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Optimal spectral sampling (OSS) is a computationally fast and accurate method for modeling sensor-band transmittances and radiances. The spectral response of a sensor channel is approximated by an optimally weighted average of monochromatic radiative transfer calculations at optimally selected points. The absorption coefficients for the selected points are obtained from prestored lookup tables. Analytical Jacobians are produced in conjunction with the radiances with very little added computational burden. A microwave version of the OSS training algorithm and forward model has been developed in parallel with the infrared version. The microwave model treats [O.sub.2] and [N.sub.2] as fixed gases and [H.sub.2]O and [O.sub.3] as variable gases. Several reference line-by-line (LBL) models are available for training. The method of tabulating and interpolating absorption coefficients has been optimized for execution speed. Results are shown for OSS application to several current and future microwave sounders. With a selected requirement of 0.05-K rms error (with respect to the reference LBL model), the number of monochromatic points required varies from one, for most window channels, to about four for channels embedded in the 60-GHz [O.sub.2] line complex and along the 183-GHz [H.sub.2]O line. Even in the cases where a single point is adequate, the optimal point does not necessarily coincide with the center frequency of the channel. Index Terms--Electromagnetic propagation in absorbing media, radiative transfer modeling, terrestrial atmosphere.

Emissivity spectra computed by the FASTEM-3 model using global positioning system dropsonde wind as input are compared to emissivity retrieved from stringently coincident WINDSAT measurements, using a variational approach. This is done in both clear and rainy conditions to assess the validity of both the emissivity model and the retrieval technique in all conditions. Results of this comparison are presented for vertical and horizontal polarizations, in moderate to high wind conditions. In particular, a slope difference is found, and its potential sources are discussed in this paper. Index Terms--Dropsonde (DS), hurricane, microwave emissivity, retrieval algorithm, variational assimilation.

The ability to provide temperature and water-vapor soundings under extreme weather conditions, such as hurricanes, could extend the coverage of space-based measurements to critical areas and provide information that could enhance outcomes of numerical weather prediction (NWP) models and other storm-track forecasting models, which, in turn, could have vital societal benefits. An NWP-independent 1D-VAR system has been developed to carry out the simultaneous restitutions of atmospheric constituents and surface parameters in all weather conditions. This consistent treatment of all components that have an impact on the measurements allows an optimal information-content extraction. This study focuses on the data from the NOAA-18 satellite (AMSUA and MHS sounders). The retrieval of the precipitating and nonprecipitating cloud parameters is done in a profile form, taking advantage of the natural correlations that do exist between the different parameters and across the vertical layers. Stability and the problem's ill-posed nature are the two classical issues facing this type of retrieval. The use of empirically orthogonal-function decomposition leads to a dramatic stabilization of the problem. The main goal of this inversion system is to be able to retrieve independently, with a high-enough accuracy and under all conditions, the temperature and water-vapor profiles, which are still the two main prognostic variables in numerical weather forecast models. Validation of these parameters in different conditions is undertaken in this paper by comparing the case-by-case retrievals with GPS-dropsondes data and NWP analyses in and around a hurricane. High temporal and spatial variabilities of the atmosphere are shown to present a challenge to any attempt to validate the microwave remote-sensing retrievals in meteorologically active areas. Index Terms--Atmospheric sounding, data assimilation, dropsonde, hurricane, microwave remote sensing, retrieval algorithm.

We show that observed biases in retrievals of temperature and water vapor profiles from a 12-channel microwave radiometer arise from systematic differences between the observed and model-calculated brightness temperatures at five measurement frequencies between 22 and 30 GHz. Replacing the value for the air-broadened half-width of the 22-GHz water vapor line used in the Rosenkranz absorption model with the 5% smaller half-width from the HITRAN compilation largely eliminated the systematic differences in brightness temperatures. An a priori statistical retrieval based on the revised model demonstrated significant improvements ill the accuracy and vertical resolution of the retrieved temperature and water vapor profiles. Additional improvements were demonstrated by combining the MWRP retrievals with those from the GOES-8 sounder and by incorporating brightness temperature measurements at off-zenith angles in the retrievals. Index Terms--Microwave remote sensing, spectroscopy, thermodynamic profile retrieval, water vapor absorption.

A sensitivity study was performed with the commonly used millimeter-wave propagation model (MPM) to assess the impact of prescribed uncertainty in the temperature dependence of the line-coupling factors used to model the oxygen line shape. In some cases, apparent nonphysical behaviors were noticed and their impact on microwave channels was found to be significant and more importantly, nonremovable by simple calibration or bias removal. The Advanced Microwave Sounding Unit configuration in particular was tested because of its prime importance in current data assimilation. This impact, which reached a maximum of a few Kelvin in some channels, is modulated nonlinearly by the amount of water vapor present in the atmosphere, with maximum impact in dry air situations, which makes it dependent on the brightness temperature itself. These errors directly impact the error budget of any physically based geophysical retrieval. The line shape of these [O.sub.2] lines has impacts across the microwave spectrum, through the wing effects. This includes channels near 23, 36, and 85 GHz, commonly used in operational radiometry. Index Terms--Microwave remote sensing, oxygen absorption, spectroscopy, temperature dependence.

Observing system assessments were made for the Earth Observing Nanosatellite-Microwave (EON-MW), a 12U CubeSat analog of the Advanced Technology Microwave Sounder (ATMS). Since the EON-MW channels and sensor specifications closely follow those of ATMS, the sensor characteristics and geophysical capabilities assessments indicate that the value of information for humidity (temperature) of EON-MW observations will be similar (very similar) to that of ATMS. Eight global observing system simulation experiments (OSSEs) were carried out to evaluate several different EON-MW constellations for data gap mitigation and/or replacement of existing sensors. In these OSSEs, adding 2 EON-MW sensors in different orbits, compared to adding a single EON-MW sensor, improves forecasts generally, and improves the analysis of at least wind and humidity. In terms of the overall OSSE impacts in the scenarios considered, a single EON-MW sensor is a close substitute for ATMS and two EON-MW sensors are a close substitute for the Special Sensor Microwave Imager Sounder (SSMIS). The analysis and forecast impacts indicate that EON-MW provides improved humidity profiles compared to SSMIS and ATMS. [ABSTRACT FROM AUTHOR]

The ERS-1 microwave radiometer was designed to correct the satellite altimeter data for the excess path delay due to tropospheric humidity. The brightness temperature calibration was tested using two radiative transfer models, applied to the same coincident meteorological profiles over ocean. Differences were analyzed by similarly comparing calculated brightness temperatures with SSM/I data, for the same meteorological data set, extracted from ECMWF (European Center for Medium range Weather Forecasting) analyses. The calibration of the two channels of the ERS-1 microwave radiometer required correction of a few kelvin. The final water vapor and altimeter path delay validation was performed by comparing routine radiosonde measurements with the retrieved water vapor and altimeter path corrections over a one-year period.

Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances. [ABSTRACT FROM AUTHOR]

The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction. [ABSTRACT FROM AUTHOR]