SC1.33 ECS

So, you've been given a time series, e.g, of hourly precipitation. That's great, but how can you generate as many as you like with exactly the same statistical properties? In this short course you'll find out.

You'll be introduced to a unified method of stochastic modelling and downscaling that makes feasible the generation of time series that preserve any desired marginal probability distribution and correlation structure including features like intermittency. The workshop includes a rapid introduction in the stochastic properties of hydroclimatic processes like precipitation, flooding, wind, temperature, etc., and highlights features like stationarity, cyclostationarity, marginal distributions, correlations structures and intermittency. We'll develop and apply on-the-spot and step-by-step: (a) the iconic AR(1) model, (b) higher order AR models as a method to approach arbitrary correlations structures; (c) the parent-Gaussian framework to simulate time series with any marginal distribution and correlation; and (d) intermittent time series modelling (like precipitation) at any time scale.

Early Career Scientists (ECS) are specifically welcome, and of course, this short course is organized in cooperation with the Young Hydrologic Society (YHS; younghs.com)!

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Co-organized as HS12.4/NH10.4
Convener: Simon Michael Papalexiou | Co-conveners: Yannis Markonis, Amir AghaKouchak, Nilay Dogulu
Thu, 11 Apr, 16:15–18:00
 
Room -2.85
So, you've been given a time series, e.g, of hourly precipitation. That's great, but how can you generate as many as you like with exactly the same statistical properties? In this short course you'll find out.

You'll be introduced to a unified method of stochastic modelling and downscaling that makes feasible the generation of time series that preserve any desired marginal probability distribution and correlation structure including features like intermittency. The workshop includes a rapid introduction in the stochastic properties of hydroclimatic processes like precipitation, flooding, wind, temperature, etc., and highlights features like stationarity, cyclostationarity, marginal distributions, correlations structures and intermittency. We'll develop and apply on-the-spot and step-by-step: (a) the iconic AR(1) model, (b) higher order AR models as a method to approach arbitrary correlations structures; (c) the parent-Gaussian framework to simulate time series with any marginal distribution and correlation; and (d) intermittent time series modelling (like precipitation) at any time scale.

Early Career Scientists (ECS) are specifically welcome, and of course, this short course is organized in cooperation with the Young Hydrologic Society (YHS; younghs.com)!