Chapter 46
PROGNOS: A Meteorological Service
of Canada (MSC) Initiative to Renew
the Operational Statistical
Post-processing Infrastructure
Stavros Antonopoulos, Christian Saad, Jacques Montpetit, Andrew Teakles
and Jonathan Baik
Abstract A new MSC initiative, named PROGNOS, aims to provide a more versatile, modular and innovative weather and air quality post-processing system to
replace the current operational system (UMOS). PROGNOS has extensible statistical modeling capabilities. Currently in development, it issues real-time experimental
air quality and temperature forecasts for cities across Canada and will eventually be
applied to other meteorological fields and numerical models. The batch updates of the
statistical models occur weekly using parallel processing in a cluster computing environment. Less flexible but more computationally efficient, online updating methods
are also being evaluated. Several statistical modeling approaches have been explored
including multiple linear regression, random forest, and Kalman filter prototypes for
air quality forecasts. Logging, parameterisation, diagnostic and visualization features are also being explored. Medium to long term milestones include integrating
seasonal and other transitional schemes as well as gridded post-processing.
S. Antonopoulos (B) · C. Saad · J. Montpetit
Meteorological Service of Canada (MSC), Environment and Climate Change Canada (ECCC),
Montreal, QC, Canada
e-mail: stavros.antonopoulos@canada.ca
C. Saad
e-mail: christian.saad@canada.ca
J. Montpetit
e-mail: jacques.montpetit@canada.ca
A. Teakles
Meteorological Service of Canada, ECCC, Dartmouth, NS, Canada
e-mail: rew.teakles@canada.ca
J. Baik
Meteorological Service of Canada, ECCC, Vancouver, BC, Canada
e-mail: jonathan.baik@canada.ca
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_46
291
PROGNOS: A Meteorological Service
of Canada (MSC) Initiative to Renew
the Operational Statistical
Post-processing Infrastructure
Stavros Antonopoulos, Christian Saad, Jacques Montpetit, Andrew Teakles
and Jonathan Baik
Abstract A new MSC initiative, named PROGNOS, aims to provide a more versatile, modular and innovative weather and air quality post-processing system to
replace the current operational system (UMOS). PROGNOS has extensible statistical modeling capabilities. Currently in development, it issues real-time experimental
air quality and temperature forecasts for cities across Canada and will eventually be
applied to other meteorological fields and numerical models. The batch updates of the
statistical models occur weekly using parallel processing in a cluster computing environment. Less flexible but more computationally efficient, online updating methods
are also being evaluated. Several statistical modeling approaches have been explored
including multiple linear regression, random forest, and Kalman filter prototypes for
air quality forecasts. Logging, parameterisation, diagnostic and visualization features are also being explored. Medium to long term milestones include integrating
seasonal and other transitional schemes as well as gridded post-processing.
S. Antonopoulos (B) · C. Saad · J. Montpetit
Meteorological Service of Canada (MSC), Environment and Climate Change Canada (ECCC),
Montreal, QC, Canada
e-mail: stavros.antonopoulos@canada.ca
C. Saad
e-mail: christian.saad@canada.ca
J. Montpetit
e-mail: jacques.montpetit@canada.ca
A. Teakles
Meteorological Service of Canada, ECCC, Dartmouth, NS, Canada
e-mail: rew.teakles@canada.ca
J. Baik
Meteorological Service of Canada, ECCC, Vancouver, BC, Canada
e-mail: jonathan.baik@canada.ca
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_46
291