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在东亚推进亚季节性到季节性多模型集成降水预测:基于深度学习的后处理以提高准确性

Uran Chung¹, Jinyoung Rhee^2,3, Miae Kim¹

¹Prediction Research Department, Climate Services and Research Division, APEC Climate Center, Busan, Republic of Korea.

|September 11, 2024

概括

深度学习改进了东亚亚亚季节性降水预测 (S2S). 这种先进的技术提高了降雨量和发生的准确性,优于传统方法.

科学领域:

气象学天气学
气候科学气候科学
人工智能的人工智能

背景情况:

对农业和能源等行业来说,季节前到季节 (S2S) 预测数据越来越重要.
人们对当前S2S预测的准确性存在担忧,特别是在降雨方面.
在S2S模型中提高降水预测精度仍然是一个重大挑战.

研究的目的:

改进东亚地区降雨量和发生情况的季节下到季节 (S2S) 预测技能.
评估基于深度学习的后处理技术与传统方法的有效性.
为了提高单个S2S气候模型的降水预测的准确性.

主要方法:

作为深度学习模型,使用了经过修改的U-Net架构,使用了TimeDistributed层.
使用6个S2S气候模型及其多模型组合 (MME) 的降水预测数据来训练模型.
定义了无雨,轻雨和大雨事件的降水发生门.

主要成果:

深度学习后处理在2-4周的降水量预测中表现优于多重线性回归 (MLR).
MLR并没有显著改善降雨发生预测的准确性.
基于深度学习的后处理大大提高了所有预测时间的降雨发生预测准确度,证明了其优越于MLR.

结论:

基于深度学习的后处理显著提高了S2S降水量和发生预测在东亚的准确性.
修改后的U-Net架构在提高S2S预测能力方面被证明是有效的.
这项研究强调了深度学习的潜力,以克服当前S2S预测模型的局限性.

关键词:

多模组合组合多模组合组合.后期处理后期处理预计降雨量预测.预测S2S的预测在U-net中,U-net是指U-net网络.

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