644 datasets of Typical Meteorological Years (TMY) created by joining twelve Typical Meteorological Months selected from a database of up to 20 years of CWEEDS hourly data. The months are chosen by statistically comparing individual monthly means with long-term monthly means for daily total global solar irradiance, mean, minimum and maximum dry bulb temperature, mean, minimum and maximum dew point temperature, and mean and maximum wind speed. These hourly datasets are used by the engineering and scientific community mainly as inputs for solar system design and analysis and building energy systems analysis tools. 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 TMY file format.

Description:Night-time sea surface temperature (SST) was acquired from the AVHRR Pathfinder project, with data distributed by NOAA, and averaged into monthly climatological composites. The data span 1990 through 2020 at 4 km pixel resolution.Methods:AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1990-2020 were removed from these maps, and set to a NaN value in the tif files. All resulting 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 0.0417 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Uncertainties: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.

Description:Night-time sea surface temperature (SST) was acquired from the AVHRR Pathfinder project, with data distributed by NOAA, and averaged into monthly climatological composites. The data span late 1981 through 2010 at 4 km pixel resolution.Methods:AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of valid data at each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1981-2010 were removed from these maps, and set to a NaN value in the tif files. All resulting 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 0.0417 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Uncertainties: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.

Dynamic Habitat Index. (2000-2005) Satellite derived estimates of photosynthetically active radiation can be obtained from satellites such as MODIS. Knowledge of the land cover allows for calculation the fraction of incoming solar radiation that is absorbed by vegetation. This fraction of photosynthetically active radiation (fPAR) absorbed by vegetation describes rate at which carbon dioxide and energy from sunlight are assimilated into carbohydrates during photosynthesis of plant tissues. The summation of carbon assimilated by the vegetation canopy over time yields the landscape's gross primary productivity. Daily MODIS imagery is the basis for periodic composites and monthly data products. Over the 6 year period from 2000-2005, we calculate the annual average cumulative total of 72 monthly fPAR measurements, to describe the integrated annual vegetative production of the landscape, the integrated average annual minimum monthly fPAR measurement, which describes the annual minimum green cover of the observed landscape, and the integrated average of the annual covariance of fPAR, which describes the seasonality of the observed landscape. We also share the combination of the annual integrated values for visualization and analysis as the Dynamic Habitat Index (with additional information in Coops et al. 2008). When using this data, please cite as: Coops, N.C., Wulder, M.A., Duro, D.C., Han, T. and Berry, S., 2008. The development of a Canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecological Indicators, 8(5), pp.754-766. ( Coops et al. 2008).

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.

The Canadian long term satellite data record (LTDR) derived from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) data was produced by the Canada Center for Remote Sensing (CCRS). Processing included: geolocation, calibration, and compositing using Earth Observation Data Manager (Latifovic et al. 2005), cloud screening (Khlopenkov and Trishchenko, 2006), BRDF correction (Latifovic et. al., 2003), atmosphere and other corrections as described in Cihlar et. al. (2004). For temporal analysis of vegetation cross-sensor correction of Latifovic et al. (2012) is advised. Data collected by the AVHRR instrument on board the National Oceanic and Atmospheric Administration (NOAA) 9,11,14,16,17,18 and 19 satellites were used to generate Canada-wide 1-km 10-day AVHRR composites. Data are available starting in 1985. It is important to note that there are three types of AVHRR sensors: (i) AVHRR-1 flown onboard TIROS-N, NOAA-6, NOAA-8, and NOAA-10; (ii) AVHRR-2 flown onboard NOAA-7, NOAA-9, NOAA-11, NOAA-12, and NOAA-14; and (iii) AVHRR-3 currently operational onboard NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19. The AVHRR-1 has four channels, AVHRR-2 has five channels and the AVHRR-3 has six channels, although only five channels of AVHRR-3 can be operational at any one time. As such, channels 3A (1.6 m) and 3B (3.7 m) work interchangeably. The processing procedure was designed to minimize artefacts in AVHRR composite images. There are thirty six 10-day image composites per year. The following three processing levels are provided: P1) top of atmosphere reflectance and brightness temperature, P2) reflectance at surface and surface temperature and P3) reflectance at surface normalized to a common viewing geometry (BRDF normalization). The processing level P1 and P2 are provided for all 36 composites while level P3 is provided for 21 composites from April – October.

Mean Temperature Difference From Normal 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.

Description:Chlorophyll-a concentration (a proxy for phytoplankton biomass) 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 and this record includes data at 4 km pixel resolution.Methods:MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-3 (version 2018), 4-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The monthly geometric mean value at all pixels was calculated for individual years, then the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to the geometric mean and geometric standard deviation factor 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. All resulting 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 0.0417 degrees. The monthly geometric mean, monthly geometric standard deviation factor, 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.

Description:Chlorophyll-a concentration (a proxy for phytoplankton biomass) 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 and this record includes data at 1 km pixel resolution.Methods:MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The pixels were aligned on a regular grid using the SeaDAS program, after which the monthly geometric mean value at all pixels was calculated for individual years. Finally, the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to these variables, 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 geometric mean, monthly geometric standard deviation factor, 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.

This product provides the variability index for selected drainage regions in Canada. Variability is measured using a coefficient of variation (CV) to compare all months over a 42-year time period and is a measure of the dispersion or variation in the monthly yield values from 1971 to 2013 (and 1971 to 2012 for drainage region 1). It is defined as the ratio of the standard deviation to the mean or the standard deviation divided by the mean, with higher CVs indicating more variability in monthly water yields. The monthly variability was not calculated for drainage regions 5, 7, 8, 16, 17, 18, or the Labrador portion of 25.