Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model Part II: Effects of Data Assimilation within the Planetary Boundary Layer

Stauffer, D.; Seaman, Nelson L.; Binkowski, Francis S.

doi:10.1175/1520-0493(1991)119<0734:uofdda>2.0.co;2

Cited by 230 publications

(199 citation statements)

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“…Only horizontal winds are nudged at all vertical levels, while no nudging is conducted for other variables within the planetary boundary layer (PBL). The strategy for grid nudging is based on previous studies (Stauffer and Seaman, 1990;Stauffer et al, 1991), which showed that this configuration reduced the bias most. For spectral nudging, same nudging strategy is used within PBL to keep the simulation consistent with grid nudging, and above PBL, geopotential field is nudged, instead of water vapor mixing ratio, which does not have large-scale features as strong as other fields and would not be nudged in the spectral nudging of WRF.…”

Section: Methodsmentioning

confidence: 99%

Differences between downscaling with spectral and grid nudging using WRF

et al. 2012

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Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

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Section: Methodsmentioning

confidence: 99%

Differences between downscaling with spectral and grid nudging using WRF

et al. 2012

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“…These methods provide a four-dimensional analysis that is somewhat balanced dynamically, and in terms of continuity, while allowing for complex local topographical or convective variations. More details about these techniques can be found on Skamarock et al (2008) and Stauffer et al (1991). The option to use grid nudging will be tested here, but the option to use observational nudging, although very interesting, is outside of the scope of this work.…”

Section: Numerical Optionsmentioning

confidence: 99%

“…This test shows that a more accurate representation of the terrain and/or the boundary layer can produce better results, in part due to lower differences between real and model grid terrain characteristics. However, model performance using finer horizontal and vertical spacing may be better, worse, or similar, due to uncertainties in the performance of the various physical parameterizations and their responses to grid resolution (Queen and Zhang, 2008;Jang et al, 1995;Wu et al, 2008;Zhang et al, 2006a, b). While several studies reported that increasing grid resolution may lead to better reproduction of finescale meteorological processes (e.g., Jimenez et al, 2006;Liu and Westphal, 2001;Mass et al, 2002) this may not necessarily correlate to better model accuracy (Gego et al, 2005).…”

Section: Simulation Domain Resolutionmentioning

confidence: 99%

A sensitivity study of the WRF model in wind simulation for an area of high wind energy

Carvalho

Rocha

Gómez‐Gesteira

et al. 2012

Environmental Modelling & Software

272

150

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The performance of the Weather Research and Forecast (WRF) model in wind simulation was evaluated under different numerical a nd physical options for an area of Portugal, located in complex terrain and characterized by its significant wind energy resource. The grid nudging and integration time of the simulations were the tested numerical options. Since the goal is to simulate the near-surface wind, the physical parameterization schemes regarding the boundary layer were the ones under evaluation. Also, the influences of the local terrain complexity and simulation domain resolution on the model results were also studied. Data from three wind measuring stations located within the chosen area were compared with the model results, in terms of Root Mean Square Error, Standard Deviation Error and Bias. Wind speed histograms, occurrences and energy wind roses were also used for model evaluation. Globally, the model accurately reproduced the local wind regime, despite a significant underestimation of the wind speed. The wind direction is reasonably simulated by the model especially in wind regimes where there is a clear dominant sector, but in the presence of low wind speeds the characterization of the wind direction (observed and simulated) is very subjective and led to higher deviations between simulations and observations. Within the tested options, results show that the use of grid nudging in simulations that should not exceed an integration tim e of 2 days is the best numerical configuration, and the parameterization set composed by the physical schemes MM5eYonsei UniversityeNoah are the most suitable for this site. Results were poorer in sites with higher terrain complexity, mainly due to limita-tions of the terrain data supplied to the model. The increase of the simulation domain resolution alone is not enough to significantly improve the model performance. Results suggest that error minimization in the wind simulation can be achieved by testing and choosing a suitable numerical and physical con figuration for the region of interest together with the use of high resolution terrain data, if available.

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“…These include four-dimensional data assimilation (FDDA) and indirect soil moisture and temperature (T ) nudging. In both the MM5 and WRF model simulations, FDDA is essentially the same in terms of the analyses used and the nudging configuration, which follow after Stauffer et al (1991) and Otte (2008a) and are described in detail for these particular simulations by Gilliam and Pleim (2010). The reanalysis fields considered are the wind components in the east-west and north-south directions, T and water vapor mixing ratio (w).…”

Section: Mm5 and Wrf Model Simulationsmentioning

confidence: 99%

Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

et al. 2010

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Abstract. This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O 3 ) mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O 3 mixing ratios are attributed to less dry deposition of O 3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO 2− 4 ), similar performance for nitrate (NO − 3 ), and slightly worse performance for nitric acid (HNO 3 ), total carbon (TC) and total fine particulate (PM 2.5 ) mass than the corresponding MM5-CMAQ results. For August, predictions of O 3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO 2− 4 were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences wet deposition was similar for both simulations for January and August.

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Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model Part II: Effects of Data Assimilation within the Planetary Boundary Layer

Cited by 230 publications

References 0 publications

Differences between downscaling with spectral and grid nudging using WRF

Differences between downscaling with spectral and grid nudging using WRF

A sensitivity study of the WRF model in wind simulation for an area of high wind energy

Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

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