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首页> 外文期刊>Journal of Applied Meteorology >The Development and Testing of a New Method to Evaluate the Operational Cloud-Seeding Programs in Texas
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The Development and Testing of a New Method to Evaluate the Operational Cloud-Seeding Programs in Texas

机译:开发和评估德克萨斯州运营云播种计划的新方法

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摘要

A method for the objective evaluation of short-term, nonrandomized operational convective cloud-seeding projects on a floating-target-area basis has been developed and tested in the context of the operational cloud-seeding projects of Texas. The computer-based method makes use of the Next-Generation Radar (NEXRAD) mosaic radar data to define fields of circular (25-km radius) floating-target analysis units with lifetimes from the first echo to the disappearance of all echoes and then superimposes thetrack and seeding actions of the project seeder aircraft onto the unit fields to define seeded (S) and non.seeded (NS) analysis units. Objective criteria (quantified herein) are used to identify "control" (C) matches for each of the seed units from the archive of NS units. To minimize potential contamination by seeding, no matching is allowed for any control unit if its perimeter came within 25 km of the perimeter of a seed unit during its lifetime. The methodology was used to evaluate seeding effects in the High Plains Underground Water Conservation District (HP) and Edwards Aquifer Authority (EA) programs during the 1999, 2000. and 2001 (EA only) seasons. Objective unit matches were selected from within and outside each operational target within 12,6. 3. and 2 h of the time on a given day that seeding of a particular unit took place. These were done to determine whether selection biases and the diurnal convective cycle confounded the results. Matches were also drawn from within and outside each target using the entire archive of days on which seeding was done. Although the results of all analyses are subjected to statistical testing, the resulting probability (P) values were used solely to determine the relative strength of the various findings. In the absence of treatment, randomization P values cannot be used as proof of seeding efficacy. The apparent effect of seeding in both programs was large--even after determining the effect of selection biases and the diurnal convective cycle. The most conservative and credible estimates of seeding effects were obtained from control matches drawn from outside the operational target within 2 h of the time that each unit was seeded initially. Under these circumstances, the percentage increase exceeds 50% and the volumetric increment was greater than 3000 acre-feet (3700 kt) per unit with strong P-value support (i.e.. <0.000l) in both the HP and EA programs. This is in good agreement with the apparent percentage effects of seeding for the randomized Texasand Thailand cloud-seeding programs, which were 43% and 48%-92%. respectively. The results and their P-value support after partitioning gave even stronger indications of positive seeding effects. Although the results of these and other analyses describedherein make a strong case for enhanced rainfall by the operational seeding programs, such programs must not be viewed as substitutes for randomized seeding efforts that arc conducted in conjunction with realistic cloud modeling and are followed by replication, preferably by independent groups for maximum credibility.
机译:在德克萨斯州的业务云播种项目的背景下,已经开发出了一种以浮动目标区域为基础的短期,非随机的对流云播种项目的客观评估方法,并进行了测试。基于计算机的方法利用下一代雷达(NEXRAD)马赛克雷达数据来定义圆形(半径为25 km)浮动目标分析单元的范围,其寿命从第一次回波到所有回波消失,然后叠加将项目播种机的跟踪和播种动作转移到单位字段上,以定义种子(S)和非种子(NS)分析单位。客观标准(在此量化)用于从NS单位档案中识别每个种子单位的“对照”(C)匹配。为了最大程度地减少播种造成的潜在污染,如果任何控制单元的周长在其使用寿命内位于播种单元周长的25公里以内,则不允许进行匹配。在1999年,2000年和2001年(仅限EA)季节,该方法用于评估高原平原地下水保护区(HP)和爱德华兹含水层管理局(EA)计划的播种效果。目标单位匹配是从12,6范围内的每个运营目标的内部和外部选择的。 3.在特定日期播种的一天中的2小时。这样做是为了确定选择偏倚和昼夜对流循环是否会混淆结果。还使用完成播种的整个天数从每个目标的内部和外部进行匹配。尽管所有分析的结果都经过统计检验,但所得的概率(P)值仅用于确定各种发现的相对强度。在没有治疗的情况下,随机P值不能用作播种功效的证据。即使在确定选择偏见和昼夜对流周期的影响之后,这两个程序中播种的表观效果还是很大的。播种效果的最保守和最可靠的估计是从每个单元最初播种后2小时内从操作目标外部绘制的对照匹配中获得的。在这种情况下,HP和EA程序均具有强大的P值支持(即<0.000l),每单位的体积增加量超过50%,体积增加量大于3000英亩-英尺(3700 kt)。这与德克萨斯州和泰国的随机云播种计划的播种的明显百分比效应非常吻合,分别为43%和48%-92%。分别。分割后的结果及其P值支持甚至更有效地表明了播种效果。尽管本文所述的这些分析和其他分析的结果为通过操作播种程序增加降雨提供了强有力的理由,但不得将此类程序视为替代结合实际云模型进行的随机播种工作的替代方案,并且最好进行复制由独立团体提供最大的信誉。

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