The objective of this paper is to provide an overview of the present status and procedures related to surface precipitation observations at Environment and Climate Change Canada (ECCC). This work was done to support the ongoing renewal of observation systems and networks at the Meteorological Service of Canada. The paper focusses on selected parameters, namely, accumulated precipitation, precipitation intensity, precipitation type, rainfall, snowfall, and radar reflectivity. Application-specific user needs and requirements are defined and captured by World Meteorological Organization (WMO) Expert Teams at the international level by Observing Systems Capability Analysis and Review (OSCAR) and WMO Integrated Global Observing System (WIGOS), and by ECCC user engagement initiatives within the Canadian context. The precipitation-related networks of ECCC are separated into those containing automatic instruments, those with human (manual) observers, and the radar network. The unique characteristics and data flow for each of these networks, the instrument and installation characteristics, processing steps, and limitations from observation to data distribution and storage are provided. A summary of precipitation instrument-dependent algorithms that are used in ECCC's Data Management System is provided. One outcome of the analysis is the identification of gaps in spatial coverage and data quality that are required to meet user needs. Increased availability of data, including from long-serving manual sites, and an increase in the availability of precipitation type and snowfall amount are identified as improvements that would benefit many users. Other recognized improvements for in situ networks include standardized network procedures, instrument performance adjustments, and improved and sustained access to data and metadata from internal and external networks. Specific to radar, a number of items are recognized that can improve quantitative precipitation estimates. Increased coverage for the radar network and improved methods for assessing and portraying radar data quality would benefit precipitation users.

The T2K experiment is a long baseline neutrino oscillation experiment aiming to observe the appearance of ν e in a ν µ beam. The ν µ beam is produced at the Japan Proton Accelerator Research Complex (J-PARC), observed with the 295 km distant SuperKamiokande Detector and monitored by a suite of near detectors at 280m from the proton target. The near detectors include a magnetized off-axis detector (ND280) which measures the un-oscillated neutrino flux and neutrino cross sections. The present paper describes the outermost component of ND280 which is a side muon range detector (SMRD) composed of scintillation counters with embedded wavelength shifting fibers and Multi-Pixel Photon Counter read-out. The components, performance and response of the SMRD are presented.

The Pan and Parapan American Games (PA15) are the third largest sporting event in the world and were held in Toronto in the summer of 2015 (10–26 July and 7–15 August). This was used as an opportunity to coordinate and showcase existing innovative research and development activities related to weather, air quality (AQ), and health at Environment and Climate Change Canada. New observational technologies included weather stations based on compact sensors that were augmented with black globe thermometers, two Doppler lidars, two wave buoys, a 3D lightning mapping array, two new AQ stations, and low-cost AQ and ultraviolet sensors. These were supplemented by observations from other agencies, four mobile vehicles, two mobile AQ laboratories, and two supersites with enhanced vertical profiling. High-resolution modeling for weather (250 m and 1 km), AQ (2.5 km), lake circulation (2 km), and wave models (250-m, 1-km, and 2.5-km ensembles) were run. The focus of the science, which guided the design of the observation network, was to characterize and investigate the lake breeze, which affects thunderstorm initiation, air pollutant transport, and heat stress. Experimental forecasts and nowcasts were provided by research support desks. Web portals provided access to the experimental products for other government departments, public health authorities, and PA15 decision-makers. The data have been released through the government of Canada’s Open Data Portal and as a World Meteorological Organization’s Global Atmospheric Watch Urban Research Meteorology and Environment dataset.

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 warm fog detection (air temperature > −5°C) algorithm using a combination of Geostationary Operational Environmental Satellite-12 (GOES-12) observations and screen temperature data based on an operational numerical model has been developed. This algorithm was tested on a large number of daytime cases during the spring and summer of 2004. Results from the scheme were compared with surface observations from four manned Canadian weather stations in Ontario, including Ottawa, Windsor, Sudbury, and Toronto. Initially, when all cases were included, fog detection (hit rate) by the satellite scheme ranged between 0.26 and 0.32. It is suggested that mid- or high-level clouds within the satellite imagery during the observed foggy periods affected the scheme’s performance in detecting surface-level fog for the majority of the cases. When cases with mid- and high-level clouds were removed using model-based screen temperatures, the hit rate ranged between 0.55 and 1.0. With an average false alarm rate of 0.10, the inclusion of model-based sounding values can be seen to improve results from the satellite-based algorithms by an average of 0.42. Average differences between the screen temperature and the surface-observed air temperature were found to be up to 2°C and this can likely account for some discrepancies in detecting fog. Finally, averaging GOES and model data to scales representing single data-point observations likely resulted in some of the failure of the fog algorithm.