Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3349-2015
https://doi.org/10.5194/gmd-8-3349-2015
Methods for assessment of models
 | 
22 Oct 2015
Methods for assessment of models |  | 22 Oct 2015

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

J. J. Gómez-Navarro, C. C. Raible, and S. Dierer

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Cited articles

Awan, N. K., Truhetz, H., and Gobiet, A.: Parameterization-Induced Error Characteristics of MM5 and WRF Operated in Climate Mode over the Alpine Region: An Ensemble-Based Analysis, J. Climate, 24, 3107–3123, https://doi.org/10.1175/2011JCLI3674.1, 2011.
Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., Halsnaes, K., Holt, T., Jylhä, K., Koffi, B., Palutikof, J., Schöll, R., Semmler, T., and Woth, K.: Future extreme events in European climate: an exploration of regional climate model projections, Climatic Change, 81, 71–95, https://doi.org/10.1007/s10584-006-9226-z, 2007.
Charney, J., Halem, M., and Jastrow, R.: Use of Incomplete Historical Data to Infer the Present State of the Atmosphere, J. Atmos. Sci., 26, 1160–1163, https://doi.org/10.1175/1520-0469(1969)026<1160:UOIHDT>2.0.CO;2, 1969.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cheng, W. Y. Y. and Steenburgh, W. J.: Evaluation of Surface Sensible Weather Forecasts by the WRF and the Eta Models over the Western United States, Weather Forecast., 20, 812–821, https://doi.org/10.1175/WAF885.1, 2005.