Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design
By C. Shields, J. Rutz, L. Leung, F. Ralph, Michael F. Wehner, B. Kawzenuk, J. Lora, E. McClenny, Tashiana Osborne, A. Payne and 26 more. Geoscientific Model Development, 2018
322 citations recorded by the source corpus.
Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/a1fe010d821c8b6b0238e74018b4de890744e222
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Citations in the corpus
- Exascale Deep Learning for Climate Analytics (2018, methodology): “For instance, the state of California receives over 50% of its rainfall through Atmospheric Rivers (ARs), and Water Resource Management planners are interested in understanding if AR tracks will shift in the future, potentially resulting in a dramatic shortfall in fresh water supply.”