Radiation Fog Prediction Using a Simple Numerical Model

Meyer, W. D.; Rao, Gandikota V.

doi:10.1007/s000240050254

Cited by 15 publications

(12 citation statements)

References 15 publications

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“…It is believed that radiation fog is generally a local problem and as such is best handled by a resident forecaster, who is familiar with the unique conditions of that site. MEYER and RAO's (1999) simple model was directed to predicting fog, using the routinely available synoptic surface observations by the military meteorologists, utilizing desk top computers, and some local parameters carefully estimated. The 24-hour prediction is based on the 1800 LT observation of dry bulb, dewpoint temperature, and wind.…”

Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

“…(b) An analytical formula of radiative cooling due to HALTINER and MARTIN (1956) was modified by MEYER and RAO (1999), for use in the model. The physical properties of the soil, such as density and thermal conductivity, were obtained from climatology (GARRATT, 1992).…”

Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

“…For example, there is no means of accounting for horizontal advection. MEYER and RAO's (1999) model can be combined with a larger 3-D model which will generate synoptic conditions. Although the 1800 LT wind speed is specified, it is strictly to calculate the diffusion coefficients of heat and moisture.…”

Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

“…The purpose of this research is to apply the MEYER and RAO's (1999) model to the prediction of mist, haze, or fog at Brunei, based on the 1800 LT routine observations of dry bulb, dewpoint temperature, and wind. Two distinct time periods were selected: one in December 1997, when the Kalimantan region was relatively dry because of the strong El Nin˜o in the eastern Pacific; the second in December 1998, when the same region was relatively wet.…”

Section: Purpose Of This Researchmentioning

confidence: 99%

“…These two months were selected for calibrating the MEYER and RAO's (1999) model, originally developed for extratropics at Brunei's tropical station. It turned out that only minor changes in radiation energy at sunset and sky radiation were needed to apply the model to Brunei.…”

Section: Datamentioning

confidence: 99%

See 4 more Smart Citations

A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

Rao¹,

O'Sullivan²

2003

Pure and Applied Geophysics

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Several radiation fog studies with emphasis on numerical simulation and prediction are reviewed. One of the earliest attempts started with a given surface diurnal variation of temperature and water vapor, and concluded by forecasting the onset of saturation at various levels; thus fog, by examining the spread of temperature and moisture in the vertical. The one-dimensional (1-D) models are still popular. Some of the recent numerical simulations use more than 100 levels in the vertical and treat various kinds of vegetation, aerosols, and soils with moisture contents. Some also employ a mesoscale model in conjunction with a 1-D model to consider the advective effects. In the following a simple 1-D numerical model was used to predict the onset of fog at Brunei, based on a desktop computer and routine surface observations of dry bulb temperature (T), dewpoint temperature (T d ), and wind speed at 1800 Local Time (LT). Optimism exists in improved predictions of fog and stratus as 1-D models incorporate many physical processes, and mesoscale models continue to improve in predicting advection and cloud cover.Key words: 1-d fog model, radiation fog, fog prediction, fog over Brunei, numerical modeling of fog, review of fog.

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Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

Section: A 1-d Fog Prediction Modelmentioning

confidence: 99%

Section: Purpose Of This Researchmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

Rao¹,

O'Sullivan²

2003

Pure and Applied Geophysics

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An analogue dynamical model for forecasting fog‐induced visibility: validation over Delhi

Goswami

Sarkar

2017

Meteorological Applications

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Accurate forecasts of fog and visibility are important for many applications; while prolonged fog can adversely affect many crops, even a short duration of dense fog can lead to disruption of air and highway traffic. The genesis and dynamics of fog are a result of many processes; accurate forecasting of fog thus continues to be a challenge. A forecast model of the occurrence of fog, measured in terms of visibility, is presented. The model is formulated as an analogue model; thus the merit of the model is primarily based on its validation against observation. Two forecasts using two sets of meteorological fields are considered: one as the benchmark forecasts with visibility calculated from observed meteorological fields and the other based on meteorological forecasts from an atmospheric mesoscale model (Weather Research and Forecasting). While the benchmark (perfect) forecasts from observed meteorological fields provide the potential skill of the model, the mesoscale forecasts provide an assessment of realizable skill in an operational setting. The validation was carried out against hourly visibility data recorded at Indira Gandhi International Airport over Delhi during the winter months (December and January) for the period 2009–2012. Error statistics show that the analogue fog model can capture a significant part of the observed variability of fog. The forecasts have more success in forecasting intense (visibility < 500 m) and persistent (duration > 4 h) fog events. The model provides a useful forecasting tool, as shown by measures such as average error, number of false warnings and the number of misses.

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Development of statistical and machine learning models to predict the occurrence of radiation fog in Japan

Negishi

Kusaka

2022

Meteorological Applications

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This study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively.

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Radiation Fog Prediction Using a Simple Numerical Model

Cited by 15 publications

References 15 publications

A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

An analogue dynamical model for forecasting fog‐induced visibility: validation over Delhi

Development of statistical and machine learning models to predict the occurrence of radiation fog in Japan

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