Operational Model Forecasts

Numerical Weather Prediction Products

Uses of NWP

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.

Limitations

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:

1. One has to face the problem of creating a sufficiently accurate picture of the state of the atmosphere at the outset of the forecast process. Errors introduced at the beginning of the forecast will propagate and amplify at each forecast interval, gradually eroding its accuracy and usefulness.
2. To move from a theoretical understanding of the weather to computer code that can generate a weather forecast, modelers will inevitably have to make some approximations.
   1. The first kind of approximation is the model's resolution, or its ability to capture smaller details.
   2. The second kind of approximation, called parametrization, becomes necessary to take into account the large-scale effects of phenomena that are too small to be picked-up at the model's resolution. For instance, individual thunderstorms are too small to be forecast by the model; yet in order to be useful the model must still produce a good approximation of the effects of thunderstorms on large-scale precipitation and temperature patterns.
3. To be successful, the model must integrate an understanding of many different phenomena and their interactions: how the wind blows; how heat is received from the sun and transformed by the oceans, the ground, the air, and the clouds; how water vapour condensates into clouds and how droplets of water turn to rain, ice and snow; how friction near the ground mixes the lower layers of air. Thus, errors in handling one type of phenomenon can contaminate other parts of the model, or amplify errors in other model sub-systems.