Bryan Mundhenk
PhD, former member of the Barnes & Maloney Research Groupsemail: bryan dot mundhenk at alumni dot colostate dot edu
Atmospheric River (AR) Research
AR Detection Algorithm
- Algorithm, Python 2.7 Web Version
- Disclaimer: This algorithm was written by an atmospheric scientist, not a formally-trained developer; as a result, inefficiencies and code quirks may exist. Comments regarding errors or potential efficiencies are welcomed.
- See Mundhenk et al., 2016 for details regarding this detection algorithm. Should you find this algorithm beneficial to your research, please acknowledge this work by citing the aforementioned article.
- See also the poster presented during the 2015 AGU Fall Meeting related to this research.
- This algorithm executes in < 1 hour in an OS X desktop environment (w/ 3.2 GHz Intel Core i5 and 16 GB RAM) for 36 years of 6-hourly MERRA data truncated to the North Pacific basin. No effort has been made to optimize the performance of the program; however, the approach would support parallel processing.
- Thanks to the ever-growing community behind the Python programming language and specifically to the developers of the modules used in this code for furthering open source programming and computing!
AR Climatology
- Mean All-Season AR Frequency of Occurrence Plot
- The plot linked above depicts the all-season mean AR frequencies over the North Pacific based on ARs detected within 36 years (1979-2014) of MERRA-derived vertically-integrated water vapor transport anomalies.
Related Publications
- Mundhenk, Bryan D., Elizabeth A. Barnes, and Eric D. Maloney, 2016: All-Season Climatology and Variability of Atmospheric River Frequencies over the North Pacific.* J. Climate, 29, doi: 10.1175/JCLI-D-15-0655.1.
- Mundhenk, Bryan D., Elizabeth A. Barnes, Eric D. Maloney, and Kyle M. Nardi, 2016: Modulation of Atmospheric Rivers near Alaska and the U.S. West Coast by Northeast Pacific Height Anomalies.^ J. Geophys. Res. Atmos., 121, doi: 10.1002/2016JD025350.
- Mundhenk, Bryan D., Elizabeth A. Barnes, Eric D. Maloney, and Cory F. Baggett, 2018: Skillful Empirical Subseasonal Prediction of Landfalling Atmospheric River Activity using the Madden-Julian Oscillation and Quasi-Biennial Oscillation. npj Climate and Atmospheric Science, 1, doi: 10.1038/s41612-017-0008-2.
* Downloads courtesy of the American Meteorological Society (AMS), who owns sole rights to them, and are subject to AMS copyright laws and statutes; for more information, visit the AMS Journals website.
^ Downloads courtesy of the American Geophysical Union (AGU), who owns sole rights to them, and are subject to AGU copyright laws and statutes; for more information, visit the AGU Publications website.
Further reproduction or electronic distribution is not permitted.