A rain-type adaptive optical flow method and its application in tropical cyclone rainfall nowcasting

Abstract

A rain-type adaptive pyramid Kanade–Lucas–Tomasi (A-PKLT) optical flow method for radar echo extrapolation is proposed. This method introduces a rain-type classification algorithm that can classify radar echoes into six types: convective, stratiform, surrounding convective, isolated convective core, isolated convective fringe, and weak echoes. Then, new schemes are designed to optimize specific parameters of the PKLT optical flow based on the rain type of the echo. At the same time, the gradients of radar reflectivity in the fringe positions corresponding to all types of rain echoes are increased. As a result, corner points that are characteristic points used for PKLT optical flow tracking in the surrounding area will be increased. Therefore, more motion vectors are purposefully obtained in the whole radar echo area. This helps to describe the motion characteristics of the precipitation more precisely. Then, the motion vectors corresponding to each type of rain echo are merged, and a denser motion vector field is generated by an interpolation algorithm on the basis of merged motion vectors. Finally, the dense motion vectors are used to extrapolate rain echoes into 0–60-min nowcasts by a semi-Lagrangian scheme. Compared with other nowcasting methods for four landfalling typhoons in or near Shanghai, the new optical flow method is found to be more accurate than the traditional cross-correlation and optical flow methods, particularly showing a clear improvement in the nowcasting of convective echoes on the spiral rainbands of typhoons.

Keywords

optical flow method / radar echo classification / adaptive / typhoon / nowcasting

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Acknowledgments

This work was supported by National Key Research and Development Program of China (No. 2018YFC1507601), National Natural Science Foundation of China (Grant No. 41775049), Scientific Research Project of Shanghai Science and Technology Commission (No.18DZ12000403), and Severe Convection S&T Innovation Team of Shanghai Meteorological Service.

Acknowledgments

This work was supported by National Key Research and Development Program of China (No. 2018YFC1507601), National Natural Science Foundation of China (Grant No. 41775049), Scientific Research Project of Shanghai Science and Technology Commission (No.18DZ12000403), and Severe Convection S&T Innovation Team of Shanghai Meteorological Service.

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