Dynamic Integrated foreCast (DICast®) System

DICast® System

The Dynamic Integrated foreCast (DICast®) system is tasked with ingesting meteorological data (observations, models, statistical data, climate data, etc.) and producing meteorological forecasts at user defined forecast sites and forecast lead times. In order to achieve this goal, DICast® generates independent forecasts from each of the data sources using a variety of forecasting techniques. A single consensus forecast from the set of individual forecasts is generated at each user-defined forecast site based on a processing method that takes into account the recent skill of each forecast module.

DICast® is a licensed technology of UCAR.


The DICast® system was first developed at NCAR in the Fall of 1998 with the goal of generating completely automated, timely, accurate forecasts out to ten days at thousands of international locations. Potential applications of this system include the transportation systems, precision agriculture, and general public-oriented forecasts. See the Operations tab for other future applications of this technology.

The DICast® system ingests data from multiple sources and applies automated forecasting techniques to each data source. Each of these forecast modules produces an "independent" forecast. The forecast skill is then improved using a fuzzy logic scheme to combine the individual forecasts.

Over the years, the numerical weather prediction (NWP) model data used by the system has evolved in response to changes to those models and appearance of new ones. The system is currently capable of using all of the US National Weather Service (NWS) modeling suite (GFS, NAM, RAP, HRRR, ensembles of GFS and CMC) as well as MOS guidance (MEX, MET, MAV, LAMP), European Forecast Center's ECMWF, the UKMET model, Environment Canada's GEM model and the Australian Bureau of Meteorology's ACCESS model. Other models such as high-resolution customized versions of WRF can be added based on the user requirements.

The system is designed to generate forecasts of standard meteorological parameters at a set of user configured locations for each forecast lead time. Forecast extent, forecast interval and update frequency are configurable but generally match the input NWP model temporal parameters. Individual module forecasts are combined using a weighted sum. The weights used in the combination are adjusted daily to reflect the recent performance of the forecast modules.