Abstract
We introduce a stochastic weather generator for the variables of minimum temperature, maximum temperature and precipitation occurrence. Temperature variables are modeled in vector autoregressive framework, conditional on precipitation occurrence. Precipitation occurrence arises via a probit model, and both temperature and occurrence are spatially correlated using spatial Gaussian processes. Additionally, local climate is included by spatially varying model coefficients, allowing spatially evolving relationships between variables. The method is illustrated on a network of stations in the Pampas region of Argentina where nonstationary relationships and historical spatial correlation challenge existing approaches.
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Apipattanavis S, Podestá G, Rajagopalan B, Katz RW (2007) A semiparametric multivariate and multisite weather generator. Water Resour Res 43:1–19. doi:10.1029/2006WR005714
Baigorria GA, Jones JW (2010) GiST: a stochastic model for generating spatially and temporally correlated daily rainfall data. J Clim 23(22):5990–6008
Beersma JJ, Buishand TA (2003) Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Clim Res 25(2):121–133
Brissette FP, Khalili M, Leconte R (2007) Efficient stochastic generation of multi-site synthetic precipitation data. J Hydrol 345:121–133
Buishand T (1978) Some remarks on the use of daily rainfall models. J Hydrol 36(3):295–308
Buishand TA, Brandsma T (2001) Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbor resampling. Water Resour Res 37(11):2761–2776
Calanca P, Semenov MA (2013) Local-scale climate scenarios for impact studies and risk assessments: integration of early 21st century ENSEMBLES projections into the ELPIS database. Theor Appl Climatol 113:445–455
Caraway NM, McCreight JL, Rajagopalan B (2014) Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling. J Hydrol 508:197–213
Chandler RE (2005) On the use of generalized linear models for interpreting climate variability. Environmetrics 16:699–715
Chilès JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New York
Fassò A, Finazzi F (2011) Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data. Environmetrics 22(6):735–748
Foufoula-Georgiou E, Georgakakos KP (1991) Hydrologic advances in space-time precipitation modeling and forecasting. In: Bowles DS, O’Connell (eds) Recent advances in the modeling of hydrologic systems. Springer, Netherlands, pp 47–65
Friend AD, Stevens AK, Knox RG, Cannell MGR (1997) A process-based terrestrial biosphere model of ecosystem dynamics. Ecol Model 95:249–287
Furrer EM, Katz RW (2007) Generalized linear modeling approach to stochastic weather generators. Clim Res 34:129–144
Furrer EM, Katz RW (2008) Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resour Res 44:1–13. doi:10.1029/2008WR007316
Harrold TI, Sharma A, Sheather SJ (2003) A nonparametric model for stochastic generation of daily rainfall amounts. Water Resour Res 39(12):1–11
Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess 25(4):475–484
Hauser T, Demirov E (2013) Development of a stochastic weather generator for the sub-polar North Atlantic. Stoch Environ Res Risk Assess 27(7):1533–1551
Katz RW (1977) Precipitation as a chain-dependent process. J Appl Meteorol 16:671–676
Khalili M, Brissette F, Leconte R (2009) Stochastic multi-site generation of daily weather data. Stoch Environ Res Risk Assess 23(6):837–849
Kim T-W, Ahn H, Chung G, Yoo C (2008) Stochastic multi-site generation of daily rainfall occurrence in south Florida. Stoch Environ Res Risk Assess 22(6):705–717
Kleiber W, Katz RW, Rajagopalan B (2012) Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes. Water Resour Res 48:1–17. doi:10.1029/2011WR011105
Kleiber W, Katz RW, Rajagopalan B (2013) Daily minimum and maximum temperature simulation over complex terrain. Ann Appl Stat 7:588–612
Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrological time series. Water Resour Res 32:679–693
Lennartsson J, Baxevani A, Chen D (2008) Modelling precipitation in Sweden using multiple step Markov chains and a composite model. J Hydrol 363(1–4):42–59. URL http://linkinghub.elsevier.com/retrieve/pii/S0022169408004848
Lima CHR, Lall U (2009) Hierarchical Bayesian modeling of multisite daily rainfall occurrence: rainy season onset, peak and end. Water Resour Res 45:1–14. doi:10.1029/2008WR007485
McCullagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, London
Mehrotra R, Sharma A (2007) A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability. J Hydrol 335:180–193
Mehrotra R, Srikanthan R, Sharma A (2006) A comparison of three stochastic multi-site precipitation occurrence generators. J Hydrol 331:280–292
Qian B, Corte-Real J, Xu H (2002) Multisite stochastic weather models for impact studies. Int J Climatol 2002:1377–1397
Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41
Rajagopalan B, Lall U (1999) A k-nearest neighbor simulator for daily precipitation and other weather variables. Water Res Res 35(10):3089–3101
Rajagopalan B, Lall U, Tarboton DG (1997a) Evaluation of kernel density estimation methods for daily precipitation resampling. Stoch Hydrol Hydraul 11(6):523–547
Rajagopalan B, Lall U, Tarboton DG, Bowles DS (1997b) Multivariate nonparametric resampling scheme for generation of daily weather variables. Stoch Hydrol Hydraul 11:523–547
Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17(1):182–190
Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables. ARS (USA) 1–83
Semenov MA (2008) Simulation of extreme weather events by a stochastic weather generator. Clim Res 35:203–212
Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414
Semenov P-BS, Pilkington-Bennett S, Calanca P (2013) Validation of ELPIS 1980–2010 baseline scenarios using the European Climate Assessment observed dataset. Clim Res 51:1–9
Sharif M, Burn DH (2007) Improved k-nearest neighbor weather generating model. J Hydrol Eng 12(1):42–51
Srikanthan R, Pegram GGS (2009) A nested multisite daily rainfall stochastic generation model. J Hydrol 371:142–153
Stern RD, Coe R (1984) A model fitting analysis of daily rainfall data. J R Stat Soc Ser A Gen 147:1–34
Wallis TW, Griffiths JF (1997) Simulated meteorological input for agricultural models. Agric Forest Meteorol 88:241–258
Wheater H, Chandler R, Onof C, Isham V, Bellone E, Yang C, Lekkas D, Lourmas G, Segond M-L (2005) Spatial-temporal rainfall modelling for flood risk estimation. Stoch Environ Res Risk Assess 19(6):403–416
Wilks DS (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydrol 210:178–191
Wilks DS (1999) Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric Forest Meteorol 96:85–101
Wilks DS, Wilby RL (1999) The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23:329–357
Woolhiser DA (1992) Modeling daily precipitation: progress and problems. Stat Environ Earth Sci 5:71–89
Yang C, Chandler RE, Isham VS, Wheater HS (2005) Spatial-temporal rainfall simulation using generalized linear models. Water Resour Res 41:1–13. doi:10.1029/2004WR003739
Yates D, Gangopadhyay S, Rajagopalan B, Strzepek K (2003) A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour Res 39:1199
Acknowledgments
Research partially supported by NSF EaSM grant 1049109. Thanks to Guillermo Podesta for providing daily weather data for Argentine Pampas.
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Verdin, A., Rajagopalan, B., Kleiber, W. et al. Coupled stochastic weather generation using spatial and generalized linear models. Stoch Environ Res Risk Assess 29, 347–356 (2015). https://doi.org/10.1007/s00477-014-0911-6
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DOI: https://doi.org/10.1007/s00477-014-0911-6