scholarly journals Fourth order, conservative discretization of horizontal Euler equations in the COSMO model and regional climate simulations

2016 ◽  
Vol 25 (5) ◽  
pp. 577-605 ◽  
Author(s):  
Jack Ogaja ◽  
Andreas Will
2016 ◽  
Vol 9 (9) ◽  
pp. 3393-3412 ◽  
Author(s):  
David Leutwyler ◽  
Oliver Fuhrer ◽  
Xavier Lapillonne ◽  
Daniel Lüthi ◽  
Christoph Schär

Abstract. The representation of moist convection in climate models represents a major challenge, due to the small scales involved. Using horizontal grid spacings of O(1km), convection-resolving weather and climate models allows one to explicitly resolve deep convection. However, due to their extremely demanding computational requirements, they have so far been limited to short simulations and/or small computational domains. Innovations in supercomputing have led to new hybrid node designs, mixing conventional multi-core hardware and accelerators such as graphics processing units (GPUs). One of the first atmospheric models that has been fully ported to these architectures is the COSMO (Consortium for Small-scale Modeling) model.Here we present the convection-resolving COSMO model on continental scales using a version of the model capable of using GPU accelerators. The verification of a week-long simulation containing winter storm Kyrill shows that, for this case, convection-parameterizing simulations and convection-resolving simulations agree well. Furthermore, we demonstrate the applicability of the approach to longer simulations by conducting a 3-month-long simulation of the summer season 2006. Its results corroborate the findings found on smaller domains such as more credible representation of the diurnal cycle of precipitation in convection-resolving models and a tendency to produce more intensive hourly precipitation events. Both simulations also show how the approach allows for the representation of interactions between synoptic-scale and meso-scale atmospheric circulations at scales ranging from 1000 to 10 km. This includes the formation of sharp cold frontal structures, convection embedded in fronts and small eddies, or the formation and organization of propagating cold pools. Finally, we assess the performance gain from using heterogeneous hardware equipped with GPUs relative to multi-core hardware. With the COSMO model, we now use a weather and climate model that has all the necessary modules required for real-case convection-resolving regional climate simulations on GPUs.


2013 ◽  
Author(s):  
Wuyin Lin ◽  
Minghua Zhang ◽  
Juanxiong He ◽  
Xiangmin Jiao ◽  
Ying Chen ◽  
...  

Author(s):  
Jennifer Tibay ◽  
Faye Cruz ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Thanh Ngo‐Duc ◽  
...  

2007 ◽  
Vol 87 (1-2) ◽  
pp. 35-50 ◽  
Author(s):  
Holger Göttel ◽  
Jörn Alexander ◽  
Elke Keup-Thiel ◽  
Diana Rechid ◽  
Stefan Hagemann ◽  
...  

2021 ◽  
Author(s):  
Emanuela Pichelli ◽  
Erika Coppola ◽  
Stefan Sobolowski ◽  
Nikolina Ban ◽  
Filippo Giorgi ◽  
...  

2016 ◽  
Vol 23 (6) ◽  
pp. 375-390 ◽  
Author(s):  
Katrin Sedlmeier ◽  
Sebastian Mieruch ◽  
Gerd Schädler ◽  
Christoph Kottmeier

Abstract. Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease.


2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.


2018 ◽  
Vol 10 (6) ◽  
pp. 1245-1265 ◽  
Author(s):  
A. Gettelman ◽  
P. Callaghan ◽  
V. E. Larson ◽  
C. M. Zarzycki ◽  
J. T. Bacmeister ◽  
...  

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