Abstract
A problem often encountered in agricultural and ecological modeling is to disaggregate daily precipitations into vectors of hourly precipitations used as input values by crop and plant models. A stochastic model for rainfall data, based on transformed censored latent Gaussian process is described. Compared to earlier similar work, our transform function provides an accurate fit for both the body and the heavy tail of the precipitation distribution. Simple empirical relationships between the parameters estimated at different time scales are established. These relationships are used for the disaggregation of daily values at stations where hourly values are not available. The method is illustrated on two stations located in the Paris basin.
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References
Ailliot P, Allard D, Monbet V, Naveau P Stochastic weather generators : a review of weather type models (submitted)
Ailliot P, Monbet V (2012) Markov-switching autoregressive models for wind time series. Environ Model Softw 30:92–101
Ailliot P, Thompson C, Thomson P (2009) Space time modeling of precipitation using a hidden Markov model and censored Gaussian distributions. J R Stat Soc C 58:405–426
Allard D (2012) Modelling spatial and spatio-temporal non Gaussian processes. In: Porcu E, Montero JM, Schlather M (eds) Advances and challenges in space-time modelling of natural events, Lecture Notes in Statistics 207. Springer, pp 141–164
Allcroft DJ, Glasbey CA (2003) A latent Gaussian Markov random field model for spatio-temporal rainfall disaggregation. J R Stat Soc C 55:1952–2005
Bevilacqua M, Gaetan C, Mateu J, Porcu E (2012) Estimating space and space-time covariance functions for large data sets : a weighted composite likelihood approach. J Am Stat Assoc 107:268–280
Brisson N, Levrault F (2010) Changement climatique, agriculture et forêt en France: simulations d’impacts sur les principales espèces. Le Livre Vert du projet CLIMATOR (2007–2010). ADEME, Orléans
Bürger G, Heistermann M, Bronstert A (2014) Towards sub-daily rainfall disaggregation via Clausius–Clapeyron. J Hydrometeorol doi:10.1175/JHM-D-13-0161.1
Caubel J, Launay M, Lannou C, Brisson N (2012) Generic response functions to simulate climate-based processes in models for the development of airborne fungal crop pathogens. Ecol Model 242:92–104
Chen J, Brissette FP, Leconte R (2012) WeaGETS-a Matlab-based daily scale weather generator for generating precipitation and temperature. Procedia Environ Sci 13:2222–2235
Chilès JP, Delfiner P (2012) Geostatistics: modeling spatial uncertainty, 2nd edn. Wiley, New York
Cressie NAC (1993) Statistics for spatial data. Wiley, New York
Flecher C, Naveau P, Allard D, Brisson N (2010) A stochastic daily weather generator for skewed data. Water Resour Res 46:W07519
Furrer EM, Katz RW (2007) Generalized linear modeling approach to stochastic weather generators. Clim Res 34:129–144
Hansen JW, Ines AVM (2005) Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agric For Meteorol 131:233–246
Huber L, Gillespie TJ (1992) Modeling leaf wetness in relation to plant-disease epidemiology. Annu Rev Phytopathol 30:553–577
Hughes JP, Guttorp P, Charles SP (1999) A non-homogeneous hidden Markov model for precipitation occurrence. Appl Stat 48:15–30
Katz RW (1977) Precipitation as a chain-dependant process. J Appl Meteorol 16:671–676
Katz RW, Parlange MB (1995) Generalizations of chain-dependent processes: application to hourly precipitation. Water Resour Res 31:1331–1341
Kleiber W, Katz RW, Rajagopolan B (2013) Daily apatio-temporal precipitation simulation using latent and transformed Gaussian processes. Water Resour Res 48:W01523
Hasan MM, Kunn PK (2010) A simple Poisson-gamma model for modelling rainfall occurrence and amount simultaneously. Agric For Meteorol 150:1319–1330
Lennartsson J, Baxevani A, Chen D (2008) Modelling precipitation in Sweden using multiple step Markov chains and a composite model. J Hydrol 363:42–59
Lantuéjoul C (2002) Geostatistical simulations. Springer, Berlin
Lindsay B (1988) Composite likelihood methods. Contemp Math 80:221–239
Olsson J (1998) Evaluation of a scaling cascade model for temporal rainfall disaggregation. Hydrol Earth Syst Sci 2:19–30
Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41
Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190
Schwartz G (1978) Estimating the dimension of a model. Ann Stat 6:261–464
Semenov AM, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:85–107
Tallis GM (1961) The moment generating function of the truncated multi-normal distribution. J R Stat Soc B 23:223–229
Thompson C, Thomson P, Zheng X (2007) Fitting a multisite rainfall model to New Zealand data. J Hydrol 340:25–39
Varin C, Vidoni P (2005) A note of composite likelihood inference and model selection. Biometrika 92:519–528
Wilks DS (2010) Use of stochastic weather generators for precipitation downscaling. Wiley Interdiscip Rev: Clim Change 1:898–907
Wilks DS, Wilby RL (1999) The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23:329–357
Zheng X, Katz RW (2008) Simulation of spatial dependence in daily rainfall using multisite generators. Water Resour Res 44:W09403
Acknowledgments
We thank Emilio Porcu and Alessandro Fassò for inviting us to join this special issue. Among others, we thank Pierre Ailliot, Valérie Monbet, Philippe Naveau and Carlo Gaetan for fruitful discussions about this work. This work was part of the project CLIMATOR (2007–2010) (http://w3.avignon.inra.fr/projet_climator/) funded by ANR (The French National Research Agency). CLIMATOR was lead by Nadine Brisson who left us in Octobre 2011. We dedicate this work to her memory.
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Allard, D., Bourotte, M. Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process. Stoch Environ Res Risk Assess 29, 453–462 (2015). https://doi.org/10.1007/s00477-014-0913-4
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DOI: https://doi.org/10.1007/s00477-014-0913-4