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This contract was for a basic research program to investigate the effects of clouds, precipitation and surface emissivity on microwave satellite sensors and their influence on atmospheric retrievals using the Unified Retrieval (UR) technique. The work consisted of the following tasks: development of databases of brightness temperatures from various satellite sensors; development databases of conventional analysis to verify the presence and amount of clouds and precipitation and for verification of retrieval results; the application of the UR techniques to the satellite sensor data; and the assessment of the accuracy of UR techniques in the presence of clouds. There was no measurable decrease in the UR performance in non-precipitating cloud cases. In cases with precipitating clouds, the UR performance was degraded as measured by the residual, which increased with increasing rain rate within the FOV.

This report summarizes results of a basic research program to investigate and develop advanced techniques for obtaining and applying meteorological parameters from spaceborne lidars. The focus of this program was to identify potentially inexpensive lidar sensor concepts which provide maximum paybacks in improvements to numerical weather prediction (NWP) model performance. As the result of a trade-off of potential lidar systems for measuring meteorological parameters such as temperature, moisture, and winds, it was determined that maximum payback for NWP could be achieved by improving the accuracy and coverage of wind observations. Since they will be available from a polar orbiter, coverage will be increased, especially for extratropical areas. The lidar will produce winds wherever there is a cloud and will likely provide boundary layer winds from maritime aerosol backscatter. The assessment of the impact of candidate satellite lidar observation concepts on numerical weather analysis and prediction models was performed through a series of observing system simulation experiments (OSSEs). In addition to NWP model impacts, the fusion of potential lidar sensor system data with that from the current Defense Meteorological Satellite Program (DMSP) suite of passive sensors was considered. Special emphasis was placed on investigating active/passive data fusion approaches for improving the retrieval of atmospheric temperature and moisture profiles and the analysis of clouds (i.e. nephanalysis) in support of tactical and strategic operations.

This reports documents the development and testing of an advanced geophysical parameter retrieval, analysis and prediction system for global numerical weather prediction. The additions and improvements to the existing data assimilation system at the Phillips Laboratory are the following: a new surface pressure analysis, modifications for high resolution analysis, an optimal retrieval of SSM/T data based on Bayesian estimation, use of precipitation data to improve the moisture and divergence analysis and as input to a diabatic nonlinear normal mode initialization. The system uses a version of the advanced physics global spectral model of PL. The results of simulation tests show a significant improvement of the moisture analyses, especially in the tropics and Southern Hemisphere, and a smaller, but detectable improvement of the other variables, except in the stratosphere.

Calibration and validation of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Water Vapor Sounder (SSM/T-2) is investigated. The objective of the effort is to obtain simultaneous independent measurements of microwave radiance, water vapor mass, specific humidity and relative humidity to quantitatively measure the performance of the instrument (calibration) and to validate the ability of the SSM/T-2 to measure the water vapor parameters, i.e. the environmental data records (EDRs) of Air Force Global Weather Central (AFGWC). Analysis of Satellite and aircraft data for calibration using both underflights and radiative transfer calculation show that the SSM/T-2 is performing within or exceeds the original contract specifications (1K). The SSM/T-2 retrieved vertical moisture profiles (EDRs) are performing within the contract specification of + or - 30 RH when compared with collocated radiosonde data except over certain ocean atmospheres, Contains color plates. All DTIC/NTIS reproductions will be in black and white.

This study investigates the derivation of reentry-vehicle erosion parameter estimates based on the inference of rain rate and hydrometeor liquid water content from microwave imagery data from the Defense Meteorological Satellite Program (DMSP) sensors. Specifically, This research focuses on evaluation of Special Sensor Microwave/Imager (SSM/I) brightness temperature data to obtain a climatology of intense atmospheric precipitation at selected relevant land based sites for a four month summer period. An integral element of the study is identification of convective cloud models to support the analysis of hydrometeor liquid water content from the inferred surface rainfall rates. Tasks include: (1) development of SSM/I rain rate climatology; (2) development of hydrometeor liquid water content parameterization; (3) application of liquid water parameterization; and (4) documentation. Software was implemented to: (a) read the SSM/I TDRs from the acquired SSM/I data tapes; (b) calibrate and antenna pattern correct the data to obtain brightness temperatures; (c) bin the data according to the location of each desired site by latitude and longitude; (d) apply the rain rate retrieval algorithm to the data; and (e) evaluate relevant climatologies. The latter included four month summer time series of average rainfall rate, spatial standard deviation, and hydrometeor integrated liquid water content for eleven Eurasian sites.

We have simulated the wind measurements which might be obtained from a low power doppler wind lidar (DWL) sensor. Wind profiles obtained from such an instrument will not be as complete as those expected from planned higher energy sensors (e.g.LAWS), with measurements only from cirrus clouds, the tops of other clouds, and from marine boundary layer aerosol. The potential impact of such a scaled down DWL system has been assessed by inserting the simulated measurements into the Air Force Phillips Laboratory Global Data Assimilation System (GDAS). Results from the observing system simulation experiment (OSSE) indicate that significant improvement in the analysis and forecast of geopotential height, horizontal wind, and relative humidity may be expected in the southern hemisphere extratropics. Comparison of impacts with those obtained in a previously conducted OSSE which assumed a full blown DWL instrument (WINDSAT) indicate that the low energy lidar yields improvements which are 1/2 to 3/4 magnitude of improvements with WINDSAT. Thus, if the development cost of a simple lidar is found to be substantially less than a LAWS-type sensor, such an instrument may warrant consideration in the planning of future space-based observing systems.

An observing system simulation experiment (OSSE) is conducted using simulated DMSP microwave sensor data, surrogate data for cloud imagery (all based on a natural run), and the application of the unified retrieval (UR) method for DMSP to obtain meteorological variables as proposed by Isaacs (23). Both the DMSP SSM/T-1 temperature sounder and SSM/T-2 moisture sounder data are simulated. The unified retrieval uses physical considerations based on a forecast or other first guess by comparing the sensor data with those simulated using the first guess profiles. Although the preliminary UR did not perform better than the statistical retrieval, enhanced UR are found to be more accurate in terms of integrated water vapor amounts. The retrieved variables, primarily temperature and moisture, are used to construct initial conditions for input to the AFGL global spectral numerical weather prediction model. The resulting analyses and forecasts based on the present statistical retrieval scheme. The impact of the preliminary UR used in this experiment was small. However the enhanced UR should provide improved humidity analyses. (JHD)

This report has described the results of a study undertaken at AER to identify and implement a state of the art nonlinear retrieval approach to characterize line of sight variability of atmospheric thermal and constituent environments. This path characterization capability was designed to interface with the existing Geophysics Laboratory (GL) line-by-line radiance/transmittance code, FASCODE. Accomplishments of the study include: (a) a review of the relevant literature concerning potential path characterization retrieval algorithms, selection of a physical least squares (PLS) nonlinear retrieval approach for implementation based on criteria including flexibility within the context of FASCODE and a certain degree of robustness in application; (b) development of a stand alone, preprocessing screening procedure to identify potential channels for path characterization based on user requirements; (c) formulation and implementation of the path characterization retrieval algorithm including suitable interfaces with FASCODE; (d) inclusion of a comprehensive error analysis capability as an integral part of the retrieval procedure; (e) demonstration of the approach for the retrieval of temperature, water vapor and ozone; and (f) comprehensive documentation of the path characterization code implementations.

An evaluation of available approaches to treat the coupled processes of multiple-scattering due to atmospheric molecules and aerosols, and nongray absorption due to atmospheric gases is performed. Based on the evaluation and consideration of the requirements of efficiency, accuracy and code structure of LOWTRAN and FASCODE models, we recommend that an approach based on the stream approximation together with the k-distribution method is best suited for implementation into these models. The parameterization is unique in the sense that the k-distribution method decouples the multiple-scattering from the treatment of non-gray gaseous absorption so that the stream approximation can be used to treat multiple scattering in both the LOWTRAN and FASCODE models. The stream approximation is extremely attractive since it retains the analytical simplicity(and hence, efficiency) of single scattering while improving the treatment of multiple-scattering(and hence, accuracy).

An extensive literature review showed a disparity between the large amount of satellite data currently or potentially available for meteorological parameters and the relatively small amount that is actually utilized. The review also documented the various existing data retrieval techniques. Algorithms were developed and modified to retrieve temperature and moisture profiles from satellite infrared and millimeter/microwave radiances. Tools were developed, based on existing Air Force computer codes, to include and test the effect of satellite data in numerical weather prediction. The use of the logarithm of specific humidity as a forecast variable was investigated. The FGGE level II and III data sets existing at AFGL were completed to include all of the special observing periods and to form the basis of data assimilation tests. Finally, an attempt was made to optimize a cloudiness parameterization scheme on the basis of satellite retrievals. A number of recommendations are made for further work in satellite data retrieval, data assimilation and numerical weather prediction. They include the continuation of some of the items presented here, as well as suggestions for new avenues of research.

This report summarizes the results of an initial study to investigate the retrieval of water vapor profiles over land from several combinations of millimeter wave frequency channels, to compare the results obtained over land to performance expected over the ocean, and to assess the feasibility of obtaining cloud information from 183 GHz measurements. Brightness temperatures were simulated for four hypothetical millimeter wave moisture sounders using a recently developed sensor simulation model. Channel frequency selecton was investigated by comparing results from individual instrumental channel sets. In performing these simulations atmospheric data sets consisting of 400 radiosonde temperature and moisture profiles obtained over both land and ocean were used. The effect of beam-filling cloud on brightness temperatures was evaluated by incorporating physical cloud submodles. For representative cloud types within the simulation algorithm. Retrievals of vertical moisture profiles were accomplished by implementing a general statistical regression method which minimizes the mean square of interest. Retrieval statistics evaluated from a dependent subset of channel brightness temperatures and vertical water vapor profiles were applied subsequently to infer water vapor profiles from an independent set of brightness temperatures.

Concurrent visible and infrared cloud imagery from four satellite sensors (DMSP OLS, NOAA AVHRR, GOES VISSR, Landsat MSS) have been intercompared. Inherent differences in cloud field analyses are noted due to sensor characteristics such as spatial and spectral resolution and scene/sensor geometry. Digital data were manipulated to simulate one sensor's data from that of another. Recommendations for further analysis of the collected data set are provided.

A simulation model applicable to the design specification of a millimeter wave moisture sounder is described. The algorithm is capable of treating the response of a multifrequency radiometer to realistic variations in vertical profiles of atmospheric temperature and moisture, surface emissivity, cloud and precipitation. Analyses were performed to investigate the sensitivity of proposed moisture sounder channels to changes in the vertical distribution of atmospheric water vapor and the presence of cloud and rain. Preliminary studies based on statistical inversion methods demonstrate moisture retrieval capabilities in clear, cloudy, and precipitating cases. Results indicate that cloud and precipitation will most likely degrade moisture sounder performance. Investigation of moisture retrieval methodologies which explicitly treat cloud effects is recommended. Keywords: Atmospheric precipitation; Satellite meteorology; Computer codes; Multiple scattering; Radiative transfer; Clouds.

This report documents the results of our two-year Phase II effort to investigate the application of data from the proposed DMSP microwave moisture sounder (SSM/T-2) to the inference of water vapor profiles, temperature profiles, cloud, precipitation, and the properties of the underlying surface. The goal of this research is to provide a unified retrieval scheme capable of processing microwave and millimeter wave sensor (i.e., sounder and imager) data which extends the adopted operational algorithm based on a purely statistically based retrieval of temperature profiles from the SSM/T-1, moisture profiles from the SSM/T-2, and surface and atmospheric parameters such as precipitation and integrated cloud liquid water from the SSM/I. The approach uses two concepts to increase retrieval accuracy. First, physical considerations based on the application of the radiative transfer equation are used to quality control the statistically derived first guess retrievals. Second, data sources from colocated microwave mission sensors (i.e., the SSM/T-1, SSM/T-2, and SSM/I) are combined when necessary to more completely characterize the spectral dependence of the observed brightness temperatures on the meteorological properties of the surface and atmosphere. By combining data from all available sensors, the desired meteorological parameters can be more accurately determined. Based on our evaluation of cloud effects on millimeter wave brightness temperatures and water vapor retrievals (Isaacs and Deblonde, 1987), we have suggested use of the visible and infrared Operational Linescan System (OLS) to provide information on cloud presence.

A basic research program is described to investigate weather effects on microwave sensors used to remote sounding and to enhance our knowledge of microwave propagation through clouds and precipitation. In this report, we describe the current RADTRAN algorithm, enumerate its deficiencies, and provide corrective solutions. The latter include; (a) updating the code structure providing user friendly menus and internal comments, (b) providing for consistency with standardized input model atmospheres currently implemented within other AFGL transmission/radiance models, (c) undertaking a comparison of the current algorithm with contemporary state-of-the-art transmittance/radiance codes to insure its fidelity and representativeness, (d) enhancing RADTRAN to treat multiple scattering, (e) providing calculations of polarized surface emissivities based on user specified surface types, and (f) document the code. Finally, we describe appropriate statistical retrieval schemes for inclusion in the code so that users can perform sensor simulation/retrieval studies. (RRH)

A FASCODE based physical retrieval algorithm for atmospheric temperature sounding is developed in support of data analysis for the SCRIBE high resolution interferometer using a modified version of FASCODE capable of treating highly opaque regions such as the CO2 Q branch at 667/cm, radiances are simulated for comparison to SCRIBE data and the effects of line mixing are evaluated. Keywords: Retrievals, Remote sensing, Line coupling.

A brief review of published studies of water vapor retrievals using th millimeter wave water vapor resonance at 183.31 GHz is presented. Both simulation and aircraft experimental studies are covered. Results are summarized based on meteorological, sensor system, and retrieval approach considerations. A number of problem areas are identified including those associated with uncertainties in millimeter wave optical properties of surfaces and gaseous absorption and the dielectric properties of liquid and solid water species, the potential effects of clouds and precipitation, and surface elevation effects.

This review critically surveys types and accuracies of available satellite data, satellite data retrieval methodologies, and data requirements of numerical weather prediction (NWP) models. NWP modeling, parameterization and data assimilation methods are reviewed with particular attention to the hydrological variables. It is concluded that the means exist to obtain improved retrievals of cloud and moisture parameters and to better parameterize the hydrological cycle. Further advances in the retrieval of humidity profiles, the parameterizations of clouds and the objective analysis of humidity are required.