Statistical forecast method and apparatus for urban heat island strength

Abstract

The invention relates to a statistical forecast method and an apparatus for the urban heat island strength, which considers the influence of at least three weather factors such as horizontal total wind speed, wind direction and air temperature to the measuring result of the urban heat island strength, applies the historical observation data of the city to be researched for establishing a data analysis process apparatus, and obtains the numerical value forecast result of the specific weather factors such as horizontal total wind speed, wind direction and air temperature of the city to be researched by a numerical value mode so as to correctly perform the statistical forecast for the urban heat island strength and provide a certain technology support for the weather study and atmosphere science.

Description

technical field [0001] The invention relates to a weather forecasting method, which is a special statistical forecasting method for urban heat island intensity. [0002] The invention also relates to a weather forecasting device, which is a special statistical forecasting device for urban heat island intensity. Background technique [0003] At present, there are mainly three methods for forecasting the intensity of urban heat island: numerical simulation, energy balance model and statistical forecasting. Among them, the calculation cost of numerical simulation is relatively high. In recent years, the rapid development of numerical models from research to application mainly benefits from the development of computer technology. Rapid development, but in the numerical simulation of the urban boundary layer, the simulation and prediction of the intensity of the urban heat island is still in the research stage, the existing computer technical conditions and numerical simulation c...

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Problems solved by technology

[0003] At present, there are mainly three methods for forecasting the intensity of urban heat island: numerical simulation, energy balance model and statistical forecasting. Among them, the calculation cost of numerical simulation is relatively high. In recent years, the rapid development of numerical models from research to application mainly benefits from the development of computer technology. Rapid development, but in the numerical simulation of the urban boundary layer, the simulation and prediction of the intensity of the urban heat island is still in the research stage, the existing computer technical conditions and numerical simulation capabilities are still not up to the level of business applications; the energy balance model is based on the energy Equilibrium principle and M-O similarity theory are used to establish a temperature calculation model to obtain the urban heat island intensity; the precondition for the establishment of this method is that the M-O similarity theory is applicable, but the applicability of the M-O similarity theory still needs to Further research shows that the theoretical basis of the energy balance model has serious flaws when it is applied to urban problems; the statistical forecasting method is to use statistical knowledge to establish the relationship between the urban heat island intensity intensity and the city size or meteorological factors, and establish the heat island intensity Mathematical model between urban environment or meteorological factors

Compared with numerical models and energy balance simulations, statistical forecasting methods are simple, easy to implement, and have strong applicability. In the prior art, there are mainly Morris statistical forecasting methods and Sundbrog statistical forecasting methods. Among them, the Morris statistical forecasting method only considers cloud cover In order to obtain the corresponding regression coefficients, the Sundbrog statistical forecast method not only considers the influence of cloud amount, wind speed, air temperature and humidity on In addition to the influence of urban heat island intensity, the diurnal variation of urban heat island intensity is also partially considered, and the intensity of urban heat island is simply divided into daytime and nighttime, and then linear regression is performed. Neither of the above two methods can effectively carry out statistics on the intensity of urban heat island. forecast

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