Friday, 3 September 2010: 11:30 AM
Alpine Ballroom A (Resort at Squaw Creek)
Ismail Gultepe, Environment Canada, Toronto, ON, Canada; and G. A. Isaac and J. Milbrandt
The objective of this work is to better understand the mountain meteorological and microphysical observations collected during the Science of Nowcasting Winter Weather for the Vancouver 2010 Olympics and Paralympics (SNOW-V10) project that was supported by the Fog Remote Sensing and Modeling (FRAM) project. The RND (Roundhouse) meteorological station was located at 1856 m height below which there were the 3 more sites called: VOA (high-level station at 1640 m), VOL (mid-level station at 1320 m), and TFT (Timing Flats at 774 m). These stations provided some or all the the following measurements at one minute resolution: precipitation rate (PR) and amount, cloud/fog microphysics, 3D wind speed (Uh) and turbulence (Uh'), visibility (Vis), IR and SW radiative fluxes, temperature (T) and relative humidity (RH), and aerosol observations. These measurements were then used for validations of the forecast products obtained from the Canadian Global Environmental Multiscale (GEM) NWP model with various configurations (horizontal grid-spacing of 15 km, 2.5 km, and 1 km).
In this work comparisons are made for 1) Vis for various weather conditions, 2) Vis parameterizations, 3) uncertainty in precipitation measurements at the extreme weather conditions, 4) the effect of blowing snow and Uh' on extreme Vis values. The ground based-CIP probe measurements of snow particles have also been summarized. In addition to these results, uncertainty in the measurements of T and RH from four different sensors has been given for the extreme weather conditions. Overall, the conclusions suggest that uncertainties in the measurements of Vis, PR, T, and RH can be as large as 50%, >25%, 20%, and >5%, respectively, and these numbers may increase depending on Uh, Uh', T, and PR. It is also concluded that differences between observed and model based Vis were strongly related to the GEM's capability of accurate prediction of liquid water content, PR, and RH over complex topography.
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