A CSI index for detection and prediction of heavy precipitation bands

The ability to deliver timely public weather warnings for heavy precipitation depends on early detection and accurate prediction. Heavy precipitation is very often organized in a form of very narrow, quasi two-dimensional bands associated with slantwise convection, making their detection and prediction very difficult. As a result, they are mostly under-detected or post-detected by numerical weather prediction (NWP) models because no scheme for slantwise convection has been incorporated into operational NWP models. An approach has been developed to detect and predict slantwise convection induced by conditional symmetric instability (CSI) by integrating the elements of slantwise convection (moisture variability, lifting conditions, and CSI) into one single index. The algorithms and associated software have been developed for generating the index fields using the Canadian Meteorological Centre operational GEM regional model outputs. The index fields serve as criteria to more accurately forecast precipitation bands. The operational products have been delivered to the Storm Prediction Centres and the Canadian Meteorological Aviation Centres. The test results indicate the success of this index in detection and prediction of the heavy snowfall events that the existing operational model failed. The detailed results will be presented in the conference.