|THE DIFFERENT FORECAST MODELS
METEOROLOGIST JEFF HABY
It is often asked why are there so many different weather forecast models. Why is there not just one? How is
it that their output can be so different? Which one is the best?
The three primary used synoptic forecast models are the North American Mesoscale Model or NAM (formally ETA), the
Global Forecast System or GFS (formally AVN and MRF), and the long standing Nested Grid Model or
NGM. There are also other models such as the RUC, Canadian Model, European Model. There are also
many variants of these models and mesoscale models.
Think of a forecast model as a set of equations that are initialized and then solved through time. The quality
of the initialization is going to depend on the input (weather data) and how realistic the equations are.
Weather data is imperfect. First, it is impossible to know weather information at every point. The weather information
is spread out by many miles and often hundreds of miles between each other (i.e. weather balloon launch points).
Thus, there are huge gaps of weather information. Second, weather data requires that a measurement take place. Any
sensor that is experiencing error, even very minor error, will contaminate the data that goes into the
forecast model. There are several additional reasons for imperfect weather data such as unrepresentativeness error
and the missing of mesoscale processes.
Not all the models have the same data input and each model has a different mathematical way that the
equations are solved. There are also differences in resolution, display of output and how physical
processes are integrated into the model. A variety of model differences and limitations are
given on link below:
The model that does the best will depend on the particular weather situation. Some models will do well
in certain weather situations but poor in others. Some models will do better in certain geographic regions
but poor in others (even with the same type of weather event). It is a good idea to see how each model
performs in certain weather situations for your particular geographic region. When the models
have similar solutions that is an indication they may all be doing well for that particular
In conclusion, differences in how the math is set up and the amount / quality of the weather data ingested
into the model accounts for the differences in the models. The model that is the best will depend
of the weather situation and geographic region.