Of the numerous climate change studies which have been performed, few of these have analyzed recent trends using an air mass‐based approach. The air mass approach is superior to simple trend analysis, as it can identify patterns which may be too subtle to influence the entire climate record. The recently‐developed ‘spatial synoptic classification’ (SSC) is thus used to identify trends over the contiguous United States for summer and winter seasons from 1948 to 1993. Both trends in air mass frequency and character have been assessed. The most noteworthy trend in frequency is a decline in air mass transitional days (TR) during both seasons. In winter, decreases of up to 1% per decade are noted in parts of the central U.S. Other notable trends include a decrease in moist tropical (MT) air in winter, and an increase in MT in summer over the southeastern states. Numerous national and local air mass character changes have been uncovered. A large overall upward trend in cloudiness is noted in summer. All air masses feature an overnight increase, yet afternoon cloudiness increases are generally limited to the three ‘dry air masses’. Also in summer, a significant warming and increase in dew point of MT air has occurred at many locales. The most profound winter trend is a large decrease in dew point (up to 1.5°C per decade) in the dry polar (DP) air mass over much of the eastern states. © 1998 Royal Meteorological Society

A growing number of climate change and variability studies, as well as applied research toward improving engineering design climatographies, require high-quality, long-term, extreme-value climate data sets for accurate and reliable estimates and assessments. As part of a historical weather data rescue project of the US government, new data quality control procedures are being developed and applied for daily maximum wind speeds. Not only are existing quality assurance procedures mostly lacking for such data but the climatological relationships upon which such quality checks may be based are also grossly underexploited. Therefore, this study seeks to elucidate relationships among peak-gust, fastest-mile, and fastest 5-min wind speeds, utilizing the peak gust factor model but generalizing it for these and other extreme wind-speed elements. The relationship between peak-gust factor and daily mean wind speed is also adapted for quality assurance and for a wider range of climates than previously studied. Fastest-interval wind-speed factors are found to follow Gaussian, gamma, or Weibull probability distributions, included within mixed models to handle zeros. Resistant prediction interval estimates about a resistant regression were developed for quality assurance of peak-gust factor, given the daily mean wind speed. Flagging thresholds were estimated using parametric bootstrapping. Flag rates from 0.05 to 0.5% are in line with rates reported in the literature, from work with similar data sets; overall Type I and Type II error rates are in the range 0.03-0.3%. The approach outlined lends itself straightforwardly to application in data quality assurance.

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A first attempt has been made toward quantifying the risk of snowmelt-related flooding in the central and southern Appalachian Mountains of the United States (from 35° to 42°N). In the last decade, two major events occurred within the region, prompting this study. Snowfall and snow depth data were collected from the cooperative observer network, quality controlled, and summarized at seasonal resolution (December–March). For establishing regional patterns, the period of 1971–2000 was selected. For testing fits of candidate probability distributions, and for focusing on the sparsely sampled higher elevations, this criterion was relaxed to include as many data from the mid- to late century as were reasonably admissible. Results indicate that the two-parameter Gumbel distribution fit best both the seasonal total snowfall and seasonal maximum snow depth. That distribution was then used to map return periods associated with critical seasonal snowfall and maximum snow depth masses. Seasonal snowfall amounts linked to a role for snowmelt in flooding were found to occur at return periods of from 2–5 yr in Pennsylvania and West Virginia to 10–200 yr in North Carolina. More generally, at elevations of at least 600 m throughout the region, return periods of 10–25 yr were estimated for critical levels of two flood-related criteria: seasonal total snowfall and maximum snow depth. In addition to providing valuable climatological information to aid forecasters and analysts, the results also support the need for further work toward understanding the role of snow in Appalachian floods.