Heavy Rainfall in Paraguay during the 2015/16 Austral Summer: Causes and Subseasonal-to-Seasonal Predictive Skill

James Doss-Gollin Columbia Water Center, and Department of Earth and Environmental Engineering, Columbia University, New York, New York

Search for other papers by James Doss-Gollin in
Current site
Google Scholar
PubMed
Close
,
Ángel G. Muñoz Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, and International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, New York

Search for other papers by Ángel G. Muñoz in
Current site
Google Scholar
PubMed
Close
,
Simon J. Mason International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, New York

Search for other papers by Simon J. Mason in
Current site
Google Scholar
PubMed
Close
, and
Max Pastén Dirección de Meteorología e Hidrología, Asunción, and Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay

Search for other papers by Max Pastén in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0805.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: James Doss-Gollin, james.doss-gollin@columbia.edu

Abstract

During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0805.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: James Doss-Gollin, james.doss-gollin@columbia.edu

Supplementary Materials

    • Supplemental Materials (PDF 10.60 MB)
Save
  • Barnston, A. G., and C. F. Ropelewski, 1992: Prediction of ENSO episodes using canonical correlation analysis. J. Climate, 5, 13161345, https://doi.org/10.1175/1520-0442(1992)005<1316:POEEUC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., S. Li, S. J. Mason, L. Goddard, D. G. DeWitt, and X. Gong, 2010: Verification of the first 11 years of IRI’s seasonal climate forecasts. J. Appl. Meteor. Climatol., 49, 493520, https://doi.org/10.1175/2009JAMC2325.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. K. Tippett, M. L. L. Heureux, S. Li, and D. G. DeWitt, 2012: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Amer. Meteor. Soc., 93, 631651, https://doi.org/10.1175/BAMS-D-11-00111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barreiro, M., 2017: Interannual variability of extratropical transient wave activity and its influence on rainfall over Uruguay. Int. J. Climatol., 37, 42614274, https://doi.org/10.1002/joc.5082.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, V., L. Chamorro, G. Coronel, and J. Baez, 2004: The major discharge events in the Paraguay River: Magnitudes, source regions, and climate forcings. J. Hydrometeor., 5, 11611170, https://doi.org/10.1175/JHM-378.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boers, N., B. Bookhagen, N. Marwan, J. Kurths, and J. Marengo, 2013: Complex networks identify spatial patterns of extreme rainfall events of the South American monsoon system. Geophys. Res. Lett., 40, 43864392, https://doi.org/10.1002/grl.50681.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boers, N., B. Bookhagen, H. M. J. Barbosa, N. Marwan, J. Kurths, and J. A. Marengo, 2014: Prediction of extreme floods in the eastern central Andes based on a complex networks approach. Nat. Commun., 5, 5199, https://doi.org/10.1038/ncomms6199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brakenridge, G., 2016: Global active archive of large flood events. Dartmouth Flood Observatory, https://www.dartmouth.edu/~floods/Archives/index.html.

  • Bravo, J. M., D. Allasia, A. R. Paz, and W. Collischonn, 2011: Coupled hydrologic-hydraulic modeling of the upper Paraguay River basin. J. Hydrol. Eng., 17, 635646, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • British Broadcasting Corporation, 2015: Flooding ‘worst in 50 years’, as 150,000 flee in Paraguay, Argentina, Brazil and Uruguay. BBC News, 27 December, http://www.bbc.com/news/world-latin-america-35184793.

  • Bröcker, J., and L. A. Smith, 2007: Scoring probabilistic forecasts: The importance of being proper. Wea. Forecasting, 22, 382388, https://doi.org/10.1175/WAF966.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campetella, C. M., and C. S. Vera, 2002: The influence of the Andes Mountains on the South American low-level flow. Geophys. Res. Lett., 29, 1826, https://doi.org/10.1029/2002GL015451.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbin, G. W., M. K. Tippett, S. P. Lillo, and H. E. Brooks, 2016: Visualizing long-range severe thunderstorm environment guidance from CFSv2. Bull. Amer. Meteor. Soc., 97, 10211031, https://doi.org/10.1175/BAMS-D-14-00136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., C. Jones, and B. Liebmann, 2004: The South Atlantic convergence zone: Intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall. J. Climate, 17, 88108, https://doi.org/10.1175/1520-0442(2004)017<0088:TSACZI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., A. E. Silva, C. Jones, B. Liebmann, P. L. S. Dias, and H. R. Rocha, 2011a: Moisture transport and intraseasonal variability in the South America monsoon system. Climate Dyn., 36, 18651880, https://doi.org/10.1007/s00382-010-0806-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., C. Jones, A. E. Silva, B. Liebmann, and P. L. Silva Dias, 2011b: The South American Monsoon System and the 1970s climate transition. Int. J. Climatol., 31, 12481256, https://doi.org/10.1002/joc.2147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Collischonn, W., C. E. M. Tucci, and R. T. Clarke, 2001: Further evidence of changes in the hydrological regime of the River Paraguay: Part of a wider phenomenon of climate change? J. Hydrol., 245, 218238, https://doi.org/10.1016/S0022-1694(01)00348-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dawson, A., 2016: Windspharm: A high-level library for global wind field computations using spherical harmonics. J. Open Res. Software, 4, e31, http://doi.org/10.5334/jors.129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., and D. A. Lowry, 1972: The use of Model Output Statistics (MOS) in objective weather forecasting. J. Appl. Meteor. Climatol., 11, 12031211, https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goddard, L., W. E. Baethgen, H. Bhojwani, and A. W. Robertson, 2014: The International Research Institute for Climate & Society: Why, what and how. Earth Perspect., 1, https://doi.org/10.1186/2194-6434-1-10.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Good, I. J., 1952: Rational decisions. J. Roy. Stat. Soc. London, B14, 107114, http://www.jstor.org/stable/2984087.

  • Green, J. K., A. G. Konings, S. H. Alemohammad, J. Berry, D. Entekhabi, J. Kolassa, J.-E. Lee, and P. Gentine, 2017: Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci., 10, 410414, https://doi.org/10.1038/ngeo2957.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., 2003: The El Niño impact on the summer monsoon in Brazil: Regional processes versus remote influences. J. Climate, 16, 263280, https://doi.org/10.1175/1520-0442(2003)016<0263:TENIOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., and R. G. Tedeschi, 2009: ENSO and extreme rainfall events in South America. J. Climate, 22, 15891609, https://doi.org/10.1175/2008JCLI2429.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., and M. T. Zilli, 2009: Interannual variability and seasonal evolution of summer monsoon rainfall in South America. J. Climate, 22, 22572275, https://doi.org/10.1175/2008JCLI2345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., V. R. Barros, and M. E. Doyle, 2000: Climate variability in southern South America associated with El Niño and La Niña events. J. Climate, 13, 3558, https://doi.org/10.1175/1520-0442(2000)013<0035:CVISSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., J. S. Pal, and F. Giorgi, 2007: Connection between spring conditions and peak summer monsoon rainfall in South America: Role of soil moisture, surface temperature, and topography in eastern Brazil. J. Climate, 20, 59295945, https://doi.org/10.1175/2007JCLI1684.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hellmuth, M. E., S. J. Mason, C. Vaughan, M. van Aalst, and R. Choularton, 2011: A better climate for disaster risk management. Climate and Society Rep. 3, 118 pp., https://iri.columbia.edu/wp-content/uploads/2013/07/CSP3_Final.pdf.

  • Herdies, D. L., 2002: Moisture budget of the bimodal pattern of the summer circulation over South America. J. Geophys. Res., 107, 8075, https://doi.org/10.1029/2001JD000997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoyer, S., and J. Hamman, 2017: Xarray: N-D labeled arrays and datasets in Python. J. Open Res. Software, 5, http://doi.org/10.5334/jors.148.

  • Huang, H.-P., R. Seager, and Y. Kushnir, 2005: The 1976/77 transition in precipitation over the Americas and the influence of tropical sea surface temperature. Climate Dyn., 24, 721740, https://doi.org/10.1007/s00382-005-0015-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunter, J. D., 2007: Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 9, 9095, https://doi.org/10.1109/MCSE.2007.55.

  • Jolliffe, I. T., and D. B. Stephenson, 2012: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd ed., John Wiley & Sons, 292 pp.

    • Crossref
    • Export Citation
  • Jones, C., and L. M. V. Carvalho, 2002: Active and break phases in the South American monsoon system. J. Climate, 15, 905914, https://doi.org/10.1175/1520-0442(2002)015<0905:AABPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, A., M. A. Cane, Y. Kushnir, A. C. Clement, M. B. Blumenthal, and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856–1991. J. Geophys. Res., 103, 18 56718 589, https://doi.org/10.1029/97JC01736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., G. N. Kiladis, C. S. Vera, A. C. Saulo, and L. M. V. Carvalho, 2004: Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone. J. Climate, 17, 38293842, https://doi.org/10.1175/1520-0442(2004)017<3829:SVORIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loader, C., 1999: Local Regression and Likelihood. Springer, 290 pp.

  • Marengo, J. A., W. R. Soares, C. Saulo, and M. Nicolini, 2004: Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: Characteristics and temporal variability. J. Climate, 17, 22612280, https://doi.org/10.1175/1520-0442(2004)017<2261:COTLJE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., and Coauthors, 2012: Recent developments on the South American monsoon system. Int. J. Climatol., 32, 121, https://doi.org/10.1002/joc.2254.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marwan, N., and J. Kurths, 2015: Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems. Chaos, 25, 097609, https://doi.org/10.1063/1.4916924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S. J., and O. Baddour, 2008: Statistical modelling. Seasonal Climate: Forecasting and Managing Risk, A. Troccoli et al., Eds., Springer, 163–201.

    • Crossref
    • Export Citation
  • Mason, S. J., and A. P. Weigel, 2009: A generic forecast verification framework for administrative purposes. Mon. Wea. Rev., 137, 331349, https://doi.org/10.1175/2008MWR2553.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S. J., and M. K. Tippett, 2017: Climate Predictability Tool version 15.5.10. Columbia University, https://doi.org/10.7916/D8G44WJ6.

    • Crossref
    • Export Citation
  • McKinney, W., 2010: Data structures for statistical computing in python. Proc. Ninth Python in Science Conf., Austin, TX, SciPy, 51–56, https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf.

    • Crossref
    • Export Citation
  • Messner, J. W., G. J. Mayr, A. Zeileis, and D. S. Wilks, 2014: Heteroscedastic extended logistic regression for postprocessing of ensemble guidance. Mon. Wea. Rev., 142, 448456, https://doi.org/10.1175/MWR-D-13-00271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michelangeli, P.-A., R. Vautard, and B. Legras, 1995: Weather regimes: Recurrence and quasi stationarity. J. Atmos. Sci., 52, 12371256, https://doi.org/10.1175/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ministerio de Obras Públicas y Comunicación, 2016: Evaluación del impacto de El Niño 2015-2016 en sector transporte y comunicación Paraguay. Tech. Rep., 75 pp.

  • Moron, V., A. W. Robertson, J.-H. Qian, and M. Ghil, 2015: Weather types across the Maritime Continent: From the diurnal cycle to interannual variations. Front. Environ. Sci., 2, https://doi.org/10.3389/fenvs.2014.00065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, Á. G., L. Goddard, A. W. Robertson, Y. Kushnir, and W. Baethgen, 2015: Cross–time scale interactions and rainfall extreme events in southeastern South America for the austral summer. Part I: Potential predictors. J. Climate, 28, 78947913, https://doi.org/10.1175/JCLI-D-14-00693.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, Á. G., J. Díaz-Lobatón, X. Chourio, and M. J. Stock, 2016a: Seasonal prediction of lightning activity in north western Venezuela: Large-scale versus local drivers. Atmos. Res., 172–173, 147162, https://doi.org/10.1016/j.atmosres.2015.12.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, Á. G., L. Goddard, S. J. Mason, and A. W. Robertson, 2016b: Cross–time scale interactions and rainfall extreme events in southeastern South America for the austral summer. Part II: Predictive skill. J. Climate, 29, 59155934, https://doi.org/10.1175/JCLI-D-15-0699.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, Á. G., X. Yang, G. A. Vecchi, A. W. Robertson, and W. F. Cooke, 2017: A weather-type-based cross-time-scale diagnostic framework for coupled circulation models. J. Climate, 30, 89518972, https://doi.org/10.1175/JCLI-D-17-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nogués-Paegle, J., and K. C. Mo, 1997: Alternating wet and dry conditions over South America during summer. Mon. Wea. Rev., 125, 279291, https://doi.org/10.1175/1520-0493(1997)125<0279:AWADCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nogués-Paegle, J., L. A. Byerle, and K. C. Mo, 2000: Intraseasonal modulation of South American summer precipitation. Mon. Wea. Rev., 128, 837850, https://doi.org/10.1175/1520-0493(2000)128<0837:IMOSAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedregosa, F., and Coauthors, 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 28252830, http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roulston, M. S., and L. A. Smith, 2002: Evaluating probabilistic forecasts using information theory. Mon. Wea. Rev., 130, 16531660, https://doi.org/10.1175/1520-0493(2002)130<1653:EPFUIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salio, P., 2002: Chaco low-level jet events characterization during the austral summer season. J. Geophys. Res., 107, 4816, https://doi.org/10.1029/2001JD001315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salio, P., M. Nicolini, and E. J. Zipser, 2007: Mesoscale convective systems over southeastern South America and their relationship with the South American low-level jet. Mon. Wea. Rev., 135, 12901309, https://doi.org/10.1175/MWR3305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos, M., and C. Lima, 2016: Identification of structural breaks in hydrological maxima time series in Paraguay River, Pantanal Region, Brazil. Geophysical Research Abstracts, Vol. 18, Abstract EGU2016-9467-2, http://meetingorganizer.copernicus.org/EGU2016/EGU2016-9467-2.pdf.

  • Saulo, C., J. Ruiz, and Y. G. Skabar, 2007: Synergism between the low-level jet and organized convection at its exit region. Mon. Wea. Rev., 135, 13101326, https://doi.org/10.1175/MWR3317.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seluchi, M. E., R. D. Garreaud, F. A. Norte, and A. C. Saulo, 2006: Influence of the subtropical Andes on baroclinic disturbances: A cold front case study. Mon. Wea. Rev., 134, 33173335, https://doi.org/10.1175/MWR3247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van der Walt, S., S. C. Colbert, and G. Varoquaux, 2011: The NumPy array: A structure for efficient numerical computation. Comput. Sci. Eng., 13, 2230, https://doi.org/10.1109/MCSE.2011.37.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velasco, I., and J. M. Fritsch, 1987: Mesoscale convective complexes in the Americas. J. Geophys. Res., 92, 95919613, https://doi.org/10.1029/JD092iD08p09591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vera, C., and Coauthors, 2006: The South American Low-Level Jet Experiment. Bull. Amer. Meteor. Soc., 87, 6377, https://doi.org/10.1175/BAMS-87-1-63.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigaud, N., A. W. Robertson, and M. K. Tippett, 2017: Multimodel ensembling of subseasonal precipitation forecasts over North America. Mon. Wea. Rev., 145, 39133928, https://doi.org/10.1175/MWR-D-17-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and Coauthors, 2017: The Subseasonal to Seasonal Prediction (S2S) Project Database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weijs, S. V., R. van Nooijen, and N. van de Giesen, 2010: Kullback–Leibler divergence as a forecast skill score with classic reliability–resolution–uncertainty decomposition. Mon. Wea. Rev., 138, 33873399, https://doi.org/10.1175/2010MWR3229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Comparison of ensemble-MOS methods in the Lorenz ’96 setting. Meteor. Appl., 13, 243256, https://doi.org/10.1017/S1350482706002192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361368, https://doi.org/10.1002/met.134.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1092 270 21
PDF Downloads 801 211 18