Utilizing the NCEP Ensemble Sensitivity Tool with GEFS and CMC Products for Atmospheric River Reconnaissance

Atmospheric Rivers (ARs) are vital sources of water for many coastal regions. However, forecasting ARs and their characteristics remains challenging due to their formation and movement over the ocean, where in-situ and ground-based observations are sparse. The AR Recon field campaign addresses this challenge by providing additional observations in upstream areas with significant forecast variability, thereby reducing short-range precipitation forecast uncertainty. As detailed in the Interagency Council for Advancing Meteorological Services (ICAMS) National Winter Season Operations Plan (NWSOP), the AR Recon Field Program has become an operational requirement. Its geographic scope has expanded to include the Gulf of Mexico, the Eastern United States, the Eastern Pacific, and the Western Pacific near Guam, covering November through March, depending on available resources. AR mission planning generally uses 00 UTC forecasts to evaluate precipitation forecast variability (e.g., 60-84 hours) and identify sensitive areas (targets) in model fields in advance (e.g., 48 hours). The National Centers for Environmental Prediction (NCEP) supports this initiative with tools that provide real-time mission targets for the AR Recon field campaign. These tools utilize ensemble sensitivities derived from a combined 31-member Global Ensemble Forecast System and a 21-member Canadian Ensemble Prediction System super ensemble. The NCEP ensemble sensitivity tool uses principal components of precipitation empirical orthogonal function analysis to identify upstream regions linked to the precipitation forecast variability. Key state variables used to measure ensemble sensitivity include integrated vapor transport (IVT), 850-hPa equivalent potential temperature, and 250-hPa potential vorticity. The tool has also been adapted to incorporate metrics such as snow, IVT, IVT landfall, 850-hPa freezing temperature, and mean sea level pressure variability. Future work will focus on the operational application of the ensemble sensitivity tool.