Editorial Type:
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Article Type:
Research Article

Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System

Jean-François Caron * Data Assimilation and Satellite Meteorology Research Section, Environment Canada, Dorval, Quebec, Canada

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Thomas Milewski Data Assimilation and Quality Control Development Section, Environment Canada, Dorval, Quebec, Canada

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Mark Buehner * Data Assimilation and Satellite Meteorology Research Section, Environment Canada, Dorval, Quebec, Canada

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Luc Fillion * Data Assimilation and Satellite Meteorology Research Section, Environment Canada, Dorval, Quebec, Canada

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Mateusz Reszka Data Assimilation and Quality Control Development Section, Environment Canada, Dorval, Quebec, Canada

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Stephen Macpherson * Data Assimilation and Satellite Meteorology Research Section, Environment Canada, Dorval, Quebec, Canada

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Judy St-James Data Assimilation and Quality Control Development Section, Environment Canada, Dorval, Quebec, Canada

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Print Publication:
01 Jul 2015
Collections:
Sixth WMO Data Assimilation Symposium
DOI:
https://doi.org/10.1175/MWR-D-14-00353.1
Page(s):
2560–2580
Restricted access

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Abstract

The modifications to the data assimilation component of the Regional Deterministic Prediction System (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area four-dimensional variational data assimilation (4DVar) algorithm for the limited-area analysis and the associated three-dimensional variational data assimilation (3DVar) scheme for the synchronous global driver analysis by the four-dimensional ensemble–variational data assimilation (4DEnVar) scheme presented in the first part of this study. It is shown that a 4DEnVar scheme using global background-error covariances can provide RDPS forecasts that are slightly improved compared to the previous operational approach, particularly during the first 24 h of the forecasts and in the summertime convective regime. Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in the first part of this study in the RDPS intermittent cycling strategy. The computational savings brought by the 4DEnVar approach are also discussed.

Corresponding author address: Jean-François Caron, Meteorological Research Division, Environment Canada, 2121 TransCanada Hwy., Dorval QC H9P 1J3, Canada. E-mail: jean-francois.caron@ec.gc.ca

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

The modifications to the data assimilation component of the Regional Deterministic Prediction System (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area four-dimensional variational data assimilation (4DVar) algorithm for the limited-area analysis and the associated three-dimensional variational data assimilation (3DVar) scheme for the synchronous global driver analysis by the four-dimensional ensemble–variational data assimilation (4DEnVar) scheme presented in the first part of this study. It is shown that a 4DEnVar scheme using global background-error covariances can provide RDPS forecasts that are slightly improved compared to the previous operational approach, particularly during the first 24 h of the forecasts and in the summertime convective regime. Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in the first part of this study in the RDPS intermittent cycling strategy. The computational savings brought by the 4DEnVar approach are also discussed.

Corresponding author address: Jean-François Caron, Meteorological Research Division, Environment Canada, 2121 TransCanada Hwy., Dorval QC H9P 1J3, Canada. E-mail: jean-francois.caron@ec.gc.ca

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

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