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气候模型模拟中偏差校正方法的综合比较：在不同时间分辨率下对ERA5-Land的应用

A comprehensive comparison of bias correction methods in climate model simulations: Application on ERA5-Land across different temporal resolutions.

作者信息

Dhawan Pranav, Dalla Torre Daniele, Niazkar Majid, Kaffas Konstantinos, Larcher Michele, Righetti Maurizio, Menapace Andrea

机构信息

Free University of Bozen-Bolzano, Faculty of Engineering, NOI Techpark - via Bruno Buozzi, 1, Bolzano, 39100, Italy.

Free University of Bozen-Bolzano, Faculty of Agricultural Environmental and Food Sciences, Piazza Università, 5, Bolzano, 39100, Italy.

出版信息

Heliyon. 2024 Nov 14;10(23):e40352. doi: 10.1016/j.heliyon.2024.e40352. eCollection 2024 Dec 15.

DOI:10.1016/j.heliyon.2024.e40352
PMID:39654746
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11625273/
Abstract

Climate data plays a crucial role in water resources management, which is becoming an increasingly relevant asset in all types of hydrological analysis not only for climate change studies but for various horizon forecasting. Though the ever-improving accuracy of climate models' spatial and temporal resolution has surged the validity of their outputs, the products of global and regional climate models need to be corrected to be reliably used for local purposes. Here, we propose a comprehensive analysis of statistical univariate and multivariate, as well as machine learning methods for bias correction, which are compared on different temporal scales, ranging from hourly time steps to monthly aggregations, in an environment of complex Alpine orthography, using ERA5-Land reanalysis data. The results reveal different trends in the performance of the bias correction methods for precipitation and temperature across the various time resolutions.

摘要

气候数据在水资源管理中起着至关重要的作用，在各类水文分析中，它正成为一种越来越重要的资产，不仅用于气候变化研究，还用于各种时间跨度的预测。尽管气候模型在空间和时间分辨率上的准确性不断提高，其输出结果的有效性也随之提升，但全球和区域气候模型的产品仍需进行校正，才能可靠地用于本地目的。在此，我们提出对用于偏差校正的统计单变量和多变量方法以及机器学习方法进行全面分析，并在复杂的阿尔卑斯山脉地形环境中，使用ERA5-Land再分析数据，在从每小时时间步长到月度汇总的不同时间尺度上对这些方法进行比较。结果揭示了在不同时间分辨率下，降水和温度偏差校正方法的性能呈现出不同趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/2d26f5776665/gr009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/51743e4b597a/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/2d26f5776665/gr009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/e4554e8fa96b/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/7beed5c544d2/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/e56a100ac3d9/gr003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/ade0a8a9705b/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/8e9433b08e26/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/51743e4b597a/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9f/11625273/2d26f5776665/gr009.jpg

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