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This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles ['or land and sea up to 73[degrees] latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 nun/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized. Index Terms--Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Sounding Unit (AMSU), microwave precipitation estimation, nficrowave radiative transfer, precipitation.

This paper develops a global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU), which observes 23-191 GHz. The algorithm was trained using a numerical weather prediction (NWP) model (MMS) for 106 globally distributed storms that predicted brightness temperatures consistent with those observed simultaneously by AMSU. Neural networks were trained to retrieve hydrometeor water-paths, peak vertical wind, and 15-min average surface precipitation rates for rain and snow at 15-km resolution at all viewing angles. Different estimators were trained for land and sea, where surfaces classed as snow or ice were generally excluded from this paper. Surface-sensitive channels were incorporated by using linear combinations [principal components (PCs)] of their brightness temperatures that were observed to be relatively insensitive to the surface, as determined by visual examination of global images of each brightness temperature spectrum PC. This paper also demonstrates that multiple scattering in high microwave albedo clouds may help explain the observed consistency for a global set of 122 storms between AMSU-observed 50-191-GHz brightness temperature distributions and corresponding distributions predicted using a cloud-resolving mesoscale NWP model (MM5) and a two-stream radiative transfer model that models icy hydrometeors as spheres with frequency-dependent densities. The AMSU/MM5 retrieval algorithm developed in Part I of this paper is evaluated in Part II on a separate paper. Index Terms--Advanced Microwave Sounding Unit (AMSU), microwave precipitation estimation, microwave radiative transfer, precipitation.

Brightness temperature histograms observed at 50-191 GHz by the Advanced Microwave Sounding Unit (AMSU) on operational NOAA satellites are shown to be consistent with predictions made using a mesoscale NWP model [the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5)] and a radiative transfer model [TBSCAT/F([lambda])] for a global set of 122 storms coincident with the AMSU observations. Observable discrepancies between the observed and modeled histograms occurred when 1) snow and graupel mixing ratios were increased more than 15% and 25%, respectively, or their altitudes increased more than ~25 mb; 2) the density, F([lambda]), of equivalent Miescattering ice spheres increased more than 0.03 g [cm.sup.-3]; and 3) the two-stream ice scattering increased more than ~1%. Using the same MM5/TBSCAT/F([lambda]) model, neural networks were developed to retrieve the following from AMSU and geostationary microwave satellites: hydrometeor water paths, 15-min average surface-precipitation rates, and cell-top altitudes, all with 15-km resolution. Simulated AMSU rms precipitation-rate retrieval accuracies ranged from 0.4 to 21 mm [h.sup.-1] when grouped by octaves of MM5 precipitation rate between 0.1 and 64 mm [h.sup.-1], and were -3.8 mm [h.sup.-1] for the octave 4-8 mm [h.sup.-1]. AMSU and geostationary microwave (GEM) precipitation-rate retrieval aecuracies for random 50-50 mixtures of profiles simulated with either the baseline or a modified-physics model were largely insensitive to changes in model physics that would be clearly evident in AMSU observations if real. This insensitivity of retrieval accuracies to model assumptions implies that MM5/TBSCAT/F([lambda]) simulations offer a useful test bed for evaluating alternative millimeter-wave satellite designs and methods for retrieval and assimilation, to the extent that surface effects are limited.

Only instruments on geostationary or comparable platforms can view global precipitation at the ~15-min interval that is necessary to monitor rapidly evolving convective events. This paper compares the abilities of 11 alternative passive microwave sensors to retrieve surface precipitation rates and hydrometeor water paths. Five instruments observe selected frequencies from 116 to 429 GHz with a filled-aperture antenna, and six instruments observe from 52 to 191 GHz with a U-shaped aperture synthesis array. The analysis is based on neural network retrieval methods and 122 global MMS-simulated storms that are generally consistent with the simultaneous Advanced Microwave Sounding Unit observations. Several instruments show considerable promise in retrieving hydrometeor water paths and 15-min average precipitation rates ~1-100 mm/h with spatial resolutions that vary from ~15 to ~50 kin. This space/time resolution is potentially adequate to support assimilation of precipitation information into cloud-resolving numerical weather prediction models. Index Terms--Aperture synthesis sounders, geostationary microwave imagers, microwave precipitation estimation, microwave radiative transfer, precipitation.

This paper addresses the following: 1) millimeter-wave scattering by icy hydrometeors and 2) the consistency between histograms of millimeter-wave atmospheric radiances observed by satellite instruments [Advanced Microwave Sounding Unit-A/B (AMSU-A/B)] and those predicted by a mesoscale numerical weather prediction (NWP) model (MM5) in combination with a two-stream radiative transfer model (TBSCAT). This observed consistency at 15-km resolution supports use of MM5/TBSCAT as a useful simulation tool for designing and assessing global millimeter-wave systems for remote sensing of precipitation and related parameters at 50-200 GHz. MM5 was initialized by National Center for Environmental Prediction NWP analyses on a 1[degrees] grid approximately 5 h prior to each AMSU transit and employed the Goddard explicit cloud physics model. The scattering behavior of icy hydrometeors, including snow and graupel, was assumed to be that of spheres having an ice density F([lambda]) and the same average Mie scattering cross sections as computed using a discrete-dipole approximation implemented by DDSCAT for hexagonal plates and six-pointed rosettes, respectively, which have typical dimensional ratios observed aloft. No tuning beyond the stated assumptions was employed. The validity of these approximations was tested by varying F([lambda]) for snow and graupel so as to minimize discrepancies between AMSU and MM5/TBSCAT radiance histograms over 122 global storms. Differences between these two independent determinations of F([lambda]) were less than ~0.1 for both snow and graupel. Histograms of radiances for AMSU and MM5/TBSCAT generally agree for 122 global storms and for subsets of convective, stratiform, snowy, and nonglaciated precipitation. Index Terms--Electromagnetic propagation, meteorology, microwave radiometry, millimeter-wave propagation, millimeter-wave radiometry, propagation, rain, remote sensing, scattering, weather forecasting.

A new set of algorithms has been developed to estimate spectral reflectance in remote sensing imagery. These algorithms are called surface prior information reflectance estimation (SPIRE) algorithms and estimate surface spectral reflectance using prior spatial and spectral information about the surface reflectance. This paper describes SPIRE algorithms that employ spatial processing of single-channel data to estimate local changes in spectral reflectance under spatially and spectrally varying multiplicative and additive noise caused by variations in illumination and atmospheric effects. Rather than modeling the physics of the atmosphere and illumination (using a physics-based code such as the Atmospheric Removal (ATREM) program), or using ground truth spectra at known locations to compensate for these effects [using the empirical line method (ELM)], prior information about the low spatial frequency content of the scene in each spectral channel is used instead. HYDICE visible near-infrared shortwave infrared (VNIR-SWIR) hyperspectral data were used to compare the performance of SPIRE, ATREM, and ELM atmospheric compensation algorithms. The spatial SPIRE algorithm performance was found to be nearly identical to the ELM ground-truth-based results, while spatial SPIRE performed better than ATREM overall and significantly better under high clouds and haze. Index Terms--Atmospheric compensation, reflectance estimation, remote sensing, spatial filtering.

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: 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. Index Terms--Hydrology, measurement, microwave propagation, microwave radiometry, precipitation, rain rate, remote sensing, weather satellite.

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-[micro]m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements. Index Terms--Climate, greenhouse gases, humidity sounder, hyperspectral, infrared, microwave, temperature sounder, weather forecasting.

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) aircraft sounder testbed (NAST) has recently been developed and deployed on the NASA ER-2 high-altitude aircraft. The testbed consists of two co-located cross-track scanning instruments: a Fourier transform interferometer spectrometer (NAST-I) [1] with spectral coverage of 3.7-15.5 [micro] m and a passive microwave spectrometer (NAST-M) with 17 channels near the oxygen absorption lines at 50-57 GHz and 118.75 GHz. The testbed provides the first coregistered imagery from high-resolution microwave and infrared sounders and will provide new data that will help 1) validate meteorological satellite environmental data record (EDR) feasibility, 2) define future satellite instrument specifications, and 3) demonstrate operational issues in ground validation, data calibration, and retrievals of meteorological parameters. To help validate the performance and potential of NAST-M, imagery was collected from more than 20 overpasses of hurricanes Bonnie and Earl during the Convection and Moisture Experiment (CAMEX-3), Florida, Summer 1998. The warm core and convection morphology of Hurricane Bonnie (August, 1998) is clearly revealed both by aircraft-based microwave brightness temperature imagery and temperature retrievals within the eye. Radiance comparisons with the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15 satellite and radiosonde observations yield root mean-squared (RMS) agreements of approximately 1 K or less. Index Terms--Aircraft instrumentation, hurricane images, microwave imaging, microwave radiometry, precipitation, temperature profile retrievals.

The use of passive 118-GHz [O.sub.2] observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feedforward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2-4.km horizontal spatial resolution by using the millimeter-wave temperature sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the Cooperative Huntsville Meteorological Experiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993. Index Terms - Microwave remote sensing, microwave spectra 118 GHz, neural network, precipitation estimation, rain cell-top altitude.

A technique for retrieving atmospheric humidity profiles using passive microwave spectral observations from satellite and Multilayer Feedforward Neural Networks (MFNN) is introduced in this paper. Relative humidity retrievals on a global scale from simulated radiances at fifteen frequencies between 23.8 and 183.3 GHz yielded fms errors in relative humidity of 6-14% over ocean and 6-15% over land at pressure levels ranging from 131 mbar to 1013 mbar. Comparison with a combined statistical and physical iterative retrieval scheme shows that superior retrievals can be obtained at a lower computational cost using MFNN.

Estimation of atmospheric relative humidity profiles using passive remote sensing techniques is difficult when the temperature profile is not well known, and such retrievals approach singularity when the atmosphere is nearly isothermal. A retrieval method that is more robust near isothermal regions and temperature inversions is described here. Its robust character results from an iterative combination of statistical methods based on a priori data, which stabilize the effects of any singularities, and physical methods that reflect the nonlinear character of the equation of radiative transfer and the dependence of measurements on uncertain surface reflectivities and temperature profiles. This method can be used to interpret data from meteorological satellites. It was tested extensively using simulated clear-sky microwave observations from space at 89 GHz, 166 GHz, and three frequencies near the 183-GHz water vapor resonance and the 60-GHz oxygen band, which is sensitive to the atmospheric temperature profile. Humidity profiles from the tropical, midlatitude, and arctic regions were retrieved. Relative humidity profiles retrieved using the statistical iterative method typically had errors between 5 and 10% in the 300-1000 mbar pressure region. These errors were somewhat less in the tropics and greater in the polar regions, and represented significantly better performances than a linear statistical retrieval method.

A CCD-based confocal microscope system that is used to measure accurate three-dimensional surface profiles is reported. For a field of view of 500 micrometers, surface samples spaced at 12 micrometers on smooth specular test objects are simultaneously resolved in depth to approximately 1 micrometer (depending on the surface being observed). A precision of 0.1 micrometer is obtained for a mirrored surface for a field of view 400 micrometers wide. Simple scaling and sampling results permit these results to be extended to other apparatus dimensions and range sampling intervals.

An experiment in which the third Stokes parameter thermal emission from a periodic water surface was measured is documented. This parameter is shown to be related to the direction of periodicity of the periodic surface and to approach brightnesses of up to 30 K at X band for the surface used in the experiment. The surface actually analyzed was a 'two-layer' periodic surface; the theory of thermal emission from such a surface is derived and the theoretical results are found to be in good agreement with the experimental measurements. These results further the idea of using the third Stokes parameter emission as an indicator of wind direction over the ocean.

The concurrent development of algorithms and systolic architectures for adaptive filtering purposes is presented. It details how the McClellan transformation is used for parametrization of a least squares filter and presented as a solution to the problem of adapting a large zero-phase finite impulse response multidimensional filter with limited parameters and arbitary directional biases.

Promising agreement over land and sea has been obtained between NEXRAD 3-GHz radar observations of precipitation rate and retrievals based on simultaneous passive observations at 50-191 GHz from the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15 meteorological satellite. A neural network with three hidden nodes and one linear output node operated on 15 km resolution data at 183 [+ or -] 1 and 183 [+ or -] 7 GHz, plus the cosine of scan angle, to produce estimates that match well the morphology of NEXRAD hurricane and frontal precipitation data smoothed to 15-km resolution. A second neural network operated on the same three parameters used in the first network, but smoothed to 50-km resolution, plus spatially-filtered cold perturbations detected in three AMSU tropospheric temperature-sounding channels (channels 4-6), which also have 50-km resolution. Comparison with the same NEXRAD data smoothed to 50-km resolution yielded root mean square (rms) discrepancies for two frontal systems and two passes over Hurricane Georges of ~1.1 mm/h, and [+ or -]1.4 dB for those precipitation events over 4 mm/h. Only 8.9% of the total AMSU-derived rainfall was in areas where AMSU saw more than 1- mm/h and NEXRAD saw less than 1-mm/h, and only 6.2% of the total NEXRAD-derived rainfall was in areas where NEXRAD saw more than 1-mm/h and AMSU saw less than 1-mm/h. Index Terms--Measurement, microwave propagation, microwave radiometry, microwave spectroscopy, precipitation, remote sensing.