‐NPP ATMS instrument prelaunch and on‐orbit performance evaluation

The first of a new generation of microwave sounders was launched aboard the Suomi-National Polar-Orbiting Partnership satellite in October 2011. The Advanced Technology Microwave Sounder (ATMS) combines the capabilities and channel sets of three predecessor sounders into a single package to provide information on the atmospheric vertical temperature and moisture profiles that are the most critical observations needed for numerical weather forecast models. Enhancements include size/mass/power approximately one third of the previous total, three new sounding channels, the first space-based, Nyquist-sampled cross-track microwave temperature soundings for improved fusion with infrared soundings, plus improved temperature control and reliability. This paper describes the ATMS characteristics versus its predecessor, the advanced microwave sounding unit (AMSU), and presents the first comprehensive evaluation of key prelaunch and on-orbit performance parameters. Two-year on-orbit performance shows that the ATMS has maintained very stable radiometric sensitivity, in agreement with prelaunch data, meeting requirements for all channels (with margins of ~40% for channels 1–15), and improvements over AMSU-A when processed for equivalent spatial resolution. The radiometric accuracy, determined by analysis from ground test measurements, and using on-orbit instrument temperatures, also shows large margins relative to requirements (specified as <1.0 K for channels 1, 2, and 16–22 and <0.75 K for channels 3–15). A thorough evaluation of the performance of ATMS is especially important for this first proto-flight model unit of what will eventually be a series of ATMS sensors providing operational sounding capability for the U.S. and its international partners well into the next decade.

Radiometer Calibration Using Colocated GPS Radio Occultation Measurements

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earths limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015.

TID Tolerance of Popular CubeSat Components

In this paper we report total dose test results of COTS components commonly used on CubeSats. We investigate a variety of analog integrated circuits, popular microcontrollers (PIC24), SD memory cards and a TCXO.

Consensus-based imaging using ADMM for a Compressive Reflector Antenna

This paper describes a novel norm-one-regularized, consensus-based imaging algorithm, based on the Alternating Direction Method of Multipliers (ADMM), that can be used by a high-sensing-capacity Compressive Reflector Antenna (CRA). The proposed method outperforms current state of the art iterative reconstruction algorithms in terms of computational cost; and it ultimately enables the use of a CRA in quasi-real-time, compressive sensing imaging applications.

Error Variance Estimation for Individual Geophysical Parameter Retrievals

Neural networks (NNs) are developed for estimating the error variances of individual infrared and microwave atmospheric temperature and humidity profile retrievals, thus potentially significantly improving their assimilation into numerical weather prediction models. Currently, most assimilation processes require error covariance matrices that are typically estimated over diverse profile ensembles. In addition to these “ensemble error variances,” this work explores the estimation of “sample error variances” that are relevant to a single sample of the ensemble (that is, an individual profile retrieval and its error at each pressure level). This analysis is facilitated by considering an individual profile retrieval as the most likely sample from a distribution of retrievals, given an individual sensor observation vector. The sample error variance is defined as the variance of this distribution. The approach described in this paper does not attempt to compute these retrieval distributions explicitly, as this is computationally prohibitive for hyperspectral sounders. Instead, NNs are trained to estimate the variances of these distributions directly. Examples over ocean utilizing AIRS/AMSU soundings on the NASA Aqua satellite and those from a proposed hyperspectral microwave sounder show that the predicted sample error variances agree well with the true sample error variances as determined by European Centre for Medium-Range Weather Forecasts analyzes colocated to the sensor observations. Furthermore, simple quality indicators derived using thresholding of the sample variance estimates compare favorably to AIRS Level-2 Version-5 quality flags.