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Climate Dynamics

, Volume 51, Issue 1–2, pp 457–472 | Cite as

An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US

  • Sepideh KhajeheiEmail author
  • Ali Ahmadalipour
  • Hamid Moradkhani
Article

Abstract

The North American multi-model ensemble (NMME) forecast system provides valuable information for climate variables at different lead times over the globe. A Bayesian ensemble post-processing approach based on Copula functions (COP-EPP) is employed to bias-correct the NMME precipitation forecast from 11 models (totaling 128 ensemble members). The study is conducted over the Continental United States (CONUS) for the hindcast period of 1982–2010 at lead-0, and the forecast period of 2012–2015 at four different lead times of lead-0 to lead-3 and the results are verified using deterministic and probabilistic measures. COP-EPP is compared with a widely-used bias-correction technique, the Quantile Mapping (QM). Although the NMME forecasts show to be more accurate across the eastern United States, large bias is found over the great plains of the central US. However, QM and COP-EPP present significant improvements over the NMME forecasts, with COP-EPP proving to be more reliable and accurate across the CONUS. In addition, COP-EPP substantially improves the temporal and spatial variability of the intra-seasonal NMME forecasts, even at lead-3.

Keywords

NMME COP-EPP Quantile mapping Bias correction CONUS 

Notes

Acknowledgements

Partial financial support for this project was provided by the National Oceanic and Atmospheric Administration (NOAA) Modeling, Analysis, Predictions, and Projections (MAPP) (Grant No. NA140AR4310234).

Supplementary material

382_2017_3934_MOESM1_ESM.docx (76 kb)
Supplementary material 1 (DOCX 75 KB)

References

  1. Ahmadalipour A, Moradkhani H, Svoboda M (2016) Centennial drought outlook over the CONUS using NASA-NEX downscaled climate ensemble. Int J Climatol. doi: 10.1002/joc.4859 Google Scholar
  2. Ahmadalipour A, Moradkhani H, Rana A (2017a) Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Clim Dyn. doi: 10.1007/s00382-017-3639-4 Google Scholar
  3. Ahmadalipour A, Moradkhani H, Yan H, Zarekarizi M (2017b) Remote sensing of drought: vegetation, soil moisture and data assimilation. Remote sensing of hydrological extremes. Springer International Publishing Switzerland, pp 121–149Google Scholar
  4. Ahmadalipour A, Moradkhani H, Demirel M (2017c) A comparative assessment of projected meteorological and hydrological droughts: elucidating the role of temperature. J Hydrol 553:785–797. doi: 10.1016/j.jhydrol.2017.08.047
  5. Aho K, Derryberry D, Peterson T (2014) Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95:631–636CrossRefGoogle Scholar
  6. Anderson JL (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev 129:2884–2903CrossRefGoogle Scholar
  7. Barnston AG, Lyon B (2016) Does the NMME capture a recent decadal shift toward increasing drought occurrence in the southwestern United States? J Clim 29:561–581. doi: 10.1175/JCLI-D-15-0311.1 CrossRefGoogle Scholar
  8. Barnston AG, Tippett MK, van den Dool HM, Unger DA (2015) Toward an improved multimodel ENSO prediction. J Appl Meteorol Climatol 54:1579–1595CrossRefGoogle Scholar
  9. Becker E, van den Dool H (2015) Probabilistic seasonal forecasts in the North American Multi-Model Ensemble: a baseline skill assessment. J Clim 151210144222001. doi: 10.1175/JCLI-D-14-00862.1 Google Scholar
  10. Becker E, den Dool H, Van Zhang Q (2014) Predictability and forecast skill in NMME. J Clim 27:5891–5906. doi: 10.1175/JCLI-D-13-00597.1 CrossRefGoogle Scholar
  11. Clark MP, Slater AG (2006) Probabilistic quantitative precipitation estimation in complex terrain. J Hydrometeor 7:3–22Google Scholar
  12. Clark M, Gangopadhyay S, Hay L et al (2004) The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. J Hydrometeorol 5:243–262CrossRefGoogle Scholar
  13. DeWitt DG (2005) Retrospective forecasts of interannual sea surface temperature anomalies from 1982 to present using a directly coupled atmosphere-ocean general circulation model. Mon Weather Rev 133:2972–2995Google Scholar
  14. Ehsan MA, Tippett MK, Almazroui M, Ismail M, Yousef A, Kucharski F, Omar M, Hussein M, Alkhalaf AA (2017) Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation. Clim Dyn 48(9–10):3309–3324Google Scholar
  15. Favre AC, El Adlouni S, Perreault L, Thiémonge N, Bobée B (2004) Multivariate hydrological frequency analysis using copulas. Water Resour Res 40(1). doi: 10.1029/2003WR002456
  16. Ficklin DL, Abatzoglou JT, Robeson SM, Dufficy A (2016) The influence of climate model biases on projections of aridity and drought. J Clim 29(4):1269–1285Google Scholar
  17. Genest C, Rémillard B, Beaudoin D (2009) Goodness-of-fit tests for copulas: A review and a power study. Insur Math Econ 44:199–213CrossRefGoogle Scholar
  18. Hersbach H (2000) Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather Forecast 15:559–570. doi: 10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2 CrossRefGoogle Scholar
  19. Infanti JM, Kirtman BP (2014) Southeastern US rainfall prediction in the North American multi-model ensemble. J Hydrometeorol 15:529–550CrossRefGoogle Scholar
  20. Kavetski D, Kuczera G, Franks SW (2006) Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resour Res 42(3). doi: 10.1029/2005WR004376
  21. Khajehei S, Moradkhani H (2017) Towards an improved ensemble precipitation forecast: a probabilistic post-processing approach. J Hydrol 546:476–489CrossRefGoogle Scholar
  22. Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Weather Rev 137(9):2908–2930Google Scholar
  23. Kirtman BP, Min D, Infanti JM et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601CrossRefGoogle Scholar
  24. Lang Y, Ye A, Gong W et al (2014) Evaluating skill of seasonal precipitation and temperature predictions of NCEP CFSv2 forecasts over 17 hydroclimatic regions in China. J Hydrometeorol 15:1546–1559CrossRefGoogle Scholar
  25. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res Atmos 115(D10). doi: 10.1029/2009JD012882
  26. Ma F, Ye A, Deng X et al (2016) Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. Int J Climatol 36:132–144. doi: 10.1002/joc.4333 CrossRefGoogle Scholar
  27. Madadgar S, Moradkhani H (2014) Spatio-temporal drought forecasting within Bayesian networks. J Hydrol 512:134–146. doi: 10.1016/j.jhydrol.2014.02.039 CrossRefGoogle Scholar
  28. Madadgar S, Moradkhani H, Garen D (2014) Towards improved post-processing of hydrologic forecast ensembles. Hydrol Process 28:104–122. doi: 10.1002/hyp.9562 CrossRefGoogle Scholar
  29. Madadgar S, AghaKouchak A, Shukla S, Wood AW, Cheng L, Hsu KL, Svoboda M (2016) A hybrid statistical-dynamical framework for meteorological drought prediction: application to the southwestern United States. Water Resour Res 52(7):5095–5110Google Scholar
  30. Maurer EP, Ficklin DL, Wang W (2016) Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies. Hydrol Earth Syst Sci 20:685–696. doi: 10.5194/hess-20-685-2016 CrossRefGoogle Scholar
  31. Mehrotra R, Sharma A (2015) Correcting for systematic biases in multiple raw GCM variables across a range of timescales. J Hydrol 520:214–223CrossRefGoogle Scholar
  32. Merryfield WJ, Lee W-S, Boer GJ et al (2013) The Canadian seasonal to interannual prediction system. Part I: models and initialization. Mon Weather Rev 141:2910–2945Google Scholar
  33. Miao C, Su L, Sun Q, Duan Q (2016) A nonstationary bias-correction technique to remove bias in GCM simulations. J Geophys Res Atmos 121(10):5718–5735Google Scholar
  34. Mizukami N, Clark MP, Gutmann ED et al (2016) Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: statistically downscaled forcing data and hydrologic models. J Hydrometeorol 17:73–98CrossRefGoogle Scholar
  35. Mo KC, Lettenmaier DP (2014) Hydrologic prediction over the conterminous United States using the national multi-model ensemble. J Hydrometeorol 15:1457–1472CrossRefGoogle Scholar
  36. Mo KC, Lyon B (2015) Global meteorological drought prediction using the North American multi-model ensemble. J Hydrometeorol 16:1409–1424CrossRefGoogle Scholar
  37. Najafi MR, Moradkhani H (2015) Ensemble combination of seasonal streamflow forecasts. J Hydrol Eng 21:4015043CrossRefGoogle Scholar
  38. Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. doi: 10.1002/hyp.8043 CrossRefGoogle Scholar
  39. New M, Hulme M, Jones P (1999) Representing twentieth-century space-time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J Clim 12:829–856Google Scholar
  40. Ozga-Zielinski B, Ciupak M, Adamowski J, Khalil B, Malard J (2016) Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland. J Hydrol 6:26–51Google Scholar
  41. Peterson TC, Heim RR, Hirsch R et al (2013) Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: state of knowledge. Bull Am Meteorol Soc 94:821–834. doi: 10.1175/BAMS-D-12-00066.1 CrossRefGoogle Scholar
  42. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192CrossRefGoogle Scholar
  43. Pierce DW, Cayan DR, Maurer EP et al (2015) Improved bias correction techniques for hydrological simulations of climate change. J Hydrometeorol. doi: 10.1175/JHM-D-14-0236.1 Google Scholar
  44. Rana A, Moradkhani H, Qin Y (2017) Understanding the joint behavior of temperature and precipitation for climate change impact studies. Theor Appl Climatol 129(1–2):321–339CrossRefGoogle Scholar
  45. Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors. Water Resour Res 46(5). doi: 10.1029/2009WR008328
  46. Roulin E, Vannitsem S (2015) Post-processing of medium-range probabilistic hydrological forecasting: impact of forcing, initial conditions and model errors. Hydrol Process 29:1434–1449CrossRefGoogle Scholar
  47. Saha S, Moorthi S, Wu X et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208Google Scholar
  48. Schaake J, Demargne J, Hartman R et al (2007) Precipitation and temperature ensemble forecasts from single-value forecasts. Hydrol Earth Syst Sci Discuss 4:655–717. doi: 10.5194/hessd-4-655-2007 CrossRefGoogle Scholar
  49. Schneider U, Becker A, Finger P et al (2011) GPCC full data reanalysis version 6.0 at 0.5: monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. doi: 10.5676/DWD_GPCC
  50. Schneider U, Becker A, Finger P et al (2014) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol 115:15–40CrossRefGoogle Scholar
  51. Shirvani A, Landman WA (2015) Seasonal precipitation forecast skill over Iran. Int J Climatol 1900:1887–1900. doi: 10.1002/joc.4467 Google Scholar
  52. Shukla S, Roberts J, Hoell A et al (2016) Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa. Clim Dyn. doi: 10.1007/s00382-016-3296-z Google Scholar
  53. Slater LJ, Villarini G, Bradley AA (2016) Evaluation of the skill of North-American multi-model ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA. Clim Dyn 1–16. doi: 10.1007/s00382-016-3286-1
  54. Stephens MA (1974) EDF statistics for goodness of fit and some comparisons. J Am Stat Assoc 69:730–737CrossRefGoogle Scholar
  55. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans A Math Phys Eng Sci 365:2053–2075. doi: 10.1098/rsta.2007.2076 CrossRefGoogle Scholar
  56. Thober S, Kumar R, Sheffield J et al (2015) Seasonal Soil Moisture Drought Prediction over Europe using the North American Multi-Model Ensemble (NMME). J Hydrometeorol. doi: 10.1175/JHM-D-15-0053.1 Google Scholar
  57. Tian D, Martinez CJ, Graham WD, Hwang S (2014) Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the southeastern United States. J Clim 27:8384–8411CrossRefGoogle Scholar
  58. Vecchi GA, Delworth T, Gudgel R et al (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016Google Scholar
  59. Willmott CJ, Matsuura K, Legates DR (2001) Terrestrial air temperature and precipitation: monthly and annual time series (1950–1999) (version 1.02). Center for Climate Research, University of Delaware, Newark, DEGoogle Scholar
  60. Xie P, Chen M, Shi W (2010) CPC unified gauge-based analysis of global daily precipitation. In: 24th Conference on Hydrology, Atlanta, GA, American Meteorological Society, vol 2Google Scholar
  61. Yang Z, Hsu K, Sorooshian S, Xu X, Braithwaite D, Verbist KM (2016) Bias adjustment of satellite-based precipitation estimation using gauge observations: a case study in Chile. J Geophys Res Atmos 121(8):3790–3806Google Scholar
  62. Yuan X, Wood EF (2013) Multimodel seasonal forecasting of global drought onset. Geophys Res Lett 40:4900–4905. doi: 10.1002/grl.50949 CrossRefGoogle Scholar
  63. Yuan X, Wood EF, Chaney NW et al (2013a) Probabilistic seasonal forecasting of African drought by dynamical models. J Hydrometeorol 14:1706–1720CrossRefGoogle Scholar
  64. Yuan X, Wood EF, Roundy JK, Pan M (2013b) CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States. J Clim 26:4828–4847CrossRefGoogle Scholar
  65. Yuan X, Roundy JK, Wood EF, Sheffield J (2015) Seasonal forecasting of global hydrologic extremes : System development and evaluation over GEWEX basins. Bull Am Meteorol Soc 96:1895–1912. doi: 10.1175/BAMS-D-14-00003.1 CrossRefGoogle Scholar
  66. Zhang S, Harrison MJ, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135:3541–3564Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Sepideh Khajehei
    • 1
    Email author
  • Ali Ahmadalipour
    • 1
  • Hamid Moradkhani
    • 1
  1. 1.Remote Sensing and Water Resources Lab, Department of Civil and Environmental EngineeringPortland State UniversityPortlandUSA

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