A data processing method for forecasting heavy precipitation weather

Abstract

The invention provides a data processing method for forecasting heavy precipitation weather. The data processing method for forecasting the heavy precipitation weather comprises the steps that firstly, thermodynamic factors, dynamic factors and a moisture index influencing occurrence of heavy precipitation are collected; secondly, according to the precipitation occurrence and development nonlinear theory, the thermodynamic factors, the dynamic factors and the moisture index are integrated, so that the physical quantity index THP is obtained; finally, according to the synoptic situations at 500hPa and at 700hPa of upper air, whether a good lifting mechanism exists or not is judged, and then the belt of the heavy precipitation and the magnitude of the heavy precipitation can be forecasted, wherein the area with a large value of the index THP is the large-probability area of precipitation in next six hours, the maximum value of the THP corresponds to the maximum amount of precipitation in next six hours, when the value of the THP is increased along with time, the amount of precipitation is increased, and otherwise the amount of precipitation is reduced. By the adoption of the data processing method for forecasting heavy precipitation weather, the belt of heavy precipitation and the development and change of precipitation can be more objectively forecasted, the magnitude of precipitation can be estimated quantitatively, and automatic operation can be achieved through a computer.

Description

technical field [0001] The invention belongs to the technical field of weather forecast guarantee, and in particular relates to a data processing method for forecasting heavy precipitation weather. Background technique [0002] In recent years, heavy precipitation in my country has shown an overall trend of increasing intensity, increasing frequency, and increasing inhomogeneity (Wang Xiaoling, Zhai Panmao, 2008; Li Jian, Yu Rucong et al., 2013). Heavy precipitation often develops rapidly and is highly catastrophic, which can easily cause secondary disasters such as urban waterlogging, landslides, and farmland waterlogging, and bring huge losses to social production and people's lives. However, the forecast of heavy precipitation is still a weak link in atmospheric science today, and its accuracy, objectivity and automation level need to be further improved. The data processing and application of heavy precipitation forecast in foreign countries has developed rapidly, and c...

AI Technical Summary

Problems solved by technology

[0009] 1) The previous data processing methods often reflect the possibility of precipitation from one aspect or several aspects, but as we all know, the atmosphere is a fluid that combines power and heat organically, and precipitation is actually a combination of various factors in the atmosphere. The result of the action is only judged by a single physical quantity index, which is relatively one-sided

Moreover, most of the indexes currently used are directly imported from abroad. Due to differences in geographical and climatic backgrounds, when some indexes are applied, the threshold range varies greatly, making it difficult to be universal.

[0010] 2) When using the fall area for forecasting, diagrams are often used for simple overlapping of areas, which is difficult to achieve automatic objective analysis, and it is impossible to objectively estimate the magnitude of precipitation

Moreover, when selecting the physical factors for nesting, there is often a large uncertainty, which is not conducive to practical application

[0011] 3) Although the weather classification method has been carried out for a long time, the classification is mainly based on the experience of the forecaster, through subjective forecasting and qualitative judgment of the possible precipitation area, resulting in too many subjective and empirical elements, and the degree of objectification Not enough, hard to quantify

And sometimes even under similar weather conditions, the resulting weather phenomena are quite different; sometimes the actual weather situation does not match any of the previously summarized flow patterns, which brings a lot of trouble to the actual work

[0012] 4) Although the products of the numerical forecast model are abundant, there are still large deviations in the forecast of specific weather phenomena, especially strong convection, and the accuracy rate is not ideal. For example, the TS score of short-term heavy precipitation at 6-hour intervals in my country is only 2.6 % (Zheng Yongguang et al., 2010), far lower than the forecast score of wind field and temperature field

On the one hand, this is due to the complexity of the dynamic and thermal processes of heavy precipitation, and the numerical model is difficult to accurately describe. On the other hand, due to the incompleteness of initial values, boundary values, and the complex combination of various disciplines, there are deviations in numerical forecast products, and the accuracy rate not ideal

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