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Discusses the use of routine meteorological measurements to estimate liquid water content on Roundtop Mountain in southern Quebec Province. Prediction of the occurrence or nonoccurrence of fog at Roundtop using the classification and regression trees algorithm; Accuracies of the hybrid statistical regression schemes in terms of no-fog and fog predictions.

Using a Bayesian context, new measures of accuracy and skill are proposed to verify weather element forecasts from ensemble prediction systems (EPSs) with respect to individual observations. The new scores are in the form of probabilities of occurrence of the observation given the EPS distribution and can be applied to individual point forecasts or summarized over a sample of forecasts. It is suggested that theoretical distributions be fit to the ensemble, assuming a shape similar to the shape of the climatological distribution of the forecast weather element. The suggested accuracy score is simply the probability of occurrence of the observation given the fitted distribution, and the skill score follows the standard format for comparison of the accuracy of the ensemble forecast with the accuracy of an unskilled forecast such as climatology. These two scores are sensitive to the location and spread of the ensemble distribution with respect to the verifying observation. The new scores are illustrated using the output of the European Centre for Medium-Range Weather Forecasts EPS. Tests were carried out on 108 ensemble forecasts of 2-m temperature, precipitation amount, and windspeed, interpolated to 23 Canadian stations. Results indicate that the scores are especially sensitive to location of the ensemble distribution with respect to the observation; even relatively modest errors cause a score value significantly below the maximum possible score of 1.0. Nevertheless, forecasts were found that achieved the perfect score. The results of a single application of the scoring system to verification of ensembles of 500-mb heights suggests considerable potential of the score for assessment of the synoptic behavior of upper-air ensemble forecasts. The paper concludes with a discussion of the new scoring method in the more general context of verification of probability distributions. [ABSTRACT FROM AUTHOR]

Cloud-to-ground lightning data have been analyzed for the years 1998–2000 for North America (Canada plus the contiguous United States) for all ground flashes, positive flashes, the percentage of positive lightning, peak currents for negative and positive lightning, and for negative and positive multiplicity. The authors examined a total of 88.7 million flashes divided among the three years: 31.1 million (1998), 29.5 million (1999), and 28.2 million (2000). Annual flash densities are derived from 245–424 km[sup 2] regions and are uncorrected for flash detection efficiency. The highest flash densities in Canada are along the U.S.–Canadian border (1–3 flashes km[sup -2] ), and in the United States along the Gulf of Mexico coast and Florida (exceeding 9 flashes km[sup -2] ). Maximum annual positive flash densities in Canada generally range primarily from 0.1 to 0.3 flashes km[sup -2] , and in the United States to over 0.7 flashes km[sup -2] (areas in the Midwest, the Gulf Coast, and Florida). Areas of greater than 20% positive lightning occur throughout British Columbia and the midwest United States extending into Manitoba and Ontario. High percent positive also occurs in Quebec and much of eastern Canada. The median negative peak current is 16.5 kA. The median positive peak current, with the peak currents less than 10 kA removed from the calculation, is 19.8 kA. Median positive peak currents exceed 35 kA in the Midwest from west Texas to Nebraska to the Canadian border. The area of maximum mean negative multiplicity, exceeding 2.6 strokes, occurs in western Canada from just east of the British Columbia–Alberta border to and including Saskatchewan. Mean negative multiplicity also peaks in the southeastern United States. Mean positive multiplicity is observed to have maximum values in Alberta, Saskatchewan, and in a region centered on Tennessee. The authors examined the time of maximum flash rate in North America and find it is... [ABSTRACT FROM AUTHOR]

ABSTRACT The Canadian Airport Nowcasting Project (CAN-Now) has developed an advanced prototype all-season weather forecasting and nowcasting system that can be used at major airports. This system uses numerical model data, pilot reports, ground in situ sensor observations (precipitation, icing, ceiling, visibility, winds), on-site remote sensing (such as vertically pointing radar and microwave radiometer) and off-site remote sensing (satellite and radar) information to provide detailed nowcasts out to approximately 6 h. The nowcasts, or short term weather forecasts, should allow decision makers such as pilots, dispatchers, de-icing crews, ground personnel or air traffic controllers to make plans with increased margins of safety and improved efficiency. The system has been developed and tested at Toronto Pearson International Airport (CYYZ) and Vancouver International Airport (CYVR). A Situation Chart has been developed to allow users to have a high glance value product which identifies significant weather related problems at the airport. New products combining observations and numerical model output into nowcasts have been tested. Some statistical verifications of forecast products, with comparisons to persistence, covering both a winter (2009/2010) and summer (2010) period have been made. Problems with the prediction of relative humidity and wind direction are outlined. The ability to forecast categorical variables such as ceiling, visibility, as well as precipitation rate and type accurately are discussed. Overall, for most variables, the nowcast systems can outperform persistence after the first 1 or 2 h, and provide more accurate forecasts than individual Numerical Weather Prediction models out to 6 h. [ABSTRACT FROM AUTHOR]