The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

Current conditions and forecasts for selected Canadian cities. Raw XML data are used to generate each city page on the Environment Canada web site https://www.weather.gc.ca/.

Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

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 Regional Ice-Ocean Prediction System (RIOPS) is based on the NEMO-CICE ice-ocean model and produces regional sea ice and ocean analyses and 84 hours forecasts daily based at [00, 06, 12, 18] UTC on a subset of the 1/12° resolution global tri-polar grid (ORCA12). RIOPS assimilates data (gridded CCMEP analysis SST product, SLA from satellite altimetry, in situ observations) using a multivariate reduced order Kalman filter and includes a 3DVar ice concentration analysis (assimilating satellite remote sensing and Canadian Ice Service ice charts). Atmospheric fluxes for 84 hours forecasts are calculated using fields from a blending of the Regional Deterministic Prediction System (RDPS) and the Global Deterministic Prediction System (GDPS).

The Regional Deterministic Air Quality Analysis (RDAQA) is an objective analysis of surface pollutants which combines numerical forecasts from the Regional Air Quality Deterministic Prediction System (RAQDPS) and hourly observational data from monitoring surface networks over North America in order to produce a better description of the air quality at every hour. Chemical constituents include 03, SO2, and NO2 gases, as well as fine particulate matter PM2.5 (2.5 micrometers in diameter or less) and coarse particulate matter PM10 (10 micrometers in diameter or less). Geographical coverage is Canada and the United States. Data is available only for the surface level, at a horizontal resolution of 10 km. The products are presented as historical, annual or monthly, averages which highlight long-term trends in cumulative effects on the environment.

CaLDAS-NSRPS was installed as an experimental system within the National Surface and River Prediction System (NSRPS) at Environment and Climate Change Canada's (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP) in July 2019. CaLDAS-NSRPS is a continuous offline land-surface assimilation system, which provides analyses of the land surface every 3 h over the domain of the High-Resolution Deterministic Prediction System (HRDPS) at a 2.5 km grid spacing. The emphasis in CaLDAS-NSRPS is to focus upon the assimilation of satellite based remote sensing observations to provide the optimal initial conditions for the predictive components of the NSRPS, the High Resolution Deterministic/Ensemble Land Surface Prediction System (HRDLPS/HRELPS) and the Deterministic/Ensemble Hydrological Prediction Systems (DHPS/EHPS). CaLDAS-NSRPS is launched 4 times per day, at 0000, 0600, 1200, and 1800 UTC.

The Global Deterministic storm Surge Prediction System (GDSPS) produces water level forecasts using a modified version of the NEMO ocean model (Wang et al. 2021, 2022, 2023). It provides 240 hours forecasts twice per day on a 1/12° resolution grid (3-9 km). The model is forced by the 10 meters winds, sea level pressure, ice concentration, ice velocity and surface currents from the Global Deterministic Prediction System (GDPS). The three dimensionnal ocean temperature and salinity fields of the model are nudged to values provided by the Global Ice-Ocean Prediction System (GIOPS) and the GDPS. During the post-processing phase, storm surge elevation (ETAS) is derived from total water level (SSH) by harmonic analysis using t_tide (Foreman et al. 2009).

Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record.