Research Mentor(s): Ashley Payne, Assistant Professor
Research Mentor School/College/Department: Climate and Space Sciences and Engineering, College of Engineering
Presentation Date: Thursday, April 22, 2021
Session: Session 4 (2pm-2:50pm)
Breakout Room: Room 16
Previous studies have identified that atmospheric features with low-level moisture transport, known as atmospheric rivers (ARs), are connected to extreme precipitation events around the globe. While these ARs provide vital water resources to communities, they are also known to cause fatalities resulting from flooding and landslides. The severity of their impacts is expected to increase with climate change due to increased atmospheric moisture. Our understanding of what drives changes in AR behavior is still incomplete. While there has been a wide breadth of research conducted on ARs in the North Pacific region, much work has yet to be done in order to fill gaps in knowledge about ARs across the globe. The investigation of South America ARs aims to quantify how our understanding of ARs depends on algorithm choice. This specific study focuses on detection algorithms run on JRA-55 reanalysis (55 km resolution). The dataset consists of six different algorithms, namely the ARConnect, GuanWaliser, IDL, Mundhenk, Payne and Reid algorithms. Python is used for intercomparison and visual representation of the differences and strength of various detection algorithms, focusing on the understudied region of southern South America (15°N-60°S, 110°W-16°W). This project aims to identify algorithms that perform poorly and common AR characteristics where there is agreement. In doing so, it is the hope that affected communities across this region will better be able to respond to incoming extreme weather associated with AR events.