Medium-Range Quantitative Precipitation Forecasts from Canada’s New 33-km Deterministic Global Operational System

Stéphane Bélair Science and Technology Branch, Environment Canada, Dorval, Quebec, Canada

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Michel Roch Science and Technology Branch, Environment Canada, Dorval, Quebec, Canada

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Anne-Marie Leduc Meteorological Service of Canada, Environment Canada, Dorval, Quebec, Canada

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Paul A. Vaillancourt Science and Technology Branch, Environment Canada, Dorval, Quebec, Canada

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Stéphane Laroche Science and Technology Branch, Environment Canada, Dorval, Quebec, Canada

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Jocelyn Mailhot Science and Technology Branch, Environment Canada, Dorval, Quebec, Canada

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Abstract

The Meteorological Service of Canada (MSC) recently implemented a 33-km version of the Global Environmental Multiscale (GEM) model, with improved physics, for medium-range weather forecasts. Quantitative precipitation forecasts (QPFs) from this new system were compared with those from the previous global operational system (100-km grid size) and with those from MSC’s short-range (48 h) regional system (15-km grid size). The evaluation is based on performance measures that evaluate bias, accuracy, and the value of the QPFs.

Results presented in this article consistently show, for these three aspects of the evaluation, that the new global forecast system (GLBNEW) agrees more closely with observations, relative to the performance of the previous global system (GLBOLD). The biases are noticeably smaller with GLBNEW compared with GLBOLD, which severely overpredicts (underpredicts) the frequencies and total amounts associated with weak (strong) precipitation intensities. The accuracy and value scores reveal gains of at least 12 h and even up to 72 h for medium-range QPFs (i.e., day 3 to day 5 predictions). The new global system even performs slightly better than MSC’s operational regional 15-km system for short-range QPFs.

In a more absolute manner, results suggest that QPFs from the new global system may still have accuracy and value even at the medium range. This seems to be true at least for the smallest precipitation threshold, related to precipitation occurrence, for which the positive area under curves of relative economic value remains important, even for day 5 QPFs.

Corresponding author address: Stéphane Bélair, Numerical Prediction Research Section, Science and Technology Branch, Environment Canada, Rm. 500, 2121 Trans-Canada Highway, Dorval, QC H9P 1J3, Canada. Email: stephane.belair@ec.gc.ca

Abstract

The Meteorological Service of Canada (MSC) recently implemented a 33-km version of the Global Environmental Multiscale (GEM) model, with improved physics, for medium-range weather forecasts. Quantitative precipitation forecasts (QPFs) from this new system were compared with those from the previous global operational system (100-km grid size) and with those from MSC’s short-range (48 h) regional system (15-km grid size). The evaluation is based on performance measures that evaluate bias, accuracy, and the value of the QPFs.

Results presented in this article consistently show, for these three aspects of the evaluation, that the new global forecast system (GLBNEW) agrees more closely with observations, relative to the performance of the previous global system (GLBOLD). The biases are noticeably smaller with GLBNEW compared with GLBOLD, which severely overpredicts (underpredicts) the frequencies and total amounts associated with weak (strong) precipitation intensities. The accuracy and value scores reveal gains of at least 12 h and even up to 72 h for medium-range QPFs (i.e., day 3 to day 5 predictions). The new global system even performs slightly better than MSC’s operational regional 15-km system for short-range QPFs.

In a more absolute manner, results suggest that QPFs from the new global system may still have accuracy and value even at the medium range. This seems to be true at least for the smallest precipitation threshold, related to precipitation occurrence, for which the positive area under curves of relative economic value remains important, even for day 5 QPFs.

Corresponding author address: Stéphane Bélair, Numerical Prediction Research Section, Science and Technology Branch, Environment Canada, Rm. 500, 2121 Trans-Canada Highway, Dorval, QC H9P 1J3, Canada. Email: stephane.belair@ec.gc.ca

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