Development and Testing of a Decision Tree for the Forecasting of Sea Fog Along the Georgia and South Carolina Coast

IF 0.8 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Operational Meteorology Pub Date : 2018-06-12 DOI:10.15191/NWAJOM.2018.0605
B. Lindner, P. Mohlin, A. Caulder, Aaron Neuhauser
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引用次数: 5

Abstract

A classification and regression tree analysis for sea fog has been developed using 648 low-visibility (<4.8 km) coastal fog events from 1998–2014 along the South Carolina and Georgia coastline. Correlations between these coastal fog events and relevant oceanic and atmospheric parameters determined the range in these parameters that were most favorable for predicting sea fog formation. Parameters examined during coastal fog events from 1998–2014 included sea surface temperature (SST), air temperature, dewpoint temperature, maximum wind speed, average wind speed, wind direction, inversion strength, and inversion height. The most favorable range in SST for sea fog formation was 10.6–23.9°C. The most favorable gaps between air temperature and SST, dewpoint temperature and SST, and dewpoint temperature and air temperature were found to be –1.7– 2.2°C, 0°C, and 0–2.2°C, respectively. The most favorable range in maximum wind speed was 11.1–20.4 km h-1, and the most favorable wind directions were parallel to the coast or SST isopleths. The most favorable range in inversion height was 70.6–617.2 m, and the most favorable inversion strength was anything >6°C. Utilizing these eight predictors, a forecasting decision tree was created and beta tested during the 2016/2017 sea fog season. The decision tree successfully predicted sea fog on 17 of the 18 dates that it occurred (94%) and successfully predicted a lack of sea fog for 189 of the 194 days where sea fog did not occur (97%). Two of the six incorrect predictions appear to have extenuating circumstances. ABSTRACT (Manuscript received 11 December 2017; review completed 16 April 2018)
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佐治亚和南卡罗来纳海岸海雾预报决策树的开发和测试
使用648个低能见度(6°C)对海雾进行了分类和回归树分析。利用这八个预测因子,创建了一个预测决策树,并在2016/2017年海雾季节进行了贝塔测试。该决策树成功预测了18天中的17天(94%)的海雾,并成功预测了194天中的189天没有海雾没有出现海雾(97%)。六个错误的预测中有两个似乎有情有可原的情节。摘要(手稿于2017年12月11日收到;审查于2018年4月16日完成)
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来源期刊
Journal of Operational Meteorology
Journal of Operational Meteorology METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
4
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