Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

The cloud-detection procedure developed by McNally and Watts(MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual(MMR) scheme for both simulated and real Advanced Infrared Sounder(AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR(Vis/NIR) cloud mask.

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

The AMSR2 microwave radiometer is the main payload of the GCOM-W1 satellite,launched by the Japan Aerospace Exploration Agency in 2012. Based on the pre-launch information extraction algorithm,the AMSR2 enables remote monitoring of geophysical parameters such as sea surface temperature,wind speed,water vapor,and liquid cloud water content. However,rain alters the properties of atmospheric scattering and absorption,which contaminates the brightness temperatures measured by the microwave radiometer. Therefore,it is difficult to retrieve AMSR2-derived sea surface wind speeds under rainfall conditions. Based on microwave radiative transfer theory,and using AMSR2 L1 brightness temperature data obtained in August 2012 and NCEP reanalysis data,we studied the sensitivity of AMSR2 brightness temperatures to rain and wind speed,from which a channel combination of brightness temperature was established that is insensitive to rainfall,but sensitive to wind speed. Using brightness temperatures obtained with the proposed channel combination as input parameters,in conjunction with HRD wind field data,and adopting multiple linear regression and BP neural network methods,we established an algorithm for hurricane wind speed retrieval under rainfall conditions. The results showed that the standard deviation and relative error of retrievals,obtained using the multiple linear regression algorithm,were 3.1 m/s and 13%,respectively. However,the standard deviation and relative error of retrievals obtained using the BP neural network algorithm were better(2.1 m/s and 8%,respectively). Thus,the results of this paper preliminarily verified the feasibility of using microwave radiometers to extract sea surface wind speeds under rainfall conditions.

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