This paper addresses the following: 1) millimeterwave 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 • 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 (λ) 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 (λ) 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 (λ) 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.

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This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

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.

A new global precipitation retrieval algorithm for the millimeter-wave Advanced Microwave Sounding Unit is presented that also retrieves Arctic precipitation rates over surface snow and ice. This algorithm improves upon its predecessor by excluding some surface-sensitive channels and by reducing the number of principal components (PCs) used to represent those that remain. The training sets were also modified to better represent cold regions. The algorithm still incorporates conversion of brightness temperatures to nadir, spatial filtering to better detect pixels scattering near 54 GHz, PC filtering of surface effects, and use of separate neural networks trained with the fifth-generation National Center for Atmospheric Research/Penn State Mesoscale Model (MM5) for land and sea, where warm and cold ocean are now treated differently. The validity of the snowfall detections is supported by nearly coincident CloudSat radar observations, and the physics of the model is largely validated by the reasonable agreement in annual precipitation obtained for 231 globally distributed rain gauges, including many at latitudes where snowfall dominates. Observed annual global precipitation statistics are also presented to permit comparisons with other algorithms and sensors.

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(λ)] 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(λ), of equivalent Mie-scattering ice spheres increased more than 0.03 g cm−3; and 3) the two-stream ice scattering increased more than ∼1%. Using the same MM5/TBSCAT/F(λ) 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−1 when grouped by octaves of MM5 precipitation rate between 0.1 and 64 mm h−1, and were ∼3.8 mm h−1 for the octave 4–8 mm h−1. AMSU and geostationary microwave (GEM) precipitation-rate retrieval accuracies 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(λ) 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.

A surface-precipitation-rate retrieval algorithm for 13-channel Advanced Microwave Sounding Unit (AMSU) millimeter-wave spectral observations from 23 to 191 GHz is described. It was trained using cloudresolving fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) simulations over 106 global storms. The resulting retrievals from the U.S. NOAA-15 and NOAA-16 operational weather satellites are compared with average annual accumulations (mm yr 21 ) for 2006-07 observed by 787 rain gauges globally distributed across 11 surface classifications defined using Advanced Very High Resolution Radiometer infrared spectral images and two classifications defined geographically. Most surface classifications had bias ratios for AMSU/gauges that ranged from 0.88 to 1.59, although higher systematic AMSU overestimates by factors of 2.4, 3.1, and 9 were found for grassland, shrubs over bare ground, and pure bare ground, respectively. The retrievals were then empirically corrected using these observed biases for each surface type. Global images of corrected average annual accumulations of rain, snow, and convective and stratiform precipitation are presented for the period 2002-07. Most results are consistent with Global Precipitation Climatology Project estimates. Evidence based on MM5 simulations suggests that near-surface evaporation of precipitation may have necessitated most of the corrections for undervegetated surfaces. A new correction for radio-frequency interference affecting AMSU is also presented for the same two NOAA satellites and improves retrieval accuracies.

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