The goal of the work in our group, is to increase the quaility of the NWP-system, and improve the forecasts on different time-and space scales. The work is done in cooperation with European research consortia, i.e. HIRLAM and ALADIN.
The NWP work is mainly focused on two different tasks:
- to improve the initial state of the models, data assimilation
- to model the physical processes, paramerization
More detailed forecasts, a challenge
When the resolution of the numerical models are increased, we must face some new problems. We are going from the dominating present resolution of typically 10 km down to 2-3 km or even less. This means that we are entering the mesoscale, where the dynamics and physics are partly different from that of the larger weather fenomea.
The flow on these scales becomes non hydrostatic which means that it is more unbalanced, and the forcing from the physical processes is more dominant than on larger scales. The parameterization of e.g. convection is doubtful, since this process is partly resolved.
This implies that the initial state is more crucial. It is also necessary with increased computer power, and effective coding of the model.
Very short forecasts, nowcasting
Many decisions in the society are strongly dependent on the weather evolution, on a very short timescale. Examples are activities at airports and road maintenance, where the largest interest is focused on the first few hours, or even less. To make short detailed forecasts is known as nowcasting.
We are doing development work in nowcasting, trying to improve local weather at airports (e.g. cloudbase). Estimations of road temperature and ice/water on the roads, is another nowcasting project, which is under development.
A third application is a model to forecast precipitation, on a short timescale, by combining model data with radar information.
Since the atmosphere is chaotic, it is not feasible to regard the weather evolution as beeing deterministic.This means that the forecast can behave very differently, if started from slightly different ( but equally probable) initial conditions, or if the physical formulation in the model, lika a parameter, is slightly changed (also within reasonable values).
By running many forecasts, the so called ensemble technique, it is possible to say something about the probability distribution of different weather developments. Work is going on in this field within our research group.
We also use statistical interpretation methods, like Kalman-filtering, to link desired prognostic information to the output of the numerical model system.