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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.

Basic view

Metadata Record Information

File Identifier
7c1070fd-af7d-40fe-9e78-49d2962f0bbc XML
Date Stamp
2024-10-04T11:42:25.53Z
Metadata language

eng; CAN

Character set
UTF8
Hierarchy Level
Dataset
Point of contact
  Government of Canada; Environment and Climate Change Canada; Meteorological Service of Canada
77 Westmorland Street, suite 260 , Fredericton , New Brunswick , E3B 6Z4 , Canada
+01-819-997-2800
+01-506-451-6010
 

Data identification

Title

UMOS statistically post-processed Forecast of the Global Deterministic Prediction System (GDPS-UMOS-MLR)

Date (Publication)
2024-07-24
Date (Creation)
2023-07-28
Status
On going
Metadata language

eng; CAN

Character set
utf8
Topic category
  • Climatology, meteorology, atmosphere
Maintenance and Update Frequency
Continual
Spatial representation type
Vector
Point of contact
  Government of Canada; Environment and Climate Change Canada; Meteorological Service of Canada
77 Westmorland Street, suite 260 , Fredericton , New Brunswick , E3B 6Z4 , Canada
+01-819-997-2800
+01-506-451-6010

Keywords

Theme
  • Statistical post-processing

  • Machine learning

  • Multiple linear regression

  • UMOS

  • Environmental forecast

  • Point forecast

Government of Canada Core Subject Thesaurus

  • Weather forecasts

  • Air temperature

  • Wind

Business Functions

  • Provide Weather Information Products and Services

  • Deliver Weather Products and Services to Clients

Branch

  • Meteorological Service of Canada

Directorate

  • Weather and Environmental Operations

GC Security Level

  • Unclassified

Geography

  • National (CA)

 
Use Limitation

Open Government Licence - Canada ( http://open.canada.ca/en/open-government-licence-canada )

Access Constraints
License
Use Constraints
License
Begin Date
2023-07-28
End Date
2024-07-25

Extent

N
S
E
W
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Ref. system Reference Systems

Reference system identifier
https://epsg.io / EPSG:4326 /
 

Distribution

Distribution Formats

Distribution format
  • GEOJSON ( RFC7946 )

 
Distributor
  Government of Canada; Environment and Climate Change Canada; Meteorological Service of Canada
77 Westmorland Street, suite 260 , Fredericton , New Brunswick , E3B 6Z4 , Canada
+01-819-997-2800
+01-506-451-6010
 
 

Overviews

N
S
E
W
thumbnail


Keywords

Environmental forecast Machine learning Multiple linear regression Point forecast Statistical post-processing UMOS
Branch
Meteorological Service of Canada
Business Functions
Deliver Weather Products and Services to Clients Provide Weather Information Products and Services
Directorate
Weather and Environmental Operations
GC Security Level
Unclassified
Government of Canada Core Subject Thesaurus
Air temperature Weather forecasts Wind

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