This paper outlines two calibration/validation efforts planned for current and future spaceborne microwave sounding instruments. First, the NPOESS Aircraft Sounder Testbed-Microwave (NAST-M) airborne sensor is used to directly validate the microwave radiometers (AMSU and MHS) on several operational satellites. Comparison results for underflights of the Aqua, NOAA, and MetOp-A satellites are shown. Second, a potential approach is presented for on-orbit field-of-view (FOV) calibration of the Advanced Technology Microwave Sounder (ATMS, to be launched in 2010). A constrained deconvolution technique is used to estimate spurious sidelobes in the ATMS antenna patterns from radiometric data collected while the sensor fields of view are scanned across the Earthpsilas limb.

This paper will provide an overview of the progress in satellite-based precipitation estimation using passive opaque microwave radiometry. Precipitation estimation has traditionally involved transparent microwave frequency bands near frequencies such as 6, 10, 18, 23, 37, and 89 GHz on instruments such as the Special Sensor Microwave Imager (SSM/I) on the DMSP (Defense Meteorological Satellite Program) satellites, the TRMM (Tropical Rainfall Measurement Mission) Microwave Imager (TMI), and the Advanced Microwave Sounding Radiometer for the Earth Observing System (AMSR-E) on the NASA Aqua satellite. Since 1990, there have been several instruments with opaque microwave channels in the 54-GHz oxygen or the 183-GHz water vapor resonance bands such as the Advanced Microwave Sounding Unit (AMSU-A/B) on NOAA-15, NOAA-16, and NOAA-17. Chen and Staelin have developed a precipitation estimation algorithm for AMSU-A/B and AMSU/HSB (Humidity Sounder for Brazil) on Aqua that relies primarily on these frequency bands. Improvements are anticipated with the launch of more instruments similar to AMSU and the launch of the Advanced Technology Microwave Sounder (ATMS) with improved resolution and sampling aboard the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) and the NPOESS satellites. Studies with the NAST-M (NPOESS Aircraft Sounder Testbed-Microwave) aircraft-based instrument which is equipped with 54-, 118-, 183-, and 425-GHz radiometers also suggest that future satellitebased precipitation estimation efforts could benefit from additional opaque bands. This paper will describe results from studies with NAST-M, the method of Chen and Staelin (2003), and the simulation system for developing a precipitation retrieval algorithm for ATMS.

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.

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.

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.

This paper reviews recent progress in neural-network-based atmospheric parameter retrieval algorithms. Satellite-based observations of the atmosphere are related to atmospheric and surface variables by the radiative transfer equation. Direct inversion of the radiative transfer equation is often intractable as the underlying atmospheric phenomenology is usually neither linearly related to the observations nor Gaussian. Neural networks provide a computationally efficient alternative with their relatively simple structure. However, effective use of neural network techniques in problems of this nature requires careful preprocessing of the input data, and this preprocessing is often highly dependent on the remote sensing scenario under consideration. In this paper, we present techniques for preprocessing neural network input data in the spectral, spatial, and temporal domains. The utility of these techniques is demonstrated with several applications, including the estimation of the atmospheric temperature profile, water vapor profile, and precipitation rate using satellite-based passive microwave and infrared measurements.

This paper describes a method for training neural nets to learn circular dependencies. Variables with circular structure (e.g. time of day, day of year, and earth location) appear in many different contexts within geoscience and remote sensing. Some common representations of circular variables (e.g. time of day in hours) can introduce discontinuities or topological distortions in estimation problems. They do not necessarily prevent a neural net from learning a relationship with circular dependencies. However, using topologically appropriate representations of circular variables can reduce the complexity necessary for a neural net to accurately learn such a relationship despite possibly increasing the number of inputs, and reducing the complexity results in shorter training times. In this paper, neural nets are trained to learn a function of time of day and a function of geolocation. In both examples, using topologically appropriate representations of time and geolocation instead of conventional representations as inputs significantly reduced RMS errors. This issue could be important in global earth science remote sensing applications where significant diurnal, seasonal, or geographical variations exist. The studies presented also suggest the development of a more general framework for training neural networks that considers the topology of variables.

A novel retrieval technique using stochastic cloud clearing and neural network estimation has been developed and evaluated. Retrieval RMS accuracies and quality control estimates are similar to physical, iterated methods while requiring substantially less computation.

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.

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.

In this paper, samples of AIRS data in the 1215 to 1615 cm(-1) spectral region are analyzed to better understand the effects of water vapor in the mid to upper tropospheric region. Two days representing mid-latitude (20 degrees- 40 degrees N) summer (warm and moist) and winter (cold and dry) maritime conditions are selected with cloud-free and 100% cloudy FOVs. The data, both in trend and differences, are well explained by the respective changes in atmospheric temperature and water vapor. These data are then compared with model simulation using MODTRAN. The results also compare favorably. Model simulation further illustrates the value of high spectral resolution for monitoring change in water vapor particularly in the upper troposphere. With the future GOES-R and NPOESS hyperspectral sensors expected to provide much improved atmospheric profile information, better monitoring of atmospheric water vapor will lead to improvements both in weather and climate applications.

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.

n Estimation techniques based on neural networks are becoming more common in high-resolution atmospheric remote sensing largely because of the simplicity, flexibility, and ability of the neural network techniques to accurately represent complex multidimensional statistical relationships. Spaceborne atmospheric sounders with increasingly finer spatial and spectral resolution are generating formidable amounts of radiance data. This abundance of data presents two major challenges in the development of algorithms that retrieve geophysical information from the radiance measurements. The first challenge concerns the robustness of the retrieval operator and involves maximal use of the geophysical content of the radiance data with minimal interference from instrument and atmospheric noise. The second challenge is the implementation of the robust algorithm within a given computational budget. The neural network estimation techniques described in this article allow both of these challenges to be overcome. Sample results are presented for retrievals of (1) atmospheric temperature and moisture profiles and (2) precipitation rates.

Certain types of two-dimensional (2-D) numerical remote sensing data can be losslessly and compactly compressed for archiving and distribution using standardized image formats. One common method for archiving and distributing data involves compressing data files using file compression utilities such as gzip and bzip2, which are widely available on UNIX and Linux operating systems. GZIP-compressed files and bzip2-compressed files must first be uncompressed before they can be read by a scientific application (e.g., MATLAB, IDL). Data stored using an image format, on the other hand, can be read directly by a scientific application supporting that format and, therefore, can be stored in compressed form, saving disk space. Moreover, wide use of image formats by data providers and wide support by scientific applications can reduce the need for providers of geophysical data to develop and maintain software customized for each type of dataset and reduce the need for users to develop and maintain or download and install such software. This letter demonstrates the utility of standardized image formats for losslessly compressing, archiving, and distributing 2-D geophysical data by comparing them with the traditional file compression utilities gzip and bzip2 on several types of remote sensing data. The formats studied include TIFF, PNG, lossless JPEG, JPEG-LS, and JPEG2000. PNG and TIFF are widely supported. JPEG2000 and JPEG-LS could become widely supported in the future. It is demonstrated that when the appropriate image format is selected, the compression ratios can be comparable to or better than those resulting from the use of file compression utilities. In particular, PNG, JPEG-LS, and JPEG2000 show promise for the types of data studied.

1. ABSTRACT, This paper ,presents ,climatological studies ,of precipitation-rate estimates ,based ,on data ,from the,Advanced ,Microwave ,Sounding ,Unit instruments, AMSU-A and AMSU-B, aboard the NOAA-15, NOAA-16, and NOAA-17 satellites; and the corresponding set of instruments, AMSU and the Humidity Sounder for Brazil (HSB), aboard the NASA Aqua satellite. A precipitation-rate retrieval algorithm,for ,these ,instruments ,has ,been developed (Chen and Staelin, IEEE Trans. Geosci. & Remote Sensing, 41(2)). This algorithm relies primarily on the ,opaque ,microwave ,bands near 54 and 183.31 GHz, which correspond to oxygen and water vapor absorption bands, respectively, and are particularly sensitive to glaciated precipitation. Most prior efforts towards satellite-based passive microwave,remote,sensing of precipitation(e.g. using TMI, SSM/I, and AMSR- Edata) have relied primarily on window,channels. The,AMSU algorithm ,has ,shown ,promising agreement,with NEXRAD over the eastern ,U.S. and plausible results globally. The NOAA-15, NOAA-16, and NOAA-17 satellites are polar- orbiting,with ,equatorial ,crossing ,times ,of

Precipitation rates (millimeters per hour) with 15- and 50-km horizontal resolution are among the initial products of Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB). They will help identify the meteorological state of the atmosphere and any AIRS soundings potentially contaminated by precipitation. These retrieval methods can also be applied to the AMSU 23-191-GHz data from operational weather satellites such as NOAA-15,-16, and -17. The global extension and calibration of these methods are subjects for future research. The precipitation-rate estimation method presented is based on the opaque-channel approach described by Staelin and Chen, but it utilizes more channels (17) and training data and infers 54-GHz band radiance perturbations at 15-km resolution. The dynamic range now reaches 100 mm/h. The method utilizes neural networks trained using the National Weather Service's Next Generation Weather Radar (NEXRAD) precipitation estimates for 38 coincident rainy orbits of NOAA-15 AMSU data obtained over the eastern United States and coastal waters during a full year. The rms discrepancies between AMSU and NEXRAD were evaluated for the following NEXRAD rain-rate categories: < 0.5, 0.5-1, 1-2, 2-4, 4-8, 8-16, 16-32, and > 32 mm/h. The rms discrepancies for the 3790 15-km pixels not used to train the estimator were 1.0, 2.0, 2.3, 2.7, 3.5, 6.9, 19.0, and 42.9 mm/h, respectively. The 50-km retrievals were computed by spatially filtering the 15-km retrievals. The rms discrepancies over the same categories for all 4709 50-km pixels flagged as potentially precipitating were 0.5, 0.9, 1.1, 1.8, 3.2, 6.6, 12.9, and 22.1 mm/h, respectively. Representative images of precipitation for tropical, mid-latitude, and snow conditions suggest the method's potential global applicability.

Arctic precipitation has been imaged by the passive Advanced Microwave Sounding Unit (AMSU) and Humidity Sounder for Brazil (HSB) aboard the Aqua satellite, and by nearly identical instruments (AMSU-A and AMSU-B) aboard the National Oceanographic and Atmospheric Administration NOAA-15, -16, and -17 satellites. Events were considered to be precipitation if they exhibited appropriate radiometric signatures and morphological evolution over consecutive satellite overpasses at 100-minute intervals. For such Arctic events, the observed cold perturbations near 183 +/- 7 GHz were as much as 20 K, and the cold perturbations near 52.8 GHz: were on the order of I K. These observed clumped cold perturbations imply the presence of scattering hydrometeors of diameter > 1 mm, which will precipitate. A comparison of these images with those of brightness temperatures from window channels that are sensitive to surface emissivity shows that such events are not likely to be the result of surface variations. Arctic precipitation rate estimates have been calculated using a precipitation-rate algorithm developed previously for mid-latitude climates [4]. For example, on July 20, 2002 a precipitation event similar to200 x 1000 km in size was observed by Aqua AMSU/HSB moving similar to100 km/h from similar to800degrees N toward northern Canada, and is consistent morphologically with numerical weather predictions. These estimates require substantial scaling to be accurate for polar atmospheres. Without careful modeling, areas of dry air can sometimes be mistakenly flagged as precipitation.