Difference from Normal Temperature values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive.Long-term average is 1991-2020.The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

Difference from Average Precipitation represents the accumulated precipitation value for a location, subtracted by the long-term average value. A negative value indicates that the location has received less than the normal amount of precipitation (mm) for that timeframe. A positive value indicates that the location has received more than the normal amount of precipitation (mm).Time periods calculated for difference from average precipitation are 1, 2, 3, 6, 9, 12, 18, 24 months.Long-term average is 1991-2020. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday.The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

This polygon layer reflects short-range (up to 84 hours) accumulated precipitation forecasts from the Regional Deterministic Prediction System (RDPS), a high-resolution (~10 km) weather model developed by Environment and Climate Change Canada (ECCC). It supports flood forecasting, hydrological modeling, and operational planning by providing refined, near-real-time precipitation guidance for Canada and surrounding areas.Short-Range Forecasts: RDPS runs multiple times per day, offering precipitation outlooks for days 0–3.5 with updates every six hours. High Resolution: At ~10 km, RDPS captures critical mesoscale phenomena like localized downpours, lake-effect snow, and terrain-driven precipitation. Hydrological Utility: Especially valuable for sub-basin-level flood risk assessment and water resource management in near-term scenarios. Technical Basis: The RDPS is a limited-area configuration of the GEM model, using initial/boundary conditions from ECCC’s Global Deterministic Prediction System (GDPS).

For nearly three decades, the SCRIBE system has been used to assist meteorologists in preparing weather reports. The philosophy behind SCRIBE is that a set of weather element matrices are generated for selected stations or sample points and then transmitted to regional weather centers. The matrices are then decoded by SCRIBE and can be modified via the graphical interface by the users. The resulting data is then provided to a text generator, which produces bilingual public forecasts in plain language.The various rules related to the Scribe matrices hinder scientific innovation, do not exploit the richness of the Numerical Weather Prediction (NWP), reduce the understanding of weather forecasts, and and may require frequent interventions from forecasters.As part of a larger modernization plan for the Meteorological Service of Canada (MSC), in which the role of the forecaster is evolving, the goal is to replace the Scribe matrices, available on the MSC Datamart, and their limited number of points across Canada with Weather Elements on the Grid ("WEonG").Weather Elements on Grid (WEonG) based on the Global Deterministic Prediction System (GDPS) 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 Global Deterministic Prediction System (GDPS).

Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

For nearly three decades, the SCRIBE system has been used to assist meteorologists in preparing weather reports. The philosophy behind SCRIBE is that a set of weather element matrices are generated for selected stations or sample points and then transmitted to regional weather centers. The matrices are then decoded by SCRIBE and can be modified via the graphical interface by the users. The resulting data is then provided to a text generator, which produces bilingual public forecasts in plain language.The various rules related to the Scribe matrices hinder scientific innovation, do not exploit the richness of the Numerical Weather Prediction (NWP), reduce the comprehension of meteorological forecasts, and and may require frequent interventions from forecasters.As part of a larger modernization plan for the Meteorological Service of Canada (MSC), in which the role of the forecaster is evolving, the goal is to replace the Scribe matrices, available on the MSC Datamart, and their limited number of points across Canada with Weather Elements on the Grid ("WEonG").Weather Elements on Grid (WEonG) based on the Regional Deterministic Prediction System (RDPS) 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 Regional Deterministic Prediction System (RDPS).

The Standardized Precipitation Index (SPI) has been recognized as the most accessible index for quantifying and reporting meteorological drought. On short timescales, the SPI is closely related to soil moisture, while at longer timescales, the SPI can be related to groundwater and reservoir storage. The model uses observed historical precipitation amounts to compute probability distributions which are then normalized using an incomplete gamma function over a range of timescales. The values can be interpreted as the number of standard deviations by which the observed anomaly deviates from the long-term mean. where positive values (greater than zero) result from above average conditions.

This dataset contains area-based management measures that have been recognized as 'other effective area-based conservation measures' (OECMs). It also contains basic information about OECMs, specifically their names, size, objectives, associated prohibitions, and DFO region. Spatial data for OECMs will be evaluated regularly, taking the most recent available information into account. In addition new 'OECMs will be identified over time. Therefore, this dataset may change over time.

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).

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in mean temperature (°C) based on an ensemble of twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. The 5th, 25th, 50th, 75th and 95th percentiles of the ensembles of projected change in mean temperature 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 mean temperature (°C) 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 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.