Weather Prediction – Statistical Optimization
RAL has been a leader in the development of intelligent weather prediction systems that blend data from numerical weather prediction models, statistics datasets, real–time observations, and human intelligence to optimize forecasts at user–defined locations. The Dynamic Integrated Forecast System (DICast®) is an example of this technology and it is currently being used by three of the nation's largest private sector weather service companies. There is a growing desire in industry to have fine–tuned forecasts for specific user–defined locations. This trend is clear in the energy, transportation, agriculture, and location–based service industries. RAL's expertise in meteorology, engineering, and applied mathematics and statistics, is being utilized to address society's growing need for accurate weather information.
DICast is an automated consensus weather forecast system that uses statistical and fuzzy logic techniques to improve upon the ingredient forecasts. The system's forecasts outperform human forecasters (beyond the first few hours) as well as automated NWS forecast products. The system was originally developed with funding from The Weather Channel. It currently is the operational forecast engine at 3 of the largest US weather providers. It is also currently being used within RAL to produce accurate site–specific forecasts that drive land–surface models for the MDSS and NASA–Agriculture projects.
LOGICast™ is a software system that is designed to address the difficult problem of producing high quality weather forecasts at locations where observations are not available. Accurate forecasts at remote locations are used to drive many user–specific applications such as road temperature forecasts along an entire roadway, or soil temperature forecasts for agriculture.