Friday, September 8, 2017

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.

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 @

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.


Other available codes

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