使用雷达深度生成模型进行熟练的降水预测
Suman Ravuri1, Karel Lenc1, Matthew Willson1
1DeepMind, London, UK.
Nature
|September 30, 2021
概括
这项研究引入了降水预测的深度生成模型,提高了对罕见的暴雨事件的预测准确性和有用性. 这种新模型提供了现实的,一致的预测,在专家评估中表现优于现有的方法.
科学领域:
- 气象学与大气科学
- 人工智能和机器学习
- 地理空间数据分析
背景情况:
- 对于依赖天气的行业来说,降雨预测 (预测时间长达2小时) 是至关重要的.
- 目前的基于雷达的方法与非线性事件如对流启动作斗争.
- 深度学习模型缺乏物理约束,导致模糊的预测和强降雨事件的表现不佳.
研究的目的:
- 根据雷达数据开发一个深度生成模型进行概率降雨预测.
- 解决现有方法的局限性,特别是对于罕见的,中等到强降雨事件和较长的交付时间.
- 提高预报质量,一致性和运营天气预报的价值.
主要方法:
- 开发一个深度生成模型用于概率性降水预测.
- 使用雷达数据进行高分辨率,时空一致的预测.
- 通过统计,经济,认知和专家气象学家的评估进行评估.
主要成果:
- 生成模型可以在很大区域 (高达1536公里×1280公里) 进行现实和一致的预测,并提供5至90分钟的预测时间.
- 专家气象学家在89%的案例中对该模型的准确性和实用性排名第一,与两种竞争方法相比.
- 量化验证显示了熟练的,不模糊的预测,提高了预测价值和运营效用.
结论:
- 深度生成模型为准确和有价值的降水预测提供了一个有希望的方法.
- 提出的模型克服了现有方法的局限性,特别是在极端天气事件中.
- 通过提供可靠的,高分辨率的概率预测,增强运营决策.
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