Computational modeling study demonstrates improved storm position, shape, and intensity prediction via an enhanced grid transformation method, indicating potential for real-time severe weather...
In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced NGT (ENGT) method. By extending the nonlinear transformation matrix to include radar reflectivity as the third dimension in addition to the grid coordinates X and Y, a 3 × 9 transformation matrix is used to describe the continuous spatial variation in the radar reflectivity field. The transformation matrix is solved using historical near-term data, enabling the extrapolation of subsequent time steps. In a set of ideal extrapolation experiments combining translation, temporal increments, and path variations, the ENGT method demonstrated better qualitative and conceptual performance than the NGT and traditional optical flow (OF) methods. In a real squall line case, the ENGT method could predict the overall movement direction of the cloud system synthesized by moving and emerging cells. In a real enhanced convective cloud cluster case, the ENGT method achieved higher scores because it generated stronger reflectivity. Although there are still mathematically unsolved and statistically insignificant problems, the ENGT method shows potential in predicting strong reflectivity, and the computational efficiency for a single weather radar is considerable.
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Yang et al. (2026) studied this question.
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