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Alternative Title:Fog diagnostic scheme
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Description:Conventional fog prediction is based on the surface visibility threshold "< 1000m", which is computed solely from the surface cloud liquid water content (LWC), in a model post processor. Because no fog physical processes are involved in the cloud schemes of current NCEP operational models, the surface cloud LWC is generally not reliable enough to represent fog LWC. As a result, this method shows very poor skill in fog prediction by current operational models. Recently a so-called multi-rule based fog diagnostic scheme, which considers several fog-related variables near the surface, is suggested and has shown a significant improvement in fog prediction. However, the drawback of the multi-rule based fog diagnosis is that it only predicts fog occurrence, not fog LWC or intensity. Thus, no fog visibility can be predicted from this method. To improve this, a new fog diagnostic scheme, based on an asymptotic analytical study of radiation fog (Zhou and Ferrier 2008, ZF08), is proposed. ZF08 revealed that there exists a critical turbulence threshold to control the balance and persistence of radiation fog. Besides saturation and cooling conditions, turbulence intensity is necessary for fog formation and persistence. Only when turbulence intensity near the ground is weaker than this critical threshold inside a fog, can it be stable and persist. Otherwise the fog can not form or will soon be dispersed even if it has formed. ZF08 also obtained a LWC vertical distribution formulation for radiation fog, based on which fog occurrence or persistence can be conveniently diagnosed in a model post processor. This office note presents a brief description of how to apply the formulation from ZF08 to develop a new fog diagnostic scheme in a model post processor. For radiation fog, the input data for this new scheme are those basic grid-point variables output from an operational model, including temperature, relative humidity, and wind speeds at the surface and high levels. To extend this scheme to other types of fog, cloud base, top and moisture horizontal advection are included. Since the LWC vertical distribution formulation involves several important fog physical processes, the LWC near the ground as well as the surface visibility computed from this scheme are more representative than those directly from operational models. In comparison to the multi-rule fog diagnostic method, the new fog scheme has following advantages: (1) it can predict fog LWC as well as visibility; (2) it detects fog condition based not only on variables at surface, but also on those variables at multiple levels, which is believed to be critical to reduce false alarms in many cases; (3) it still can obtain the fog LWC even if the RH values at lowest levels are less than 100%, which is an efficient way to correct model bias in fog prediction; and (4) in case of extremely low temperature as ice fog may happen, the saturation RH threshold with reference to ice is used. Two different types of fog events, a warm fog event in Gulf coast and an ice fog event in Yellowknife, Canada are used to validate the new scheme on fog prediction. Finally, the errors and uncertainties of this scheme are discussed.
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Content Notes:Binbin Zhou.
"May 31, 2011."
System requirements: Adobe Acrobat Reader.
Includes bibliographical references (pages 40-43).
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Rights Information:Public Domain
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