Extrapolation Algorithms for Radar Echoes Based on Characteristic Conditional Diffusion Models
With the extension of lead time for extrapolation,the attenuation of radar echoes becomes increasingly pronounced,and the predictive performance for intense echoes rapidly deteriorates.These constitute two quintessential characteristics of the current radar extrapolation results'inaccuracy.To address the issues mentioned above,a novel methodology called DiffREE(diffusion radar echo ex-trapolation algorithm)was introduced.This algorithm skillfully fuses the spatial and temporal information from past radar echo frames using a conditional encoding module.It employs a Transformer encoder to automatically extract spatiotemporal features from the echoes,which are then used as conditions to drive the diffusion model in reconstructing the current radar echo frames.Experimental results demonstrate that this method can produce high-precision,high-quality radar forecast frames,achieving significant improvements of 42.2％,51.1％,49.8％,and 39.5％in CSI,ETS,HSS,and POD,respectively,compared to the best-performing baseline algo-rithm.
deep learningshort-term forecastingradar echo extrapolationdiffusion modelconditional coding
NETL
NSTL
万方数据
