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Method for statistically analyzing long-term change trend and seasonal change rule of atmospheric methane

A technology of long-term change trend and seasonal change, applied in statistical analysis of atmospheric methane long-term change trend and seasonal change law, statistical analysis of gas change trend and law field, can solve the problems of wrong analysis results, easy to give, unstable analysis results, etc.

Inactive Publication Date: 2018-03-30
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] Existing trend analysis techniques are mainly based on loose data statistical distribution assumptions in estimating long-term methane change trends and seasonal changes. Usually, the analysis results are unstable, and it is easy to give wrong analysis results. The practicability has not yet been proven.

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  • Method for statistically analyzing long-term change trend and seasonal change rule of atmospheric methane
  • Method for statistically analyzing long-term change trend and seasonal change rule of atmospheric methane
  • Method for statistically analyzing long-term change trend and seasonal change rule of atmospheric methane

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Embodiment Construction

[0030] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0031] Part 1: Methods for statistical analysis of long-term and seasonal changes in atmospheric methane

[0032] The method provided by the present invention to statistically analyze the long-term variation trend and seasonal variation of atmospheric methane mainly comprises the following two steps:

[0033] 1. Establish long-term change trend and seasonal change law models

[0034] The long-term trend and seasonal variation models are based on the low-order Fourier series, which establishes a certain relationship between the long-term observed methane series and the year.

[0035] The low-order Fourier series is represented by the function model V:

[0036] V(t,b)=b 0 +b 1 cos2πt+b 2 sin2πt+b 3 cos4πt+b 4 sin4πt+...

[0037] In the formula, t is the annual time series, b is the fitting coefficient of seasonal change, and the sea...

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Abstract

The invention discloses a method for statistically analyzing the long-term change trend and seasonal change rule of atmospheric methane. The method includes the steps of firstly, building a long-termchange trend and seasonal change rule model V: V(t, b)=b0+b1cos2Pit+b2sin2Pit+b3cos4Pit+b4sin4Pit+..., and building a function model F: F(t, a, b)=at+V(t, b) for base-, airborne- or satellite-borne-observed active-passive atmospheric methane total column concentration data; secondly, using the bootstrapped sampling technology to perform strict fitting analysis on non-Gaussian-distribution data. The method has the advantages that by building the low-order Fourier series model and strict statistical analysis, the average trend and strict estimated seasonal change value of a given data set can beobtained, and the long-term change trend and seasonal change rule of the methane can be accurately quantified; the method is significant to fields such as atmospheric probing and weather change, applicable to the analysis of base-, airborne- or satellite-borne-observed active-passive atmospheric methane observation data and capable of providing valuable data sources for the source-sink analysis and dynamic change research and simulation of the atmospheric methane.

Description

technical field [0001] The invention relates to a method for statistically analyzing gas change trends and rules, in particular to a method for statistically analyzing long-term change trends and seasonal change rules of atmospheric methane, and belongs to the technical fields of atmospheric detection and climate change. Background technique [0002] Global climate change is one of the most important environmental problems facing the world today. A key element of this problem is understanding the behavior in the atmosphere of radiation-active gases and greenhouse gases that participate in atmospheric chemical reactions. Long-term observations of these gases provide experimental data for studying the evolution of these gases and the changes in source and sink. [0003] As one of the most important greenhouse gases, methane is much less abundant in the atmosphere than carbon dioxide. Due to the large number of seasonal changes and other factors in the long-term methane observa...

Claims

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Application Information

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IPC IPC(8): G06F17/14
CPCG06F17/141
Inventor 孙友文田园刘诚刘文清王薇单昌功胡起后
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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