Short-term climate forecasting method based on empirical mode decomposition and numerical value set forecasting

Abstract

The invention discloses a short-term climate forecasting method based on empirical mode decomposition and numerical value set forecasting. The invention adopts a way of integrating a numerical value set forecasting technology and a mean generating function stepwise regression model and combines a new empirical mode decomposition (EMD) method for processing a data sequence. The short-term climate forecasting method comprises the following steps of: firstly, decomposing a non-stationary climate data sequence into a stationary intrinsic mode function (IMF) component with multi-scale feature; then constructing different forecasting models for each IMF by a way of set forecasting and stepwise regression analysis; and finally linearly fitting to form a forecasting result. When the system is used for short-term forecasting, a user can cut out the appointed sequence length and forecasting length according to the actual data demand and vnlrpfalgp select a forecasting model parameter in a set forecasting process. Compared with a direct or single forecasting method, the invention has better forecasting capacity for the variation trend of climate and sudden climate.

Description

technical field [0001] The invention relates to a short-term prediction (5-10 years) of climate sequence data (20-50 years) based on empirical mode decomposition and ensemble forecasting technology, which is suitable for situations where sudden changes in climate need to be sensitively grasped, such as frost (cooling), heavy rain and other disastrous weather. Background technique [0002] Climate prediction is an important branch of atmospheric science, and climate change prediction is one of the most important contents of short-term climate prediction for meteorological stations at all levels. [0003] The climate system is a dissipative, high-order nonlinear system with multiple sources of instability, and its complex internal interactions and free changes lead to climate variability and complexity, as well as to the multi-scale characteristics of climate change [1-3] . Since we currently know very little about the lithosphere, hydrosphere, cryosphere, and biosphere (inc...

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Problems solved by technology

Since we currently know very little about the lithosphere, hydrosphere, cryosphere, and biosphere (including human activities), the accuracy and completeness of the accumulated data is very insufficient. Therefore, in terms of current human understanding of the climate system, it is necessary to establish It is impossible to accurately reflect the nonlinear dynamical equations of climate system changes [4] , which brings great difficulties to the study of dynamic theory of climate change prediction

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