Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany
Creators: name: Nguyen, Viet Dung
name: Merz, Bruno
name: Hundecha, Yeshewatesfa
name: Haberlandt, Uwe
name: Vorogushyn, Sergiy
Publisher: name: Chichester [u.a.] : Wiley
Year: 2021
Subjects: subject: correlation
subject: extreme
subject: flood
subject: large-scale
subject: multi-variate
subject: weather generator
subject: Normal distribution
subject: Precipitation (meteorology)
subject: Risk assessment
subject: Solar radiation
subject: Stochastic systems
subject: Auto regressive models
subject: Comprehensive evaluation
subject: Extreme precipitation
subject: Marginal distribution
subject: Spatial correlation matrix
subject: Spatial correlation structures
subject: Statistical properties
subject: Transition probabilities
subject: Floods
subject: extreme event
subject: flooding
subject: multivariate analysis
subject: precipitation (climatology)
subject: probability
subject: weather
subject: Germany
subject: Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften

description: In this work, we present a comprehensive evaluation of a stochastic multi-site, multi-variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km2. The regional weather generator, which is based on a first-order multi-variate auto-regressive model, is setup using 53-year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi-day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma-Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non-precipitation variables. The weather generator can be used to generate synthetic event footprints for large-scale trans-basin flood risk assessment. © 2021 The Authors. International Journal of Climatology published by John Wiley &amp; Sons Ltd on behalf of Royal Meteorological Society.