Gögnin innhalda staðsetningu veðurstöðva sem eru í eigu Vegagerðarinnar og staðsettar eru við þjóðvegi en einnig veðurstöðvar í eigu Veðurstofunnar og annarra.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in snow depth based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in snow depth is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of snow depth change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in snow depth (%) 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.

The Agri-Environmental Indicator Residual Soil Nitrogen (RSN) dataset estimates the amount of nitrogen remaining in the soil at the end of the growing season (after harvest) on Canadian agricultural lands annually.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem. The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications. Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: projected number of large fires (>200 ha) across Canada for the long-term (2071-2100) under the RCP 2.6 (rapid emissions reductions). Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are short-wave and long-wave radiation emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product. Other products are based on the enhancement of channel data for a single wavelength, also aimed at highlighting meteorological features of the observed surface or clouds, but in a simpler way since only a single wavelength is involved. This older approach is still useful today, as its simplicity makes image interpretation easier in some cases.

The Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The maximum daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Last Spring Frost (0 °C) is defined as the average day, during the first half of the year, of the last occurrence of a minimum temperature at or below 0 °C. These values are calculated across Canada in 10x10 km cells.

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).

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.