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Blizzard conditions occur regularly in the Canadian Arctic, with high impact on travel and life there. These extreme conditions are challenging to forecast for this vast domain because the observation network is sparse and remote sensing coverage is limited. To establish occurrence statistics we analyzed aviation routine weather reports (METARs) from Canadian Arctic stations between October and May 2014–18. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with the highest frequency found on the northwest side of Hudson Bay and over flat terrain in central Baffin Island. Except in sheltered locations, the reported cause of reduced visibility is blowing snow without precipitating snow in about one-half to two-thirds of METARs made by a human observer, even higher at some stations. We produce three products that forecast blizzard conditions from postprocessed NWP model output. The blizzard potential (BP), generated from expert’s rules, is intended for warning well in advance of areas where blizzard conditions may develop. A second product (BH) stems from regression equations for the probability of visibility ≤ 1 km in blowing snow and/or concurrent snow derived by Baggaley and Hanesiak. A third product (RF), generated with the random forest ensemble classification algorithm, makes a consensus YES/NO forecast for blizzard conditions. We describe the products, provide verification, and show forecasts for a significant blizzard event. Receiver operator characteristic curves and critical success index scores show RF forecasts have greater accuracy than BP and BH forecasts at all lead times. [ABSTRACT FROM AUTHOR]

Statistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance. Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models. Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004. [ABSTRACT FROM AUTHOR]

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]

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.

Analyzes regression models for measuring ground ultraviolet (UV) radiation in the presence of clouds and other environmental factors. Archive for regression model variables; Methods used in the derivation of regressions; Tests for the accuracy of a three-predictor classification and regression trees model; Effect of forest fire smoke on UV radiation.

The goal of the Canadian Arctic Weather Science (CAWS) project is to conduct research into the future operational monitoring and forecasting programs of Environment and Climate Change Canada in the Arctic where increased economic and recreational activities are expected with enhanced transportation and search and rescue requirements. Due to cost, remoteness and vast geographical coverage, the future monitoring concept includes a combination of space-based observations, sparse in situ surface measurements, and advanced reference sites. A prototype reference site has been established at Iqaluit, Nunavut (63°45'N, 68°33'W), that includes a Ka-band radar, water vapor lidars (both in-house and commercial versions), multiple Doppler lidars, ceilometers, radiation flux, and precipitation sensors. The scope of the project includes understanding of the polar processes, evaluating new technologies, validation of satellite products, validation of numerical weather prediction systems, development of warning products, and communication of their risk to a variety of users. This contribution will provide an overview of the CAWS project to show some preliminary results and to encourage collaborations. [ABSTRACT FROM AUTHOR]

Pays tribute to the late Louis J. Luzbetak, a pioneering Roman Catholic missionary anthropologist.

The purpose of this study was to focus on how anomalies in the normalized difference vegetation index (NDVI; a proxy for soil moisture) over the Canadian Prairies can condition the convective boundary layer (CBL) so as to inhibit or facilitate thunderstorm activity while also considering the role of synoptic-scale forcing. This study focused on a census agricultural region (CAR) over central Alberta for which we had observed lightning data (proxy for thunderstorms), remotely sensed NDVI data, and in situ rawinsonde data (to quantify impacts of vegetation vigor on the CBL characteristics) for 11 summers from 1999 to 2009. The authors' data suggest that the occurrence of lightning over the study area is more likely (and is of longer duration) when storms develop in an environment in which the surface and upper-air synoptic-scale forcing are synchronized. On days when surface forcing and midtropospheric ascent are present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. Additionally, the authors found the response of thunderstorm duration to NDVI anomalies to be asymmetric. That is, the response of lightning duration to anomalies in NDVI is marked when NDVI is below average but is not necessarily discernible when NDVI is above average. The authors propose a conceptual model, based largely on observations, that integrates all of the above findings to describe how a reduction in vegetation vigor-in response to soil moisture deficits-modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating lifting condensation level heights, which in turn affect lightning activity. [ABSTRACT FROM AUTHOR]

Linkages between the terrestrial ecosystem and precipitation play a critical role in regulating regional weather and climate. These linkages can manifest themselves as positive or negative feedback loops, which may either favor or inhibit the triggering and intensity of thunderstorms. Although the Canadian Prairies terrestrial system has been identified as having the potential to exert a detectable influence on convective precipitation during the warm season, little work has been done in this area using in situ observations. The authors present findings from a novel study designed to explore linkages between the normalized difference vegetation index (NDVI) and lightning duration (DUR) from the Canadian Lightning Detection Network for 38 census agricultural regions (CARs) on the Canadian Prairies. Statistics Canada divides the prairie agricultural zone into CARs (polygons of varying size and shape) for the purpose of calculating agricultural statistics. Here, DUR is used as a proxy for thunderstorm activity. Statistical analyses were undertaken for 38 CARs for summers [June--August (JJA)] between 1999 and 2008. Specifically, coefficients of determination were calculated between pairs of standardized anomalies of DUR and NDVI by season and by month. Correlations were also calculated for CARs grouped by size and/or magnitude of the NDVI anomalies. The main findings are as follows: 1) JJA lightning activity is overwhelmingly below average within larger dry areas (i.e., areas with below-average NDVI); that is, the linkages between NDVI and DUR increased significantly as both the area and magnitude of the dry anomaly increased. 2) In contrast, CARs with above-average NDVI did not consistently experience above-average lightning activity, regardless of the CAR size. 3) The lower threshold for the length scale of the dry anomalies required to affect the boundary layer sufficiently to reduce lightning activity was found to be approximately 150 km (~18 000 km2). 4) The authors'' analysis suggests that the surface-convection feedback appears to be a real phenomenon, in which drought tends to perpetuate drought with respect to convective storms and associated rainfall, within the limits found in 1) and 3). [ABSTRACT FROM AUTHOR]

Cloud-to-ground (CG) lightning data have been analyzed for the years 2001--09 for North America, which includes Alaska, Canada, and the lower 48 U.S. states. Flashes recorded within the North American Lightning Detection Network (NALDN) are examined. No corrections for detection efficiency variability are made over the 9 yr of the dataset or over the large geographical area comprising North America. There were network changes in the NALDN during the 9 yr, but these changes have not been corrected for nor have the recorded data been altered in any way with the exception that all positive lightning reports with peak currents less than 15 kA have been deleted. Thus, the reader should be aware that secular changes are not just climatological in nature. All data were analyzed with a spatial resolution of 20 km. The analyses presented in this work provide a synoptic view of the interannual variability of lightning observations in North America, including the impacts of physical changes in the network during the 9 yr of study. These data complement and extend previous analyses that evaluate the U.S. NLDN during periods of upgrade. The total (negative and positive) flashes for ground flash density, the percentage of positive lightning, and the positive flash density have been analyzed. Furthermore, the negative and positive first stroke peak currents and the flash multiplicity have been examined. The highest flash densities in Canada are along the U.S.--Canadian border (1--2 flashes per square kilometer) and in the United States along the Gulf of Mexico coast from Texas through Florida (exceeding 14 flashes per square kilometer in Florida). The Gulf Stream is ''outlined'' by higher flash densities off the east coast of the United States. Maximum annual positive flash densities in Canada range primarily from 0.01 to 0.3 flashes per square kilometer, and in the United States to over 0.5 flashes per square kilometer in the Midwest and in the states of Louisiana and Mississippi. The annual percentage of positive lightning to ground varies from less than 2%% over Florida to values exceeding 25%% off the West Coast, Alaska, and the Yukon. A localized maximum in the percentage of positive lightning in the NALDN occurs in Manitoba and western Ontario, just north of North Dakota and Minnesota. When averaged over North America, first stroke negative median peak currents range from 19.8 kA in 2001 to 16.0 kA in 2009 and for all years, average 16.1 kA. First stroke positive median peak currents range from a high of 29.0 kA in 2008 and 2009 to a low of 23.3 kA in 2003 with a median of 25.7 kA for all years. There is a relatively sharp transition from low to high median negative peak currents along the Gulf and Atlantic coasts of the United States. No sharp transitions are observed for the median positive peak currents. Relatively lower positive peak currents occur throughout the southeastern United States. The highest values of mean negative multiplicity exceed 3.0 strokes per flash in the NALDN with some variation over the 9 yr. Lower values of mean negative multiplicity occur in the western United States. Positive flash mean multiplicity is slightly higher than 1.1, with the highest values of 1.7 observed in the southwestern states. As has been noted in prior research, CG lightning has significant variations from storm to storm as well as between geographical regions and/or seasons and, consequently, a single distribution for any lightning parameter, such as multiplicity or peak current, may not be sufficient to represent or describe the parameter. [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]

Land-based meteorological measurements at two locations on the Danish coast are used to predict offshore wind speeds. Offshore wind-speed data are used only for developing the statistical prediction algorithms and for verification. As a first step, the two datasets were separated into nine percentile-based bins, with a minimum of 30 data records in each bin. Next, the records were randomly selected with approximately 70% of the data in each bin being used as a training set for development of the prediction algorithms, and the remaining 30% being reserved as a test set for evaluation purposes. The binning procedure ensured that both training and test sets fairly represented the overall data distribution. To base the conclusions on firmer ground, five permutations of these training and test sets were created. Thus, all calculations were based on five cases, each one representing a different random selection from the same data, but maintaining the (approximate) 70-30 split in each bin. This procedure served to ensure that conclusions were not based on a single randomly-selected case. Two statistical methods are employed: multiple linear regression (MLR), and Classification and Regression Trees (CART). MLR produces excellent results using only land-based predictors. The CART results are similar to those from MLR, and tend to be slightly better. [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]

Alternate approaches for estimating deposition of acidifying compounds by fog are needed. This paper presents empirical models for estimating concentration of SO42− in fog at high elevation locations in eastern North America. They are based upon meteorological predictors, pollutant emissions data and ambient ozone measurements and were developed using fog measurements from the chemistry of high elevation fog (CHEF) and the mountain cloud chemistry program (MCCP). Optimum models have a relative error (RE) of 0.37, indicating that the variance in the fog SO42− observations could be reduced by 63%. In addition to meteorological observations from nearby airports, the best models also utilized information on air mass history determined from back-trajectories. Ozone, measured at the fog collection site, was also found to be a potentially useful predictor of SO42− in fog. The best models performed as well as models developed from on-site meteorological measurements, indicating that it is possible to estimate fog SO42− from readily available meteorological data. [Copyright &y& Elsevier]