in

Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding

  • 1.

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

    Article  Google Scholar 

  • 2.

    Ralph, F. M., Dettinger, M. D., Cairns, M. M., Galarneau, T. J. & Eylander, J. Defining “Atmospheric River”: how the glossary of meteorology helped resolve a debate. Bull. Am. Meteor. Soc 99, 837–839 (2018). This article provides the definition of an atmospheric river.

    Article  Google Scholar 

  • 3.

    Ralph, F. M. et al. (eds) In Atmospheric Rivers p. 286 (Springer, 2020).

  • 4.

    Ralph, F. M., Neiman, P. J. & Rotunno, R. Dropsonde observations in low‐level jets over the Northeastern Pacific Ocean from CALJET‐1998 and PACJET‐2001: mean vertical‐profile and atmospheric‐river characteristics. Mon. Weather Rev. 133, 889–910 (2005).

    Article  Google Scholar 

  • 5.

    Browning, K. A. & Pardoe, C. W. Structure of low-level jet streams ahead of mid-latitude cold fronts. Quart. J. Roy. Meteor. Soc. 99, 619–638 (1973).

    Article  Google Scholar 

  • 6.

    Sodemann, H. & Stohl, A. Moisture origin and meridional transport in atmospheric rivers and their association with multiple cyclones. Monthly Weather Rev. 141, 2850–2868 (2013).

    Article  Google Scholar 

  • 7.

    Ralph, F. M. et al. Dropsonde observations of total integrated water vapor transport within North Pacific atmospheric rivers. J. Hydrometeor. 18, 2577–2596 (2017).

    Article  Google Scholar 

  • 8.

    Browning, K. A. Conceptual models of precipitation systems. Weather Forecasting 1, 23–41 (1986).

    Article  Google Scholar 

  • 9.

    Wernli, H. & Davies, H. C. A Lagrangian-based analysis of extratropical cyclones. I: the method and some applications. Quart. J. Roy. Meteor. Soc. 123, 467–489 (1997).

    Article  Google Scholar 

  • 10.

    Madonna, E., Wernli, H., Joos, H. & Martius, O. Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part I: climatology and potential vorticity evolution. J. Climate 27, 3–26 (2014).

    Article  Google Scholar 

  • 11.

    Sodemann, H. et al. (eds) In Atmospheric Rivers p. 286 (Springer, 2020).

  • 12.

    Doyle, J. D., Amerault, C., Reynolds, C. A. & Reinecke, P. A. Initial condition sensitivity and predictability of a severe extratropical cyclone using a moist adjoint. Monthly Weather Rev. 142, 320–342 (2014).

    Article  Google Scholar 

  • 13.

    Schäfler, A. & Harnisch, F. Impact of the inflow moisture on the evolution of a warm conveyor belt. Quart. J. Roy. Meteor. Soc. 141, 299–310 (2015).

    Article  Google Scholar 

  • 14.

    Rodwell, M. J., Richardson, D. S., Parsons, D. B. & Wernli, H. Flow-dependent reliability: a path to more skillful ensemble forecasts. Bull. Am. Meteor. Soc. 99, 1015–1026 (2018).

    Article  Google Scholar 

  • 15.

    Lavers, D. A. et al. Winter floods in Britain are connected to atmospheric rivers. Geophys. Res. Lett. 38, L23803 (2011).

    Article  Google Scholar 

  • 16.

    Lavers, D. A. & Villarini, G. The nexus between atmospheric rivers and extreme precipitation across Europe. Geophys. Res. Lett. 40, 3259–3264 (2013).

    Article  Google Scholar 

  • 17.

    Ramos, A. M., Trigo, R. M., Liberato, M. L. & Tomé, R. Daily precipitation extreme events in the Iberian Peninsula and its association with atmospheric rivers. J. Hydrometeor. 16, 579–597 (2015).

    Article  Google Scholar 

  • 18.

    Ralph, F. M. et al. Flooding on California’s Russian River: role of atmospheric rivers. Geophys. Res. Lett. 33, L13801 (2006).

    Article  Google Scholar 

  • 19.

    Neiman, P. J., Schick, L. J., Ralph, F. M., Hughes, M. & Wick, G. A. Flooding in western Washington: the connection to atmospheric rivers. J. Hydrometeor. 12, 1337–1358 (2011).

    Article  Google Scholar 

  • 20.

    Viale, M. & Nunez, M. N. Climatology of winter orographic precipitation over the subtropical central Andes and associated synoptic and regional characteristics. J. Hydrometeor. 12, 481–507 (2011).

    Article  Google Scholar 

  • 21.

    Kingston, D. G., Lavers, D. A. & Hannah, D. M. Floods in the Southern Alps of New Zealand: the importance of atmospheric rivers. Hydrol. Process. 30, 5063–5070 (2016).

    Article  Google Scholar 

  • 22.

    Pasquier, J. T., Pfahl, S. & Grams, C. M. Modulation of atmospheric river occurrence and associated precipitation extremes in the North Atlantic Region by European weather regimes. Geophys. Res. Lett. 46, 1014–1023 (2019).

    Article  Google Scholar 

  • 23.

    UK Met Office. Record Breaking Rainfall. https://www.metoffice.gov.uk/weather/warnings-and-advice/uk-storm-centre/storm-dennis (UK Met Office, 2020).

  • 24.

    Insured losses from Europe’s Storm Victoria (aka Dennis) estimated at €286M: PERILS. Insurance J. https://www.insurancejournal.com/news/international/2020/03/30/562719.htm (2020).

  • 25.

    Corringham, T. W., Ralph, F. M., Gershunov, A., Cayan, D. R. & Talbot, C. A. Atmospheric rivers drive flood damages in the western United States. Sci. Adv. 5, eaax4631 (2019).

    Article  Google Scholar 

  • 26.

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

    CAS  Article  Google Scholar 

  • 27.

    Khouakhi, A. & Villarini, G. On the relationship between atmospheric rivers and high sea water levels along the US West Coast. Geophys. Res. Lett. 43, 8815–8822 (2016).

    Article  Google Scholar 

  • 28.

    Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J. & Cayan, D. Atmospheric rivers, floods, and the water resources of California. Water 3, 445–478 (2011).

    Article  Google Scholar 

  • 29.

    Baggett, C. F., Barnes, E. A., Maloney, E. D. & Mundhenk, B. D. Advancing atmospheric river forecasts into subseasonal‐to‐seasonal time scales. Geophys. Res. Lett. 44, 7528–7536 (2017).

    Article  Google Scholar 

  • 30.

    DeFlorio, M. J. et al. Global assessment of atmospheric river prediction skill. J. Hydrometeor. 19, 409–426 (2018).

    Article  Google Scholar 

  • 31.

    Lavers, D. A., Pappenberger, F., Richardson, D. S. & Zsoter, E. ECMWF Extreme Forecast Index for water vapor transport: a forecast tool for atmospheric rivers and extreme precipitation. Geophys. Res. Lett. 43, 11,852–11,858 (2016).

    Google Scholar 

  • 32.

    Lavers, D. A., Zsoter, E., Richardson, D. S. & Pappenberger, F. An assessment of the ECMWF extreme forecast index for water vapor transport during boreal winter. Weather Forecast. 32, 1667–1674 (2017). This paper describes the ECMWF Extreme Forecast Index product for integrated vapour transport and highlights the increased possible awareness of atmospheric rivers and extreme precipitation.

    Article  Google Scholar 

  • 33.

    Nayak, M. A., Villarini, G. & Lavers, D. A. On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States. Geophys. Res. Lett. 41, 4354–4362 (2014).

    Article  Google Scholar 

  • 34.

    Wick, G. A., Neiman, P. J., Ralph, F. M. & Hamill, T. M. Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather Forecast. 28, 1337–1352 (2013).

    Article  Google Scholar 

  • 35.

    Leutbecher, M. & Palmer, T. N. Ensemble forecasting. J. Comput. Phys. 227, 3515–3539 (2008).

    Article  Google Scholar 

  • 36.

    Lavers, D. A. et al. The gauging and modeling of rivers in the sky. Geophys. Res. Lett. https://doi.org/10.1029/2018GL079019 (2018).

  • 37.

    Rutz, J. J. et al. The atmospheric river tracking method intercomparison project (ARTMIP): quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. 2019, 13777–13802 (2019).

    Article  Google Scholar 

  • 38.

    Martin, A. C., Ralph, F. M., Wilson, A., DeHaan, L. & Kawzenuk, B. Rapid cyclogenesis from a mesoscale frontal wave on an atmospheric river: impacts on forecast skill and predictability during atmospheric river landfall. J. Hydrometeor. 20, 1779–1794 (2019).

    Article  Google Scholar 

  • 39.

    Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).

    CAS  Article  Google Scholar 

  • 40.

    Lavers, D. A. et al. Earlier awareness of extreme winter precipitation across the western Iberian Peninsula. Meteorol. Appl. 25, 622–628 (2018).

  • 41.

    Lavers, D., Tsonevsky, I., Richardson, D. & Pappenberger, F. The Extreme Forecast Index for water vapour flux, ECMWF Newslett. 160, https://www.ecmwf.int/en/newsletter/160/news/extreme-forecast-index-water-vapour-flux (2019).

  • 42.

    Ralph, F. M. et al. A scale to characterize the strength and impacts of atmospheric rivers. Bull. Am. Meteor. Soc. 100, 269–289 (2019).

    Article  Google Scholar 

  • 43.

    Ralph, F. M. et al. West Coast forecast challenges and development of atmospheric river reconnaissance. Bull. Am. Meteor. Soc., 101, E1357–E1377, https://doi.org/10.1175/BAMS-D-19-0183.1 (2020). This paper provides an overview of Atmospheric River Reconnaissance in the northeast Pacific which is key to the ideas proposed for AR Recon Atlantic.

  • 44.

    Stone, R. E. et al. Atmospheric river reconnaissance observation impact in the navy global forecast system. Monthly Weather Rev. 148, 763–782 (2020).

    Article  Google Scholar 

  • 45.

    Lavers, D. A. et al. Forecast errors and uncertainties in Atmospheric Rivers. Weather Forecast. https://doi.org/10.1175/WAF-D-20-0049.1 (2020).

  • 46.

    National Winter Season Operations Plan. Winter Season Reconnaissance https://www.ofcm.gov/publications/nwsop/nwsop2.htm (2019).

  • 47.

    Schäfler, A. et al. The North Atlantic Waveguide and Downstream Impact Experiment. Bull. Amer. Meteor. Soc. 99, 1607–1637 (2018). This paper describes the NAWDEX observational campaign in the North Atlantic and AR Recon Atlantic would build on these findings.

    Article  Google Scholar 

  • 48.

    Grams, C. M., Magnusson, L. & Madonna, E. An atmospheric dynamics perspective on the amplification and propagation of forecast error in numerical weather prediction models: a case study. Quart. J. R. Meteor. Soc. 144, 2577–2591 (2018).

    Article  Google Scholar 

  • 49.

    Schäfler, A. et al. Observation of jet stream winds during NAWDEX and characterization of systematic meteorological analysis errors. Monthly Weather Rev. https://doi.org/10.1175/MWR-D-19-0229.1 (2020).

  • 50.

    Rennie, M. & Isaksen, L. Use of Aeolus observations at ECMWF. ECMWF Newslett. 163, https://www.ecmwf.int/en/newsletter/163/news/use-aeolus-observations-ecmwf (2020).

  • 51.

    Guan, B., Waliser, D. E., Molotch, N. P., Fetzer, E. J. & Neiman, P. J. Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Monthly Weather Rev. 140, 325–342 (2012).

    Article  Google Scholar 

  • 52.

    Ralph, F. M. et al. The impact of a prominent rain shadow on flooding in California’s Santa Cruz mountains: a CALJET case study and sensitivity to the ENSO cycle. J. Hydrometeor. 4, 1243–1264 (2003).

    Article  Google Scholar 

  • 53.

    Lavers, D. A., Villarini, G., Allan, R. P., Wood, E. F. & Wade, A. J. 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 (2012).

    Google Scholar 

  • 54.

    Jasperse J. et al. Preliminary viability assessment of Lake Mendocino forecast informed reservoir operations. Technical report. http://pubs.er.usgs.gov/publication/70192184 (USGS, 2017).


  • Source: Resources - nature.com

    The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems

    A sciaenid swim bladder with long skinny fingers produces sound with an unusual frequency spectrum