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Algorithm Summary

Code for identifying atmospheric river (AR) outlines by applying size, shape, and transport direction criteria to fields of integrated water vapor transport (IVT) from gridded meteorological analyses.

Version 1 of the algorithm is described in Mattingly et al. (2018). The AR identification criteria are based on the Guan and Waliser (2015) and Mundhenk et al. (2016) algorithms, but with less strict requirements for minimum IVT threshold, object shape, and transport direction in order to capture ARs in the polar regions. This version (v2) includes the following updates:

To date, the algorithm has been successfully used to identify ARs in MERRA-2 and ERA5 reanalysis data, as well as ARTMIP Tier 2 paleoclimate simulations. Please feel free to contact me if you are interested in applying the algorithm to other gridded meteorological data sources.

Dependencies

The code requires a python 3 environment with the following packages:

Additionally, the directory containing the project code must be included in the user’s python path.

How to run

The AR identification procedure requires gridded fields of IVT magnitude, u/v-IVT components, and IVT values at the desired climatological percentile rank threshold (default: 85th percentile) as input. Optionally, lower-tropospheric mean wind may be used in place of u/v-IVT components for transport direction criteria.

The code is somewhat flexible with regard to the time properties of input and output files (the start time, end time, and interval between timesteps in hours); for data with 6-hourly or more frequent temporal resolution, structuring the data as monthly input and output files is recommended.

The typical work flow for producing AR data is:

Two important notes on running ARs_ID.py:

The scripts/ directory contains miscellaneous scripts that may be useful, including driver scripts for processing multiple years of data, downloading ERA5 data, chunking large ERA5 files for manageable processing, and working with ARTMIP data.

References
  1. Mattingly, K. S., Mote, T. L., & Fettweis, X. (2018). Atmospheric River Impacts on Greenland Ice Sheet Surface Mass Balance. Journal of Geophysical Research: Atmospheres, 123(16), 8538–8560. 10.1029/2018jd028714
  2. Guan, B., & Waliser, D. E. (2015). Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. Journal of Geophysical Research: Atmospheres, 120(24), 12514–12535. 10.1002/2015jd024257
  3. Mundhenk, B. D., Barnes, E. A., & Maloney, E. D. (2016). All-Season Climatology and Variability of Atmospheric River Frequencies over the North Pacific. Journal of Climate, 29(13), 4885–4903. 10.1175/jcli-d-15-0655.1