Open AccessParameterization of Runway Visual Range as a Function of Visibility: Implications for Numerical Weather Prediction Models
Author(s) -
Faisal S. Boudala,
George A. Isaac,
Robert W. Crawford,
Janti Reid
Publication year - 2012
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech-d-11-00021.1
Subject(s) - visibility , nowcasting , snow , environmental science , meteorology , precipitation , relative humidity , weather research and forecasting model , numerical weather prediction , atmospheric sciences , range (aeronautics) , climatology , physics , geology , materials science , composite material
A parameterization of runway visual range (RVR) has been developed using relevant meteorological parameters such as visibility (Vk), relative humidity (RH), temperature (T), precipitation intensity (PI), and precipitation type (PT) measured in years between 2009 and 2011 at Toronto Pearson International Airport during the Canadian Airport Nowcasting Project. The FD12P probe measured PI, Vk, and PT. The observed Vk and PT were tested against data reported by hourly surface observations (SAs). The measured Vk has correlated well with the SA with a correlation coefficient (r) of 0.76 for Vk < 5 km, but the FD12P underestimated visibility by about 20% with a mean difference (MD) of about 196 m. For Vk < 2 km, the FD12P overestimated visibility by about 7% with an MD of 60 m. The SA reported slightly more snow events—22% as compared to 17%—but the FD12P reported many more snow grain cases than the SA. Both the SA and the FD12P reported rain at similar frequency—4% and 5%, respectively. Using a theoretical approach, a parameterization that can be used to determine RVR as a function of Vk has been developed. Using the observed T, RH, and dewpoint temperature (Td), a new parameterization for predicting Vk/RVR in fog has been also developed. These parameterizations agreed with observations (r ≈ 0.8). The parameterizations have been tested using the Canadian Environmental Multiscale Regional model. The results show that when PI, RH, and T are reasonably predicted and the fog events are correctly diagnosed, the model can be used to forecast RVR.