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

Authors :
Jean-François Ribaud
Martial Haeffelin
Jean-Charles Dupont
Marc-Antoine Drouin
Felipe Toledo
Simone Kotthaus
Laboratoire de Météorologie Dynamique (UMR 8539) (LMD)
Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris
École normale supérieure - Paris (ENS-PSL)
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL)
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Institut Pierre-Simon-Laplace (IPSL (FR_636))
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
Association Nationale de la Recherche et de la Technologie, ANRT
Direction Générale de l’Armement, DGA: DGA 2018 60 0074
Acknowledgements. The contribution of the first author and the PFG2 project were supported by the 'Direction Générale de l’Armement' under grant DGA 2018 60 0074. Felipe Toledo thanks the French Association Nationale de la Recherche et de la Technolo-gie (ANRT) and the company Meteomodem for their funding contribution. The authors would like to thank Rafael Eigenmann and Ulrich Goersdorf (DWD) for kindly providing the data for Munich airport, Maxime Hervo (Meteoswiss) for kindly providing the data for Zurich airport, Philipp Kneringer (Universität Innsbruck) for kindly providing the data for Vienna airport, and Météo-France for kindly providing the data for Paris-Roissy airport. Also, we would like to thank the PROBE COST action for setting up an EU-wide discussion framework regarding applications and methods for fog nowcasting methods based on remote sensing measurements.
Source :
Atmospheric Measurement Techniques, Atmospheric Measurement Techniques, 2021, 14 (12), pp.7893-7907. ⟨10.5194/amt-14-7893-2021⟩, Atmospheric Measurement Techniques, Vol 14, Pp 7893-7907 (2021)
Publication Year :
2021

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 PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) 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 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

Details

Language :
English
ISSN :
18678548 and 18671381
Database :
OpenAIRE
Journal :
Atmospheric Measurement Techniques, Atmospheric Measurement Techniques, 2021, 14 (12), pp.7893-7907. ⟨10.5194/amt-14-7893-2021⟩, Atmospheric Measurement Techniques, Vol 14, Pp 7893-7907 (2021)
Accession number :
edsair.doi.dedup.....4799e362bbe4128e5a80d370340591c9
Full Text :
https://doi.org/10.5194/amt-14-7893-2021⟩