Method for correcting sliding deviation of refinement temperature prediction

Abstract

The invention discloses a method for correcting sliding deviation of refinement temperature prediction. The method comprises: obtaining a weather prediction product with temperature numerical value ofa real-time point; calculating daily highest and lowest temperature prediction of the real-time point; determining a temperature prediction deviation statistic and an optimal sliding statistic period; performing a deviation sliding statistic of the daily highest and lowest temperature prediction; performing the correction of the deviation of the refinement temperature prediction according to thecorrected deviation of the daily highest and lowest temperature prediction; reporting the corrected temperature from a real-time point to a prediction site or a intelligent grid point; developing a timing running program according to the numerical prediction product and the real-time product which automatically runs every day; and outputting a corrected temperature site or a intelligent grid prediction product in real-time to realize the correction of the sliding deviation of the temperature prediction. According to the method for correcting the sliding deviation of the refinement temperatureprediction, the deviation correction can be realized by the sliding statistic on the numerical model temperature prediction, accuracy of the numerical prediction product can be not only improved, a data statistics collation process of long-term sequences can be simplified.

Description

technical field [0001] The invention belongs to the field of meteorological forecasting, and in particular relates to a sliding correction method for temperature refinement forecast deviation. Background technique [0002] Temperature forecasting is an essential element in weather forecasting. With the improvement of the level of numerical weather prediction and the development of interpretation techniques such as MOS, the accuracy of temperature prediction has been continuously improved. Taking the numerical forecast products of the European Center for Medium-Range Numerical Forecasting (ECMWF) as an example, the accuracy rate of the temperature forecast within 2°C of the ECMWF model has reached more than 80%. There are often systematic errors. MOS forecasting has achieved good interpretation effects on temperature forecasting through methods such as building equations based on historical data statistics, but traditional MOS forecasting has the following disadvantages: (1...

AI Technical Summary

Problems solved by technology

MOS forecasting has achieved good interpretation effects on temperature forecasting through methods such as building equations based on historical data statistics, but traditional MOS forecasting has the following disadvantages: (1) It requires a lot of historical data, generally more than 2-3 years The effect of building equations is better

However, the current numerical model develops and changes rapidly, and the model often has a large adjustment within 3 years, resulting in a decline in the effect of the MOS forecasting method.

(2) Large amount of calculation

(3) For more obvious processes such as strong cooling, the traditional MOS forecasting method often lags behind

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