Weather Elements on Grid (WEonG) based on the High Resolution Deterministic Prediction System (HRDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the pan-Canadian High Resolution Deterministic Prediction System (HRDPS-NAT).

The Regional Ensemble Prediction System (REPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 3 days into the future. The probabilistic predictions are based on 20 ensemble members that are perturbed through their initial and boundary conditions as well as physical tendencies. A control member that is not perturbed is also available. Atmospheric elements include temperature, precipitation, cloud cover, wind speed and direction, humidity and others. This product contains raw numerical results of these calculations. Geographical coverage includes Canada and the United States. Data is available at a horizontal resolution of 10 km. Data is available on ten vertical levels. Predictions are performed four times a day.

A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area (within a 20km radius from the urban center) rather than a single precise location.

The daily mean is the average of all unit values for a given day.

The Canadian Lightning Detection Network (CLDN) provides lightning monitoring across most of Canada. The data distributed here represents a spatio-temporal aggregation of the observations of this network available with an accuracy of a few hundred meters. More precisely, every 10 minutes, the reported observations are processed in the following way: The location of observed lightning (cloud-to-ground and intra-cloud) in the last 10 minutes is extracted. Using a regular horizontal grid of about 2.5km by 2.5km, the number of observed lightning flashes within each grid cell is calculated. These grid data are normalized by the exact area of each cell (in km2) and by the accumulation period (10min) to obtain an observed flash density expressed in km-2 and min-1. A mask is applied to remove data located more than 250km from Canadian land or sea borders.

The monthly mean is the average of daily mean values for a given month.

The Global Ensemble Wave Prediction System (GEWPS) uses the third generation spectral wave model WaveWatch III® (WW3) to arrive at probabilistic predictions of wave elements from the current day out to 16 days into the future. The probabilistic predictions are based on 20 ensemble members and a control member that are forced by the 10 meters winds from the Global Ensemble Prediction System (GEPS). The GEPS forecast is a coupled atmosphere-ice-ocean model, its sea ice forecast is used by the GEWPS to dampen or suppress wave growth in areas covered respectively with 25% to 75% and more than 75% ice. WW3 (WAVEWATCH III® Development Group, WW3DG 2019) is a third generation spectral wave prediction model that solves the evolution of the energy balance equation for the 2-D wave energy spectrum without any prior assumptions on the shape of the spectrum. The WW3 model has been implemented by a growing number of national operational forecasting centres over the last several years.

The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

Statistical post-processing of weather and environmental forecasts issued by numerical models, including the Global Deterministic Prediction System (GDPS), reduces systematic bias and error variance of raw numerical forecasts. This is achieved by establishing an optimal relationship between observations recorded at stations and co-located numerical model outputs. The Updatable Model Output Statistics (UMOS) system at Environment Canada carries out this task. The statistical relationships are built using the Model Output Statistics (MOS) method and a multiple linear regression (MLR) technic. The weather and environmental variable being statistically post-processed by UMOS consists of air temperature at approximately 1.5 meters above ground. The absence of a statistically post-processed forecast can be caused by a missing statistical model due to insufficient observation data quality or quantity. Geographical coverage includes weather stations across Canada. Statistically post-processed forecasts are available at the same frequency of emission as the numerical model producing the raw forecasts and at 3-hourly lead times up to 144 hours (6 days) for the GDPS.

Anomalous weather resulting in Temperature and Precipitation extremes occurs almost every day somewhere in Canada. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. Virtual Climate stations correspond with the city pages of weather.gc.ca. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.