Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network
Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network
复制标题
DOI：
10.1175/aies-d-22-0042.1
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
B. Scarino;K. Itterly;Kristopher Bedka;C. Homeyer;J. Allen;S. Bang;Daniel J. Cecil
中科院分区：
文献类型：
--
作者：
B. Scarino;K. Itterly;Kristopher Bedka;C. Homeyer;J. Allen;S. Bang;Daniel J. Cecil
对地静止卫星成像仪提供了通常与强对流有关的云顶模式的历史和近实时观测。有利的环境条件恶劣的天气被认为是代表很好的再分析。然而，仅仅使用模型或卫星图像来准确预测对流和冰雹等代价高昂的风暴灾害将在哪里发生是极具挑战性的。卫星观测的云模式与再分析环境参数的多变量组合，与下一代天气雷达（NEXRAD）使用深度神经网络（DNN）估计的最大预期冰雹大小（MESH）相关联，可以估计任何观测到的风暴单元的潜在严重冰雹可能性。这些估计是在卫星观测到位于有利风暴环境中的冷云（指示对流）时做出的。我们寻求一种方法，可以用来估计气候冰雹的频率和风险，在整个历史卫星数据记录。从卫星和再分析的对流参数的统计分布显示分离的非严重/严重的冰雹类的预测因子，包括过冲云顶温度和面积特征，垂直风切变，对流抑制。这些复杂的多变量预测关系在DNN中被利用，以产生具有0.511的关键成功指数和0.407的Heidke技能得分的似然估计，这在类似的冰雹研究中是例外的。此外，DNN的案例研究的应用程序表现出良好的定性协议之间的冰雹可能性和MESH。这些冰雹分类汇总在一个11年的GOES-12/13图像数据库，以获得冰雹的频率和严重程度的气候学，这表示中部平原，中西部和墨西哥西北部作为研究领域内最容易发生冰雹的地区。
Geostationary satellite imagers provide historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.