Extended Abstract (856K)P1.20
Warm season extreme quantitative precipitation forecasting for the Burlington, VT region
John R. Gyakum, McGill University, Montreal, QC, Canada; and E. Atallah, P. Sisson, M. Kimball, and A. Roberge
The problem of quantitative precipitation forecasting (QPF) continues to be a challenge. Large-scale numerical guidance is helpful, but improvements in QPFs have lagged those of the 500-hPa height forecasts. Substantial warm-season precipitation events include those that are associated with former tropical cyclones, mesoscale convective systems, or relatively weak baroclinic zones associated with elevated convection. Examples of recent heavy precipitation cases resulting in devastating flooding and a Presidential declaration of Disaster are the 12 June 2002 flooding in northeast VT and the 27 June 1998 flooding in the Green Mountains of Vermont. This study focuses on predicting extreme warm-season precipitation amounts for the complex topography in the Burlington, Vermont (BTV) region. Regional scale events were identified from rainfall data including BTV and other surrounding stations. The unified precipitation data set (UPD), a gridded data set available from Climate Diagnostics Center (CDC) that includes cooperative observing stations precipitation amounts, was used. Cooperative observers report 24-h totals at 1200 UTC; the Burlington station data were chosen to correspond with the method of reporting of the cooperative stations. The UPD was used to eliminate events attributed to isolated small-scale convection over Burlington, leaving regional scale precipitation events (greater areal extent) in the Burlington area for study. The daily maximum precipitation at any grid point in the region bounded by 43.50-45.00 deg N latitude and 74.25-72.00 deg W longitude for June, July, and August for the 50-year period from 1948 to 1998 were considered. The mean and standard deviation of the precipitation were calculated, and categories for the cases were identified from the statistics. Extreme cases were represented by 1200 UTC 24-h totals exceeding 2 standard deviations above the mean or greater than 37 mm (1.45 in). Heavy cases were those between 1 and 2 standard deviations above the mean, or 24-37 mm (0.95-1.45 in). Moderate cases were those between 0.5 and 1 standard deviations above the mean, or 18-24 mm (0.70-0.95 in). To insure independence among the events, a separation of at least 7 days between cases was imposed. During the 50-year period, using the stipulation of a 7-day separation, we found 53 extreme cases, 94 heavy cases, and 61 moderate cases. Extreme events, which are rare, were preferentially eliminated from the more common heavy events. In addition, dates before 1963 were eliminated owing to the poor quality of the available NCEP reanalysis. To create composites comparable to the extreme cases, the same numbers (53) of heavy and moderate cases were used and were chosen at random. The National Centers for Environmental Prediction (NCEP) global reanalysis and 30 year climatologies were used to construct composites of each intensity category. A composite field, anomaly from climatology, and statistical significance were plotted for standard meteorological variables. Fields investigated include sea level pressure, 500-hPa heights, 1000-500 hPa thickness, precipitable water, Showalter and Coupling Indices, and dynamic tropopause height and wind. The recently-released NCEP North American Regional reanalysis, with 32-km resolution, is used to highlight the distinctions in temperature and moisture stratification among the three intensity categories. Preliminary composites from the extreme, heavy, and moderate categories and selected cases will be presented. The categories will be compared to each other and climatology. The selected cases will illustrate the variability of the areal precipitation distribution. Through the identification of large-scale anomalous circulation precursors to warm season precipitation events in and near the Burlington, VT forecast region, it is expected that forecasters will be able to recognize the intensity of an event in advance and improve forecast and warning lead times.
Poster Session 1, Conference Posters
Monday, 1 August 2005, 5:30 PM-7:00 PM, Regency Ballroom
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