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Cloud Climatologies for Rocket Triggered Lightning from Launches at Cape Canaveral Air Force Station and Kennedy Space Center

机译:卡纳维拉尔角空军基地和肯尼迪航天中心发射的火箭触发闪电的云气候

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摘要

We have conducted a study on the development of detailed climatological probabilities of violating cloud related Lightning Launch Commit Criteria (LLCC) used by Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS and KSC). This study was conducted to provide the 45th Weather Squadron with improved capabilities for operational forecasting for launches from CCAFS and KSC. Our focus was on developing methods to produce climatological probabilities of violating one of the LLCC, the thick cloud layer rule. We developed a hybrid process of blending data from the Climate Forecast System Reanalysis (CFSR), meteorological aerodrome reports (METARs), radiosonde observations (RAOBs), and expert meteorologist data sets to create a merged data set for determining the probability of violating the thick cloud layer rule. Using our blended hybrid process, we computed cloud thicknesses, and probabilities of violating the thick cloud LLCC for each day of the year at 00Z and 12Z. Additionally, we conducted a sensitivity analysis to identify the potential for modifying the thick cloud LLCC. A primary result from our study is a sub-daily data set of the climatological probabilities of violating the thick cloud layer rule. We conducted eight validation case studies that demonstrated our calculated violations match well with observed violations. The development of a merged data set that provides more useful information than any one of the individual data sets is a technique that is likely to be useful in solving many other climatological problems
机译:我们已经进行了一项研究,研究了卡纳维拉尔角空军基地和肯尼迪航天中心(CCAFS和KSC)所使用的违反与云有关的闪电发射承诺标准(LLCC)的详细气候概率的发展。进行这项研究是为了向第45气象中队提供改进的功能,以进行CCAFS和KSC发射的业务预报。我们的重点是开发产生违反LLCC之一(厚云层规则)的气候概率的方法。我们开发了一种混合过程,将气候预测系统重新分析(CFSR),气象机场报告(METAR),无线电探空仪观测(RAOB)和专家气象学家数据集的数据混合在一起,以创建合并的数据集,以确定违反厚层天气的可能性云层规则。使用我们的混合混合过程,我们计算了每年00Z和12Z的云厚度以及违反厚云LLCC的概率。此外,我们进行了敏感性分析,以发现修改厚云LLCC的潜力。我们研究的主要结果是违反厚云层规则的气候概率的次日数据集。我们进行了八次验证案例研究,证明我们计算出的违规行为与观察到的违规行为非常匹配。提供比单个数据集更有用的信息的合并数据集的开发是一种可能对解决许多其他气候问题有用的技术

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    Strong Greg J.;

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  • 年度 2012
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