PARAFOG v2.0: a near real-time decision tool to support nowcasting fog formation events at local scales

Jean-François Ribaud, M. Haeffelin, J. Dupont, M. Drouin, F. Toledo, S. Kotthaus
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引用次数: 2

Abstract

Abstract. An improved version of the near-real time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and -STL modules is evaluated based on 9 years of measurements at the SIRTA observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types, and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 minutes to fog onset, with first high alerts occurring earlier for RAD than STL cases.
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PARAFOG v2.0:一个近乎实时的决策工具,支持在局部尺度上的临近预报雾形成事件
摘要提出了近实时决策工具PARAFOG (PFG2)的改进版本,用于检索雾前警报级别并区分辐射(RAD)和层降(STL)雾情况。PFG2有两个不同的模块,用于监测RAD和STL雾形成的物理过程,并在欧洲站点进行评估。这些模块基于创新的模糊逻辑算法,可以在雾发生前几分钟到几小时,根据RAD/STL条件检索雾警报级别(低、中、高)。PFG2-RAD模块还评估雾的厚度。PFG2-RAD和PFG2-STL模块都依赖于能见度观测和自动激光雷达和ceilometer (ALC)测量的组合。PFG2-RAD和-STL模块的整体性能是基于在巴黎附近的SIRTA天文台9年的测量和在巴黎-鲁瓦西、维也纳、慕尼黑和苏黎世机场长达两个雾季的测量结果进行评估的。在所有地点,由PFG2获取的雾前警戒级别均与本地天气分析相符。先进的PFG2算法对两种考虑的雾类型的命中率都在100%左右,对于RAD (STL)雾情况,虚警率约为10%(30%)。最后,发现导致随后雾事件的第一个高警报发生的时间范围从- 120分钟到雾开始,RAD的第一个高警报比STL病例发生得更早。
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