DOI: 10.5281/zenodo.17100520
title: DARK (formerly Buffet v1.0) AR Detection Catalog (MERRA-2, 1980–2017, ARTMIP format)

description: This dataset provides yearly binary atmospheric-river (AR) masks derived from MERRA-2 reanalysis, produced by the DARK detection algorithm. Although the method was developed with a polar (Antarctic) emphasis, the implementation operates globally and scans the full MERRA-2 grid. Output files follow the ARTMIP Tier-1 conventions (variable name, attributes, dimensions, CF metadata, and filename pattern).

Method 

Input field. The algorithm uses MERRA-2 total IVT directly from ARTMIP yearly files (DOI: 10.5065/D62R3QFS).

Overview of the steps :





Rolling seasonal threshold (98th percentile).For each leap day-of-year (1…366) we compute a 15-day rolling window climatology of IVT at each grid cell and take the 98th percentile across all selected days and years. Non-leap years are mapped to the leap DOY by shifting days ≥ 29 Feb (+1).




Exceedance mask.A grid cell is marked 1 if the local IVT of a given day exceeds its local and day-of-year threshold (98th percentile as defined in 1), else 0.




Area filtering (< 20, 000 km²).

We treat longitude as periodic so features crossing that seam aren’t split and drop components with area < 20,000 km². Grid-cell areas are computed on WGS-84 using geocentric radius and the local Δlat/Δlon.




Length filtering ( < 2000 km) with seam-free centerlines.For each remaining component (AR candidate) and time:



We compute a skeleton of each AR candidate, build a pixel-adjacency graph, enumerate endpoint-to-endpoint paths, and select the one most aligned with the component’s PCA axis. The path is extended to the object edges and smoothed with a B-spline. Geodesic segment lengths are summed using WGS-84 (geopy) to get a sub-grid AR length. Components shorter than 2000 km are discarded.





Output assembly.We write a yearly byte (i1) mask ar_binary_tag(time,lat,lon), with 1 where an AR is detected and 0 elsewhere.



Files & Format





One NetCDF per year:MERRA2.ar_tag.Buffet.<freq>.<YYYYMMDD-YYYYMMDD>.ncwhere <freq> is inferred (e.g., 3hourly, 6hourly).




Variables





ar_binary_tag(time, lat, lon) — byte; 1 = AR, 0 = no AR.Attributes:





description = "binary indicator of atmospheric river"




scheme = "Buffet"




version = "1.0"




credits = "Developed by Victoire Buffet, Vincent Favier, and Benjamin Pohl"






time(time) — CF time with standard_name, long_name, units = "minutes since <year>-01-01 00:00:00", calendar = "standard", axis = "T".




lat(lat) — degrees_north; CF/ARTMIP attributes (standard_name, long_name, axis).




lon(lon) — degrees_east; CF/ARTMIP attributes (standard_name, long_name, axis).






Compression: data variable uses NetCDF-4 deflate (zlib=True, complevel=1, shuffle=True). time is unlimited.



Spatial/Temporal Coverage





Spatial: Global MERRA-2 native 2D grid (the algorithm targets polar/Antarctic ARs but evaluates the globe). 0.5° in latitude, 0.625° in longitude.




Temporal: 1980–2017, using the time resolution provided by the input MERRA-2 IVT files (3-hourly).



Parameters (defaults)





Rolling window: 15 days




Percentile: 98th




Minimum area: 20,000 km²




Minimum length: 2,000 km



Reproducibility

Inputs





Yearly MERRA-2 files with a 2D IVT variable:ARTMIP_MERRA_2D_{year}_3h.nc (or equivalent pattern)



Two stages





Build rolling‐DOY percentiles (once):percentile_98_IVT_rolling_15D_merra2.nc




Tag each year to produce ar_binary_tag yearly files.



Software





Python ≥ 3.9; key libraries: xarray, numpy, scipy, scikit-image, geopy, networkx, matplotlib.




Geodesics computed with geopy (WGS-84).




Files written CF-compliant with netCDF4.



Credits

Developed by Victoire Buffet, Vincent Favier, and Benjamin Pohl at Institut des Géosciences de l'Environnement (Grenoble) and Biogéosciences (Dijon).

Keywords

Atmospheric Rivers; MERRA-2; ARTMIP; IVT; Antarctic; Polar Meteorology

Related Material





ARTMIP format and Tier-1 conventions.




MERRA-2 reanalysis documentation.