Observation, Simulation and Predictability of Fog: Review and Perspectives

Bergot, Thierry; Koračin, Darko

doi:10.3390/atmos12020235

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…The processes involved during each transition phase of fog life are complicated to be correctly incorporated into the forecasting models; this, in turn, increases the prospect of false alarms or biases. The current models present still are in need of more satisfactory vertical and horizontal resolution (Bergot and Koracin 2021 ). Queries such as identifying and understanding various mechanisms involved in fog formation and sustenance and interaction of micro-environments inside and outside the fog layer and how it affects fog life cycles can be explored with a varied range of tools and techniques available (Cuxart and Jiménez 2012 ).…”

Section: Fog Forecasting and Detectionmentioning

confidence: 99%

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Lakra

Avishek

2022

Rend. Fis. Acc. Lincei

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With the changing climate and environment, the nature of fog has also changed and because of its impact on humans and other systems, study of fog becomes essential. Hence, the study of its controlling factors such as the characteristics of condensation nuclei, microphysics, air–surface interaction, moisture, heat fluxes and synoptic conditions also become crucial, along with research in the field of prediction and detection. The current review expands for the period between 1976 to 2021, however, especially focused on the research articles published in the last two decades. It considers 250 research papers/research letters, 24 review papers, four book chapters/manuals, five news articles, 15 reports, six conference papers and five other online readings. This review is a compilation of the pros and cons of the techniques used to determine the factors influencing fog formation, its classification, tools and techniques available for its detection and forecast. Some recent advanced are also discussed in this review: role of soil properties on fogs, application of microwave communication links in the detection of fog, new class of smog, and how the cognitive abilities of humans are affected by fog. Recently India and China are facing an emergence and repetitions of fog haze/smog and thus their policies initiatives are also briefly discussed. It is concluded that the complexity in fog forecasting is high due to multiple factors playing a role at multiple levels. Most of the researchers have worked upon the role of humidity, temperature, wind, and boundary layer to predict fogs. However, the role of global wind circulations, soil properties, and anthropogenic heat requires further investigations. Literature shows that fog is being harnessed to address water insecurity in various countries, however, coastal areas of Angola, Namibia and South Africa, Kenya, Eastern Yemen, Oman, China, India, Sri Lanka, Mexico, along with the mountainous regions of Peru, Chile, and Ecuador, are some of the potential sites that can benefit from the installation of fog water harvesting systems.

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Section: Fog Forecasting and Detectionmentioning

confidence: 99%

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Lakra

Avishek

2022

Rend. Fis. Acc. Lincei

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“…Poor visibility associated with fog and/or low stratus (FLS) affects many socioeconomic sectors, such as aviation, marine, and road transportation [1][2][3][4][5][6][7]. This is why the monitoring of fog phenomenon has become a scientific topic of interest [8][9][10][11][12][13][14]. In recent years, with the rapid development of socio-economic sectors, fog detection and forecasting have become more and more important.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Bari,

Lasri,

Souri

et al. 2023

Atmosphere

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Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause. This paper presents an image-based approach for the short-term nighttime forecasting of FLS during the next 5 h over Morocco, based on geostationary satellite observations (Meteosat SEVIRI). To achieve this, a dataset of hourly night microphysics RGB product was generated from native files covering the nighttime cold season (October to April) of the 5-year period (2016–2020). Two optical flow techniques (sparse and dense) and three deep learning techniques (CNN, Unet and ConvLSTM) were used, and the performance of the developed models was assessed using mean squared error (MSE) and structural similarity index measure (SSIM) metrics. Hourly observations from Meteorological Aviation Routine Weather Reports (METAR) over Morocco were used to qualitatively compare the FLS existence in METAR, where it is also shown by the RGB product. Results analysis show that deep learning techniques outperform the traditional optical flow method with SSIM and MSE of about 0.6 and 0.3, respectively. Deep learning techniques show promising results during the first three hours. However, their performance is highly dependent on the number of filters and the computing resources, while sparse optical flow is found to be very sensitive to mask definition on the target phenomenon.

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“…The improvements in the numerical weather prediction (NWP) models in terms of vertical/horizontal resolutions [16][17][18], physical parameterization schemes [19][20][21], and land surface processes [22][23][24] have been possible due to the support of extensive fog field observations from across the globe [4,[25][26][27][28][29]. Several research centers have evaluated the performance of the NWP models and attempted to operationalize them for short-range (12-72 h) fog prediction on the local to regional scale [30][31][32][33][34]. Although the NWP models have made significant progress, the spatiotemporal variability of fog poses a major problem for forecasters due to the uncertainty associated with individual model forecasts.…”

Section: Introductionmentioning

confidence: 99%

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

et al. 2022

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One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts.

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Observation, Simulation and Predictability of Fog: Review and Perspectives

Abstract: Fog affects human activities in various ways, but the societal impact of fog has significantly increased during recent decades due to increasing air, marine and road traffic [...]

Cited by 9 publications

References 17 publications

A review on factors influencing fog formation, classification, forecasting, detection and impacts

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

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