Object-Based Verification of Atmospheric River Predictions in the Northeast Pacific

Abstract

AbstractAccurate forecasts of atmospheric rivers (ARs) provide advance warning of flood and landslide hazards and greatly aid effective water management. It is, therefore, critical to evaluate the skill of AR forecasts in numerical weather prediction (NWP) models. A new verification framework is proposed that leverages freely available software and metrics previously used for different applications. Specifically, AR detection and statistics are computed for the first time using the Method for Object-Based Diagnostic Evaluation (MODE). In addition, the measure of effectiveness (MoE) is introduced as a new metric for understanding AR forecast skill in terms of size and location. The MoE provides a quantitative measure of the position of an entire forecast AR relative to observation, regardless of whether the AR is making landfall. In addition, the MoE can provide qualitative information about the evolution of a forecast by lead time, with implications about the predictability of an AR. We analyze AR forecast verification and skill using 11 years of cold-season forecasts from two NWP models: one global and one regional. Four different thresholds of integrated vapor transport (IVT) are used in the verification, revealing differences in forecast skill that are based on the strength of an AR. In addition to MoE, AR forecast skill is also addressed in terms of intensity error, landfall position error, and contingency-table metrics.