Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings
Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings
复制标题
使用 ERA5 邻近探测对美国重大龙卷风和冰雹进行机器学习分类
DOI：
10.1175/waf-d-21-0056.1
复制
发表时间：
2021
影响因子：
2.9
通讯作者：
Taszarek, Mateusz
中科院分区：
文献类型：
--
作者：
Gensini, Vittorio A.;Converse, Cody;Ashley, Walker S.;Taszarek, Mateusz
先前的研究已经确定了能够巧妙区分恶劣天气事件和重大恶劣天气事件的环境特征，但它们在很大程度上受到样本量和/或预测变量数量的限制。鉴于严重恶劣天气的社会影响加剧，我们使用 1996 年至 2019 年期间龙卷风和冰雹报告最近且之前的网格点提取的超过 150 000 个 ERA5 再分析得出的垂直剖面重新审视了这一主题。配置文件经过质量控制并用于计算 84 个变量。根据这些数据对几种机器学习分类算法进行了训练、测试和交叉验证，以评估预测严重或极严重龙卷风和冰雹报告的技能。根据交叉验证的关键成功指数得分和接受者操作特征曲线值下的面积来衡量，随机森林分类优于所有测试方法。此外，随机森林分类比其他方法更可靠，并且频率偏差可以忽略不计。龙卷风最重要的三个随机森林分类变量是 500 hPa 的风速、850 hPa 的风速和 0-500 米风暴相对螺旋度。对于冰雹，3-6公里和-10°至-30°C层的风暴相对螺旋度以及0-6公里的大风切变被发现是最重要的。使用博弈论方法来帮助解释随机森林分类器的输出，并为操作临近预报和预测建立关键特征阈值。还提出了随机森林模型的空间适用性的用例，展示了业务预测的潜在效用。总体而言，这项研究支持越来越多的天气和气候研究发现随机森林分类应用中令人钦佩的技能。
Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and −10° to −30°C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.