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Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge...
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... was decided that the simulation must not generate more than 1% duplicated days. Figure 2 shows the results for January. The figure shows the mean of 50 runs and a fitted two-degree polynomial curve to derive a reasonable value of k. ...
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... mean numbers of dry and wet days are well simulated ( Figure 12). In the FSS version, the mean length of dry spells is under- estimated slightly (2.9%), and the mean length of wet spells is overestimated (1.8%) ( Figure 13). ...
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... demonstrate the seasonality better, the validation was done at monthly time scales. The mean monthly temperature of all temperature stations is plotted in Figure 20 and is well reproduced. ...
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... the standard deviation ( Figure 21) all observations are within the grey areas. The average deviation at all stations is below 1%. ...
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... model also performs well with respect to extremes. For minimum temperatures (Figure 22), almost all observations are in the simulated grey areas, with some exceptions (especially in January). The dotted lines in Figure 22 denote the 5 th and 95 th percentiles of simulations without the suggested modulus trans- formations. ...
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... minimum temperatures (Figure 22), almost all observations are in the simulated grey areas, with some exceptions (especially in January). The dotted lines in Figure 22 denote the 5 th and 95 th percentiles of simulations without the suggested modulus trans- formations. Minor improvements can be detected for autumn and winter at station 15 ( Figure 22(a)), and spring and summer at sta- tion 19 (Figure 22(c)). ...
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... dotted lines in Figure 22 denote the 5 th and 95 th percentiles of simulations without the suggested modulus trans- formations. Minor improvements can be detected for autumn and winter at station 15 ( Figure 22(a)), and spring and summer at sta- tion 19 (Figure 22(c)). ...
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... dotted lines in Figure 22 denote the 5 th and 95 th percentiles of simulations without the suggested modulus trans- formations. Minor improvements can be detected for autumn and winter at station 15 ( Figure 22(a)), and spring and summer at sta- tion 19 (Figure 22(c)). ...
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... situation looks different for the simulated maximum temperatures ( Figure 23). Without the modulus transformation, Figure 20. ...
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... situation looks different for the simulated maximum temperatures ( Figure 23). Without the modulus transformation, Figure 20. Monthly characteristics of mean temperature for all temperature stations. ...
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... results can be achieved only when transforming the data. The ARMA models are able to reproduce the autocorrelation (Figure 24) and the inter-site correlations very well (Figure 25). The correlated random numbers are able to reproduce the spa- tial temperature fields. ...
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... results can be achieved only when transforming the data. The ARMA models are able to reproduce the autocorrelation (Figure 24) and the inter-site correlations very well (Figure 25). The correlated random numbers are able to reproduce the spa- tial temperature fields. ...
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... be conducted with cor- related uniform random numbers, for example through a Cholesky decomposition (see Watkins (2010) for details), to make sure that there are similar data pairs that can be reshuffled. The correlated random numbers can match the inter-site correlations of the observations and should be computed separately for each season. Fig. 2 gives an overview of the proposed precipitation generator and all components described in this ...
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... Other methods alter the temporal dependence structure of hydroclimatic timeseries, for instance by modifying the seasonality or the persistence of wet and dry conditions. Various techniques are used in this case, including Markov chain models (Breinl et al., 2015;Ullrich et al., 2021), spectral analysis and wavelet transforms (Steinschneider and Brown, 2013;Quinn et al., 2018;Fletcher et al., 2023), and copula methods (Borgomeo et al., 2015b;Nazemi et al., 2020). Lastly, Borgomeo et al. (2015a) proposes a versatile tool that lets the user choose the objective function of the streamflow generator to optimize the streamflow properties of interest. ...
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... In this respect, parametric approaches falling into the class of the time series models have been proven a viable option to address the problem of large-domain precipitation simulation. However, also in this case, only few examples showed capabilities to simulate precipitation at a large number of locations Serinaldi & Kilsby, 2014b;Ullrich et al., 2021), while more often they are applied to a few tens of sites (Benoit et al., 2022;Breinl et al., 2013Breinl et al., , 2015Verdin et al., 2019) or even less. In fact, large-domain modeling faces limitations such as the dramatic increase of CPU time with increasing number of locations, or the feasibility of Cholesky factorization of large covariance matrices (Benoit et al., 2018). ...
Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water‐related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large‐domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large‐domain simulations in short time. The proposed approach focuses on correlated processes with mixed (zero‐inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed‐Uniform space enables a substantial simplification of the so‐called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1,000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non‐Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, and the use of advection, anisotropy, and nonstationary spatiotemporal correlation functions.
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... The SWR uses the time (t) dependent temperature model published by Breinl et al. [23]. Let T t be the temperature, N t be the normal temperature (the average temperature for that day in the year), and σ t be the standard deviation for the normal temperature. ...
... This mean temperature is smoothed using a fifth-order Fourier series as a low pass filter to produce N t . Such Fourier series smoothing of mean temperature to yield normal temperature is a common technique [23][24][25]. ...
... The SWR uses the time (t) dependent temperature model published by Breinl et al [23]. Let Tt be the temperature, Nt be the normal temperature (the average temperature fo that day in the year), and σt be the standard deviation for the normal temperature. ...
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Over the last two decades, scientific discovery has increasingly been driven by the large availability of data from a multitude of sources, including high-resolution simulations, observations and instruments, as well as an enormous network of sensors and edge components. In such a dynamic and growing landscape where data continue to expand, advances in Science have become intertwined with the capacity of analysis tools to effectively handle and extract valuable information from this ocean of data. In view of the exascale era of supercomputers that is rapidly approaching, it is of the utmost importance to design novel solutions that can take full advantage of the upcoming computing infrastructures. The convergence of High Performance Computing (HPC) and data-intensive analytics is key to delivering scalable High Performance Data Analytics (HPDA) solutions for scientific and engineering applications. The aim of this paper is threefold: reviewing some of the most relevant challenges towards HPDA at scale, presenting a HPDA-enabled version of the Ophidia framework and validating the scalability of the proposed framework through an experimental performance evaluation carried out in the context of the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE). The experimental results show that the proposed solution is capable of scaling over several thousand cores and hundreds of cluster nodes. The proposed work is a contribution in support of scientific large-scale applications along the wider convergence path of HPC and Big Data followed by the scientific research community.
... Other studies have also looked into using WGs as constituting a downscaling tool in climate change impact studies because of the relative ease with which their parameters can be modified to represent climate variability (Semenov and Barrow 1997, Kilsby et al. 2007, Zhuang et al. 2016, Keller et al. 2017. Despite this advantage, only a few studies have looked at the potential of using stochastic WGs for long-term streamflow forecasting (Li et al. 2013, Breinl et al. 2015, Shield and Dai 2015, Breinl 2016, and an even smaller number implemented a WG into streamflow forecasts. Of note, Šípek and Daňhelka (2015) used a WG for ESP based on a limited number of observed years selected on the basis of large-scale climate indices, and Hwang et al. (2011) generated several precipitation scenarios to study the impact of uncertainty on streamflow simulations. ...
Resampling historical time series remains one of the main approaches used to generate long-term probabilistic streamflow forecasts, while there is a need to develop more flexible approaches taking into account non-stationarities. One possible approach is to use a modelling chain consisting of a stochastic weather generator and a hydrological model. However, the ability of this modelling chain to generate adequate probabilistic streamflows must first be evaluated. The aim of this paper is to compare the performance of a stochastic weather generator against resampling historical meteorological time series in order to produce ensemble streamflow forecasts. The comparison framework is based on 30 years of forecasts for a single Canadian watershed. Forecasts resulting from the two methods are evaluated using the continuous ranked probability score (CRPS) and rank histograms. Results indicate that while there are differences between the methods, they nevertheless perform similarly, thus showing that weather generators can be used as substitutes for resampling the historical past.
... Risk assessment studies often apply stochastic generators in order to generate long series of different meteorological variables, such as precipitation, temperature, solar radiation or wind (e.g., Richardson, 1981;Wilks and Wilby, 1999;Apipattanavis et al., 2007;Leander and Buishand, 2009;Steinschneider and Brown, 2013;Chen et al., 2014;Li, 2014;Breinl et al., 2015). These long scenarios are then used as inputs of environmental models. ...
