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A Statistical Downscaling Method Based on Non-stationary Time Series Decomposition

A time series and downscaling technology, applied in the direction of climate sustainability, complex mathematical operations, ICT adaptation, etc., to achieve the effect of reducing input uncertainty and strong generalization ability

Active Publication Date: 2022-06-17
SHANDONG UNIV
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  • Claims
  • Application Information

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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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  • A Statistical Downscaling Method Based on Non-stationary Time Series Decomposition
  • A Statistical Downscaling Method Based on Non-stationary Time Series Decomposition
  • A Statistical Downscaling Method Based on Non-stationary Time Series Decomposition

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Embodiment 1

[0099] This embodiment takes the downscaling of monthly average maximum temperature, minimum temperature, and precipitation at the plain site 1 and the mountain site 2 as examples to illustrate a statistical downscaling method based on non-stationary time series decomposition. Site 1 is a plain site with an average altitude of 18.1m and is a fixed meteorological observation station near a farm in a city; Site 2 is a mountain site with an average altitude of 1890.9m and 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 higher altitude of Site 2, the area 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. Downscale data preparation:

[0101] Collect the large-scale data of the CMIP5 global climate model GCM and the small-scale m...

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Abstract

The present invention relates to a statistical downscaling method based on non-stationary time series decomposition. The specific steps of the method include: S1. Downscaling data preparation; S2. Non-stationary time series decomposition; S3. Selection of optimal time series decomposition results; S4. Random forest model training; S5. Synthesis of time series decomposition components after training; S6. Model evaluation; S7. Downscaling of future scenarios. This method looks for the statistical relationship between the steady-state components, and then solves the problem of the consistency assumption of the statistical relationship in the statistical downscaling, solves the ever-existing scale mismatch problem between the climate model and the hydrological model, and also provides a basis for the use of hydrological models. Modeling watershed responses in a range of studies, including future climate change, provides a more reliable approach.

Description

technical field [0001] The invention relates to a statistical downscaling method based on decomposition of non-stationary time series, and belongs to the cross 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 the acceleration of 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 provided by the hydrological models provide the possibility to study the future and past spatial and temporal characteristics of water resources at the regional scale...

Claims

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Application Information

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Patent Type & Authority Patents(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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