Abstract

HAILCAST is a numerical model developed specifically to predict the size of the largest hail reaching the ground. It consists of a steady-state cloud model combined with a time-dependent hailstone growth model. The regional version of the Canadian Global Environmental Multiscale (GEM) model is used to provide prognostic model soundings that are used as input data for HAILCAST. A map of forecasted maximum hail size is thereby obtained. Because hail is typically accompanied by rain, it would be advantageous if the GEM–HAILCAST system were to predict the occurrence of hail only in those regions where the GEM model was predicting precipitation. Hence, the utility of applying a forecast rainfall mask from the GEM model to restrict hail forecasts to those areas where rainfall is forecast during a 12-h window centered on 0000 UTC was tested. The accumulated precipitation filter is objective and integrates both the thermodynamic and dynamic output from the GEM model over many time steps.

To test the utility of applying the GEM forecast precipitation mask, the masking technique was applied to HAILCAST-predicted maximum hail size maps for the three Canadian prairie provinces between 1 June and 31 August 2000. Several case studies will be presented to illustrate the usefulness of adding the precipitation mask. Verification statistics confirm that applying the rainfall mask tends to slightly reduce the false alarm ratio while still identifying the majority of hail events within a special study area over southern Alberta. The performance of the precipitation masking technique was not as effective on severe hail days, especially when attempting to identify both the occurrence and location of severe hail swaths.

Abstract

Lagrangian trajectories were computed for three extreme summer rainfall events (with rainfall exceeding 100 mm) over the southern Mackenzie River basin to test the hypothesis that the low-level moisture feeding these rainstorms can be traced back to the Gulf of Mexico. The three-dimensional trajectories were computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT).

For all three events, parcel trajectories were identified that originated near the Gulf of Mexico and terminated over the southern Mackenzie River basin. Specifically, the transport of low-level moisture was found to occur along either quasi-continuous or stepwise trajectories. The time required to complete the journey varied between 6 and 10 days.

Closer examination of the data suggests that, for the three cases in question, the transport of modified Gulf of Mexico moisture to high latitudes was realized when the northward extension of the Great Plains low-level jet to the Dakotas occurred in synch with rapid cyclogenesis over Alberta, Canada. In this way, modified low-level moisture from the Gulf of Mexico arrived over the northern Great Plains at the same time as a strong southerly flow developed over the Dakotas and Saskatchewan, Canada, in advance of the deepening cutoff low over Alberta. This moist air was then transported northward over Saskatchewan and finally westward over the southern Mackenzie River basin, where strong ascent occurred.

Abstract

A one-dimensional steady-state cloud model was combined with a time-dependent hail growth model to predict the maximum hailstone size on the ground. Model runs were based on 160 proximity soundings recorded within the Alberta Hail Project area for three summers between 1983 and 1985. The forecast hail sizes were verified against reports of maximum hail size gathered from a high-density observation network within the project area. The probability of detection (POD), the false-alarm ratio (FAR), and the Heidke skill score (HSS) were computed for the hail model forecasts and were compared with the skill scores for a nomogram method developed to forecast hail size in Alberta. The hail model was skillful in forecasting hail (POD = 0.85, FAR = 0.26, HSS = 0.64). On days with hail larger than 2 cm in diameter, the hail model performed slightly better (POD = 0.90, FAR = 0.40, HSS = 0.67). Analysis of the skill scores and hail-size forecasts suggests that employing a coupled cloud and hail model noticeably improves the overall skill and accuracy of hail forecasts as compared with those determined using the nomogram.

Abstract

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

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

Forecasting the occurrence of hail and the maximum hail size is a challenging problem. This paper investigates the feasibility of producing maps of the forecast maximum hail size over the Canadian prairies using 12-h prognostic soundings from an operational NWP model as input for a numerical hail growth model. Specifically, the Global Environmental Multiscale model run by the Canadian Meteorological Center is used to provide the initial data for the HAILCAST model on a 0.5° × 0.5° grid. Maps depicting maximum hail size for the Canadian prairies are generated for 0000 UTC for each day from 1 June to 31 August 2000. The forecast hail-size maps are compared with radar-derived vertically integrated liquid data over southern Alberta and surface hail reports. Verification statistics suggest that the forecast technique is skillful at identifying the occurrence of a hail day versus no-hail day up to 12 h in advance. The technique is also skillful at predicting the main threat areas. The maximum diameter of the hailstones is generally forecast quite accurately when compared with surface observations. However, the technique displays limited skill when forecasting the distribution of hail on a small spatial scale.

Severe thunderstorms are a common occurrence in summer on the Canadian prairies, with a large number originating along the Alberta, Canada, foothills, just east of the Rocky Mountains. Most of these storms move eastward to affect the Edmonton–Calgary corridor, one of the most densely populated and fastest-growing regions in Canada. Previous studies in the United States, Europe, and Canada have stressed the importance of mesoscale features in thunderstorm development. However, such processes cannot be adequately resolved using operational observation networks in many parts of Canada. Current conceptual models for severe storm outbreaks in Alberta were developed almost 20 years ago and do not focus explicitly on mesoscale boundaries that are now known to be important for thunderstorm development.

The Understanding Severe Thunderstorms and Alber ta Boundary Layers Experiment (UNSTABLE) is a field and modeling study aiming to improve our understanding of the processes associated with the initiation of severe thunderstorms, to refine associated conceptual models, and to assess the ability of convectivescale NWP models to simulate relevant physical processes. As part of UNSTABLE in 2008, Environment Canada and university scientists conducted a pilot field experiment over the Alberta foothills to investigate mesoscale processes associated with the development of severe thunderstorms. Networks of fixed and mobile surface and upper-air instrumentation provided observations of the atmospheric boundary layer at a level of detail never before seen in this region. Preliminary results include the most complete documentation of a dryline in Canada and an analysis of variability in boundary layer evolution across adjacent forest and crop vegetation areas. Convective-scale NWP simulations suggest that although additional information on convective mode may be provided, there is limited benefit overall to downscaling to smaller grid spacing without assimilation of mesoscale observations.