ARTMIP-early start comparison of atmospheric river detection tools: how many atmospheric rivers hit northern California’s Russian River watershed?

Abstract

Many atmospheric river detection tools (ARDTs) have now been developed. However, their relative performance is not well documented. This paper compares a diverse set of ARDTs by applying them to a single location where a unique 12-year-long time-series from an atmospheric river observatory at Bodega Bay, California is available. The study quantifies the sensitivity of the diagnosed number, duration, and intensity of ARs at this location to the choice of ARDT, and to the choice of reanalysis data set. The ARDTs compared here represent a range of methods that vary in their use of different variables, fixed vs. percentile-based thresholds, geometric shape requirements, Eulerian vs. Lagrangian approaches, and reanalyses. The ARDTs were evaluated first using the datasets documented in their initial publication, which found an average annual count of 19 ± 7. Applying the ARDTs to the same reanalysis dataset yields an average annual count of 19 ± 4. Applying a single ARDT to three reanalyses of varying grid sizes (0.5°, 1.0°–2.5°) showed little sensitivity to the choice of reanalysis. While the annual average AR event count varied by about a factor of two (10–25 per year) depending on the ARDT, average AR duration and maximum intensity varied by less than ± 10%, i.e., 24 ± 2 h duration; 458 ± 44 kg m− 1 s− 1 maximum IVT. ARDTs that use a much higher threshold for integrated vapor transport were compared separately, and yielded just 1–2 ARs annually on average. Generally, ARDTs that include either more stringent geometric criteria or higher thresholds identified the fewest AR events.

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References

  1. AMS (2017) Definition of “atmospheric river” in the glossary of meteorology. http://glossary.ametsoc.org/wiki/Atmospheric_river. Accessed 15 Apr 2018

  2. Baggett C, Lee S, Feldstein S (2016) An investigation of the presence of atmospheric rivers over the north Pacific during planetary-scale wave life cycles and their role in Arctic warming. J Atmos Sci 73:4329–4347

    Article  Google Scholar 

  3. Blamey R, Ramos A, Trigo R, Tomé R, Reason C (2018) The influence of atmospheric rivers over the South Atlantic on winter rainfall in South Africa. J Hydrometeor. https://doi.org/10.1175/JHM-D-17-0111.1

    Google Scholar 

  4. Brands S, Gutiérrez JM, San-Martín D (2017) Twentieth-century atmospheric river activity along the west coasts of Europe and North America: algorithm formulation, reanalysis uncertainty and links to atmospheric circulation patterns. Clim Dyn 48:2771–2795

    Article  Google Scholar 

  5. Dee DP et al (2011) The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. QJR Meteorol Soc 137(654):553–597

    Article  Google Scholar 

  6. DeFlorio M, Waliser D, Guan B, Lavers D, Ralph F, Vitart F, (2018) Global assessment of atmospheric river prediction skill. J Hydrometeorol 19:409–426

    Article  Google Scholar 

  7. Dettinger MD (2011) Climate change, atmospheric rivers, and floods in California—a multimodel analysis of storm frequency and magnitude changes. J Am Water Resour Assoc 47:514–523

    Article  Google Scholar 

  8. Dettinger MD (2013) Atmospheric rivers as drought busters on the U.S. West Coast. J Hydrometeor 14:1721–1732

    Article  Google Scholar 

  9. Dettinger MD, Ralph FM, Das T, Neiman PJ, Cayan DR (2011) Atmospheric rivers, floods and the water resources of California. Water 3(2):445–478

    Article  Google Scholar 

  10. Dettinger MD, Ralph FM, Lavers D (2015) Setting the stage for a global science of atmospheric rivers. EOS Trans Amer Geophys Union. https://doi.org/10.1029/2015EO038675

    Google Scholar 

  11. Gelaro R et al (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim 30:5419–5454

    Article  Google Scholar 

  12. Gershunov A, Shulgina TM, Ralph FM, Lavers D, Rutz JJ (2017) Assessing climate-scale variability of atmospheric rivers affecting western North America. Geophys Res Lett. https://doi.org/10.1002/2017GL074175

    Google Scholar 

  13. Guan B, Waliser DE (2015) Detection of atmospheric rivers: evaluation and application of an algorithm for global studies. J Geophys Res Atmos 120:12514–12535

    Article  Google Scholar 

  14. Guan B, Waliser DE (2017) Atmospheric rivers in 20 year weather and climate simulations: a multi-model, global evaluation. J Geophys Res Atmos 122:5556–5581

    Article  Google Scholar 

  15. Guan B, Waliser DE, Ralph FM (2018) An inter-comparison between reanalysis and dropsonde observations of the total water vapor transport in individual atmospheric rivers. J Hydrometeor 19:321–337

    Article  Google Scholar 

  16. Hagos SM, Leung LR, Yoon J-H, Lu J, Gao Y (2016) A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the Large Ensemble CESM simulations. Geophys Res Lett 43:1357–1363

    Article  Google Scholar 

  17. Hecht CW, Cordeira JM (2017) Characterizing the influence of atmospheric river orientation and intensity on precipitation distributions over North-Coastal California. Geophys Res Lett. https://doi.org/10.1002/2017GL074179

    Google Scholar 

  18. Hsu K, Gao X, Sorooshian S (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190

    Article  Google Scholar 

  19. Jackson DL, Hughes M, Wick GA (2016) Evaluation of landfalling atmospheric rivers along the U.S. West Coast in reanalysis data sets. J Geophys Res Atmos 121:2705–2718

    Article  Google Scholar 

  20. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteorol Soc 77(3):437–471

    Article  Google Scholar 

  21. Lamjiri MA, Dettinger MD, Ralph FM, Guan B (2017) Hourly storm characteristics along the U.S. West Coast: role of atmospheric rivers in extreme precipitation. Geophys Res Lett. https://doi.org/10.1002/2017GL074193

    Google Scholar 

  22. Lavers DA, Villarini G (2015) The contribution of atmospheric rivers to precipitation in Europe and the United States. J Hydrol 522:382–390

    Article  Google Scholar 

  23. Lavers DA, Villarini G, Allan RP, Wood EF, Wade AJ (2012) The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation. J Geophys Res 117:D20106

    Article  Google Scholar 

  24. Lavers DA, Allan RP, Villarini G, Lloyd-Hughes B, Brayshaw DJ, Wade AJ (2013) Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ Res Lett 8:034010

    Article  Google Scholar 

  25. Lavers DA, Ralph FM, Waliser DE, Gershunov A, Dettinger MD (2015) Climate change intensification of horizontal water vapor transport in CMIP5. Geophys Res Lett 42:5617–5625

    Article  Google Scholar 

  26. Lavers DA, Waliser DE, Ralph FM, Dettinger MD (2016) Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting Western U.S. extreme precipitation and flooding. Geophys Res Lett 43:2275–2282

    Article  Google Scholar 

  27. Livneh B, Rosenberg EA, Lin C, Nijssen B, Mishra V, Andreadis KM, Maurer EP, Lettenmaier DP (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J Clim 26:9384–9392

    Article  Google Scholar 

  28. Lora JM, Mitchell JL, Risi C, Tripati AE (2017) North Pacific atmospheric rivers and their influence on western North America at the Last Glacial Maximum. Geophys Res Lett 44:1051–1059

    Article  Google Scholar 

  29. Mahoney K, Jackson DL, Neiman PJ, Hughes M, Darby L, Wick G, White AB, Sukovich E, Cifelli R (2016) Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States. Mon Weather Rev 144:1617–1632

    Article  Google Scholar 

  30. Mundhenk BD, Barnes EA, Maloney ED (2016) All-season climatology and variability of atmospheric river frequencies over the North Pacific. J Clim 29:4885–4903

    Article  Google Scholar 

  31. Newell RE, Zhu Y (1994) Tropospheric rivers: a one-year record and a possible application to ice core data. Geophys Res Lett 21:113–116

    Article  Google Scholar 

  32. Newell RE, Newell NE, Zhu Y, Scott C (1992) Tropospheric rivers?—a pilot study. Geophys Res Lett 19:2401–2404

    Article  Google Scholar 

  33. Ralph FM, Dettinger MD (2012) Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull Am Meteorol Soc 93:783–790

    Article  Google Scholar 

  34. Ralph FM, Neiman PJ, Wick GA (2004) Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon Weather Rev 132:1721–1745

    Article  Google Scholar 

  35. Ralph FM, Neiman PJ, Wick GA, Gutman SI, Dettinger MD, Cayan DR, White AB (2006) Flooding on California’s Russian River: role of atmospheric rivers. Geophys Res Lett 33:L13801. https://doi.org/10.1029/2006GL026689

    Article  Google Scholar 

  36. Ralph FM, Coleman T, Neiman PJ, Zamora RJ, Dettinger MD (2013) Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal northern California. J Hydrometeorol 14:443–459

    Article  Google Scholar 

  37. Ralph FM, Iacobellus SF, Neiman PJ, Cordeira JM, Spackman JR, Waliser DE, Wick GA, White AB, Fairall C (2017) Dropsonde observations of total water vapor transport within North Pacific atmospheric rivers. J Hydrometeorol 18:2577–2596

    Article  Google Scholar 

  38. Ralph FM, Dettinger MD, Cairns MM, Galarneau T, Eylander J (2018) Development of the definition of the term “atmospheric river” for the Glossary of Meteorology. Bull Am Meteorol Soc 99:837–839

    Article  Google Scholar 

  39. Ramos AM, Tome R, Trigo RM, Liberato MLR, Pinto JG (2016) Projected changes in atmospheric rivers affecting Europe in CMIP5 models. Geophys Res Lett 43:9315–9323

    Article  Google Scholar 

  40. Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648

    Article  Google Scholar 

  41. Rutz JJ, Steenburgh WJ, Ralph FM (2014) Climatological characteristics of atmospheric rivers and their inland penetration over the western United States. Mon Weather Rev 142:905–921

    Article  Google Scholar 

  42. Sellars S, Nguyen P, Chu W, Gao X, Hsu K-I, Sorooshian S (2013) Computational Earth science: Big data transformed into insight. Eos Trans Am Geophys Union 94:277–278

    Article  Google Scholar 

  43. Sellars SL, Kawzenuk B, Nguyen P, Ralph FM, Sorooshian S (2017a) Genesis, pathways, and terminations of intense global water vapor transport in association with large-scale climate patterns. Geophys Res Lett 44:12465–12475

    Article  Google Scholar 

  44. Sellars SL, Nguyen P, Kawzenuk B (2017b) The CONNected object, or CONNECT algorithm applied to National Aeronautics and Space Administration (NASA) Modern-era retrospective analysis for research and applications, version 2 (MERRA V2)—integrated water vapor from 1980 to 2016. UC San Diego Library Digital Collections. https://doi.org/10.6075/J01834P8

  45. Shields C et al (2018) Atmospheric river tracking method intercomparison project (ARTMIP): project goals and experimental design. Geosci Model Dev 11:2455–2474

    Article  Google Scholar 

  46. Trenberth KE, Fasullo JT, Mackaro J (2011) Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J Clim 24(18):4907–4924

    Article  Google Scholar 

  47. Waliser D, Guan B (2017) Extreme winds and precipitation during landfall of atmospheric rivers. Nat Geosci 10:179–183

    Article  Google Scholar 

  48. Warner MD, Mass CF, Salathe EP Jr (2015) Changes in winter atmospheric rivers along the North American West Coast in CMIP5 climate models. J Hydrometeorol 16:118–128

    Article  Google Scholar 

  49. White AB, co-authors (2013) A twenty-first-century California observing network for monitoring extreme weather events. J Atmos Oceanic Technol 30:1585–1603

    Article  Google Scholar 

  50. Wick GA (2014) Implementation and initial application of an atmospheric river detection tool based on integrated vapor transport. 2014 Fall Meeting, San Francisco, Amer. Geophys. Union, Abstract A34E-06

  51. Wick GA, Neiman PJ, Ralph FM (2013a) Description and validation of an automated objective technique for identification and characterization of the integrated water vapor signature of atmospheric rivers. IEEE Trans Geosci Remote Sens 51:2166–2176

    Article  Google Scholar 

  52. Wick GA, Neiman PJ, Ralph FM, Hamill TM (2013b) Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather Forecast 28:1337–1352

    Article  Google Scholar 

  53. Young AM, Skelly KT, Cordeira JM (2017) High-impact hydrologic events and atmospheric rivers in California: an investigation using the NCEI Storm Events Database. Geophys Res Lett 44:3393–3401

    Article  Google Scholar 

  54. Zhu Y, Newell RE (1994) Atmospheric rivers and bombs. Geophys Res Lett 21:1999–2002. https://doi.org/10.1029/94GL01710

    Article  Google Scholar 

  55. Zhu Y, Newell RE (1998) A proposed algorithm for moisture fluxes from atmospheric rivers. Mon Weather Rev 126:725–735

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Grant Number W912HZ-15-2-0019 from the US Army Corps of Engineers. It was partially supported, with authors Alexander Gershunov and Tamara Shulgina, by Department of the Interior via the Bureau of Reclamation (USBR-R15AC00003, Seasonal and extended-range predictability of atmospheric rivers and their associated precipitation) and by the California Department of Water Resources (4600010378 UCOP2-11, Development of seasonal outlooks for Atmospheric Rivers). The Atmospheric River Observatory data were provided by NOAA’s Physical Sciences Division, Earth System Research Laboratory, from their website at http://www.esrl.noaa.gov/psd/. The authors would like to thank UC San Diego Qualcomm/Calit2 and Pacific Research Platform (ACI-1541349) for supporting the PRP/CONNECT pilot project with network data transfer and storage support for the Sellars et al. (2017b) dataset. The authors would also like to thank two anonymous reviewers for their comments that helped us to strengthen the paper.

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Correspondence to Anna M. Wilson.

Appendices

Appendix 1: Estimating the precipitation contribution from ARs identified with different ARDTs

To quantify the impact of AR activity detected using different AR tracking schemes on precipitation regime at BBY, the contribution of AR-related precipitation to total annual precipitation accumulated at the area (certain grid cell) during the water years of 2005–2013 (Table 6) was estimated using Livneh’s (2013) precipitation dataset. Precipitation during AR days, defined as days with at least one 3-h time step associated with AR conditions, and the day after an AR day are counted. The set of ARDTs with the least strict criteria such as RSR2014, GSR2017 and GW2015 ranged from 55 to 60% of AR contribution per year, while WNR2013-IVT250 and MBM2016 ranged from 45 to 53% per year. The ARDTs focused on much stronger ARs, Ralphetal2013-OBS47, SGS2013 and WNR2013-IVT500 contribute roughly 10% of AR-related precipitation per year. The annual behavior of AR-related precipitation contribution is illustrated in Figure AI-1. In particular, during wet years such as 2006 the contribution of AR-related precipitation was as much as 70% for ARDT outputs with the least strict criteria, whereas strict AR detection schemes account for up to 30% of the contribution. During dry years both AR activity (Fig. 7 from the main text) and AR precipitation contribution (Fig. 11) are about 25% lower.

Proving the statement on the connection of AR strength and precipitation intensity (Sect. 6) we estimated the contribution of AR precipitation to all precipitation summed in the different percentile categories (Fig. 12). The results show that in general moderate to extreme precipitation accumulations are most likely to be associated with AR events. Namely, more than 40% of heavy precipitation and 80% extreme precipitation events are associated with ARs. The catalogs focused on much stronger ARs (SGS2013, WNR2013-IVT500 and Ralphetal2013-OBS47) tend to catch predominantly heavy and extreme precipitation cases. The set of ARDT outputs based on simpler (or no) geometric characteristics (GSR2017, RSR2014, GW2015) cover a wider spectrum of precipitation events (Table 6; Fig. 11, 12).

Table 6 Annual average contribution of AR-related precipitation to all precipitation
Fig. 11
figure11

Annual average contribution of precipitation associated with AR events counted by each MERRA2-based AR catalog at the grid cell containing BBY during water years 2005–2013

Fig. 12
figure12

Contribution of precipitation associated with AR days in different daily precipitation percentile categories counted by each MERRA2-based AR catalog at the BBY grid cell during water years 2005–2013

Appendix 2: Quantification of the difference between choice of ARDT and choice of reanalysis

Sensitivity of AR frequency, duration and intensity to the detection methodology (the reanalysis datasets) is quantified by the amount of shared variance in AR catalogs obtained from applying different (the same) detection algorithms to the same (different) reanalysis dataset. The percentage of shared variance represented by square of average correlation coefficient between pairs of AR catalogs shows the amount overlap variation of those catalogs. Two sets of catalogs are considered: six MERRA2-based AR catalogs developed using GSR2017, GW2015, RSR2014, MBM2016, WNR2013-IVT and WNR2013-IWV with solid/percentile-based IVT/IWV thresholds and with/without geometry characteristics at AR detection schemes (see Sect. 4), and three AR catalogs obtained from applying RSR2014 algorithm to NCEP/NCAR, ERA-Interim and MERRA-2 reanalysis datasets (see Sect. 5) with different spatial and temporal resolutions. The number of AR events, their average duration and IVT intensity were computed from November through April during 2005–2010 water years according to data availability in considered AR catalogs. The results (Table 6) show that the AR catalogs based on different ARDTs applied to the same reanalysis share 70% of interannual AR variability, whereas AR catalogs based on the same detection method applied to different reanalyses share 84% of AR variability. This illustrates that the choice of reanalysis has about 14% less of an effect on AR frequency than does the choice of ARDT. Shared variations in average duration and IVT intensity of different reanalysis based ARs are 14% and 20% higher, respectively (Table 7).

Table 7 Shared variance in AR catalogs obtained from applying GSR2017, GW2015, RSR2014, MBM2016 and WNR2013-IVT AR detection algorithms to MERRA2 Reanalysis dataset and RSR2014 algorithm to NCEP/NCAR, ERA-Interim and MERRA-2 reanalysis datasets

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Ralph, F.M., Wilson, A.M., Shulgina, T. et al. ARTMIP-early start comparison of atmospheric river detection tools: how many atmospheric rivers hit northern California’s Russian River watershed?. Clim Dyn 52, 4973–4994 (2019). https://doi.org/10.1007/s00382-018-4427-5

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Keywords

  • Atmospheric Rivers (AR)
  • Russian Rivers
  • Average Annual Count
  • Reanalysis Dataset
  • Geometric Criteria