Atmospheric rivers (ARs) are elongated corridors of water vapor in the lower troposphere that cause extreme precipitation over many coastal regions around the globe. They play a vital role in the water cycle in the western US, fueling the most extreme west coast precipitation and sometimes accounting for more than 50% of total annual west coast precipitation (Gershunov et al., 2017). Severe ARs are associated with extreme flooding and damages while weak ARs are typically more beneficial to our society as they bring much needed drought relief (Ralph et al., 2019). Future climate projections show an increase in US west coast precipitation variability caused by AR precipitation increasing and non-AR precipitation decreasing (Gershunov et al., 2019). From 2012 to 2016, California experienced a historic drought, which was followed by the state's second wettest year on record. 2020 and 2021 are two of the driest years on record over much of the western US (Williams et al., 2022). The extreme interannual variability in western US precipitation in recent years coinciding with climate change projections of increased precipitation variability is a serious cause for concern over how patterns may change in the coming decades (Polade et al., 2017;Shields & Kiehl, 2016).A necessary step in understanding changing patterns in ARs as a function of climate change is employing an AR detection method. There are a variety of different algorithms used to track ARs due to their relatively diverse definitions (Shields et al., 2018). The Atmospheric River Tracking Intercomparison Project (ARTMIP) organizes and provides information on all the widely accepted algorithms that exist. Nearly all the algorithms included in ARTMIP rely on absolute and relative numerical thresholds. Absolute thresholds are static constraints that are required for an AR to exist, typically coming in the form of length, width, minimum inte-Abstract There is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight into future precipitation trends. There are already a variety of different methods used to identify ARs, but many are used in contexts that are often difficult to apply to large climate datasets due to their computational cost and requirement of integrated vapor transport as an input variable, which can be expensive to compute in climate models at high temporal frequencies. Using deep learning (DL) to track ARs is a unique approach that can alleviate some of the computational challenges that exist in more traditional methods. However, some questions still remain regarding its flexibility and robustness. This research investigates the consistency of a DL methodology of tracking ARs with more established algorithms to demonstrate its high-level performance for future studies.Plain Language Summary Atmospheric rivers (ARs) are long corridors of water vapor in the lower atmosphere that are...
Forecast skill of a numerical weather prediction (NWP) model depends on many factors, including observations of the initial atmospheric state and the ocean/land surface, data assimilation techniques, and model
<p>Atmospheric rivers (ARs) are elongated corridors of water vapor in the lower Troposphere that cause extreme precipitation over many coastal regions around the globe. They play a vital role in the water cycle in the western US, fueling most extreme west coast precipitation and sometimes accounting for more than 50% of total annual west coast precipitation (Gershunov et al. 2017). Severe ARs are associated with extreme flooding and damages while weak ARs are typically more beneficial to our society as they bring much needed drought relief.</p><p>Precipitation is particularly difficult to predict in traditional climate models.&#160; Predicting water vapor is more reliable (Lavers et al. 2016), allowing IVT (integrated vapor transport) and ARs to be a favorable method for understanding changing patterns in precipitation (Johnson et al. 2009).&#160; There are a variety of different algorithms used to track ARs due to their relatively diverse definitions (Shields et al. 2018). The Atmospheric River Tracking Intercomparison Project (ARTMIP) organizes and provides information on all of the widely accepted algorithms that exist. Nearly all of the algorithms included in ARTMIP rely on absolute and relative numerical thresholds, which can often be computationally expensive and have a large memory footprint. This can be particularly problematic in large climate datasets. The vast majority of algorithms also heavily factor in wind velocity at multiple vertical levels to track ARs, which is especially difficult to store in climate models and is typically not output at the temporal resolution that ARs occur.</p><p>A recent alternative way of tracking ARs is through the use of machine learning. There are a variety of neural networks that are commonly applied towards identifying objects in cityscapes via semantic segmentation. The first of these neural networks that was applied towards detecting ARs is DeepLabv3+ (Prabhat et al. 2020). DeepLabv3+ is a state of the art model that demonstrates one of the highest performances of any present day neural network when tasked with the objective of identifying objects in cityscapes (Wu et al. 2019). We employ a light-weight convolutional neural network adapted from CGNet (Kapp-Schwoerer et al. 2020) to efficiently track these severe events without using wind velocity at all vertical levels as a predictor variable. When applied to cityscapes, CGNet's greatest advantage is its performance relative to its memory footprint (Wu et al. 2019). It has two orders of magnitude less parameters than DeepLabv3+ and is computationally less expensive. This can be especially useful when identifying ARs in large datasets. Convolutional neural networks have not been used to track ARs in a regional domain. This will also be the first study to demonstrate the performance of this neural network on a regional domain by providing an objective analysis of its consistency with eight different ARTMIP algorithms.</p>
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