Objective calibration of numerical weather prediction models

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

doi:10.1016/j.atmosres.2017.02.007

Cited by 18 publications

(18 citation statements)

References 21 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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“…On the other side, its increase causes a general increase of temperature, especially the minimum value (up to 1.5 • C). In fact, an increase of tkhmin implies that the turbulent kinetic energy is maintained in stable conditions, eliminating strong inversions [10]. A reduction of tkhmin does not cause variation in precipitation, while its increase causes a growth, since it increases the small convective cloudiness [24].…”

Section: Discussionmentioning

confidence: 99%

“…Baldauf et al [9] presented results of the operational NWP COSMO-LM and the related sensitivity activity performed for the convective scale. In 2017, Voudouri et al [10] examined the feasibility to calibrate a COSMO-LM model using an approach based on an objective multi-variate calibration method built on a quadratic meta-model (MM), originally developed in [11] and then adapted for applications to regional climate models [12]. The MM is a model emulator that performs a calibration based on sampling of the parameter space using several COSMO-LM simulations, and then fitting a (continuous) quadratic regression in this space.…”

Section: Introductionmentioning

confidence: 99%

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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“…The feasibility of calibrating COSMO at about 3 km resolution using an objective multivariate calibration method built on a quadratic metamodel (MM) was investigated by Voudouri et al [8]. This metamodel makes a calibration by sampling the parameter space and then fitting a continuous quadratic regression.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of High-Resolution Simulations with COSMO over South Italy

Mercogliano

2020

Atmosphere

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This study aims to assess the capabilities of a weather forecasting system based on simulations performed with the COSMO (COnsortium for Small-scale Modeling) model over a domain located in southern Italy, employing a spatial resolution of about 1 km, driven by ECMWF-IFS global data. The model is run daily at the Italian Aerospace Research Center (CIRA), and the evaluation was performed from January to May 2018 using a combination of observational data, specifically data provided by the CIRA meteorological station, wind profiler and ceilometer. Moreover, data provided by radio sounding located at Pratica di Mare and ground stations at two other locations were also used. A model configuration optimized through a tuning procedure over the domain considered was employed, while the evaluation was performed by comparing daily values of several variables and using standard monitoring indices. The results highlight that the model has good capability in reproducing daily values of temperature, while precipitation intensity is generally underestimated, even if rain patterns are well captured (alternating rainy and dry days). Good agreement is also reported for wind speed, especially at 100 and 500 m altitude. Regarding radio sounding data, the COSMO model configuration selected can reproduce the vertical profile of temperature and dew point, with the exception of inversion points. Evaluation against ceilometer data is achieved in terms of cloud height and planetary boundary layer height.

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Objective calibration of numerical weather prediction models

Cited by 18 publications

References 21 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

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

Performance Evaluation of High-Resolution Simulations with COSMO over South Italy

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