NRIET rainstorm intelligent similar analysis method

Abstract

The invention discloses an NRIET rainstorm intelligent similar analysis method. In a meteorological big data similar forecasting method, a similar criterion is obtained by means of machine learning, asimilar calculation scheme can be established more accurately, meanwhile, similar deviation is used as a measurement standard, such that not only numerical similarity of elements is considered, but deformation factors of a weather system are also added. In addition, the level of similarity of different variable fields in the weather process is quantitatively measured by combining image big data similarity level, element fields are gradually introduced for similar filtering, and finally the dates of similar cases are obtained, and the historical live scenes corresponding to the similar cases are taken as a basis for making a weather forecast. According to the method, a graph big data technology and a traditional similar algorithm are combined, the atmospheric circulation evolution trend provided by a numerical forecast product is utilized, the three-dimensional structural features of the weather development process are considered, the linear and nonlinear change rules of the atmosphereare considered, and the method has the characteristics of clear idea and strong intuition and is used for assisting in forecast and analysis.

Description

technical field [0001] The present invention relates to a meteorological big data forecast analysis method, in particular to an NRIET rainstorm intelligent similarity analysis method. Background technique [0002] The traditional similar situation method is used to forecast meteorological elements. It is necessary to use historical data in advance to summarize the ground or air situation when various weathers occur into several types—weather-climate models, and to count the similar weather processes of various types and the weather in the forecast area. Relationship. When making a forecast, as long as one finds historically similar weather patterns based on the weather situation at that time and its evolution characteristics, a corresponding weather forecast can be made. The traditional rainstorm similarity forecast has strong empirical properties and uncertainties in the selection of meteorological elements. In many cases, the criterion in the rainstorm similarity forecast...

AI Technical Summary

Problems solved by technology

The traditional rainstorm similarity forecast has strong empirical properties and uncertainties in the selection of meteorological elements. In many cases, the criterion in the rainstorm similarity forecasting process depends on the experience selection of the forecaster, which is not calculated by an objective algorithm, which will cause Subjective errors in forecast results

At the same time, the existing rainstorm similarity analysis method mostly uses the distance method in the process of calculating the similarity, and converts the two-dimensional meteorological elements into one-dimensional data. The consideration of the shape ignores the overall information of the situation field of meteorological elements, which makes the final similarity analysis results inaccurate

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