Statistical downscaling method based on non-stationary time series decomposition

A time-series, time-scale technology for climate sustainability, complex mathematical operations, ICT adaptation, etc.

Active Publication Date: 2020-08-28
SHANDONG UNIV
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Problems solved by technology

[0006] Aiming at the deficiencies of the prior art, the present invention provides a statistical downscaling method based on non-stationary time series decomposition, which decomposes non-stationary climate series into stationary climate components, and overcomes statistical downscaling by finding the statistical relationship between the stationary components The problem with methodologically inconsistent assumptions

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  • Statistical downscaling method based on non-stationary time series decomposition
  • Statistical downscaling method based on non-stationary time series decomposition
  • Statistical downscaling method based on non-stationary time series decomposition

Examples

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Embodiment 1

[0099] This embodiment takes the downscaling of the monthly average maximum temperature, minimum temperature, and precipitation located at the plain station 1 and the mountain station 2 as an example to illustrate a statistical downscaling method based on non-stationary time series decomposition. Station 1 is a plain station with an average altitude of 18.1m, which is a fixed meteorological observation station near a farm in a certain city; Station 2 is an alpine station with an average altitude of 1890.9m, which is a fixed meteorological observation station for a lake. Site 1 has a typical Mediterranean climate with mild and humid winters and hot and dry summers; due to the high altitude of Site 2, the region has a Mediterranean continental climate with warm and dry summers. The technical roadmap of the downscaling method is as follows: figure 1 As shown, the specific steps include:

[0100] S1. Downscaling data preparation:

[0101] The large-scale data of the CMIP5 global...

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Abstract

The invention relates to a statistical downscaling method based on non-stationary time series decomposition. The method comprises the specific steps of S1, downscaling data preparation; S2, decomposing a non-stationary time sequence; S3, selecting an optimal time sequence decomposition result; S4, training a random forest model; S5, synthesizing decomposition components of the time sequence aftertraining; S6, performing model evaluation; and S7, downscaling a future scene. According to the method, a statistical relationship is searched among steady-state components; therefore, the problem ofrelated statistical relationship consistency hypothesis in statistical downscaling is solved, the problem of scale mismatching always existing between a climate model and a hydrological model is solved, and a more reliable way is provided for basin response in a series of researches such as future climate change by utilizing the hydrological model.

Description

technical field [0001] The invention relates to a statistical downscaling method based on non-stationary time series decomposition, which belongs to the intersection field of climate and hydrology. Background technique [0002] Climate change is a precursor and an important part of global change. Data analysis shows that with the development of society, human activities, land use and urbanization, climate change has produced multi-scale, all-round and multi-level impacts. Climate change affects the terrestrial water circulation system through changes in temperature, precipitation, evaporation and other factors, resulting in the redistribution of water resources at different temporal and spatial scales. The emergence of global climate models and the simulation results of large-scale climate factors provide the possibility to use hydrological models to study the future and past temporal and spatial characteristics of water resources at the regional scale. Usually, the global ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/10
CPCG06F17/10Y02A90/10
Inventor 李欣桐张晓东王曙光
Owner SHANDONG UNIV
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