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Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The main objective of the proposed study was to investigate the complementary information provided by microwave and infrared sensors in order to enhance both the microwave retrieval and the current cloud analysis. This primary task investigated and assessed the impact of incorporating cloud horizontal and vertical spatial distribution information on the performance and accuracy of simultaneous physical retrievals of atmospheric profiles from microwave sensors. The second task explored additional attributes of the microwave measurements, particularly related to ice clouds, to investigate the possibility of identifying multi-layer clouds from simultaneous microwave, visible and infrared data, and to provide infrared emission properties needed for threshold-based cloud detection. We also applied the UR approach to DMSP data to provide the basis for screening out cases where the infrared/visible-derived low cloud properties were uncertain and to assess ability to characterize the vertical structure of multi-layer clouds. The third task investigated the feasibility of predicting infrared emission properties using the microwave data, and assessed if this information could be used to enhance low cloud and thin high cloud identification in totally overcast conditions, especially at night. This task included deriving empirical relationships between microwave and infrared surface emission based on dependent clear data sets, and performing exploratory cloud analyses using both the surface temperature data from the current surface temperature data base and the microwave derived surface emission properties. The last task addressed the application of the UR algorithm to land and to snow/ice backgrounds and assessed the algorithm's capability using new data collected over the Northern Hemisphere in the winter.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The main objective of the proposed study was to investigate the complementary information provided by microwave and infrared sensors in order to enhance both the microwave retrieval and the current cloud analysis. This primary task investigated and assessed the impact of incorporating cloud horizontal and vertical spatial distribution information on the performance and accuracy of simultaneous physical retrievals of atmospheric profiles from microwave sensors. The second task explored additional attributes of the microwave measurements, particularly related to ice clouds, to investigate the possibility of identifying multi-layer clouds from simultaneous microwave, visible and infrared data, and to provide infrared emission properties needed for threshold-based cloud detection. We also applied the UR approach to DMSP data to provide the basis for screening out cases where the infrared/visible-derived low cloud properties were uncertain and to assess ability to characterize the vertical structure of multi-layer clouds. The third task investigated the feasibility of predicting infrared emission properties using the microwave data, and assessed if this information could be used to enhance low cloud and thin high cloud identification in totally overcast conditions, especially at night. This task included deriving empirical relationships between microwave and infrared surface emission based on dependent clear data sets, and performing exploratory cloud analyses using both the surface temperature data from the current surface temperature data base and the microwave derived surface emission properties. The last task addressed the application of the UR algorithm to land and to snow/ice backgrounds and assessed the algorithm's capability using new data collected over the Northern Hemisphere in the winter.
Author: R. G. Isaacs Publisher: ISBN: Category : Languages : en Pages : 131
Book Description
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. (r.h.).
Author: R. G. Isaacs Publisher: ISBN: Category : Languages : en Pages : 76
Book Description
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. Keywords: Satellite meteorology; Water vapor; Clouds; Unified retrieval; Numerical weather prediction. (JHD).
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Author: David K. Lynch
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Author: R. G. Isaacs
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Author: Bruce Anthony Wielicki
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Author: R. G. Isaacs
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Author: Shona Louise Mackie