Air Monitoring Networks and Studies produce data that represent a wide variety of observations and measurements. Multiple data types (also called collections) can be produced by a single network and data collections can have contributions from multiple networks. The data are organised as follows: 1. Atmospheric Gases, 2. Atmospheric Particles, 3. Atmospheric Precipitation Chemistry, 4. Combined Atmospheric Gases and Particles 5. Special Studies of Atmospheric Gases, Particles and Precipitation Chemistry Networks and Studies contributing to the Canadian National Atmospheric Chemistry Database and Analysis system (NAtChem) are from Canadian federal and provincial networks (past and present) and also include U.S. historical networks (these data are not available elsewhere). Information about these contributing networks, for each of these collection and product groups, can be found in each network's description documentation.

Patterns of wet deposition of the nitrate (NO3), non-sea-salt sulfate (xSO4) and ammonium (NH4) ions across areas of Canada and the United States are based on measurements of precipitation depth and ion concentrations in precipitation samples. xSO4 refers to the wet deposition of sulfate with the sea-salt sulfate contribution removed at coastal sites. These measurements were collected and quality controlled by their respective networks: in Canada, the federal Canadian Air and Precipitation Monitoring Network (CAPMoN) and provincial or territorial networks in Alberta, New Brunswick, the Northwest Territories, Nova Scotia, Ontario and Quebec. In the United States, wet deposition measurements were made by two coordinated networks: the National Atmospheric Deposition Program (NADP) / National Trends Network (NTN) and the NADP/Atmospheric Integrated Research Monitoring Network (AIRMoN). Only data from sites that were designated as regionally representative were used in the mapping. Wet deposition amounts were interpolated by ordinary kriging using ArcMap Geostatistical Analyst. The map is limited to the contiguous U.S. and southeastern or southern Canada because outside that region, the interpolation error exceeds 30% due to the larger distances between stations. Links to annual and five-year average maps are available in the associated resources.

This polygon layer displays ensemble-based, medium-range precipitation forecasts from the Global Ensemble Prediction System (GEPS), offering a probabilistic view of future rainfall or snowfall over a 16‑day horizon. It aids in uncertainty analysis, risk assessment, and strategic resource planning.Ensemble Approach: GEPS runs multiple perturbed members of ECCC’s GEM model, capturing a range of atmospheric evolutions and yielding probability distributions for precipitation. Global Domain: Similar coverage to the GDPS but focuses on ensemble mean, spreads, and probabilities rather than a single deterministic outcome. Longer-Range Outlook: Extends up to 16 days, supporting risk-based planning for potential floods, extended rainfall events, or dryness. Data Utility: Allows decision-makers to weigh confidence levels in precipitation scenarios, vital for water management, agriculture, and emergency contingency strategies.

Precipitation Percentiles represents the accumulated precipitation (mm) for the time period compared to historical information for the same time period. This comparison ranks the current precipitation amount and assigns it a percentile value based on a historic record.Products are produced for the following timeframes: Agricultural Year, Growing Season and Winter Season as well as rolling products for 30, 60, 90 and 180 days

This program summarizes long term water chemistry and chlorophyll a monitoring in north central Ontario lakes developed under Canada’s Long Range Transport of Air Pollutants (LRTAP) initiative to understand and track lake acidification caused by atmospheric deposition. Centered on the intensively studied Turkey Lakes Watershed since 1979 and complemented by broader headwater and mesoscale lake networks around Sault Ste. Marie, Ontario, as well as a few locations near Parry Sound, Ontario, the monitoring integrates catchment scale approaches to link atmospheric inputs, watershed processes, and aquatic responses. Water chemistry measurements quantify acid–base status, major ions, nutrients, and trace metals to diagnose sensitivity to acidification, episodic stress during events such as spring snowmelt, and long term chemical recovery following emission controls. Chlorophyll a is used alongside chemistry to indicate phytoplankton biomass, lake productivity, and overall water quality, providing biological context for fish habitat and ecosystem condition. Together, these coordinated datasets support both detailed process studies and regional assessments of lake sensitivity, productivity, and ecological recovery across a heterogeneous landscape.

This polygon layer presents 7‑day and 10‑day accumulated precipitation forecasts from the Global Deterministic Prediction System (GDPS), aggregated by sub-basin. It is designed to help hydrologists, water resource managers, and emergency planners pinpoint watersheds facing higher rainfall or snowfall totals in the medium-to-long range, enabling proactive flood risk assessment, drought monitoring, and resource allocation.Developed by Environment and Climate Change Canada (ECCC), the GDPS is a global numerical weather prediction model running at approximately 15km resolution, updated twice daily (00Z and 12Z). This layer integrates 168-hour (7‑day) and 240-hour (10‑day) precipitation forecasts into sub-basin polygons, offering a comprehensive view of expected cumulative precipitation. By focusing on watershed boundaries, decision-makers can quickly gauge regional vulnerabilities to prolonged rainfall or snowfall events.Key highlights: Global Model Insight: Captures large-scale, multi-day weather systems (e.g., atmospheric rivers, persistent low-pressure systems). Sub-Basin Aggregation: Delivers averaged precip values per basin, simplifying hydrological analysis for flood or drought outlooks. Extended Outlook: Spanning from day 0 to day 10, covers both medium- and longer-term forecast horizons, essential for strategic planning and mitigation efforts. Typical Uses:Flood Forecasting – Identifying basins prone to heavy or prolonged precipitation. Water Resource Management – Adjusting reservoir release schedules or irrigation planning based on expected accumulations. Emergency Preparedness – Deploying resources or issuing advisories in vulnerable watersheds.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in total precipitation are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily precipitation (mm/day) from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded precipitation dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected relative change in total precipitation is with respect to the reference period of 1986-2005 and expressed as a percentage (%). Seasonal and annual averages of projected precipitation change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of projected precipitation change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled total precipitation (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

This polygon layer presents the spatial distribution of forecasted accumulated precipitation from the Global Forecast System (GFS) over watershed sub‑basins. GFS APCP raster data are overlaid with global watershed boundaries, and zonal statistics are computed to derive average precipitation per sub‑basin over a 7‑day (168‑hour) period. This product aids in global disaster preparedness and water management planning.GFS model output is processed into APCP rasters that capture accumulated precipitation over a 7‑day forecast period. These rasters are then combined with watershed boundary data, and zonal statistics are applied to compute average precipitation for each sub‑basin. The final polygon features provide a clear depiction of global rainfall and snowfall patterns, offering critical information for disaster risk management and international water resource planning.

Accumulated Precipitation represents the amount of total precipitation in mm (solid and/or liquid) which has been recorded over a given period of time. Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season, as well as rolling products for 7, 14, 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of precipitation amounts that occurred over a period of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar, satellite imagery and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at a horizontal resolution of 10 km. The 24 hour analysis is produced twice a day and is valid at 06 and 12 UTC. A preliminary analysis is available approximately 1 hour after the end of the accumulation period and a final one is generated 7 hours later in order to assimilate more gauge data.