Using Connected Vehicle Data to Fill In the Observations Gap for Data Assimilation

An insufficiently dense surface observation network presents a major limitation in obtaining the current state of the atmosphere for numerical weather prediction efforts focused on high spatial and temporal resolutions. Advances in both computing capabilities and data assimilation methods will soon allow operational weather models to run over both local and larger areas at very fine (1 to 5 km) scales; however, these finer resolution models will require finer resolution observations to accurately determine the initial state of the atmosphere. Connected vehicle technologies, where public, private, and commercial vehicles serve as weather-observing platforms, can be used to fill in these gaps in the surface weather observation network.

To study the impact of a dense network of observations along roadways, such as would exist with fully implemented connected vehicle technologies, a simulated vehicle probe data set was created. These data were assimilated into the Weather Research and Forecasting (WRF) model for select case studies, and the resulting output compared against observations and a baseline WRF run without the vehicle data assimilation. Various verification methods, including those as part of the Model Evaluation Tools (MET) package, were used on the cases to determine the impact of the assimilated vehicle observations on the forecast.