Optimization of high resolution COSMO model performance over Switzerland and Northern Italy

Voudouri, A.; Khain, Pavel; Carmona, I.; Avgoustoglou, E.; Kaufmann, P.; Grazzini, Federico; Bettems, Jean-Marie

doi:10.1016/j.atmosres.2018.05.026

Cited by 19 publications

(22 citation statements)

References 18 publications

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“…Bellprat et al (2012b) firstly used their method for the calibration of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM for the Coordinated Regional Climate Downscaling Experiment (CORDEX; Giorgi et al, 2009) European domain. The same method has successively been employed in the study of Bellprat et al (2016) for investigating the transferability of the COSMO-CLM model configuration to other regions such as the North America CORDEX domain and for the tuning of the same model for high-resolution numerical weather predictions over western Europe (Voudouri et al, 2017(Voudouri et al, , 2018.…”

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confidence: 99%

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Russo

Sørland

Kirchner³

et al. 2020

Geosci. Model Dev.

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Abstract. The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent subgrid-scale processes. In order to assess a model's reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for regional climate models (RCMs), whose performance is domain dependent. In this study, the parameter space of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM is investigated for the Central Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, using a perturbed physics ensemble (PPE) obtained by performing 1-year simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for subregions characterized by different climate conditions, assessing by which degree it is possible to improve model performance by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a subset of model parameters present relevant changes in model performance for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behaviour among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameter perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be retuned, and its parameter uncertainty properly investigated, when setting up model experiments for different domains of study.

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confidence: 99%

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Russo

Sørland

Kirchner³

et al. 2020

Geosci. Model Dev.

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“…Bellprat et al (2012b) applied a quadratic metamodel on a subsample of model parameters in order to objectively tune the model for the region. Their method is considered the reference for COSMO-CLM for determining optimal parameters values and has been further developed and applied to the Mediterranean region by Avgoustoglou et al (2017) and for higher resolution for the Alpine region by Voudouri et al (2018). Bellprat et al (2016) additionally used the same method for the European and the North American domain, finding quite similar values of optimal model parameters for the two regions.…”

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confidence: 99%

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

et al. 2019

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Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role.In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is twofold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration.Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of cli-matological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability.This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

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“…It was found that this method is affordable in terms of computing resources and effective in terms of improved forecast quality. Successively, Voudouri et al [13] applied the proposed methodology for the calibration of COSMO-LM at high horizontal resolution (2 km) over a domain including Switzerland and Northern Italy. They found that this method allows a temperature bias reduction of about 0.2 • C and an improvement of the overall performances of the model.…”

Section: Introductionmentioning

confidence: 99%

“…The sensitivity analysis to parameters, through a tuning procedure, is aimed to select those that have been shown to play a significant role in determining model response [12]. As stated in [13], the computational resources required for the application of automatic calibration methods are rather heavy due to the high number of parameters to be considered and the related number of simulations to be performed. For example, the cost of the method proposed in [13] is associated with the number of simulations required to fit the meta-model: as demonstrated in [11], the minimum number of simulations for calibrating n parameters is equal to 2n + n × (n − 1)/2.…”

Section: Introductionmentioning

confidence: 99%

“…As stated in [13], the computational resources required for the application of automatic calibration methods are rather heavy due to the high number of parameters to be considered and the related number of simulations to be performed. For example, the cost of the method proposed in [13] is associated with the number of simulations required to fit the meta-model: as demonstrated in [11], the minimum number of simulations for calibrating n parameters is equal to 2n + n × (n − 1)/2. Thus, calibration of a model over an entire year and adopting a fine resolution, such as 1 km, requires a considerable amount of computing power.…”

Section: Introductionmentioning

confidence: 99%

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A Sensitivity Analysis with COSMO-LM at 1 km Resolution over South Italy

Voudouri¹,

Mercogliano

2020

Atmosphere

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The results of a sensitivity analysis based on COSMO-LM (COnsortium for Small-Scale MOdeling—Lokal Model) simulations driven by ECMWF-IFS (European Centre for Medium-Range Weather Forecasts—Integrated Forecasting System). global data over a domain located in southern Italy are presented. Simulations have been performed at very high resolution (about 1 km). The main aim of this study is to individuate the most sensitive physical and numerical parameters of the model configuration, comparing a set of 18 simulations in terms of temperature and precipitation against ground observations. The parameters that result in having more influence for a proper representation of temperature and precipitation fields are the heat resistance length of laminar layer (which accounts for the high complexity of the interaction of the atmosphere with the underlying surface) and the minimal diffusion coefficient for heat. Temperature values are strongly influenced also by the vertical variation of critical relative humidity. An optimized tuning of these parameters allows COSMO-LM to improve the representation of simulated main features of this area, with significant bias reductions.

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Optimization of high resolution COSMO model performance over Switzerland and Northern Italy

Cited by 19 publications

References 18 publications

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

A Sensitivity Analysis with COSMO-LM at 1 km Resolution over South Italy

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