Calibration of a convective parameterization scheme in the WRF model and its impact on the simulation of East Asian summer monsoon precipitation

Yang, Ben; Zhang, Yaocun; Qian, Yun; Huang, Anning; Yan, Huiping

doi:10.1007/s00382-014-2118-4

Cited by 54 publications

(30 citation statements)

References 87 publications

(124 reference statements)

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“…In addition, compared to VFRGCM, RCM has more complex model physical parameterizations, which will inevitably induce large model uncertainty. The uncertainty in model physics can evidently affect the simulated results (Yang et al, 2015a(Yang et al, , 2015b. This may be one of the reasons for the more realistic trend of air temperature produced by the VFRGCM compared with RCM dynamical downscaling.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Guo

Wang

2016

Adv. Atmos. Sci.

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Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.

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Section: Discussionmentioning

confidence: 99%

“…However, RCM dynamic downscaling produced a smaller bias than the VFRGCM despite their almost equal horizontal resolutions. The differences may be attributable to the more comprehensive physical processes of the RCM dynamical downscaling approach compared with the VFRGCM (Yang et al, 2015a(Yang et al, , 2015b.…”

Section: Discussionmentioning

confidence: 99%

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Guo

Wang

2016

Adv. Atmos. Sci.

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“…Moreover, the algorithm has convergence issues when the simplex becomes ill-conditioned. Besides the downhill simplex, a few global optimization algorithms are introduced to tune uncertain parameters of climate system models, such as simulated stochastic approximation annealing (SSRR) , MVFSA (Yang et al, 2014), and multi-objective particle swarm optimization (MOPSO) (Gill et al, 2006). SSRR requires at least 10 000 steps to get a stable solution (Liang et al, 2013), and MVFSA also requires thousands of steps to get a stable solution (Jackson et al, 2004).…”

Section: T Zhang Et Al: Parameter Optimization Methods For Model Tuningmentioning

confidence: 99%

An automatic and effective parameter optimization method for model tuning

Zhang

Wang

et al. 2015

Geosci. Model Dev.

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Abstract. Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

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“…The Weather Research and Forecasting (WRF) model is a latest generation mesoscale NWP system that has been widely used for rainfall simulation and prediction (Efstathiou et al, 2013;Yang et al, 2015). Although rainfall products can be directly used due to the high accuracy of rain or no rain predictions, the WRF model still cannot ensure accurate rainfall quantities or spatiotemporal distributions 10 (GPS) radio occultation (RO) data were used with WRF-3DVar (Ha et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Doppler radar and GTS Data Assimilation for NWP Rainfall Prediction of an Extreme Summer Storm in Northern China: from the Hydrological Perspective

Liu¹,

Tian²,

Yan³

et al. 2018

Preprint

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Abstract. Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction 10 (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations from the Doppler weather radar and the Global Telecommunication System (GTS) in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing-Tianjin-Hebei region of northern China on 21 July 2012 is selected in this study. The Weather Research and Forecasting (WRF) model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-15 dimensional variational (3D-Var) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Results show that the assimilation can largely improve the WRF rainfall products especially the accumulative process of rainfall, which is of great importance for hydrologic applications through the rainfall-runoff transformation process. Both radar reflectivity and GTS data are good choices for assimilation in improving the rainfall products, whereas special attentions should be paid for assimilating radial 20 velocity where unsatisfactory results are always found. Simultaneously assimilating GTS and radar data always perform better than assimilating radar data alone. The inclusion of GTS data in the nested domains when radar reflectivity and radial velocity are assimilated in the innermost domain show the best results among all the 11 assimilation modes. The assimilation efficiency of the GTS data is higher than both radar reflectivity and radial velocity considering the number of data assimilated and its effect. It is also found that the assimilation of more observations cannot guarantee further improvement of 25 the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are also made.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Calibration of a convective parameterization scheme in the WRF model and its impact on the simulation of East Asian summer monsoon precipitation

Cited by 54 publications

References 87 publications

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

An automatic and effective parameter optimization method for model tuning

Evaluation of Doppler radar and GTS Data Assimilation for NWP Rainfall Prediction of an Extreme Summer Storm in Northern China: from the Hydrological Perspective

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