Rainfall forecast displacement correction algorithm based on object diagnosis

Abstract

The invention discloses a rainfall forecast displacement correction algorithm based on object diagnosis. According to the algorithm, the defects of an MODE method and a CRA method are overcome, a reasonable object displacement deviation calculation method is designed, the thought of an object diagnosis method and the thought of an observation similarity method are combined, the defects of a point-to-point diagnosis method are overcome, and the rainfall forecasting performance of the mode is directly improved from the perspective of object displacement deviation correction. Data experiments andstatistical cross tests show that the prediction performance of different prediction periods of validity after displacement correction is improved, the critical success index and the detection rate are improved, and the false alarm rate is reduced.

Description

Technical field [0001] The present invention relates to the technical field of numerical forecast statistics post-processing technology in weather forecasting business, in particular to numerical model precipitation forecast correction technology. Background technique [0002] In the practice of weather forecasting, forecasters often find that the heavy precipitation rainbands predicted by numerical models have intensity deviations and north-south deviations compared with the actual observations. At present, most of the existing methods correct for the intensity deviation of the model precipitation forecast. Although the forecast performance of the model can be improved to a certain extent, the intensity of the heavy precipitation is usually accompanied by excessive false alarm ratio (FAR). However, there are few correction methods for rainband displacement deviation. The observation similarity method finds the historical precipitation forecast field similar to the current preci...

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Problems solved by technology

However, this method is based on traditional point-to-point error indicators (such as root mean square error, correlation coefficient, etc.) There is a "double penalty" problem in the observation grid

[0003] The Method for Object-based Diagnostic Evaluation (MODE) is a new type of spatial inspection technology, which can extract forecast and observed precipitation objects and calculate displacement deviations, avoiding the disadvantages of point-to-point diagnostic methods, but no scholars have yet Combining the method with the observation similarity method for displacement correction of precipitation forecasting objects

In addition, in the calculation of the displacement deviation of the object, the MODE method only uses the center position of the object to calculate the displacement deviation, and the continuous rain area method (Contiguous Rain Area, CRA) calculates the displacement deviation by moving the object to achieve the maximum overlap. In practice, two methods have been found None of them can adapt well to the precipitation objects with different shapes, and often produce calculation results that deviate greatly from the subjective judgment of the forecaster

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