PROGNOS: A Meteorological Service of Canada (MSC) Initiative to Renew the Operational Statistical Post-processing Infrastructure

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.