Climate mode evaluation and deviation correction method based on information entropy

Abstract

The invention discloses a climate mode evaluation and deviation correction method based on information entropy, and the method comprises the following steps: selecting a climate evaluation result anda climate measurement result of each climate model according to the actual demands; calculating information entropy and mutual information of each climate model and the measurement result; obtaining aVI image and an RVI image; evaluating the advantages and disadvantages of the climate model; n climate evaluation models with good evaluation results are selected to construct a multi-mode ensemble forecasting system; and performing result correction by using a least square method. According to the method, the output data of the multiple climate modes are used as independent variables, the deviation correction model of the output data of the multiple climate modes is constructed, evaluation results are further corrected while climate mode evaluation is achieved, and accurate prediction of future climate scenes is facilitated.

Description

technical field [0001] The invention relates to the field of uncertainty assessment methods for multi-climate model output data, in particular to a climate model assessment and deviation correction method based on information entropy. Background technique [0002] As an important part of the Qinghai-Tibet Plateau, the main initiation area and adjustment area of ​​climate change in the northern hemisphere, the source region of the Yangtze River has a huge impact on the ecological environment and socio-economic development of the eastern and southwestern parts of my country. It is also the most special and fragile area of ​​my country's ecosystem one. Under the influence of global changes, the climate in the source region of the Yangtze River has a clear trend of warming and drying, glaciers are retreating, permafrost is degrading, and lakes, river beaches, and swamp-type wetlands tend to be desiccated and developed. At present, the ecological environment in the source areas o...

AI Technical Summary

Problems solved by technology

Therefore, this evaluation method has two shortcomings: (1) There is not a strictly monotonic correspondence between the root mean square error and the model performance, that is, the reduction of the root mean square error does not necessarily correspond to the improvement of the climate model performance

(2) Although the root mean square error can be written as a function of the correlation coefficient and standard deviation, that is, the root mean square error contains two aspects of information, the correlation coefficient and the standard deviation, but only using the root mean square error cannot explicitly describe the correlation coefficient and standard deviation For example, the same root mean square error can correspond to completely different correlation coefficients and standard deviations

In many cases, variables have non-linear dependencies, a fact that cannot be correctly identified with linear dependencies

In addition, in the presence of outliers, the correlation between the two variables may be low, and the correlation coefficient cannot be used to reflect the relationship between the two variables

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