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Korean Meteorological Society (한국기상학회)
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Inter-comparison of Prediction Skills of Multiple Linear Regression Methods Using Monthly Temperature Simulated by Multi-Regional Climate Models

다중 지역기후모델로부터 모의된 월 기온자료를 이용한 다중선형회귀모형들의 예측성능 비교

  • Seong, Min-Gyu (Department of Atmospheric Sciences, Kongju National University) ;
  • Kim, Chansoo (Department of Applied Mathematics, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Sciences, Kongju National University)
  • Received : 2015.08.25
  • Accepted : 2015.10.19
  • Published : 2015.12.31
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Abstract

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

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References

  1. Chandler, R. E., 2005: On the use of generalized linear models for interpreting climate variability. Environmetrics, 16, 699-715. https://doi.org/10.1002/env.731
  2. Choi, S. J., D. K. Lee, and S. G. Oh, 2011: Regional climate simulations over East-Asia by using SNURCM and WRF forced by HadGEM2-AO. J. Korean Earth Sci. Soc., 32, 750-760. https://doi.org/10.5467/JKESS.2011.32.7.750
  3. Christensen, J. H., and O. B. Christensen, 2007: A summary of PRUDENCE model projections of changes in European climate by the end of this century. Clim. Change, 81, 7-30. https://doi.org/10.1007/s10584-006-9210-7
  4. Christensen, J. H., E. Kjellstrom, F. Giorgi, G. Lenderink, and M. Rummukainen, 2010: Weighting assignment in regional climate models. Climate Res., 44, 179-194. https://doi.org/10.3354/cr00916
  5. Chu, P. S., and X. Zhao, 2004: Bayesian change-point analysis of tropical cyclone activity: The Central North Pacific case. J. Climate, 17, 4893-4901. https://doi.org/10.1175/JCLI-3248.1
  6. Cui, B., Z. Toth, Y. Zhu, and D. Hou, 2012: Bias correction for global ensemble forecast. Wea. Forecasting, 27, 396-410. https://doi.org/10.1175/WAF-D-11-00011.1
  7. Dai, Y. H., 2002: Convergence properties of the BFGS algorithm. SIAM J. Optim, 13, 693-701. https://doi.org/10.1137/S1052623401383455
  8. Feng, J., D. K. Lee, C. Fu, J. Tang, Y. Sato, H. Kato, J. Megregor, and K. Mabuchi, 2011: Comparison of four ensemble methods combining regional climate simulations over Asia. Meteor. Atmos. Phys., 111, 41-53. https://doi.org/10.1007/s00703-010-0115-7
  9. Fu, C., S. Wang, Z. Xiong, W. J. Gutowski, D. K. Lee, J. L. McGregor, Y. Sato, H. Kato, J. W. Kim, and M. S. Suh, 2005: Regional climate model intercomparison project for Asia. Bull. Amer. Meteor. Soc., 86, 257-266. https://doi.org/10.1175/BAMS-86-2-257
  10. Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 1962-1970. https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2
  11. Gemmer, M., T. Fischer, T. Jiang, B. Su, and L. L. Liu, 2011: Trends in precipitation extremes in the Zhujiang river basin, South China. J. Climate, 24, 750-761. https://doi.org/10.1175/2010JCLI3717.1
  12. Giorgi, F., M. R. Marinucci, and G. T. Bates, 1993a: Development of a second generation regional climate model (RegCM2). Part I: boundary layer and radiative transfer processes. Mon. Wea. Rev., 121, 2794-2813. https://doi.org/10.1175/1520-0493(1993)121<2794:DOASGR>2.0.CO;2
  13. Giorgi, F., M. R. Marinucci, G. T. Bates, and G. DeCanio, 1993b: Development of a second generation regional climate model (RegCM2). Part II: convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814-2832. https://doi.org/10.1175/1520-0493(1993)121<2814:DOASGR>2.0.CO;2
  14. Giorgi, F., and Coauthors, 2001: Regional climate change information-Evaluation and projections. In J. Climate change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 583-638.
  15. Giorgi, F., C. Jones, and G. R. Asrar, 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175-183.
  16. Giorgi, F., and Coauthors, 2012: RegCM4: model description and preliminary tests over multiple CORDEX domains. Climate Res., 52, 7-29. https://doi.org/10.3354/cr01018
  17. Gneiting, T., A. E. Raftery, A. H. Westveld III, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098-1118. https://doi.org/10.1175/MWR2904.1
  18. Grell, G., J. Dudhia, and D. Stauffer, 1994: A description of the fifthgeneration Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398 1 STR, 121 pp.
  19. Hamill, T. M. 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560. https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2
  20. Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea.Forecasting, 15, 559-570. https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2
  21. Hong, S. Y., and Coauthors, 2013: The global/regional integrated model system (GRIMs). Asia-Pacific J. Atmos. Sci., 49, 219-243. https://doi.org/10.1007/s13143-013-0023-0
  22. IPCC, 2014: Climate Change 2014: Impacts, Adaption, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, V. R. Barros et al., Eds., Cambridge University Press, 688 pp.
  23. Juang, H. M. H, S. Y. Hong, and M. Kanamitsu, 1997: The NCEP regional spectral model: An update. Bull. Amer. Meteor. Soc., 78, 2125-2143. https://doi.org/10.1175/1520-0477(1997)078<2125:TNRSMA>2.0.CO;2
  24. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.
  25. Kanamitsu, M., A. Kumar, H. M. H. Juang, J. K. Schemm, W. Wang, F. Yang, S. Y. Hong, P. Peng, W. Chen, S. Moorthi, and M. Ji, 2002: NCEP dynamical seasonal forecast system 2000. Bull. Amer. Meteor. Soc., 83, 1019-1037. https://doi.org/10.1175/1520-0477(2002)083<1019:NDSFS>2.3.CO;2
  26. Kim, C., and M. S. Suh, 2013: Prospects of using Bayesian model averaging for the calibration of one-month forecasts of surface air temperature over South Korea. Asia-Pacific J. Atmos. Sci., 49, 301-311. https://doi.org/10.1007/s13143-013-0029-7
  27. Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhan, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from a multimodel superensemble. Science, 285, 1548-1550. https://doi.org/10.1126/science.285.5433.1548
  28. Lee, J. W., S. Y. Hong, E. C. Chang, M. S. Suh, and H. S. Kang, 2014: Assessment of future climate change over East Asia due to RCP scenarios downscaled by GRIMs-RMP. Climate Dyn., 42, 733-747. https://doi.org/10.1007/s00382-013-1841-6
  29. Marzban, C., R. Wang, F. Kong, and S. Leyton, 2011: On the effect of correlations on rank histograms: reliability of temperature and wind speed forecasts from finescale ensemble reforecasts. Mon. Wea. Rev., 139, 295-310. https://doi.org/10.1175/2010MWR3129.1
  30. Mearns L. O., M. Hulme, T. R. Carter, R. Leemans, M. Lal, P. Whetton, L. Hay, R. N. Jones, R. Katz, T. Kittel, J. Smith, and R. Wilby, 2001: Climate scenario development. In Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 741-770.
  31. Meehl, G. A., F. Zwiers, J. Evans, T. Knutson, L. Mearns, and P. Whetton, 2000a: Trends in extreme weather and climate events: Issues Related to Modeling Extremes in Projections of Future Climate Change. Bull. Amer. Meteor. Soc., 81, 427-436. https://doi.org/10.1175/1520-0477(2000)081<0427:TIEWAC>2.3.CO;2
  32. Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 2000b: The Coupled Model Intercomparison Project (CMIP). Bull. Amer. Meteor. Soc., 81, 313-318. https://doi.org/10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2
  33. Myoung, J. S., S. G. Oh, and M. S. Suh, 2012: Improvement of simulated air temperature of regional climate model using linear regression method. Korean J. Climate Research, 7, 255-270.
  34. Peng, P., A. Kumar, and H. van den Dool, 2002: An analysis of multimodel ensemble prediction for seasonal climate anomalies. J. Geophys. Res., 107, doi:10.1029/2002JD002712.
  35. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155-1174. https://doi.org/10.1175/MWR2906.1
  36. Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.
  37. Suh, M. S., and D. K. Lee, 2004: Impacts of land use/cover changes on surface climate over east Asia for extreme climate cases using RegCM2. J. Geophys. Res., 109, doi:10.1029/2003JD003681.
  38. Suh, M. S., S. G. Oh, D. K. Lee, D. H. Cha, S. J. Choi, C. S. Jin, and S. Y. Hong, 2012: Development of new ensemble methods based on the performance skills of regional climate models over South Korea. J. Climate, 25, 7067-7082. https://doi.org/10.1175/JCLI-D-11-00457.1
  39. van der Linden, P., and J. F. B., Mitchell, 2009: ENSEMBLES: Climate change and its impacts at seasonal, decadal and centennial timescales. Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, 160 pp.
  40. Wilks, D. S., 2002: Smoothing forecast ensembles with fitted probability distributions. Quart. J. Roy. Meteor. Soc., 128, 2821-2836. https://doi.org/10.1256/qj.01.215
  41. Wilks, D. S., 2006: Statistical methods in the atmospheric science, 2nd ed. International Geophysics Series, 59, Academic Press, 627 pp.
  42. Yun, K. S., K. Y. Heo, J. E. Chu, K. J. Ha, E. J. Lee, Y. Choi, and A. Kitoh, 2012: Change in climate classification and extreme climate indices from a high-resolution future projection in Korea. Asia-Pac. J. Atmos. Sci., 48, 213-226. https://doi.org/10.1007/s13143-012-0022-6
  43. Yun, W. T., L. Stefanova, and T. N. Krishnamurti, 2003: Improvement of the multimodel superensemble technique for seasonal forecasts. J. Climate, 16, 3834-3840. https://doi.org/10.1175/1520-0442(2003)016<3834:IOTMST>2.0.CO;2

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