Abstract
Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.
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References
Apipattanavis, S., 2008: Stochastic nonparametric methods for multi-site weather generation and flood frequency estimation: applications to construction delay, hydrology and agricultural modeling. PhD dissertation, University of Colorado, 199 pages.
Apipattanavis, S., G. P. Podestá, B. Rajagopalan, and R. W. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resour. Res., 43, W11401, doi: 10.1029/2006WR005714.
Apipattanavis, S., F. Bert, G. P. Podestá, and B. Rajagopalan, 2010a: Linking weather generators and crop models for assessment of climate forecast outcomes. Agriculture and Forest Meteorology, 150, 166–174.
Apipattanavis, S., K. Sabol, K. Molenaar, B. Rajagopalan, Y. Xi, B. Blackard, and S. Patil, 2010b: Integrated framework for quantifying and predicting weather-related highway construction delays. Journal of Construction Engineering and Management, 136, 1160–1168.
Barnston, A. G., S. H. Li, S. J. Mason, D. G. DeWitt, L. Goddard, and X. F. Gong, 2010: Verification of the first 11 years of IRI’s seasonal climate forecasts. Journal of Applied Meteorology and Climatology, 49, 493–520.
Beersma, J. J., and T. Adri Buishand, 2003: Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Climate Research, 25, 121–134.
Benestad, R. E., I. Hanssen-Bauer, and D. L. Chen, 2008: Empirical Statistical Downscaling. World Scientific, 228 pps.
Briggs, W. M., and D. S. Wilks, 1996: Extension of the Climate Prediction Center long-lead temperature and precipitation outlooks to general weather statistics. J. Climate, 9, 3496–3504.
Buishand, T. A., 1978: Some remarks on the use of daily rainfall models. J. Hydrol., 36, 295–308.
Caldwell, J., B. Rajagopalan, and E. Danner, 2014: Statistical modeling of daily water temperature attributes on the Sacramento River. Journal of Hydrologic Engineering, 20, 04014065, doi: 10.1061/(ASCE)HE.1943-5584.0001023.
Cleveland, W. S., 1979: Robust locally-weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829–836.
Furrer, E. M., and R. W. Katz, 2007: Generalized linear modeling approach to stochastic weather generators. Climate Research, 34, 129–144.
Giorgi, F., and L. O. Mearns, 1991: Approaches to the simulation of regional climate change: A review. Rev. Geophys., 29, 191–216.
Hansen, J. W., and T. Mavromatis, 2001: Correcting lowfrequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology, 109, 297–310.
Hastie, T. J., and R. J. Tibshirani, 1990: Generalized Additive Models. Chapman and Hall.
Hostetler, S. W., J. R. Alder, and A. M. Allan, 2011: Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users. U.S. Geological Survey Open-File Report 2011–1238, 64 pp.
Katz, R. W., and M. B. Parlange, 1998: Overdispersion phenomenon in stochastic modeling of precipitation. J. Climate, 11, 591–601.
Kim, Y., R. W. Katz, B. Rajagopalanc, G. P. Podestá, and E. M. Furrer, 2012: Reduced overdispersion in stochastic weather generators using a generalized linear modeling approach. Climate Research, 53, 13–24.
MacDonald, I. L., and W. Zucchini, 1997: Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman and Hall.
Mannig, B., and Coauthors, 2013: Dynamical downscaling of climate change in Central Asia. Global and Planetary Change, 110, 26–39.
McCullagh, P., and J. A. Nelder, 1989: Generalized Linear Models. 2nd ed. Chapman and Hall, 206 pages.
Rajagopalan, B., and V. Lall, 1999: A k-nearest neighbor simulator for daily precipitation and other weather variables. Water Resour. Res., 35, 3089–3101.
Richardson, C. W., 1981: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res., 17, 182–190.
Stern, R. D., and R. Coe, 1984: A model fitting analysis of daily rainfall data. Journal of the Royal Statistical Society: Series A, 147, 1–34.
Verdin, A., B. Rajagopalan, W. Kleiber, and R. W. Katz, 2015: Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environmental Research and Risk Assessment, 29, 347–356.
Wilby, R. L., and T. M. L. Wigley, 1997: Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, 21, 530–548.
Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks, 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 2995–3008.
Wilby, R. L., S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns, 2004: Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material for Data Distribution Centre of Intergovernmental Panel on Climate Change. [Available online at http://www.ipcc-data.org/guidelines/dgmno2400v1092004.pdf].
Wilks, D. S., 1989: Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour. Res., 25, 1429–1439.
Wilks, D. S., and R. L. Wilby, 1999: The weather generator game: A review of stochastic weather models. Progress in Physical Geography, 23, 329–357.
Xu, Z. F., and Z. L. Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J. Climate, 25, 6271–6286.
Yates, D., S. Gangopadhyay, B. Rajagopalan, and K. Strzepek, 2003: A technique for generating regional climate scenarios using a nearest neighbor algorithm. Water Resour. Res., 39, 1199, doi: 10.1029/2002WR001769.
Yoon, J. H., L. Y. R. Leung, and J. Correia Jr., 2012: Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J. Geophys. Res., 117, D21109, doi: 10.1029/2012JD017650.
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Kim, Y., Rajagopalan, B. & Lee, G. Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator. Adv. Atmos. Sci. 33, 175–183 (2016). https://doi.org/10.1007/s00376-015-5115-6
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DOI: https://doi.org/10.1007/s00376-015-5115-6