Impacts of Spectral Nudging Parameters on Dynamical Downscaling in Summer over Mainland China

Mai, Xiaoping; Qiu, Xiaobin; Yang, Yi; Ma, Yuanyuan

doi:10.3389/feart.2020.574754

Cited by 17 publications

(8 citation statements)

References 55 publications

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“…A common nudging coefficient (0.0003 s −1 ) was used for all variables (geopotential, horizontal wind, and temperature) to adjust the strength of the nudging force in the governing equations. The wavenumber truncations were set to 6 and 4, corresponding to cut-off wavelengths of approximately 1095 and 1238 km in the zonal and meridional directions, respectively; this is close to the value of 1000 km suggested by Gómez and Miguez-Macho (2017) and Mai et al (2020).…”

Section: Wrf Model Configurationmentioning

confidence: 99%

Wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the plateau

Chen

Tang

et al. 2023

Clim Dyn

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The Tibetan Plateau (TP), also called the Third Pole, is considered to be “the world water tower”. The northwestern TP (NWTP), which has an average elevation higher than 4800 m, is an arid region where the summer precipitation is largely overestimated by the ERA5 global reanalysis product. We hypothesize that this wet bias is mainly caused by unrealistic lower-level winds that trigger strong convection over the region; it can be reduced by using a high-resolution regional climate model with a large domain that allows realistically representing interactions between the Westerlies and Asian summer monsoons. Here, downscaling using the Weather Research and Forecasting (WRF) model driven by ERA5 was conducted with a large domain (8°‒50° N, 65°‒125° E) at 9 km for the period 1979‒2019 (WRF9km). Precipitation values from WRF9km and ERA5 were evaluated against satellite observations; compared with ERA5, WRF9km captured the climatological summer precipitation over the NWTP with a much-reduced wet bias. The ERA5 overestimation is mainly caused by excessive convective precipitation, likely linked to strong vertical motions over the NWTP induced by an overestimated lower-level southerly wind.

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Section: Wrf Model Configurationmentioning

confidence: 99%

Wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the plateau

Chen

Tang

et al. 2023

Clim Dyn

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“…To prevent the WRF simulation from drifting away from the global reanalysis forcing, the spectral nudging (SN) method, which is considered as an indirect assimilation method (von Storch et al 2000), was applied to wind elds above the PBL. SN is an e cient method to improve the performance of dynamical downscaling in WRF (Tang et al 2017;Mai et al 2020). SN can force model close to the long waves of the driving data, whereas adding values in the smaller scales (Miguez-Macho et al 2005).…”

Section: Model and Experimental Designmentioning

confidence: 99%

WRF gray-zone dynamical downscaling over the Tibetan Plateau during 1999–2019: model performance and added value

Zhou

Shao

et al. 2022

Clim Dyn

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The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate is poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000-2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparison against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate model's performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, signi cant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically. Key Points1. WRF outperforms ERA5 in capturing the climatological characteristics, inter-annual variability, and seasonal variation of surface air temperature and precipitation with signi cantly reduced cold bias and wet bias at the gray-zone resolution 2. Less simulated snow cover related surface albedo causes reduced cold bias over the southeastern TP and less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF 3. The diurnal variation of precipitation is more realistically simulated in WRF with more realistic peak time compared to ERA5

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“…Therefore, analysis nudging was used for domain 1 and above the boundary layer for the wind, temperature and specific humidity. Nudging was used to relax the model solution toward the analysis data, and it was proven to be effective in reducing the wind speed error [70,71].…”

Section: Model Setupmentioning

confidence: 99%

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

Saadatabadi,

Hamzeh,

Kaskaoutis

et al. 2024

Applied Sciences

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This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

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Impacts of Spectral Nudging Parameters on Dynamical Downscaling in Summer over Mainland China

Cited by 17 publications

References 55 publications

Wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the plateau

Wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the plateau

WRF gray-zone dynamical downscaling over the Tibetan Plateau during 1999–2019: model performance and added value

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

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