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Radar Nowcasting of Cloud-to-Ground Lightning over Houston, Texas

机译:德克萨斯州休斯顿的云到地面闪电的雷达临近预报

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摘要

Ten years (1997-2006) of summer (June-August) daytime (1400-0000 UTC) Weather Surveillance Radar-1988 Doppler data for Houston, Texas, were examined to determine the best radar-derived predictors of the first cloud-to-ground lightning flash from a convective cell. Convective cells were tracked using a modified version of the Storm Cell Identification and Tracking (SCIT) algorithm and then correlated to cloud-to-ground lightning data from the National Lightning Detection Network (NLDN). Combinations of three radar reflectivity values (30, 35, and 40 dBZ) at four isothermal levels (-10°, -15°, -20°, and updraft -10℃) and a new radar-derived product, vertically integrated ice (VII), were used to optimize a radar-based lightning forecast algorithm. Forecasts were also delineated by range and the number of times a cell was identified and tracked by the modified SCIT algorithm. This study objectively analyzed 67 384 unique cells and 1 028 510 lightning flashes to find the best lightning forecast criteria. Results show that using 30 dBZ at the -15° or -20 ℃ isotherm on cells within 75 km of the radar that have been tracked for at least two consecutive scans produces the best lightning forecasts with a critical success index (CSI) of 0.68. The best VII predictor values were 0.42 or 0.58 kg m~(-2) on cells within 75 km of the radar that have been tracked for at least two consecutive scans, producing a CSI of 0.67. Lead times for these predictors were 10.0 and 13.4 min, respectively. Lead times greater than 10 min occurred with less stringent predictors (e.g., 30 dBZ at -10℃or VII greater than 0.25 kg m~(-2) on cells within 125 km with a minimum track count of 2), but lower CSI values result. In general, cells tracked for multiple scans provide higher CSIs and lead times than decreasing the range from the radar or changing the reflectivity threshold and height.
机译:研究了德克萨斯州休斯顿的夏季(6月至8月)白天(1400-0000 UTC)十年(1997-2006)的天气监视雷达1988多普勒数据,以确定第一个云到云的最佳雷达衍生预报器对流电池发出的地面闪电。对流细胞使用风暴细胞识别和跟踪(SCIT)算法的改进版本进行跟踪,然后与来自国家闪电检测网络(NLDN)的云对地闪电数据相关联。在四个等温水平(-10°,-15°,-20°和上升气流-10℃)下三个雷达反射率值(30、35和40 dBZ)的组合,以及一个新的雷达衍生产品,垂直积分冰( VII),用于优化基于雷达的闪电预报算法。预测还通过范围和经过修改的SCIT算法识别和跟踪单元的次数来描绘。这项研究客观地分析了67384个独特的电池单元和1028510个闪电,以找到最佳的闪电预报标准。结果表明,在雷达75公里以内的至少连续两次扫描的小区中,在-15°或-20℃等温线上使用30 dBZ可以产生最佳的闪电预报,其临界成功指数(CSI)为0.68。至少在连续两次扫描中,在距雷达75公里以内的单元上,VII的最佳VII预测值是0.42或0.58 kg m〜(-2),CSI值为0.67。这些预测变量的交货时间分别为10.0分钟和13.4分钟。提前时间大于10分钟,且预测指标不太严格(例如,在125 km内的单元上,在-10℃时30 dBZ或VII大于0.25 kg m〜(-2),最小轨道数为2),但CSI值较低结果。通常,与减少从雷达的范围或更改反射率阈值和高度相比,为多次扫描跟踪的单元提供了更高的CSI和提前期。

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