The increasing amount of data implies that new visualization techniques will be needed including 3D techniques. Sophisticated diagnostic tools will be needed that can sift through the data and highlight meteorological processes at work. The large volume of data will lead to increased reliance on artificial intelligence techniques to monitor current and forecast conditions and support decision making. In other words, we will need techniques that extract and present the most relevant information from the mass of data that will be available.
As an enabling technology these IT advances open new possibilities for local forecast offices. Once the realm of experts at national centers, numerical modeling is now taking place at local offices on a limited basis. This opens the door to many new possibilities such as high resolution mesoscale forecasts that capture severe weather, and local ensemble modeling. This in turn would facilitate new kinds of forecast information such as probability distributions for forecast variables. Another new possibility is coupling local weather models to distributed local hydrologic models to produce various runoff/flooding scenarios. These developments would support increasing lead times for warnings, in effect allowing warning issuance based on forecasts instead of observations.
Forecasters will continue to interact with grids as the final medium to depict the forecast. This will need to be done in an integrated manner rather than using separate techniques for observations and forecasts. The line between observations and forecasts will be blurred as "synthetic" observations will allow presentations (such as loops) to start with recent observations and transition seamlessly into the future. Powerful tools (such as interpolation) that can affect changes through time and space will be needed. Tools that allow forecasters to define meteorological abstractions such as fronts and troughs and tie forecast conditions to these features will be needed as well.