Wind speed downscaling method based on climate model and joint distribution function

Abstract

The invention discloses a wind speed downscaling method based on a climate model and a joint distribution function, and belongs to the technical field of regional climate change. The method comprises the steps of 1, collecting historical near-surface wind direction and wind speed, grid near-surface wind speed and HadGEM3-GC31-LL climate model data; 2, repairing the near-surface wind speed of a site lacking in space and time by using an inverse distance weighting method and a linear interpolation method respectively; 3, performing dynamic downscaling on the target region by using a RegCM region climate model; 4, performing statistical downscaling on the target region, and determining an interaction relationship between a simulation value and an observation value of the regional climate model; and 5, estimating the future near-surface wind speed. According to the method, the defect that only single power or statistical downscaling is applied in the current research is overcome, and a scientific basis is provided for development and utilization of wind energy in China and even in the global region.

Description

technical field [0001] The invention relates to the technical field of regional climate change, in particular to a wind speed downscaling method based on a climate model and a joint distribution function. Background technique [0002] In recent years, with the intensification of climate change, the wind speed in most parts of the world has decreased, which will have a profound impact on the development and utilization of wind energy resources. Therefore, the impact of future climate change on near-surface wind speed has always been one of the research hotspots. However, climate change has significant dynamics, non-uniformity and spatial heterogeneity. The low horizontal resolution (150-400 km) of global climate models (GCMs) is difficult to reflect these complexities. Therefore, there is an urgent need to develop high-resolution or even ultra-high-resolution (spatial resolution less than 10 km) near-surface wind speed data under climate change conditions. [0003] At pres...

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Problems solved by technology

However, most studies in this area only use a single dynamical or statistical downscaling, which is insufficient to analyze the impact of medium- and long-term climate change on the inconsistency and spatial heterogeneity of near-surface wind speed

In addition, the current research lacks a high-temporal-spatial precision near-surface wind speed downscaling method based on a regional climate simulation model and a multivariate joint distribution function.

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