Advancements in Study on the Application of MODE Verification Method in Weather Forecasting
The objective evaluation of small-scale variables'forecast performance is vital for the application and development of Numerical Weather Prediction(NWP).Traditional point-to-point verification has significant limitations in the evaluation of high-resolution NWP.The Object-based Diagnostic Evaluation(MODE)method utilizes convolution functions and a given threshold to identify objects in the forecast and observation fields,extract their attributes,and diagnose the performance of the NWP.It has been widely applied in weather forecasting.This paper systematically reviews the academic ideas,technical framework,algorithm flow,and verification indices of the MODE spatial verification method.Subsequently,this paper summarizes the typical applications of MODE verification in precipitation forecasting,weather radar,satellite cloud images,ensemble forecasting,and other elements.It elaborates on the significance of verification results in evaluating the quality of NWP and their role in improving the accuracy of weather forecast results,both subjectively and objectively.Furthermore,it introduces recent updates and developments in MODE verification methods.These include the comprehensive evaluation index MODE Composite Score(MCS),which considers the mismatched attributes of objects,three-dimensional spatiotemporal object tracking using ellipsoids as targets,and the verification method,MODE Time Domain(MTD).Finally,it discusses the MODE verification method's applicability,advantages,and limitations while considering its future development direction and application prospects.The purpose of this study is to provide references for better application and diagnosis of NWP performance using the MODE method.
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