UMOS statistically post-processed Forecast of the Global Deterministic Prediction System (GDPS-UMOS-MLR)
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.
- Publisher - Current Organization Name: Environment and Climate Change Canada
- Licence: Open Government Licence - Canada
Data and Resources
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MSC DatamartGEOJSONEnglish dataset GEOJSON
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MSC DatamartGEOJSONFrench dataset GEOJSON
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MSC Datamart AMQPGEOJSONEnglish dataset GEOJSON
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MSC Datamart AMQPGEOJSONFrench dataset GEOJSON
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MSC Open Data documentationHTMLEnglish guide HTML
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MSC Open Data documentationHTMLFrench guide HTML
Contact Information
Delivery Point: 77 Westmorland Street, suite 260
City: Fredericton
Administrative Area: New Brunswick
Postal Code: E3B 6Z4
Country: Canada
Electronic Mail Address: ECWeather-Meteo@ec.gc.ca