Preparation method for ground-based observation air temperature space-time data set

Abstract

The invention discloses a preparation method of a ground-based observation air temperature space-time data set. The method comprises the following steps: firstly determining space-time resolution ratio and area range of a precast data set; preprocessing air temperature observation data so as to form a normative driving data set; preparing a macro geographic factor and microcosmic geographic factor grid data set of a research area so as to enable the macro geographic factor and microcosmic geographic factor grid data set to correspond to the space-time resolution ratio of the research area; constructing a wavelet neural network dynamic combined prediction model in the time dimension, performing simulation prediction on the change characteristics of air temperature data so as to form a future air temperature change time sequence set; calculating air temperature numerical values of grids in a target area one by one in the space dimension, so as to generate a spatial data set of air temperature spatial change trends; performing simulation calculation on air temperature in the time dimension and the space dimension simultaneously based on key influence factors so as to generate an air temperature future space-time change scene predication data set. The preparation method has favorable expansibility and can be popularized and applied to preparation and production of space-time data products of other observation elements of a geoscience station.

Description

technical field [0001] The invention relates to the fields of data mining, data fusion and data preparation in the field of geosciences, and specifically relates to a method for preparing a ground observation air temperature spatio-temporal data set. Background technique [0002] Air temperature has an important impact on human and natural systems. As a comprehensive environmental index reflecting the heat status of the earth's surface system, it is not only a key indicator to participate in the division of natural territorial system boundaries, but also an important parameter for the simulation of land surface process models. Therefore, air temperature is It is widely used in various fields of global change research. Exploring the properties of unknown geographic spaces based on the properties of known geographic spaces is the first step in many geographical studies, and it is also a basic problem in geography. The demand for environmental spatial information in modern s...

AI Technical Summary

Problems solved by technology

The air temperature data comes from the ground observation network, such as meteorological stations, hydrological stations, ecological observation stations, and some temporary sampling points. Although these stations are generally more and more dense, due to the limitations of observation methods, resource allocation, and regional characteristics, we It is impossible to establish observation stations or set up observation instruments for each geographical grid point (or belt), especially in remote areas, where the distribution of geoscience observation stations is very sparse, for example, there are less than 200 national meteorological stations in the five provinces and regions of Northwest China, so we can generally only get Discrete, irregularly distributed, and varying degrees of detail observation point (or sampling point) data

These methods only conduct theoretical verification research and application on a certain aspect of the spatio-temporal characteristics of environmental observation elements, but do not consider the effective coupling and integration of methods from a systematic perspective, forming a spatio-temporal analysis system of environmental observation elements, especially future situation prediction , so far there is no effective method for preparing surface-observed air temperature spatio-temporal datasets

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