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The products that are made available on this site originate from the regional and global operational runs of the GEM model. Traditionally, model forecast output has been processed into raster graphics as four-panel charts that can be printed out on large sheets of paper. In the course of the forecast process, meteorologists often like to annotate these charts, compare them with previous ones, and with the output from other models.
The oldest and most familiar of these products is the classic four-panel chart, which depicts several of the traditional parameters used in general meteorology. More specialized charts are also made available, depicting model forecasts of fields such as air turbulence, aircraft icing, or parameters used in forecasting seasonal severe weather and ocean waves.
Numerical Weather Prediction (NWP) is the forecasting of weather elements through the use of numerical models, such as CMC's Global Environmental Multiscale (GEM) model. All NWP models are based on the following idea:
If we know enough about the state of the atmosphere at the present time, and we know the physical laws that govern the atmosphere, then we can code these laws into a sufficiently fast computer and do a virtual "fast-forward" to see what the atmosphere's evolution is going to be, through the next few hours or days.
Much like a digital camera contains an array of pixels instead of a smooth, continuous picture, the model performs its calculations on a three-dimensional grid. You could also think of a huge 3D spreadsheet where each cell holds the forecast for a particular point in the atmosphere. For instance, the global configuration of the GEM model operates on cells that are each 0.3 degree of latitude by 0.45 degree of longitude Timewise, the evolution of the weather is also calculated in steps. The current global GEM model "sees" time in increments of 45 minutes.
Model forecasts have become an indispensable source of information in virtually every aspect of weather forecasting. The level of detail in modern models allows for a wide variety of products and forecast fields to be delivered, for use not only in general meteorology, but also in specialized areas such as aviation and air quality. Advances in data visualization and delivery methods show great promise for the users of meteorological products and the practitioners of the science. However, managing the mass of forecast data created by the models is fast becoming a science of its own. Output from modern NWP models requires post-processing to make it intelligible and, most importantly, expert human interpretation in order to assess its meaning, qualities, and possible flaws.
While NWP is the greatest success achieved by the science of meteorology, its application can still be said to be only partially effective. There are three main reasons for this: