Shaimaa Fathy El Sharkawy, Mona Safar, Mohamed A. Gad.
Association of Egyptian-American Scholars
Rainfall forecasting at short lead-times (i.e., nowcasting) constitutes an active research field and considerable efforts have been done to improve the accuracy of forecasting over the last decades. Accurate short-term forecasts are considered a very dire need in fields such as aviation safety, flash floods prediction, and real-time operation of water resources and storm drainage systems. This paper presents a novel tracking and forecasting model that performs both pattern tracking and forecasting of the motion fields of the rainfall patterns that are detected using raster-based remote sensors (Weather Satellites and Radars). The technique uses a distributed version of the cross-correlation idea on subsets of the subsequent images to determine the velocity field at any time step. The velocity field obtained from the subsets is spatially interpolated to the pixel level to determine a high-resolution version of the velocity field. An exponential filter with parameter updating is used to adaptively fit the temporal evolution of the velocity vectors at every pixel. The effectiveness of the model is illustrated using Meteosat images. We were able to effectively track and forecast the velocity vectors of the cloud patterns at every pixel of the Meteosat extent. Our initial experience indicates that the developed model shall benefit many application domains.
This article was published in 3alamaltanmya
sponsored by Future Builders International Academy
Led by Dr.Maha Fouad