Dense fog appears to be decreasing in many parts of the world, especially in western cities. Dense fog (visibility \400 m) is disappearing in the urban southern California area also. There the decrease in dense fog events can be explained mainly by declining particulate levels, Pacific sea surface temperatures (SST), and increased urban warming. Using hourly data from 1948 to the present, we looked at the relationship between fog events in the region and contributing factors and trends over time. Initially a strong relationship was suggested between the occurrence of dense fog and the phases of an atmosphere-ocean cycle: the Pacific Decadal Oscillation (PDO). However, closer analysis revealed the importance to fog variability of an increasing urban heat island and the amount of atmospheric suspended particulate matter. Results show a substantial decrease in the occurrence of very low visibilities (\400 m) at the two airport stations in close proximity to the Pacific Ocean, LAX (Los Angeles International) and LGB (Long Beach International). A downward trend in particulate concentrations, coupled with an upward trend in urban temperatures were associated with the decrease in dense fog occurrence at both LAX and LGB. LAX dense fog that reached over 300 h in 1950 dropped steadily, with 0 h recorded in 1997. Since 1997, there has been a slight recovery with both 2008 and 2009 recording over 30 h of dense fog at both locations. In this study we examine whether the upturn is a temporary reversal of the trend. To remove the urban effect, we also included fog data from Vandenberg Air Force Base (VBG), located in a relatively sparsely populated area approximately 200 km to the north of metropolitan Los Angeles. Particulates, urban heat island, and Pacific SSTs all seem to be contributing factors to the decrease in fog in southern California, along with large-scale atmosphereocean interaction cycles. Case studies of local and regional dense fog in southern California point to the importance of strong, low inversions and to a lesser contributor, Santa Ana winds. Both are associated with large-scale atmospheric circulation patterns, which have changed markedly over the period of studied. These changes point to continued decreases in dense fog in the region.
Several experimental products derived from Geostationary Operational Environmental Satellite (GOES) Sounder retrievals (vertical profiles of temperature and moisture) have been developed to assist weather forecasters in assessing the potential for convective downbursts. The product suite currently includes the wind index (WINDEX), a dry microburst index, and the maximum difference in equivalent potential temperature (e) from the surface to 300 hPa. The products are displayed as color-coded boxes or numerical values, superimposed on GOES visible, infrared, or water vapor imagery, and are available hourly, day and night, via the Internet. After two full summers of evaluation, the products have been shown to be useful in the assessment of atmospheric conditions that may lead to strong, gusty surface winds from thunderstorms. Two case studies are presented: 1) a severe downburst storm in southern Arizona that produced historic surface wind speeds and damage, and 2) multiple dry and wet downbursts in western Kansas that resulted in minor damage. Verification involved comparing the parameters with radiosonde data, numerical model first guess data, or surface wind reports from airports, mesonetworks, or storm spotters. Mean absolute WINDEX from the GOES retrievals differed from the mean surface wind gust reports by Ͻ2 kt (1 m s Ϫ1) for 82 events, but underestimated wind gusts for 7 nighttime events by 22 kt (11 m s Ϫ1). GOES WINDEX was also slightly better than that derived from the concurrent National Centers for Environmental Prediction's Eta Model first guess. There are plans to incorporate these downburst parameters into a future upgrade of the National Weather Service's Advanced Weather Interactive Processing System, with the option to derive them from either GOES Sounder data, radiosondes, or numerical model forecast data.
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