In June 2013, excessive rainfall associated with an intense weather system triggered severe flooding in southern Alberta, which became the costliest natural disaster in Canadian history. This article provides an overview of the climatological aspects and large-scale hydrometeorological features associated with the flooding event based upon information from a variety of sources, including satellite data, upper air soundings, surface observations and operational model analyses. The results show that multiple factors combined to create this unusually severe event. The event was characterized by a slow-moving upper level low pressure system west of Alberta, blocked by an upper level ridge, while an associated well-organized surface low pressure system kept southern Alberta, especially the eastern slopes of the Rocky Mountains, in continuous precipitation for up to two days. Results from air parcel trajectory analysis show that a significant amount of the moisture originated from the central Great Plains, transported into Alberta by a southeasterly low level jet. The event was first dominated by significant thunderstorm activity, and then evolved into continuous precipitation supported by the synoptic-scale low pressure system. Both the thunderstorm activity and upslope winds associated with the low pressure system produced large rainfall amounts. A comparison with previous similar events occurring in the same region suggests that the synoptic-scale features associated with the 2013 rainfall event were not particularly intense; however its storm environment was the most convectively unstable. The system also exhibited a relatively high freezing level, which resulted in rain, rather than snow, mainly falling over the still snow-covered mountainous areas. Melting associated with this rain-on-snow scenario likely contributed to downstream flooding. Furthermore, above-normal snowfall in the preceding spring helped to maintain snow in the high-elevation areas, which facilitated the rain-on-snow event.

A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dualpolarized radar (WKR). Horizontal reflectivity Z and differential reflectivity Z DR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, Z DR ) algorithms. Specific differential phase K DP was used to compute rain rates from three R(K DP ) algorithms, one modified to use positive and negative K DP , and an R(K DP , Z DR ) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. Hightemporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dualpolarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, Z DR ) algorithms. Rainfall estimation from the Brandes et al. R(K DP ) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and 26.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(K DP ) and R(A) algorithms were comparable to the NEXRAD S-band estimates.

A devastating, flood-producing rainstorm occurred over southern Alberta, Canada, from 19 to 22 June 2013. The long-lived, heavy rainfall event was a result of complex interplays between topographic, synoptic, and convective processes that rendered an accurate simulation of this event a challenging task. In this study, the Weather Research and Forecasting (WRF) Model was used to simulate this event and was validated against several observation datasets. Both the timing and location of the model precipitation agree closely with the observations, indicating that the WRF Model is capable of reproducing this type of severe event. Sensitivity tests with different microphysics schemes were conducted and evaluated using equitable threat and bias frequency scores. The WRF double-moment 6-class microphysics scheme (WDM6) generally performed better when compared with other schemes. The application of a conventional convective/stratiform separation algorithm shows that convective activity was dominant during the early stages, then evolved into predominantly stratiform precipitation later in the event. The HYSPLIT back-trajectory analysis and regional water budget assessments using WRF simulation output suggest that the moisture for the precipitation was mainly from recycling antecedent soil moisture through evaporation and evapotranspiration over the Canadian Prairies and the U.S. Great Plains. This analysis also shows that a small fraction of the moisture can be traced back to the northeastern Pacific, and direct uptake from the Gulf of Mexico was not a significant source in this event.

A polarimetric melting-layer detection algorithm developed for an S-band radar has been modified for use by the King City C-band radar in southern Ontario, Canada. The technique ingests radar scan volume data to determine the melting-layer top and bottom and to diagnose temporal and spatial variations of the meltinglayer heights. The thickness of the melting layer is also derived from the algorithm. Detailed case studies of two frontal systems over this region are described, comparing the radar-derived melting-layer height with aircraft measurement of the height of the 08C isotherm. The analysis demonstrated the ability to detect rapidly changing melting-layer heights during frontal passages in the region. A range of melting-layer heights for a 3-yr period was investigated and produced detections from close to the ground up to about 5.0 km. Comparison of algorithm results to output from a numerical weather prediction model over the 3-yr period showed good agreement. The correlation coefficient of the heights of the 08C wet-bulb temperature with the radar-derived melting-layer tops was 0.96. The time series of the algorithm output was used to detect frontal passages and showed that the algorithm should be useful for approximately 19 frontal passages per year in this region.

Weather radar provides real-time, spatially distributed precipitation estimates, whereas traditional gauge data are restricted in space. The use of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications has increased with the development of weather radar worldwide. New dual-polarization technology and algorithms are showing improvements to radar QPEs. This study evaluates radar QPEs from C-band radar at King City, Canada (WKR), and NEXRAD S-band radar at Buffalo, New York (KBUF), to verify the reliability and accuracy for operational use in the Humber River (semiurban) and Don River (urban) watersheds in the Greater Toronto Area (GTA), Canada. Twenty rainfall events that occurred from 2011 to 2017 were determined from hourly gauge measurements and compared with nine radar QPEs. Rain rates were estimated with different algorithms using three dual-polarized reflectivity values: horizontal reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP. The correlation coefficient, bias, detection, and root-mean-square error were calculated and averaged over all events for each gauge station to show the spatial distribution and in a similar pattern to represent the variation by the event. The quality of the results in terms of accuracy and reliability indicates that the radar QPEs from KBUF S-band and WKR C-band multiparameter rain rate estimators can be effectively used as precipitation forcing of hydrological models for hydrometeorological applications. The high spatiotemporal resolution, long-term data archive, and good percent detection of radar QPEs can facilitate hydrometeorological applications by providing a continuous time series for hydrological models.

Data obtained from a variety of sources including, the Canadian Lightning Detection Network, weather radars, weather stations and operational numerical weather model analyses were used to address the evolution of precipitation during the June 2013 southern Alberta flood. The event was linked to a mid-level closed low pressure system to the west of the region and a surface low pressure region initially to its south. This configuration brought warm, moist unstable air into the region that led to dramatic, organized convection with an abundance of lightning and some hail. Such conditions occurred in the southern parts of the region whereas the northern parts were devoid of lightning. Initially, precipitation rates were high (extreme 15-min rainfall rates up to 102 mm h -1 were measured) but decreased to lower values as the precipitation shifted to longlived stratiform conditions. Both the convective and stratiform precipitation components were affected by the topography. Similar flooding events, such as June 2002, have occurred over this region although the 2002 event was colder and precipitation was not associated with substantial convection over southwest Alberta.

Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

Flood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.