Atmospheric and Climate Science Directorate

Numerical Weather Prediction

Whether on an urban or planetary scale, covering a few hours or a few centuries, weather and oceanic forecasting and numerical modelling are considered to be among the leading environmental challenges of our time. Over the past four decades, Numerical Prediction Research (NPR) has significantly advanced progress on this challenge, both nationally and internationally. As this new century begins, an active program of multidisciplinary research and development (physicists, mathematicians, meteorologists, chemists and computer experts, etc.) are making a cutting-edge contribution to weather and environmental prediction and modelling.

Since the groundbreaking work of V. Bjerknes (1904) and L.F. Richardson (1922), the challenge of weather forecasting has been related to the initial conditions of mathematical physics (based on the non-linear equations governing fluid flow) and has been approached using numerical means. The success of the first numerical prediction by Charney, Fjortoft and von Neumann (1950) launched the unprecedented trend of recent decades in scientific research on earth sciences. Numerical Prediction Research has made major strides, as reflected by the work of American E. Lorenz in the early sixties on 'chaos' and, closer to home, the contributions of Canadian A. Robert to improving the efficiency of numerical weather prediction models. Numerical weather prediction models currently rely on increasingly powerful computers that will soon make it possible to expand and significantly improve meteorological computer applications to an ever greater extent.

The impact of extreme weather phenomena on society is growing more and more costly, causing infrastructure damage, injury and the loss of life. One aim of meteorological research is to limit the impact of extreme summer and winter weather through improved detection and through faster and more accurate prediction techniques. NPR will continue to expand the unified modelling approach using the multi-scale global environmental modelling (GEM) model for short and mid-term operational weather forecasting. GEM forecasts better represent physical, chemical and hydrological processes (cloud formation, rain, snow, etc.) and surface simulations (lacustrine, forest environments, etc.) with improved statistical approaches.

Global Environmental Modelling (GEM) Prediction:

The NPR division of the Meteorological Research Branch is prioritising the development of a coupled version of the GEM model to resolve atmosphere ocean and atmosphere-hydrology environmental forecasting problems. The research centre will endeavour to incorporate these capabilities into research projects on environmental forecasting in relation to ecosystems, in collaboration with partners from Environment Canada. These efforts will encourage the development of analytical and prediction products for determining precipitation speed and heights. These applications are intended for hydrological prediction and cryosphere related activities, such as flood prediction, using hydrological models and ice cover simulations using the coupled GEM regional model.

Below is a joint collaboration between Environment Canada (NPR) and Fisheries and Oceans Canada (IML):

A second NPR priority will focus on developing integrated atmospheric and chemical models for predicting air quality. NPR is working towards developing a regional version of the unified GEM non-hydrostatic model (with scales ranging from one to hundreds of kilometres) for use Canadian and the international scientific community use in meteorological and environmental applications.

At the dawn of this new century, significant research and development challenges remain to be met before acceptable meteorological and environmental forecasts can be produced over every spatial scale (from regional to planetary) and time scale (from a few hours to a few seasons) across Canada. The major goal of Numerical Prediction Research division is to meet these challenges for the greater good of the Canadian community.

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