Method for carrying out fusion processing on meteorological data and generating numerical weather forecast

Abstract

The invention discloses a method for carrying out fusion processing on meteorological data and generating a numerical weather forecast, which comprises the following steps: step 1, collecting and receiving various meteorological element data from meteorological observation detection equipment or CCTV to obtain a meteorological element set X1; step 2, performing standardized preprocessing on the acquired meteorological element data to obtain a standardized meteorological element set X; step 3, performing three-dimensional data variation assimilation on the meteorological element set X, performing characteristic error analysis and cross covariance analysis on the data in the meteorological element set X to obtain a covariance error root mean square of each data in the meteorological elementset X, and obtaining an optimal meteorological element data type according to the covariance error root mean square; step 4, performing WRF numerical weather forecast mode compiling according to the optimal meteorological element type; and step 5, performing forecast post-processing on the WRF mode result to generate meteorological element numerical forecast.

Description

technical field [0001] The invention relates to the field of weather forecasting, in particular to a method for merging weather data and generating numerical weather forecasting. Background technique [0002] The numerical model itself has been able to simulate the actual atmospheric movement quite accurately, and the resolution is becoming more and more refined; Three-dimensional variation method, four-dimensional variation method and Kalman filter method. The four-dimensional variational method and the Kalman filter method are considered to be the development direction of data assimilation, but due to the large amount of calculation, the large amount of storage, and the difficulty in obtaining the precise initial value of the model state error covariance matrix, only a few countries use it in business operations. WRF's assimilation system WRFda is a relatively complete three-dimensional variational assimilation system, which has achieved remarkable results in business app...

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Problems solved by technology

The four-dimensional variational method and the Kalman filter method are considered to be the development direction of data assimilation, but due to the large amount of calculation, the large amount of storage, and the difficulty in obtaining the precise initial value of the model state error covariance matrix, only a few countries use it in business operations

[0009] The current research is mainly based on the assimilation of conventional observation and detection data, high-altitude sounding wind data, automatic observation station data, Doppler weather radar data, wind profile radar data, and even lightning data and other meteorological data. There is also a method of combining ground conventional observation data with other data, but there is no further research on effectively analyzing the advantages and disadvantages of various meteorological data and combining them

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