A temperature forecast method and system based on Kalman filter

Abstract

The application belongs to the technical field of meteorological services, and in particular relates to a temperature forecast method and system based on Kalman filtering. The temperature forecasting method based on the Kalman filtering comprises: step a: input initial meteorological data into the HAPS system, output the HAPS temperature forecast result of the automatic weather station by the HAPS system; step b: calculate the position of the automatic weather station, and use the Kalman The filter method is used to interpret the single element of the HAPS temperature forecast results to obtain the interpretation results of each automatic weather station; step c: based on the interpretation results of the automatic weather stations, the deviation correction method is used to correct the HAPS temperature forecast results to obtain the correction of the automatic weather stations The subsequent temperature forecast results. This application uses the Kalman filter method to correct the HAPS temperature forecast results, which can effectively overcome the errors of the HAPS system and keep the forecast quality stable.

Description

technical field [0001] The application belongs to the technical field of meteorological services, and in particular relates to a temperature forecast method and system based on Kalman filtering. Background technique [0002] With the development of society and economy and the demand for more and more refined meteorological guarantee services for urban disaster prevention and mitigation, numerical forecasting has become one of the most important means to improve the accuracy and level of refinement of urban weather forecast and early warning. In recent years, with the rapid economic development of large and medium-sized cities such as Beijing, Shanghai and Shenzhen, various large-scale domestic and foreign large-scale sports events, exhibitions, conferences and other major social and public activities have been held frequently in these cities. The degree of modernization puts forward new requirements. [0003] As one of the pilot units for fine-grained forecasting services i...

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Problems solved by technology

[0005] To sum up, from the analysis of the forecast system itself, there are three reasons that affect the accuracy of forecast application. One is that there is noise in the live observation of the automatic station, which leads to forecast errors in the HAPS system; the other is that the HAPS numerical forecast model is affected by the terrain and other initial fields The accuracy of the initial field parameters has a certain impact on the forecast accuracy, resulting in a large error in the forecast temperature and affecting the service effect; the third is that the numerical forecast model of the HAPS system itself has shortcomings

The combined effects of the above factors often affect the prediction accuracy of the HAPS system, so it is very urgent to revise the refined numerical prediction results. varies from person to person

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