Skip to main content
U.S. flag

An official website of the United States government

Publication Date
27 April 2021

Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Subtitle
Different algorithm designs bring uncertainty in the understanding of atmospheric rivers.
Print / PDF
Powerpoint Slide
Science

Existing detection methods for studying atmospheric rivers have been designed to answer a variety of scientific questions. Consequently, conclusions from these studies do not agree with each other sometimes. In this study, the authors investigate how these detection methods differ in terms of understanding atmospheric river lifecycles, and whether their differences are of consequence.

Impact

Understanding the activity of atmospheric rivers is important because they are often linked to high-risk weather events. This study comprehensively analyzed the measure of atmospheric rivers by different detection methods. The results provide a benchmark for the sensitivity of atmospheric rivers to detection designs. This study is potentially helpful for increased the forecast skill of atmospheric rivers.  

Summary

Atmospheric rivers (ARs) are one of the major mechanisms by which water vapor is transported from the tropics to high latitudes and, therefore, dictate water resource availability in many coastal regions such as the west coast of North America. As such, many studies have developed detection algorithms to isolate and study the characteristics of ARs. However, conclusions from these studies may differ because of algorithm design assumptions. Our team selects nine detection algorithms that have been applied to a common dataset to evaluate uncertainties in AR measures. This analysis framework enables us to identify the disagreements in AR characteristics across algorithms including AR size, event number, lifetime, intensity, and landfall activity. Results suggest that basic AR characteristics vary significantly depending on the detection algorithm. However, algorithm differences may be ameliorated when AR behavior is analyzed over intraseasonal and interannual time scales.

Point of Contact
William D. Collins
Institution(s)
Lawrence Berkeley National Laboratory
Funding Program Area(s)
Publication