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  • Author or Editor: J. D. W. Kahl x
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W. P. Elliott, D. J. Gaffen, J. K. Angell, and J. D. W. Kahl


Mean layer virtual temperature estimates, based on geopotential height measurements, form the basis for one approach being used to monitor changes in upper-air temperature. However, virtual temperature is a function of atmospheric moisture content as well as temperature. This paper investigates the impact of real or apparent changes in atmospheric moisture on changes in mean layer virtual temperature. Real changes in mean layer specific humidity of up to 50% would cause changes in mean layer virtual temperature of less than 1°C, except in the tropical boundary layer, where the high moisture content would lead to larger virtual temperature changes. The effect of humidity changes is negligible in polar regions and most pronounced in the tropics, which could influence the interpretation of the latitudinal gradient of virtual temperature trend estimates. Improvements in radiosonde humidity sensors since 1958 have led to an apparent decrease in atmospheric humidity. On global average, for the 850–300-mb layer, such changes are estimated to contribute to an apparent cooling of between 0.05° and 0.1°C, or about 10% to 20% of the observed warming trend since 1958.

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Victoria A. Lang, Teresa J. Turner, Brandon R. Selbig, Austin R. Harris, and Jonathan D. W. Kahl


Wind gusts present challenges to operational meteorologists, both to forecast accurately and also, to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts.

This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for 7 specific weather conditions over an 8-year period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 hours. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the rain with thunder weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.

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