Machine Learning of Maritime Fog Forecast Rules

Tag, Paul M.; Peak, James E.

doi:10.1175/1520-0450(1996)035<0714:mlomff>2.0.co;2

Cited by 24 publications

(15 citation statements)

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“…Some influences have been cited by various authors, mainly dealing with conditions out over the open ocean. For instance, the presence of a sufficiently shallow cloudy marine boundary layer capped by a strong inversion and a sufficiently moist subcloud layer (dew point higher than the sea-surface temperature by a few degrees) have been cited by PEAK and TAG (1989) and TAG and PEAK (1996) as important factors. Furthermore, cloud base lowering has been shown to be tied to the diurnal cycle of stratiform boundary layer clouds related to the interaction of the cloud with radiation (DUYNKERKE and HIGNETT, 1993).…”

Section: Definition and Fog Classificationmentioning

confidence: 99%

Fog Research: A Review of Past Achievements and Future Perspectives

Gültepe

Tardif

Michaelides³

et al. 2007

Pure appl. geophys.

542

348

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The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ''fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

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Section: Definition and Fog Classificationmentioning

confidence: 99%

Fog Research: A Review of Past Achievements and Future Perspectives

Gültepe

Tardif

Michaelides³

et al. 2007

Pure appl. geophys.

542

348

View full text Add to dashboard Cite

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“…Oliver et al (1978) studied in detail the interactions between radiative and turbulence processes and found that radiative cooling at fog top is an important process in the fog life cycle. Telford and Chai (1984) have shown that microphysical processes and entrainment are the two major mechanisms that drive the lowering of stratus to fog whereas Tag and Peak (1996) showed that the presence of a sufficiently shallow stratus cloud capped by a strong inversion and moist sub-cloud layer are important factors.…”

Section: Introductionmentioning

confidence: 99%

Stratus–Fog Formation and Dissipation: A 6-Day Case Study

Dupont

Haeffelin

Protat

et al. 2012

Boundary-Layer Meteorol

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A suite of active and passive remote sensing instruments and in-situ sensors deployed at the SIRTA Observatory (Instrumented Site for Atmospheric Remote Sensing Research), near Paris, France, for a period of six months (October 2006-March 2007 document simultaneously radiative, microphysical and dynamic processes driving the continental-fog life cycle. The study focuses on a 6-day period between 23 and 29 December 2006 characterized by several stratus-cloud lowering and lifting events and almost 18 h of visibility below 1 km. Conceptual models and different possible scenarios are presented here to explain the formation, the development and the dissipation phases of three major stratus-fog events and to quantify the impact of each driving process. For example, slowly evolving large-scale conditions characterized by a slow continuous cloud-base lowering, followed by a rapid transient period conductive to fog formation and dissipation, are observed for cases 1 and 3. During this stable period, continuous cloud-top radiative cooling (≈ −160 Wm −2 ) induces a progressive and slow lowering of the cloud base: larger droplets at cloud top (cloud reflectivity approximately equals to −20 dBZ) induce slow droplet fall to and beyond cloud base (Doppler velocity ≈ −0.1 m s −1 ), cooling the sub-cloud layer by evaporation and lowering the saturation level to 100 m (case 1) or to the surface (cases 2 and 3). Suddenly, a significant increase in Doppler velocity magnitude ≈ −0.6 m s −1 and of turbulent kinetic energy dissipation rate around 10 −3 m 2 s −3 occurs at cloud base (case 1). These larger cloud droplets reach the surface leading to fog formation over 1.5 h. The Doppler velocity continues to increase over the entire cloud depth with a maximum value of around −1 m s −1 due to the collection of fog droplets by the drizzle drops with high collection efficiency. As particles become larger, they fall to the ground and lead to fog dissipation. Hence, falling particles play a major role in both the formation and also in the dissipation of the fog. These roles co-exist and the balance is driven by the characteristics of the falling particles, such as the concentration of drizzle drops, the size distribution of drizzle drops compared to fog droplets, Doppler velocity and thermodynamic state close to the surface.

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“…Admittedly, this deficiency in weather data is significant, but recently developed methods can estimate the presence of fog. ( 24 )…”

Section: Data Preparationmentioning

confidence: 99%

“…The index for “the existence of fog” (represented by a binary variable) was calculated using the determination rules shown below as described by Tag and Peak. ( 24 ) Base layer height (BLH), sea surface temperature (SST), and dew point temperature (DPT) are the required inputs. Fog is assumed to be present when the following conditions are met:…”

Section: Data Preparationmentioning

confidence: 99%

The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels

Pelot

Hilliard

2009

Risk Analysis

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There is a long history of studying the relationship between weather and maritime activities. This article analyzes the link between relative incident rate (RIR) and general weather factors within certain gridded areas and time periods. The study area, which encompasses a broad extent of Atlantic Canadian waters, includes fishing incidents recorded by the Canadian Coast Guard from 1997 to 1999. Methodologies used for traffic track generation in a geographical information system and aggregation of all relevant weather data needed for the statistical analyses are presented. Ultimately, a regression tree was built to illustrate the relationship between incident rate and the following six weather factors: wave height; sea surface temperature; air temperature; ice concentration; fog presence; and precipitation. Results from the regression tree reveal that the RIR defined as (incident number per area-day)/(traffic amount per area-day) across grid cells with incidents, increases as the weather conditions deteriorate in a general way, and the concentration of ice has the biggest influence on the magnitude of incident rates for a given level of traffic exposure. The results from this analysis may assist administrators of maritime traffic, especially those associated with fishing activities, through a better understanding of the influence on RIR of certain weather conditions within given areas in specific time periods.

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Machine Learning of Maritime Fog Forecast Rules

Cited by 24 publications

References 0 publications

Fog Research: A Review of Past Achievements and Future Perspectives

Fog Research: A Review of Past Achievements and Future Perspectives

Stratus–Fog Formation and Dissipation: A 6-Day Case Study

The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels

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