The accurate detection and quantification of light precipitation is problematic, particularly in the Arctic region. Satellite and ground-based observations of light precipitation are frequently underestimated at high latitudes. Remote sensing and in-situ observations from the Iqaluit, NU supersite (64oN, 69oW) were integrated to train, develop, and validate a random forest (RF) model that can diagnose precipitation type and other weather element occurrences. Observations from multiple lidars, optical disdrometers, traditional precipitation gauges and meteorological aerodrome (METAR) reports from 2015-2020 were integrated and used in the RF model development. The model was trained at Iqaluit, validated over different time periods, and applied to another region (Whitehorse, YT; 61oN, 135oW). Results indicate the importance of accurate visibility observations to train the model. Overall, the RF model was capable of distinguishing precipitation types and demonstrated the potential to be used at all sites/networks where similar automated and cost-effective instruments are already deployed (e.g. radar sites, airports with ceilometers, etc.). This would reduce the dependency on METARs while improving weather element occurrence accuracy. [Traduit par la redaction] Une methode d'apprentissage automatique pour integrer les observations des supersites de l'Arctique et diagnostiquer la presence d'elements meteorologiques. La detection et la quantification precises des precipitations legeres posent probleme, en particulier dans la region arctique. Les observations satellitaires et terrestres des precipitations legeres sont souvent sous-estimees aux hautes latitudes. La teledetection et les observations in situ du supersite d'Iqaluit, au Nunavut (64oN, 69oW) ont ete integrees pour former, etablir et valider un modele de foret aleatoire (FA) qui peut diagnostiquer le type de precipitations et d'autres occurrences d'elements meteorologiques. Des observations provenant de multiples lidars, de disdrometres optiques, de pluviometres traditionnels et de rapports d'aerodromes meteorologiques (METAR) de 2015 a 2020 ont ete integrees et utilisees dans l'elaboration du modele de foret aleatoire. Le modele a ete mis a l'essai a Iqaluit, valide sur differentes periodes et applique a une autre region (Whitehorse, Yukon; 61oN, 135oW). Les resultats denotent l'importance d'observations precises de la visibilite pour mettre a l'essai le modele. Dans l'ensemble, le modele FA a ete capable de distinguer les types de precipitations et a montre qu'il pouvait etre utilise sur tous les sites/reseaux ou des instruments automatises et rentables similaires sont deja deployes (p. ex. sites radar, aeroports dotes de ceilometres, etc.). Cela permettrait de reduire la dependance a l'egard des METAR tout en ameliorant la precision de l'occurrence des elements meteorologiques.

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 METeorological Aerodrome Reports (METARs) from Canadian Arctic stations between October and May 2014-2018. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with 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 post-processed 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 (2005). 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.

This study presents the spatial and temporal features of more than 45 million cloud-to-ground (CG) lightning flashes recorded by the Canadian Lightning Detection Network for the years 1999-2018. Although sensor upgrades have improved the detection efficiency and location accuracy of CG lightning, the large-scale spatial patterns remain about the same as found in a previous study covering the years 1999-2008. Analyses, using equal-area squares with 10 km sides, describe the regional and seasonal characteristics of negative and positive flashes, the percentage and flash density of positive lightning, and the first-stroke peak currents of both polarities. Lightning activity over the provinces and territories is greatest in the summer, varying from 95.9% to 76.8% of the annual activity in the Northwest Territories and Ontario, respectively. Winter lightning is rare, usually occurring in extreme southern Ontario and the Atlantic Provinces, as well as over offshore regions west of Vancouver Island and the coastal waters off Nova Scotia. Preliminary analysis suggests that, compared with the 1999-2008 period, the majority of western and northern Canada has experienced more lightning days during the 2009-2018 period, whereas much of eastern Canada has experienced fewer lightning days. A statistical analysis performed on 154 stations across Canada found that the decadal increases (decreases) at 5 (31) stations were significant at the 90% confidence level or higher, and 4 (16) of these were significant at the 95% confidence level.

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.

Anthropogenic climate change is anticipated to increase severe thunderstorm potential in North America, but the resulting changes in associated convective hazards are not well known. Here, using a novel modelling approach, we investigate the spatiotemporal changes in hail frequency and size between the present (1971–2000) and future (2041–2070). Although fewer hail days are expected over most areas in the future, an increase in the mean hail size is projected, with fewer small hail events and a shift toward a more frequent occurrence of larger hail. This leads to an anticipated increase in hail damage potential over most southern regions in spring, retreating to the higher latitudes (that is, north of 50° N) and the Rocky Mountains in the summer. In contrast, a dramatic decrease in hail frequency and damage potential is predicted over eastern and southeastern regions in spring and summer due to a significant increase in melting that mitigates gains in hail size from increased buoyancy.

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.

The relationship between cloud-to-ground (CG) lightning and convective precipitation across Canada is examined. A database of coincident 6-hourly rain-gauge and lightning data, constructed from 64 weather stations spanning Canada's ecozones, from April to October 1999 to 2003, was used to calculate rainfall yields (defined as the ratio of the total volume of precipitation to the total CG flash count, in units of kilograms per flash (kg fl(-1))). Warm season rain yields have been found to vary between 1.06 x 10(8) kg fl(-1) and 21.8 x 10(8) kg fl(-1) over the ecozones of eastern Canada and between 1.05 x 10(8) kg fl(-1) and 41.5 x 10(8) kg fl(-1) over western ecozones. The rainfall yields derived from station data were used to predict convective precipitation in 2004 and 2010. Overall, the warm season correlation coefficients between predicted and gauge-measured precipitation were 0.65 and 0.77 for 2004 and 2010, respectively, and 0.71 for both years combined. Regional differences reflecting the complexity of convective activity were found. Correlation coefficients of 0.69, 0.75, and 0.71 were obtained for 2004, 2010, and both years combined, respectively, for ecozones in eastern Canada and 0.50, 0.87, and 0.68, respectively, for ecozones in western Canada. A predictive capability to estimate convective rainfall using lightning information may be feasible in data-sparse regions without radar coverage, but the predictions exhibit greater uncertainty in some ecozones than in others and over the western region of Canada than over the eastern region, when ecozone-averaged rainfall yield relationships are used.

A high-resolution Canadian lightning climatology is presented. Generating the high-resolution flash density climatology from a relatively short observation period (approximately 10 years) of the Canadian Lightning Detection Network can be challenging because of the natural variations in the lightning frequency. To address this, an objective methodology was developed with the intended purpose of reducing random variations while still retaining the real spatially significant local variations in the cloud-to-ground lightning flash densities. This technique is applied to the annual 1 km lightning flash density values across most of Canada (south of 60 degrees-70 degrees N) to generate a high-resolution lightning climatology. Lightning flash density maps for selected areas are presented that demonstrate typical patterns resulting from the optimizing methodology. This high-resolution climatology can be used to assess lightning occurrence and risk for many applications including protection measures for buildings and other structures such as electrical transmission lines, insurance purposes, as well as general climatological knowledge and public safety. An example that applies the high-resolution lightning climatology to wind turbine lightning protection is provided because it was the initial motivation for this research.

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.

Two non-linear, machine-learning/statistical methods, i.e., Bayesian neural network (BNN) and support vector regression (SVR), plus multiple linear regression (MLR), were used to forecast surface wind speeds at lead times of 12, 24, 48 and 72 h. Three different schemes, a statistical downscaling model (Scheme 1) using daily reforecast data from the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS), an autoregressive model (Scheme 2) based on past wind observations, and a full model (Scheme 3) combining the two, were investigated in this study for the October–March winds from two meteorological stations in the Canadian Arctic (Clyde River and Paulatuk). At very short lead times, Scheme 2 provides better wind speed prediction than Scheme 1, but its forecast scores decrease rapidly with lead time. Scheme 3 generally performs best, especially at shorter lead times. All the linear and non-linear downscaling methods have significantly higher forecast scores at the two stations than the GFS reforecast. The non-linear methods tended to have slightly better forecast scores than linear methods (MLR and the linear version of SVR). There is particular interest in high-wind events, defined as having wind speeds over 22 knots (11.3 m s−1). After rescaling, the continuous wind predictions from Scheme 3 were classified into two types — high-wind event or non-event. For high-wind event forecasting, the non-linear methods have marginally better binary forecast scores than the linear methods for Clyde River but not for Paulatuk. The alternative approach of using support vector classification (SVC) did not perform better, but weighting the high-wind events more heavily than the non-events during model training improved the binary forecast scores.

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 (similar to 18 000 km(2)). 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).

The 2004 lightning season and related wildfire activity in Yukon, Canada, was exceptional in many aspects. The synoptic environment during the summer was dominated by a persistent upper level ridge over Alaska and Yukon, bringing above-normal temperatures and below-normal precipitation to Yukon. The number of cloud-to-ground (CG) flashes, lightning-initiated forest fires, and extent of the area burned exceeded historic records. Forest fire smoke affected most of Yukon during the summer. Thunderstorms forming in this northern environment in July exhibited unusual lightning characteristics as detected by Canadian Lightning Detection Network. Changes in the frequency of extreme lightning days and in the fraction of nocturnal lightning occurrence were observed. Lightning properties in July differed from climatology in several ways. The central regions of Yukon experienced enhanced positive CG lightning activity. First-stroke positive peak currents were found to be stronger, while negative peak currents were weaker than climatology. Our observations are consistent with the previous findings reported in southerly climates. Although thunderstorms related to the diurnal heating and cooling cycle influenced positive lightning occurrences in Yukon, other possible sources, including pyrocumulonimbus clouds and inverted-polarity thunderstorms, cannot be overlooked. Evidence is presented suggesting that both atmospheric conditions and smoke from the fires may have influenced the electrification process of thunderstorms to enhance +CG production. The extreme summer of 2004 experienced in Yukon may provide a hint of future impacts due to climate change.

For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic.

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.

Cloud-to-ground (CG) lightning from thunderstorms initiating forest fires is a typical summer hazard in the rugged terrain of the Yukon Territory of northern Canada. The lightning season usually starts in May, peaks during July and is usually over in September. This region typically experiences 141 wildfires annually which burn over 160,000 hectares between June and August. An annual average of over 20 thousand CG flashes account for about 54% of these ignitions. During the summer of 2004, a persistent upper level ridge dominated throughout the Yukon. Maximum temperatures were above normal for much of the season and precipitation amounts were about 60% of normal. The number of CG flashes, lightning-initiated forest fires and extent of the area burned surpassed historic records. Specifically, over 40 thousand CG flashes accounted for 88% of the 282 wildfires consuming over 1.7 million hectares of the territory. (Green, 2004; WFRP, 2005). The 2004 fire season in Alaska burned about 2.7 million hectares.