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    The "Canada's First Fall Frost Normals (1981-2010)" dataset contains the Mean and Median First Fall Frost Julian day calculated from the ANUSPLIN gridded data set using the date range from January 1, 1981 - December 31, 2010. The dataset also includes the Mean and Median Frost Free Period (given as a count of calendar days). For the purposes of this dataset a Frost Free day is defined as a day where the minimum daily temperature is greater than 0.0 Celsius.For more information, visit: http://open.canada.ca/data/en/dataset/c293739c-4e16-4384-bff8-e3fdaddc5e5f

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    The "Canada's First Fall Frost Normals (1981-2010)" dataset contains the Mean and Median First Fall Frost Julian day calculated from the ANUSPLIN gridded data set using the date range from January 1, 1981 - December 31, 2010. The dataset also includes the Mean and Median Frost Free Period (given as a count of calendar days). For the purposes of this dataset a Frost Free day is defined as a day where the minimum daily temperature is greater than 0.0 Celsius.For more information, visit: http://open.canada.ca/data/en/dataset/c293739c-4e16-4384-bff8-e3fdaddc5e5f

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    Anomalous weather resulting in Temperature and Precipitation extremes occurs almost every day somewhere in Canada. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. Virtual Climate stations correspond with the city pages of weather.gc.ca. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

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    Statistical post-processing of weather and environmental forecasts issued by numerical models, including the Global Deterministic Prediction System (GDPS), reduces systematic bias and error variance of raw numerical forecasts. This is achieved by establishing an optimal relationship between observations recorded at stations and co-located numerical model outputs. The Updatable Model Output Statistics (UMOS) system at Environment Canada carries out this task. The statistical relationships are built using the Model Output Statistics (MOS) method and a multiple linear regression (MLR) technic. The weather and environmental variable being statistically post-processed by UMOS consists of air temperature at approximately 1.5 meters above ground. The absence of a statistically post-processed forecast can be caused by a missing statistical model due to insufficient observation data quality or quantity. Geographical coverage includes weather stations across Canada. Statistically post-processed forecasts are available at the same frequency of emission as the numerical model producing the raw forecasts and at 3-hourly lead times up to 144 hours (6 days) for the GDPS.

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    Statistical post-processing of weather and environmental forecasts issued by numerical models, including the Regional Deterministic Prediction System (RDPS), reduces systematic bias and error variance of raw numerical forecasts. This is achieved by establishing an optimal relationship between observations recorded at stations and co-located numerical model outputs. The Updatable Model Output Statistics (UMOS) system at Environment Canada carries out this task. The statistical relationships are built using the Model Output Statistics (MOS) method and a multiple linear regression (MLR) technic. The weather and environmental variables being statistically post-processed by UMOS include air temperature and dew point temperature at approximately 1.5 meters above ground as well as wind speed and direction at 10 meters above ground or at the anemometer level in the case of a buoy. The absence of a statistically post-processed forecast can be caused by a missing statistical model due to insufficient observation data quality or quantity. In addition, the absence of a post-processed forecast for wind direction could also be due to weak forecasted wind components preventing the calculation of reliable results. The forecasts of wind speed and direction are produced from independent statistical post-processing models. Geographical coverage includes weather stations across Canada. Statistically post-processed forecasts is available at the same frequency of emission as the numerical model producing the raw forecasts and at 3-hourly lead times for the RDPS.

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    CaLDAS-NSRPS was installed as an experimental system within the National Surface and River Prediction System (NSRPS) at Environment and Climate Change Canada's (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP) in July 2019. CaLDAS-NSRPS is a continuous offline land-surface assimilation system, which provides analyses of the land surface every 3 h over the domain of the High-Resolution Deterministic Prediction System (HRDPS) at a 2.5 km grid spacing. The emphasis in CaLDAS-NSRPS is to focus upon the assimilation of satellite based remote sensing observations to provide the optimal initial conditions for the predictive components of the NSRPS, the High Resolution Deterministic/Ensemble Land Surface Prediction System (HRDLPS/HRELPS) and the Deterministic/Ensemble Hydrological Prediction Systems (DHPS/EHPS). CaLDAS-NSRPS is launched 4 times per day, at 0000, 0600, 1200, and 1800 UTC.

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    This dataset is included the following meteorological parameters: wind speed, wind direction, visibility, total clouds cover, air temperature, sea level pressure, pressure tendency, amount of pressure tendency, present weather(code), sea surface temperature, height of wind waves,direction of primary swell waves and etc. Research vessel:"Viktor Buynitsky". Callsign:"UAJX".

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    This dataset is included the following parameters: water temperature, salinity,air temperature,visibility (code). Research vessel:"Mikhail Somov".

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    This dataset is included the following meteorological parameters:wind speed,wind direction,visibility,total clouds cover,air temperature,dew point temperature,sea level pressure,pressure tendency,amount of pressure tendency,present weather,sea surface temperature,height of wind waves,direction of primary swell waves,concentration of arrangement of sea ice. Research vessel: "Akademik Fedorov". Call sign: "UCKZ".

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    Measurement data on the "Rossiya" icebreaker.This dataset is included the following parameters:temperature of water,salinity,dencity.Additionally, meteorological data are presented:wind speed,wind direction, air temperature, visibility.