Long-term hail risk assessment with deep neural networks

Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov, Ilya Makarov Moscow Institute of Physics, Technologies, Skolkovo Institute of Science, Technology, Los Alamos National Laboratory, A. I. R. Institute
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Abstract

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as"hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades.
基于深度神经网络的长期冰雹风险评估
冰雹风险评估对于估计和减少对农作物、果园和基础设施的损害是必要的。此外,它还有助于估计和减少企业,特别是保险公司的损失。但冰雹预报具有挑战性。用于为此目的设计模型的数据是三维地理空间时间序列。就可用数据集的分辨率而言,冰雹是一个非常局部的事件。此外,冰雹事件是罕见的-在观测中只有1%的目标被标记为“冰雹”。临近预报和短期冰雹预报的模式正在改进。将机器学习模型引入气象学领域并不是什么新鲜事。也有各种气候模式反映未来气候变化的可能情景。但是,目前还没有机器学习模型来预测特定地区冰雹频率的变化。对于后一项任务,第一种可能的方法是忽略空间和时间结构,并开发一种能够将给定的气象变量垂直剖面划分为有利于或不利于冰雹形成的模式。尽管这种方法肯定会忽略重要的信息,但它的权重非常轻,并且易于扩展,因为它将观察结果视为彼此独立的。更先进的方法是设计一个能够处理地理空间数据的神经网络。我们的想法是将负责处理空间数据的卷积层与能够处理时间结构的递归神经网络块结合起来。本研究比较了两种方法,并介绍了一种适合于预测几十年来冰雹频率变化的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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