A characteristic analysis of fog using GPS‐derived integrated water vapour

Lee, Jaewon; Park, Jong‐Uk; Cho, Jungho; Baek, Ji-Hye; Kim, Hyung Woo

doi:10.1002/met.182

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…where P 0 represents surface pressure in hPa and f pθ, Hq " 1´0.00266¨cos2θ´0.00028¨H (2) After the ZHD is calculated, ZWD can be generated by following equation:…”

Section: Gps Pwv Estimationmentioning

confidence: 99%

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Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Jiang

Chen

et al. 2016

Remote Sensing

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GPS has become a very effective tool to remotely sense precipitable water vapor (PWV) information, which is important for weather forecasting and nowcasting. The number of geodetic GNSS stations set up in China has substantially increased over the last few decades. However, GPS PWV derivation requires surface pressure to calculate the precise zenith hydrostatic delay and weighted mean temperature to map the zenith wet delay to precipitable water vapor. GPS stations without collocated meteorological sensors can retrieve water vapor using standard atmosphere parameters, which lead to a decrease in accuracy. In this paper, a method of interpolating NWP reanalysis data to site locations for generating corresponding meteorological elements is explored over China. The NCEP FNL dataset provided by the NCEP (National Centers for Environmental Prediction) and over 600 observed stations from different sources was selected to assess the quality of the results. A one-year experiment was performed in our study. The types of stations selected include meteorological sites, GPS stations, radio sounding stations, and a sun photometer station. Compared with real surface measurements, the accuracy of the interpolated surface pressure and air temperature both meet the requirements of GPS PWV derivation in most areas; however, the interpolated surface air temperature exhibits lower precision than the interpolated surface pressure. At more than 96% of selected stations, PWV differences caused by the differences between the interpolation results and real measurements were less than 1.0 mm. Our study also indicates that relief amplitude exerts great influence on the accuracy of the interpolation approach. Unsatisfactory interpolation results always occurred in areas of strong relief. GPS PWV data generated from interpolated meteorological parameters are consistent with other PWV products (radio soundings, the NWP reanalysis dataset, and sun photometer PWV data). The differences between them were approximately 1~3 mm at most at our selected stations, and GPS data processing is the main factor influencing the agreement of the GPS PWV results with those of other methods.

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“…where P 0 represents surface pressure in hPa and f pθ, Hq " 1´0.00266¨cos2θ´0.00028¨H (2) After the ZHD is calculated, ZWD can be generated by following equation:…”

Section: Gps Pwv Estimationmentioning

confidence: 99%

“…Water vapor plays a key role in many weather and climate processes (e.g., see [1,2]). Many instruments can be employed to retrieve the atmospheric water vapor content, such as radiosondes, microwave radiometers, sun photometers, and ground-based GPS devices [3].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Jiang

Chen

et al. 2016

Remote Sensing

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“…An integrated Water Vapor (IWV) model based on GPS observations has been used in climatology along with the numerical weather prediction (NWP) model [63]. The GPS IWV model is also applied as a new approach to fog detection and assessments by studying (in time domain) the relationship between the GPS IWV and meteorological observations during formation, evolution, and dissipation of dense fog [64,65].…”

Section: Fog Modeling and Forecastingmentioning

confidence: 99%

Fogs: Physical Basis, Characteristic Properties, and Impacts on the Environment and Human Health

Pérez-Díaz

Ivanov

Peshev

et al. 2017

Water

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This work presents a selective overview of natural fogs in terms of fog types, forms and states of occurrence, physical, micro-physical, chemical and dynamic properties, basic characterizing parameters, etc. In focus are related achievements and contributions reported mainly during the last decade and a half, as a result of both laboratory studies and field observations. Processes of homogeneous and heterogeneous nucleation are analyzed in the aspects of condensation, nuclei diversity and specifics, as related to the activation, growth and deposition of fog droplets. The effect is highlighted of the water vapor's partial pressure on the surface tension of the liquid water-air interface and the freezing point of the water droplets. Some problems and aspects of fog modeling, parameterization, and forecasting are outlined and discussed on the examples of newly developed relevant 1D/3D theoretical models. Important issues of fog impacts on the air quality, ecosystems, water basins, societal life, and human health are also addressed and discussed, particularly in cases of anthropogenically modified (chemical, radioactive, etc.) fogs. In view of reducing the possible negative effects of fogs, conclusions are drawn concerning the new demands and challenges to fog characterization imposed by the changing natural and social environment and the needs for new data on and approaches to more adequate observations of fog-related events.

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“…It is difficult to monitor the spatial and temporal extent of fog over large‐scale areas such as the IG plains using a limited conventional and ground based observational network, so satellite based fog monitoring becomes popular due to its enhanced spatial, spectral and temporal resolution and offers new opportunities for real‐time fog detection and monitoring using upcoming geostationary satellite such as INSAT‐3D. Apart from the use of satellite data (Lee et al , 2010) modelling studies can also be carried out for the occurrence of prolonged fog over the Indian region (Choi and Speer, 2006; Shi et al , 2010).…”

Section: Introductionmentioning

confidence: 99%

Night time fog detection using MODIS data over Northern India

Chaurasia

Sathiyamoorthy

Shukla

et al. 2011

Meteorological Applications

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An algorithm for night time fog detection using satellite data was used to study 2009 and 2010 fog episodes over the North Indian plains. The algorithm employs a bispectral thresholding technique involving brightness temperature difference (BTD) between two spectral channels: 3.9 and 10.75 µm, combining radiative transfer simulations and satellite data. The prolonged fog episode of January 2010 was analysed in detail using this algorithm in conjunction with the meteorological parameters and it was found that upper tropospheric long waves passed over this region during the fog episode. These waves affect the circulation close to the surface and appear to have played an important role in the formation and persistence of fog during 2010. The present thresholding method can be used to monitor night time fog over the Indian subcontinent on an operational basis using the forthcoming geostationary satellite INSAT-3D.

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A characteristic analysis of fog using GPS‐derived integrated water vapour

Cited by 13 publications

References 18 publications

Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Fogs: Physical Basis, Characteristic Properties, and Impacts on the Environment and Human Health

Night time fog detection using MODIS data over Northern India

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