Numerous regional models have sprung up to provide numerical forecasts of local weather events with a level of detail unheard of in the past, based on the rapid availability of appropriate data and computational resources.
When I first joined the office in 1962, the first numerical forecast had just been issued.
Both statistical and numerical forecast models had contributed to the improving forecasts, with numerical forecast models taking the lead since the 1990s.
However, numerical models are not perfect, and the initial conditions of numerical forecasts include errors.
We describe a numerical forecast system designed for prediction of physical and biological dynamics of a coastal inlet.
The main advantage of this method is that it can produce high forecast rates using all types of numerical forecast models, even those that are not rate-based.
This dataset is the result of assimilation of in situ and remotely sensed measurements into a numerical forecast model of the stratosphere and troposphere.
In almost all cases, numerical forecast models can be easily converted to an alarm-based forecast because the information provided by a numerical value assignment on a given space-time grid can be used as an alarm function.
Such profile, for example the numerical forecast model output, is supposed to contain the error and affect the output surface temperature and emissivity accuracy.
This representation could be, for example, obtained from numerical forecast models that assimilate in situ and remote-sensing data, such as the European Centre for Medium-Range Weanalysesrecand (ECMWF) analyses, and produce the required global fields of atmospheric parameters.
In what follows, we describe one iteration of the combination procedure using a current model (the initial rate-based forecast model at the first iteration) and an input model (any type of numerical forecast model) to compute a new rate-based forecast model.
