Weather Generation (according to Korbinian Breinl)

How one can reasonably cope with simulating future hydrometeorological forcing for hydrological purposes ? Clearly, since meteorology is dominated by unpredictable phenomena (in the sense of chaotic ones) and we cannot pretend to simply use forecasts, when we are looking just a little far away. An option would be to use climatic models and doing dynamic downscaling of their outcomes. The previous lecture given by Jeremy Pal (GS), followed this research path. However, we can produce statistical weather scenarios using stochastic weather generators (SWG) too, once  we have an idea of what will be the mean characteristics of such system.
Literature is full of SWG that covers mainly temperature and rainfall, but it seems there exists systems also that covers other meteorological variables, like wind and radiation.
Today we had a talk on the subject given at our Department of Civil, Environmental and Mechanical Engineering given by Korbinian Breinl. He is at present a post-doc at Uppsala University with Giuliano Di Baldassarre (GS) and we are collaborating in the SteepStreams project.

As usual you can find his presentation by clicking on the figure above. But you can see the talk on YouTube

Besides flooding (and solid flooding) which is one of the scopes of the projects, I hope we succeed in modeling all the main components of the hydrological cycle by a combine use of Korbinian’s Generator and JGrass NewAGE. I have also the video record of his presentation, but not yet the approval to share it publicly on YouTube. However you can ask it to me writing to abouthydrology @ gmail.com.

Korbinian's generator is written in Matlab, and it is available through Github.

Please find below a reference list which include, besides Korbinian’s one, some other references that I could gather through time.

References

• Apipattanavis, S., G. Podesta´, B. Rajagopalan, and R. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resour. Res., 43, 1–19. 
• Baigorria, G. A., & Jones, J. W. (2010). GiST: A stochastic model for generating spatially and temporallycorrelated daily rainfall data. Journal of Climate, 23(22), 5990-6008.
• Baxevani, A., and J. Lennartsson (2015), A spatiotemporal precipitationgenerator based on a censored latentGaussian field, Water Resour. Res., 51,4338–4358, doi:10.1002/2014WR016455.
• Beersma, J. J., and T. A. Buishand, 2003: Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Climate Res., 25, 121–133.
• Brissette, F. P., M. Khalili, and R. Leconte, 2007: Efficient stochastic generation of multi-site synthetic precipitation data. J. Hydrol., 345, 121–133.
• Breinl, K., Turkington, T. and Stowasser, M. (2015), Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards. Met. Apps, 22: 334–347. doi:10.1002/met.1459
• Breinl, K., Di Baldassarre, G., Lopez, M.G., Hagenlocher, M., Vico, G., Rutgersson, A.,2017. Can weather generation capture precipitation patterns across different climates,spatial scales and under data scarcity? NATURE Scientific Reports 7.
• Burton, A., C. G. Kilsby, H. J. Fowler, P. S. P. Cowpertwait, and P. E. O’Connell, 2008: RainSim: A spatial–temporal stochastic rainfall modelling system. Environ. Modell. Software, 23, 1356–1369.
• Cannon, A., 2008: Probabilistic multisite precipitation downscaling by an expanded Bernoulli–Gamma density network. J. Hydrometeor., 9, 1284–1300 
• Cordano E. and Eccel E. (2016), Tools for stochastic weather series generation in R environment, Italian Journal of Agrometeorology <doi:10.19199/2016.3.2038-5625.031> (Codes and manual can be retrieved here).
• Donatelli, M., Bellocchi, G., Habyarimana, E., Bregaglio, S., Confalonieri, R., Baruth, B., CLIMA: a weather generator framework, 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009 http://mssanz.org.au/modsim09
• Fatichi, S., Ivanov, V.Y., E. Caporali (2011). Simulation of future climate scenarios with a weather generator, Advances in Water Resources, 34, 448–467, doi:10.1016/j.advwatres.2010.12.013 (code here)
• Fowler, H. J., C. G. Kilsby, P. E. O’Connell, and A. Burton, 2005: A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change. J. Hydrol., 308, 50–66.
• Geng, S., Penning de Vries, F.W.T., Supit, I., 1986. A simple method for generating daily rainfall data. Agric. ForestMeteorol. 36, 363–376.
• Hodges, T., French, V., LeDuc, S.K., 1985. Estimating solar radiation for plant simulation, models. AgRISTARS Tech. Rep. JSC-20239; YM-15e00403, Columbia, MO, USA.
• N. Z. Jovanovic , J. G. Annandale , N. Benadè & G. S. Campbell (2003) CLIMGEN-UP: A user-friendly weather data generator, South African Journal of Plant and Soil, 20:4, 203-205, DOI: 10.1080/02571862.2003.10634936
• Ivanov, V. Y., R. L. Bras, and D. C. Curtis (2007), A weather generator for hydrological, ecological, and agricultural applications, Water Resour. Res., 43, W10406, doi:10.1029/2006WR005364. (code here)
• Keller, D.E., A weather generator for current and future climate conditions, Ph.D. Dissertation, ETH Zurich, 2015
• Khalili, M., R. Leconte, and F. Brissette, 2007: Stochastic multisite generation of daily precipitation data using spatial autocorrelation. J. Hydrometeor., 8, 396–412.
• Koutsoyiannis, D. and Onof, C. Rainfall disaggregation using adjusting procedures on a Poisson clustermodel. J. Hydrol. 246 (2001).
• Leander, R., and T. A. Buishand, 2009: A daily weather generator based on a two-stage resampling algorithm. J. Hydrol., 374, 185–195.
• Müller, H. & Haberlandt, U. Temporal Rainfall Disaggregation with a Cascade Model: From Single-StationDisaggregation to Spatial Rainfall. J Hydrol Eng 20 (2015).
• Palutikof, J. P., C. M. Goodess, S. J. Watkins, and T. Holt, 2002: Generating rainfall and temperature scenarios at multiple sites: Examples from the Mediterranean. J. Climate, 15, 3529– 3548.
• Qian, B., J. Corte-Real, and H. Xu, 2002: Multisite stochastic weather models for impact studies. Int. J. Climatol., 22, 1377– 1397
• Richardson CW. 1981. Stochastic simulation of daily precipitation,temperature, and solar-radiation. Water Resour. Res. 17: 182–190
• Richardson, C.W., Wright, D.A., 1984. WGEN: A Model for Generating Daily Weather Variables. U.S. Department of Agriculture, Agricul- tural Research Service, ARS-8.
• Mikhail A. Semenov, LARS-WG A Stochastic Weather Generator for Use in Climate Impact Studies. 2002
• Mikhail A. Semenov, Roger J. Brooks, Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain, CLIMATE RESEARCH,Vol. 11: 137–148, 1999
• Stöckle, C.O., Nelson, R.L., Donatelli, M., Castellvı`, F., 2001. ClimGen: a flexible weather generation program. In: Bindi, M., Donatelli, M., Porter, J.R., Van Ittersum, M.K. (Eds.), Proceedings of the Second International Symposium on Modelling Cropping Systems, Florence, Italy, pp. 229e230.
• Srikanthan, R., and G. G. S. Pegram, 2009: A nested multisite daily rainfall stochastic generation model. J. Hydrol., 371, 142–153.
• Wilby, R. L., O. J. Tomlinson, and C. W. Dawson, 2003: Multi-site simulation of precipitation by conditional resampling. Climate Res., 23, 183–194.
• Wilks, D. S., 1998: Multisite generalization of a daily stochastic precipitation generation model. J. Hydrol., 210, 178–191
• Wilks, D.S., Wilby, R.L., 1999. The weather generation game: a review of stochastic weather models. Progress in Physical Geography 23, 329e357.

Other available codes

• GMLWGEN (R)
• WeatherGen (C++)