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

Rao, Gandikota V.; O'Sullivan, James M.

doi:10.1007/978-3-0348-7970-5_14

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Thus, a three-dimensional (3-D) model, which incorporates processes like horizontal pressure gradient, advection and diffusion, is 1 required for predicting fog. Mesoscale models such as Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research Forecasting (WRF) were developed and used to forecast fog, especially the low visibility in fog by different researchers (Ballard et al 1991;Rao and Sullivan 2003;Pagowski et al 2004;Fu et al 2006;Van der Velde et al 2010). These models use parameterization schemes to forecast fog visibility as they do not have detailed microphysical processes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of fog/visibility over India using NWP Model

2018

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India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.

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Section: Introductionmentioning

confidence: 99%

Prediction of fog/visibility over India using NWP Model

2018

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“…The first such fog prediction with a regional 3-D model has been attempted by Ballard et al [8]. Since then many further similar models have been attempted for fog prediction [9][10][11][12][13][14]. Still, there are only few specific studies [15][16][17][18], over India, for fog simulation and prediction.…”

Section: Introductionmentioning

confidence: 99%

Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

Payra

Mohan

2014

Advances in Meteorology

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The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

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“…Numerical simulation studies had focused on radiation fog, which has a relatively clear formation process [e.g., Taylor , 1917; see a review by Rao and O ' Sullivan , 2003]. Bergot and Guedalia [1994] performed a sensitivity test with a 1‐D model to improve forecast of radiation fog.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial characteristics of fog occurrence over the Korean Peninsula

Lee¹,

Lee²,

Park³

et al. 2010

J. Geophys. Res.

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In this study, fogs are classified based on the spatial and temporal characteristics over South Korea using the visibility data and the empirical orthogonal function (EOF) and wavelet analyses. With fog defined in terms of visibility (<1 km), the EOF analysis is performed to extract spatial distribution characteristics via dimension reduction, whereas the space‐time wavelet expansion is applied to the EOF time series to specify the fog characteristics in the space of time versus scale (i.e., period in this study). The first EOF mode occupies 48.9% of total variance and shows the fog distribution covering almost entire areas of South Korea with one sign (+), except at the eastern coast and western part of the southern coast. The wavelet analysis reveals that this fog occurs based on meteorological conditions of various scales from daily to seasonal, thus classified as mixed fog. The second EOF mode, which occupies 19.5% of total variance, shows distinct separation of spatial distribution of fog, with a negative (−) sign in winter over northwestern coastal/inland, western coastal, and south central mountain areas of South Korea and a positive (+) sign in other seasons elsewhere. With cycles of 1–2 weeks and 1–2 months being dominant in the wavelet analysis, this fog is considered to be strongly affected by synoptic scale weather systems and monsoon. Fog over the positive area is mostly affected by monsoon and/or cyclonic frontal systems, thus classified as frontal fog, whereas that over the negative area is affected by the cold‐core anticyclones moving over warm sea surface in winter or by radiative cooling, thus classified as steam fog (coastal/sea) or radiation fog (inland), respectively. The mountain area may have upslope fog because of orographic lifting. The third EOF mode, occupying 6.7% of total variance, depicts distinct spatial separation of fog distribution around the coastal areas with a negative (−) sign and in the inland areas with a positive (+) sign. The former, with a dominant 1–2 week cycle, is classified as sea fog affected by migratory anticyclones and monsoon in late spring and summer, while the latter, with a dominant diurnal variation, represents radiation fog under clear sky in autumn. It turns out that the combined EOF and wavelet analyses are useful to assess the detailed spatial and temporal characteristics of various types of fog occurrence in South Korea.

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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

Cited by 5 publications

References 9 publications

Prediction of fog/visibility over India using NWP Model

Prediction of fog/visibility over India using NWP Model

Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

Temporal and spatial characteristics of fog occurrence over the Korean Peninsula

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