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We have found 2,797 datasets for the keyword "meteorological data". You can continue exploring the search results in the list below.
Datasets: 99,338
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2,797 Datasets, Page 1 of 280
Canadian Weather Energy and Engineering Datasets (CWEEDS)
644 datasets of hourly meteorological data for all of Canada from various periods (1998 to 2020). The values of the records for solar irradiance are primarily based on satellite-derived solar estimates. This dataset has been updated with the most recent changes made in March 2023. The solar values in these files are based on 0.1° x 0.1° (11 km x 11 km grid) for all of Canada. Refer to Data Resources below for additional information on the CWEEDS file format and revision history.
Ocean Weather Station Papa, 1949-1981
The Canadian Weathership Program collected meteorological data at Station Papa (50N, 145W) in the North Pacific Ocean between 1949 and 1981. In 2014, researchers at the University of Washington (UW) Applied Physics Laboratory (APL) and the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) analyzed this historic data to determine its efficacy as a scientific tool. The data available here are the Government of Canada data files that were utilized for this analysis. The "OWSP Full Data (1949-1981)" file contains the entire Canadian Weathership Program record of data collected from Station Papa and the "OWSP Daily Averaged Wind Speed and Wave Height Data (1949-1981)" file contains daily averaged values of wind speed and wave height generated by the UW APL and NOAA PMEL researchers. The Data Dictionary for each data file contains notes on any quality controls that were applied to the data by the UW APL and NOAA PMEL researchers. The UW documents titled, "Data Documentation for Dataset 1170 (DSI-1170), Surface Marine Data, National Climatic Data Center" (https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/25570/td1170.pdf?sequence=6&isAllowed=y) and "Table detailing units of data values in each file" (https://digital.lib.washington.edu/researchworks/handle/1773/25570), provide further information on the key values, point scales, and other units that were used in these datasets.
AERMOD Input File Download by Location
This dataset is a locational record of the meteorological input files publically available on Saskatchewan GeoHub that can be used with the Environmental Protection Agency approved Regulatory Model (AERMOD). Each file represents the meteorology over an area of the province while minimizing the influences of local terrain on air flow. Additional attribute information for each location includes coordinates and a link to download the AERMOD data as a zip file.The Air Quality Section of the Ministry of Environment uses air quality modelling to simulate how air pollutants disperse in the ambient atmosphere in order to help manage the air quality in the province. The models are used to estimate the impact of air pollutants emitted from emission sources, and are typically employed to determine whether existing or new proposed industrial facilities are or will be in compliance with the ambient air quality standards outlined in Table 20 of the province's Environmental Code, June 1, 2015 under The Environmental Management and Protection Act, 2010. The information needed to run dispersion models consists primarily of emissions and meteorological data. Five years (2012-2016) of preprocessed meteorological datasets in an AERMOD ready format is publicly available. This file is contained in the downloadable zipped file. The zipped file contains five files: the SFC and PFL files are the AERMOD ready files required to run AERMOD (i.e., data, sensible heat flux, frictional velocity, potential temperature gradient, vertical velocity, mixing height, monin-obukhov length, surface roughness, Bowen ratio, albedo, scalar wind speed, wind direction, ambient temperature, precipitation, precipitation rate, relative humidity, surface pressure, and total cloud amounts); the DAT file contains the land use information (i.e., Surface roughness, Bowen ratio and albedo) chosen for each month in the SFC file; the KMZ file contains the wind rose for that location which can be used on Google Earth; and the PNG file contains various graphs of monthly or diurnal meteorological distribution (i.e., temperature, wind speed, daytime mixing heights and sensible heat flux, and stability) which can be used to help determine if that location is representative of the area proposed for modelling. Please note: Since this data is newly developed, it is possible there may be issues with the data as it gets used in more applications. Ongoing changes, edits and updates may be made by the Air Quality Section of the Ministry of Environment. Is is recommended for any future modelling to download the latest version of the input files and not archive any input files on your own server for future use, unless this notification no longer exists. If there are any issues discovered with data in the zipped file, please contact Dennis Fudge at dennis.fudge@gov.sk.ca or at 306-519-7105. Your support will be greatly appreciated. There may be times you feel that the input files are not representative of the proposed modelling domain due to the surrounding features (i.e., forest/agricultural or rural/urban) being different than those used to generate the input files. If that is the case, the modeler can generate the input modelling files themselves. The relevant files to generate these input files are available upon request. Please contact Dennis Fudge at dennis.fudge@gov.sk.ca or at 306-519-7105.
Tower
Towers -- structures or buildings that are typically higher than their diameter and high relative to their surroundings -- are shown in this data set. They include: * communication towers * fire towers * microwave towers * radio towers * navigation beacons * lighthouses * lightning locators * meteorological towers
Bathymetric Gridded Data Overview
CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.
Seasonal Climatologies of the Northeast Pacific Ocean (1980-2010)
Description:Seasonal climatologies (temperature, salinity, and sigma-t) of the Northeast Pacific Ocean were computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period in spatial resolution ranging from approximately 100m to 70km.Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.
Upwelling indices derived from GLORYS12 Model and ERA5 surface wind on the Scotian Shelf during 1993-2022
Estimates of wind-driven upwelling of colder water on the Scotian Shelf along the Nova Scotia coastline from 1993 to 2022 (inclusive) are presented, calculated using surface and 55m-depth water temperatures from the Global Ocean Physics Reanalysis (GLORYS12v1) product, and also ERA5 surface winds. GLORYS12v1 is a 1/12o data-assimilative reanalysis modelling product from Mercator Ocean International, implemented by the Copernicus Marine Environment Monitoring Service (CMEMS; (https://doi.org/10.48670/moi-00021). ERA5 is a weather forecast produced by the European Centre for Medium-Range Weather Forecasts (ECMWF; https://doi.org/10.24381/cds.adbb2d47). Daily estimates are given of upwelling area and intensity (temperature anomaly between upwelled and non-upwelled water), calculated over the area of interest (AOI) on the Scotian Shelf. Yearly estimates are given of total upwelling duration and cumulative area for the year in question, further broken down into seasons: Spring (March-May), Summer (June-August), and Fall (September-November). Lastly, estimates of the yearly start/end dates of the cold-water upwelling season (lasting generally from March to November) are estimated. The sea surface temperature (SST) data from GLORYS were validated against in-situ buoy observations (https://www.meds-sdmm.dfo-mpo.gc.ca/alphapro/wave/waveshare/metaData/meta_c44258.csv) and satellite-derived SST produced by Canadian Meteorological Centre (https://doi.org/10.5067/GHCMC-4FM02 and https://doi.org/10.5067/GHCMC-4FM03. These products may be used to gain knowledge of interannual variability of coastal upwelling on the ScS over the past 30 years.Cite this data as: Tao, J., Casey, M., Lu, Y., and Shen, H. Upwelling indices derived from GLORYS12 Model and ERA5 surface wind on the Scotian Shelf during 1993-2022.Published: December 2024. Ecosystems and Oceans Science, Maritimes region, Fisheries and Oceans Canada, Dartmouth NS. https://open.canada.ca/data/en/dataset/a2da6bfd-92e3-434e-b9bd-456b7fc9e92b
Trend of Mean Temperature for 1948-2016 based on Canadian gridded data
Seasonal and annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990). CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.
Topographic Data of Canada - CanVec Series
CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards.CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis.Related Products:**[Constructions and Land Use in Canada - CanVec Series - Manmade Features](https://open.canada.ca/data/en/dataset/fd4369a4-21fe-4070-914a-067474da0fd6)****[Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features](https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b)****[Administrative Boundaries in Canada - CanVec Series - Administrative Features](https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97)****[Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features](https://open.canada.ca/data/en/dataset/92dbea79-f644-4a62-b25e-8eb993ca0264)****[Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features](https://open.canada.ca/data/en/dataset/80aa8ec6-4947-48de-bc9c-7d09d48b4cad)****[Transport Networks in Canada - CanVec Series - Transport Features](https://open.canada.ca/data/en/dataset/2dac78ba-8543-48a6-8f07-faeef56f9895)****[Elevation in Canada - CanVec Series - Elevation Features](https://open.canada.ca/data/en/dataset/64aad38d-f692-4ab6-bf2c-f938586c1249)****[Map Labels - CanVec Series - Toponymic Features](https://open.canada.ca/data/en/dataset/b3fdcd34-4533-415f-8f83-68f17f9d5d68)**
Monthly Satellite Sea Surface Temperature Climatology of the Canadian Pacific Exclusive Economic Zone (2003-2020) – 1 km Resolution
Description:Night-time sea surface temperature (SST) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020; records were created at 1 km pixel resolution to be consistent with other satellite products.Methods:MODIS-Aqua night long-wave Sea Surface Temperature (SST) images were acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, spanning the period 2003-01-01 to 2020-12-31. Image pixels were aligned and mapped to a regular grid using the SeaDAS program, retaining all pixels with a quality level of ‘1’ or lower, which is recommended for scientific analysis. The monthly mean value at all pixels was calculated for individual years, and used to produce maps of the monthly climatological mean and standard deviation of SST. Additionally, the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. A few small gaps between pixels (near the edges of individual images) were filled using the median value of surrounding pixels, provided there were greater than 4 values. Finally, all rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.01 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Data Sources:NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.
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