A Random Forest Method to Forecast Downbursts Based on Dual-Polarization Radar Signatures
A Random Forest Method to Forecast Downbursts Based on Dual-Polarization Radar Signatures
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基于双偏振雷达特征的下击暴流预测随机森林方法
DOI：
10.3390/rs11070826
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
R. Blakeslee
中科院分区：
文献类型：
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作者：
Bruno L. Medina;L. Carey;C. G. Amiot;R. Mecikalski;W. Roeder;Todd M. McNamara;R. Blakeslee
美国空军第45天气中队在卡纳维拉尔角空军站和肯尼迪航天中心(CCAFS/KSC)提供风力警告，包括下击暴流警告。这项研究的目的是提供一个随机森林模型，它使用35节风速阈值来区分雷暴的下击暴流和零事件，以区分这两类。利用CCAFS/KSC周围密集的风观测网络评估了下击暴流的发生。对209个风暴的8个双极化雷达特征信号进行了自动计算，并将其纳入随机森林模型。随机森林模型预测空事件比预测下暴流事件更准确，True Skill统计为0.40。强下击暴流事件的分类效果好于风级较弱的下击暴流事件。最重要的雷达特征是最大的垂直积分冰和峰值反射率。与基于单个雷达特征阈值的自动预测方法相比，随机森林模型提供了更可靠的性能。基于这些结果，建议继续使用随机森林方法进行业务开发和测试。
The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events using a 35-knot wind threshold to separate these two categories. The downburst occurrence was assessed using a dense network of wind observations around CCAFS/KSC. Eight dual-polarization radar signatures that are hypothesized to have physical implications for downbursts at the surface were automatically calculated for 209 storms and ingested into the Random Forest model. The Random Forest model predicted null events more correctly than downburst events, with a True Skill Statistic of 0.40. Strong downburst events were better classified than those with weaker wind magnitudes. The most important radar signatures were found to be the maximum vertically integrated ice and the peak reflectivity. The Random Forest model presented a more reliable performance than an automated prediction method based on thresholds of single radar signatures. Based on these results, the Random Forest method is suggested for continued operational development and testing.