Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design
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Published:2018-06-20
Issue:6
Volume:11
Page:2455-2474
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Shields Christine A., Rutz Jonathan J., Leung Lai-YungORCID, Ralph F. Martin, Wehner MichaelORCID, Kawzenuk BrianORCID, Lora Juan M., McClenny Elizabeth, Osborne Tashiana, Payne Ashley E.ORCID, Ullrich Paul, Gershunov Alexander, Goldenson Naomi, Guan Bin, Qian Yun, Ramos Alexandre M.ORCID, Sarangi ChandanORCID, Sellars Scott, Gorodetskaya IrinaORCID, Kashinath Karthik, Kurlin Vitaliy, Mahoney Kelly, Muszynski Grzegorz, Pierce Roger, Subramanian Aneesh C.ORCID, Tome RicardoORCID, Waliser Duane, Walton Daniel, Wick Gary, Wilson AnnaORCID, Lavers DavidORCID, Collow AllisonORCID, Krishnan Harinarayan, Magnusdottir GudrunORCID, Nguyen PhuORCID,
Abstract
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.
Publisher
Copernicus GmbH
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