Nowcasting effect evaluation method, system, terminal and storage medium

Abstract

The invention relates to a nowcasting effect evaluation method, a system, a terminal and a storage medium. The method comprises the steps that the membership degree of radar echo data to a set threshold value is calculated, based on the membership degree, the probability mean value in the neighborhood range of each grid point is calculated through a spatial neighborhood probability method, and the fuzzy probability of each grid point is obtained according to the probability mean value; calculating a score skill score IFSS of the radar echo data based on Gaussian membership intensity characteristics; a time neighborhood self-adaptive weighting algorithm is adopted to distribute weights for the forecast echo data at each moment, a space neighborhood probability method is adopted to calculate the probability of each grid point after the weights are distributed, and a score skill score TFSS of the radar echo data based on the time neighborhood self-adaptive weighting algorithm is calculated; and calculating a score skill score TIFSS based on the intensity feature and the spatial-temporal feature according to the IFSS and TFSS scoring results. According to the method, more objective and reasonable evaluation and inspection can be realized, and the defects of advanced forecasting and delayed forecasting are overcome.

Description

technical field [0001] The application belongs to the technical field of weather forecast verification, and in particular relates to a nowcasting effect evaluation method, system, terminal and storage medium. Background technique [0002] As people's demand for accurate forecasts of disastrous extreme weather continues to increase, the importance of strong convective weather forecasts has become increasingly prominent. Doing a good job in forecasting and preventing convective weather is of great significance to disaster prevention, mitigation and relief. Due to the small temporal and spatial scales of severe convective-scale weather systems, rapid evolution of generation and consumption, and highly nonlinear dynamic and physical processes, the refined forecasting of severe convective weather is still facing great challenges. [0003] Forecast verification is an important part of the development of severe convective weather forecasting and technology. Its purpose is to give t...

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Problems solved by technology

However, the traditional deterministic forecasting method for severe convective weather is based on site observations or eyewitness reports, and the test indicators calculated through the two-dimensional contingency table have very high requirements for spatial consistency, and it is necessary to compare the forecast with the actual situation in the grid. Strict "one-to-one correspondence" on points or stations, there can be no position deviation and intensity deviation, and there are double penalties of "prediction occurs but reality does not occur" and "prediction does not occur but reality occurs" for grid points. When the predicted target appears When there is a certain deviation, the TS score may be very low, but even a forecast with a very low TS score may contain valuable information required by the user (such as a forecaster), such as a position with a large deviation but a better description Prediction of Structural Characteristics of Convective Systems

In addition, conventional inspection methods only give the evaluation of whether the forecast is accurate or not, but cannot give the reason for the deviation of the forecast. For some regional targets, only a simple right or wrong evaluation is given, and some positive aspects in the forecast are covered up. information

[0004] In order to solve the test of weather forecast with high spatio-temporal resolution, some new spatial test techniques are constantly emerging. The current space test techniques mainly include neighborhood method (Neighborhood), scale decomposition method (Scale Decomposition) (Mittermaier et al, 2010; Casati et al. al,2004,2010) and spatial inspection methods for object attributes, etc. However, these methods cannot effectively solve the problems of time lead and lag or intensity errors in the forecast results

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