An assessment of numerical weather prediction models in forecasting atmospheric rivers
Date
2017
Authors
Nardi, Kyle M., author
Barnes, Elizabeth A., author
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Abstract
Atmospheric rivers (ARs), narrow corridors of high atmospheric water vapor transport, influence large regions of the West Coast of North America, from southern California to British Columbia and Alaska. Regardless of location, areas influenced by landfalling ARs face various threats and disruptions from excessive rainfall and associated runoff. Therefore, improving forecasts of AR occurrence and characteristics is of great importance to those responsible for protecting life and property. When providing the public with outlooks and warnings related to ARs, forecasters must confront the challenge of assessing the output of different numerical weather prediction (NWP) models. Specifically, forecasters must understand how performance varies across different time scales, geographical regions, and individual models. Prior work, such as Wick et al. (2013), has examined the forecast skill
of several NWP models at different lead times, yet as models are continuously updated, a fresh perspective on AR forecast performance is desired. This study aims to assess how different weather forecast models perform at varying lead times and for distinct regions of the West Coast of North America. Re-forecasts from several operational NWP models, obtained from the International S2S Project Database, are run out to approximately 60 days. An atmospheric river detection algorithm is applied to the model output in order to quantify how the models handle such features. The study examines atmospheric river re-forecasts for the West Coast of North America as well as three non-overlapping sub-regions along the coast. The first sub-region extends from southern California to the Oregon border. The second sub-region covers the Pacific Northwest from southern Oregon to the northern extent of Vancouver Island. The third and final sub-region consists of the coasts of British Columbia and southeastern Alaska. Together, these regions represent a large fraction of the AR landfall locations for western North America. Model performance is studied through the lens of AR occurrence, intensity, and location. Results indicate variations in re-forecast skill as a function of lead time, geographic region, and model used. A desired near-term outcome of this work is an increased awareness of both the utility and limitations of NWP models in the prediction of atmospheric river events at short, medium, and long-range leads. A desired long-term outcome is the use of these results as a bridge to understanding what gives rise to the differing characters of atmospheric rivers over the northeast Pacific and how models can improve their depictions of such features.
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Subject
weather forecasting
extreme weather
model verification