Defining Uncertainties through Comparison of Atmospheric River Tracking Methods
The 2nd ARTMIP workshop provided a forum for the AR community to 1) discuss analyses of the Tier 1 dataset, 2) synthesize the results and implications of the Tier 1 analyses, 3) use this information to define the experimental designs for the various Tier 2 experiments, 4) work toward developing a set of recommendations regarding the advantages and disadvantages of different AR algorithms for various scientific questions, and 5) discuss gaps and emerging opportunities for advancing the tracking and science of ARs.
Metrics and analysis discussed for Tier 1 data (including AR frequency, duration, intensity), will be used to craft guidance and recommendations on choice of methodology for specific science questions. Advantages and disadvantages for classes of algorithms will greatly help individual AR resources choose an appropriate methodology for their science problem. In addition to recommendations, a database of all ARTMIP catalogues will be available to the community to further AR research.
ARTMIP is a grassroots effort initiated by DOE and NOAA scientists to understand and quantify the implications of the diverse set of AR identification and tracking methods found in the literature. The 2nd Atmospheric River Tracking Method Intercomparison Project (ARTMIP) workshop, sponsored by the U.S. Department of Energy, built upon the framework established by the 1st ARTMIP workshop (held in May 2017 in San Diego, CA). The goal of ARTMIP is to understand and quantify uncertainties in atmospheric river science based on choice of identification and/or tracking methodology (i.e. AR algorithms) and communicate this to the AR research and stakeholder communities. The climatological characteristics of ARs, such as AR frequency, duration, intensity, and seasonality, are all strongly dependent on the method used to identify ARs. Understanding the uncertainties and how the choice of detection algorithm impacts quantities such as precipitation is imperative for stakeholders such as water managers, city and transportation planners, agriculture, or any industry that depends on global and regional water cycles information for the near term and into the future. Understanding and quantifying AR algorithm uncertainty is also important for developing metrics and diagnostics for evaluating model fidelity in simulating ARs and their impacts.