Latest revision as of 13:15, 12 February 2021

Abstract

Abstract. Radiation fog occurs over many locations around the world in stable atmospheric conditions. Air traffic at busy airports can be significantly disrupted because low visibility at the ground makes it unsafe to take off, land and taxi on the ground. Current numerical weather prediction forecasts are able to predict general conditions favorable for fog formation, but not the exact time or location of fog occurrence. A selected set of observations available in near-real time at strategic locations could also be useful to track the evolution of key processes and key parameters that drive fog formation. Such observations could complement the information predicted by numerical weather prediction (NWP) models that is made available to airport forecasters in support of their fog forecast. This paper presents an experimental setup based on collocated automatic lidar and ceilometer measurements, relative humidity measurements and horizontal visibility measurements to study hygroscopic growth of fog condensation nuclei. This process can take several minutes to hours, and can be tracked using lidar- or ceilometer-attenuated backscatter profiles. Based on hygroscopic growth laws we derive a set of parameters that can be used to provide alerts minutes to hours prior to formation of radiation fog. We present an algorithm that uses the temporal evolution of attenuated backscatter measurements to derive pre-fog formation alerts. The performance of the algorithm is tested on 45 independent pre-fog situations at two locations (near Paris, France, and Brussels, Belgium). We find that an alert for pre-fog conditions predominantly occurs 10–50 min prior to fog formation at an altitude ranging 0 to 100 m above ground. In a few cases, alerts can occur up to 100 min prior to fog formation. Alert durations are found to be sensitive to the relative humidity conditions found a few hours prior to the fog.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.5194/amt-9-5347-2016 under the license https://creativecommons.org/licenses/by

• https://www.atmos-meas-tech.net/9/5347/2016 under the license https://creativecommons.org/licenses/by/3.0

• https://www.atmos-meas-tech.net/9/5347/2016/amt-9-5347-2016.pdf

• http://www.atmos-meas-tech-discuss.net/amt-2016-182/amt-2016-182.pdf,

http://dx.doi.org/10.5194/amt-2016-182 under the license cc-by

• https://amt.copernicus.org/articles/9/5347/2016/amt-9-5347-2016.pdf,

http://dx.doi.org/10.5194/amt-9-5347-2016 under the license https://creativecommons.org/licenses/by/3.0/

• https://www.atmos-meas-tech.net/9/5347/2016/amt-9-5347-2016.pdf,

https://amt.copernicus.org/articles/9/5347/2016,

https://amt.copernicus.org/articles/9/5347/2016/amt-9-5347-2016.pdf,

http://ui.adsabs.harvard.edu/abs/2016AMT.....9.5347H/abstract,

https://www.atmos-meas-tech.net/9/5347/2016,

https://noa.gwlb.de/receive/cop_mods_00011253,

http://www.atmos-meas-tech-discuss.net/amt-2016-182/amt-2016-182.pdf,

https://core.ac.uk/display/145794043,

https://academic.microsoft.com/#/detail/2437848696 under the license https://creativecommons.org/licenses/by/3.0/

• https://www.atmos-meas-tech.net/9/5347/2016/amt-9-5347-2016.pdf,

https://doaj.org/toc/1867-1381,

https://doaj.org/toc/1867-8548

DOIS: 10.5194/amt-9-5347-2016 10.5194/amt-2016-182