The Sensitivity of Atmospheric River Identification to Integrated Water Vapor Transport Threshold, Resolution, and Regridding Method.

Atmospheric rivers (ARs) are elongated narrow bands of enhanced water vapor that can cause intense rainfall and flooding. ARs only appeared in the literature the last 30 years, and there has been much debate about how to define ARs and how to identify them. As a result, a wide range of AR identification algorithms have been produced with variations in the conditions required for an object to be classified as an AR and differences in the input data. One of the key conditions in most AR identification algorithms is a minimum threshold of water vapor flux, along with geometric criteria. The aim of this study is to explore uncertainties in global AR identification based on a single integrated water vapor transport (IVT)‐based identification method. We conduct a sensitivity analysis under one algorithmic framework to explore the effects of different IVT thresholds, input data resolutions, and regridding methods during the Years of Tropical Convection operational analysis (May 2008 to April 2010). We found that the resolution and regridding method affects the number of ARs identified but the seasonal cycle is maintained. AR identification is highly sensitive to the choice of IVT threshold; importantly, the commonly used 250 kg m−1 s−1 IVT threshold is not appropriate for global studies with detection methods that also include a restrictive geometric condition as this combination can lead to the strongest systems failing to be identified. The uncertainties within a single AR detection method and input data parameters may be as large as uncertainties across AR detection methodologies. Plain Language Summary: Atmospheric rivers (ARs) are large corridors of increased moisture flow in the lower atmosphere. ARs can cause hazards such as heavy rainfall, damaging winds, landslides, and flash flooding. Recently, there has been a lot of discussion about how scientists should define these weather phenomena; for example, what is the minimum amount of water needed for a weather system to be considered an AR? In order to study ARs, scientists often look at large data sets over multiple decades. So instead of searching for ARs manually in these huge data sets, they create computer algorithms that identify ARs automatically. As a result, there has been a large variety of different algorithms developed. Using a single algorithm, we answer the following question: How do small differences between data sets and moisture content of ARs affect the detection results? We found that when ARs are defined using lower thresholds of vapor transport and geometric conditions, algorithms may fail to identify the strongest ARs. Additionally, we quantified some of the uncertainty associated with using data sets of different fidelity. These results are useful for helping scientists choose the best threshold and data sets for their study. Key Points: An IVT‐based identification method was used to perform extensive sensitivity tests on IVT threshold and data set parameters (resolution and regridding)IVT thresholds below 350 kg m−1 s−1 may miss the strongest ARs when combined with a geometric criterionSimple methodological changes to resolution, order of operations, regridding methods, and IVT threshold can result in considerable changes to the detection results [ABSTRACT FROM AUTHOR]