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

The Agri-Environmental Indicator Particulate Matter dataset provides an estimated net emissions of particulate matter from agricultural lands.

Met Norway's operational numerical wave model MyWaveWam is run on a 4 km grid covering Europe and the Arctic. The model is run twice daily with ECMWF and AROME atmospheric forcing to give forecasts to +66 hrs

This web site provides adjusted and homogenized climate data for many climatological stations in Canada. These data were created for use in climate research including climate change studies. They incorporate a number of adjustments applied to the original station data to address shifts due to changes in instruments and in observing procedures. Sometimes the observations from several stations were joined to generate a long time series. Users are strongly cautioned to determine the data suitability for their application. They should also be aware that ongoing research on adjustment techniques may result in future revisions of the datasets. The datasets are updated annually with the most recent data. The adjusted and homogenized data are provided for four climate elements: Surface air temperature, Precipitation, Surface pressure, and Surface wind speed. References Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., M. M. Hartwell, and X. L. Wang, 2020: A third generation of homogenized temperature for trend analysis and monitoring changes in Canada’s climate. Atmosphere-Ocean., 58:3, 173-191, doi:10.1080/07055900.2020.1765728. Wan, H., X. L. Wang, V. R. Swail, 2010: Homogenization and trend analysis of Canadian near-surface wind speeds. Journal of Climate, 23, 1209-1225. Wan, H., X. L. Wang, V. R. Swail, 2007: A quality assurance system for Canadian hourly pressure data. J. Appl. Meteor. Climatol., 46, 1804-1817.

Cumulative heat-energy satisfies the essential requirement of field crop growth and development towards a high yield and good quality of agricultural crop products. 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.

Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression. The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C. Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using 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. Multiple layers are provided. First, the fire season length is shown across Canada for a reference period (1981-2010). Difference in projected fire season length compared to reference period is shown for the short- (2011-2040), medium- (2041-2070), and long-term (2071-2100) under the RCP 8.5 (continued emissions increases) and, for the long-term (2071-2100), under RCP 2.6 (rapid emissions reductions).

Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. 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 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).

The Canadian Precipitation Analysis System (CaPA) produces a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. In order to produce the High Resolution Deterministic Precipitation Analysis (HRDPA) at a resolution of 2.5 km, CaPA is connected to the continental HRDPS for its trial field. CaPA-HRDPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. A preliminary production is started 1 hour after valid time and a final one is launched 7 hours later. This translates into a production of 12 analyses per day.

The Agri-Environmental Indicator Risk of Water Contamination by Pesticides dataset reports the annual and semi-decadal status of pesticide transport to surface water, the concentration of pesticide in ground water, and the risk of water contamination by pesticide. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values.