Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

doi:10.1007/s00382-017-3729-3

Cited by 22 publications

(18 citation statements)

References 45 publications

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“…With the application of model calibrations for improving numerical weather forecasting, the sensitivity analysis results here can provide important land surface parameter candidates for calibration or SA problems associated with numerical weather prediction models (Santanello et al 2013;Quan et al, 2016;Duan et al, 2017;Di et al, 2018). Duan et al (2017) and Di et al (2018) calibrated several parameters from different physical parameterizations of the Weather Research and Forecasting (WRF) model, in which one or two crucial soil parameters from the coupled land surface model were incorporated. They reported better forecast results for precipitation and temperature using the optimal parameter values than using the default parameter values.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Peng

Sun

2020

Tellus A: Dynamic Meteorology and Oceanography

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To provide scientific support for improvements in land surface modeling on the Tibetan Plateau (TP) by reducing uncertainties in the physical parameters of models, comprehensive uncertainty and sensitivity evaluations were performed for the simulation of surface soil moisture (SSM). Five observational stations were selected for the study. The conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach and the Common Land Model (CoLM) with 28 uncertain parameters were employed to evaluate the maximal uncertainty of the simulated SSM. The uncertainty analysis indicated that the parameter errors could induce large uncertainties. These uncertainties in the SSM generally fluctuated over the range from 0.33 to 0.64 m 3 m À3 in terms of absolute changes and from 235% to 510% in terms of percentage changes. The uncertainty analysis addressed the necessity of decreasing the uncertainties in the parameters. When resources are limited, the most important and sensitive parameter set should first be identified in order to reduce uncertainties. To find this parameter set, a sensitivity analysis framework based on the CNOP-P approach was applied. The results showed that the most sensitive and important combinations of 4 parameters changed slightly among the study sites and consisted of soil texture-related parameters. Although the most sensitive and important parameter combinations only had 4 elements, the uncertainties that they could induce accounted for a large proportion of the uncertainties caused by all 28 uncertain parameters. Furthermore, the decreases in parameter errors, which were derived from the CNOP-Ps of the most sensitive and important parameter combinations, led to the maximal reductions in the uncertainties of the simulated SSM. The above results imply that we should prioritize reducing the uncertainty of sensitive parameters or parameter combinations in order to improve prediction and simulation abilities.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Peng

Sun

2020

Tellus A: Dynamic Meteorology and Oceanography

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“…The ASMO method had been used to optimize the parameters of the weather forecasting and research model for improving precipitation and typhoon intensity simulations [38,39]. The advantage of the method is that it speeds up the search, with the help of the statistical surrogate model (or regression model).…”

Section: Adaptive Surrogate Modeling-based Optimization (Asmo) Paramementioning

confidence: 99%

Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method

Zhang

Duan

et al. 2020

Water

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Land surface evapotranspiration (ET) is important in land-atmosphere interactions of water and energy cycles. However, regional ET simulation has a great uncertainty. In this study, a highly-efficient parameter optimization framework was applied to improve ET simulations of the Community Land Model version 4.0 (CLM4) in China. The CLM4 is a model at land scale, and therefore, the monthly ET observation was used to evaluate the simulation results. The optimization framework consisted of a parameter sensitivity analysis (also called parameter screening) by the multivariate adaptive regression spline (MARS) method and sensitivity parameter optimization by the adaptive surrogate modeling-based optimization (ASMO) method. The results show that seven sensitive parameters were screened from 38 adjustable parameters in CLM4 using the MARS sensitivity analysis method. Then, using only 133 model runs, the optimal values of the seven parameters were found by the ASMO method, demonstrating the high efficiency of the method. For the optimal parameters, the ET simulations of CLM4 were improved by 7.27%. The most significant improvement occurred in the Tibetan Plateau region. Additional ET simulations from the validation years were also improved by 5.34%, demonstrating the robustness of the optimal parameters. Overall, the ASMO method was found to be efficient for conducting parameter optimization for CLM4, and the optimal parameters effectively improved ET simulation of CLM4 in China.

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“…P21 is the profile shape exponent for calculating the momentum diffusivity coefficient. A higher value of P21 increases the momentum diffusivity leading to an increase in the eddy turbulence diffusivity and impacting the planetary boundary layer height and convection formation (Di et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

Assessment of WRF Model Parameter Sensitivity for High‐Intensity Precipitation Events During the Indian Summer Monsoon

Chinta

Sai

Balaji

2021

Earth and Space Science

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Default values for many parameters in Numerical Weather Prediction models are typically adopted based on theoretical or experimental investigations by scheme designers. Short‐range forecasts are substantially affected by the specification of parameters in the Weather Research and Forecasting (WRF) model. The presence of a multitude of parameters and several output variables in the WRF model renders appropriate parameter value identification quite challenging. This study aims to identify the parameters that most strongly influence the model output variables using a Global Sensitivity Analysis (GSA) method. Morris One‐At‐a‐Time (MOAT), a GSA method, is used to identify the sensitivities of 23 chosen tunable parameters corresponding to seven physical parameterization schemes of the WRF model. The sensitivity measures (MOAT mean and standard deviation) are evaluated for 11 output variables simulated by the WRF model, corresponding to different parameters. Twelve high‐intensity 4‐day precipitation events during the Indian summer monsoon during 2015, 2016, and 2017 over India's monsoon core region are considered for the study. Though the parameter sensitivities vary depending on the model output variable, overall results suggest a general trend. The consistency of sensitivity analysis results with different initial and lateral boundary conditions is also assessed.

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Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

Cited by 22 publications

References 45 publications

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method

Assessment of WRF Model Parameter Sensitivity for High‐Intensity Precipitation Events During the Indian Summer Monsoon

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