A Method of Improving Cloud Predictions for Real-Time Weather Forecasting and Visualization

Indirect or passive observations using satellite remote sensing in the visible, infrared and microwave spectra provide global coverage of the thermal states of the cloud tops or the ground surface. The commonly employed temperature profile matching techniques using satellite data and numerical weather prediction models are only relatively successful in estimating the cloud top height (CTH) for optically dense middle and high clouds (cloud tops at heights generally greater than two kilometers). Therefore, accurate predictions of low-level CTH present a formidable challenge to the forecasting and nowcasting community. In this study, we present an approach to estimating low-level CTH by combining the above-cloud information extracted from the satellite imagery and the below-cloud information obtained from weather station measurements. Assumed ranges of brightness temperature and CTH are used to process the cloudy pixels for visualization and classification purposes. Our study indicates that the CTH evaluated using satellite data confirms the presence of low-level clouds in the range 400-1000 m. Accurate estimates of the boundary layer CTH can provide better low-level cloud products (

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., fog or clouds formed by fog lifting) for improved weather forecasting and applications in the research community.