Accurate forecasts of precipitation during landfalling atmospheric rivers (ARs) are critical because ARs play a large role in water supply and flooding for many regions. In this study, we have used hundreds of observations to verify global and regional model forecasts of atmospheric rivers making landfall in Northern California and offshore in the midlatitude northeast Pacific Ocean. We have characterized forecast error and the predictability limit in AR water vapor transport, static stability, onshore precipitation, and standard atmospheric fields. Analysis is also presented that apportions the role of orographic forcing and precipitation response in driving errors in forecast precipitation after AR landfall. It is found that the global model and the higher-resolution regional model reach their predictability limit in forecasting the atmospheric state during ARs at similar lead times, and both present similar and important errors in low-level water vapor flux, moist-static stability, and precipitation. However, the relative contribution of forcing and response to the incurred precipitation error is very different in the two models. It can be demonstrated using the analysis presented herein that improving water vapor transport accuracy can significantly reduce regional model precipitation errors during ARs, while the same cannot be demonstrated for the global model.

[1] A metric to quantify the value added by high-resolution models is introduced. It is based on a characteristic spatial distribution of skill rather than the averages of skill values. Normal distribution functions are fit to the model skill distribution of coarse-and fine-resolution models, and a new metric (Added Value Index, AVI) is defined as the area enclosed by the two distribution functions, with information on the way the two curves cross each other. The AVI is computed for a case of downscaling seasonal forecasts and is shown to properly provide a different degree of added value by high-resolution models.Citation: Kanamitsu, M., and L. DeHaan (2011), The Added Value Index: A new metric to quantify the added value of regional models,

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