Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

Geophysical Research Letters

Volumn 42, Issue 2, 2015, Pages 579-587

  Di, Zhenhua ^a,b   Duan, Qingyun ^a   Gong, Wei ^a   Wang, Chen ^a,c   Gan, Yanjun ^a   Quan, Jiping ^a   Li, Jianduo ^a   Miao, Chiyuan ^a   Ye, Aizhong ^a   Tong, Charles ^c  

^a BEIJING NORMAL UNIVERSITY   (China)

^b INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

^c LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

global sensitivity analysis; MOAT method; uncertainty quantification; WRF model calibration; WRF model parameter sensitivities

Indexed keywords

FORECASTING; SENSITIVITY ANALYSIS; UNCERTAINTY ANALYSIS;

GLOBAL SENSITIVITY ANALYSIS; MOAT METHOD; PARAMETER SENSITIVITIES; PHYSICAL INTERPRETATION; PRECIPITATION FORECASTING; UNCERTAINTY QUANTIFICATIONS; WEATHER RESEARCH AND FORECASTING MODELS; WRF MODEL;

WEATHER FORECASTING;

CLIMATE MODELING; GLOBAL CLIMATE; PARAMETERIZATION; PRECIPITATION (CLIMATOLOGY); SENSITIVITY ANALYSIS; SUMMER; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

BEIJING [CHINA]; CHINA;

EID: 84923638866     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2014GL061623     Document Type: Article

References (53)

1. Aksoy, A., F. Zhang, and, J. W. Nielsen-Gammon, (2006), Ensemble-based simultaneous state and parameter estimation, Geophys. Res. Lett., 33, L12801, doi: 10.1029/2006GL26186.
2. Bastidas, L. A., T. S. Hogue, S. Sorooshian, H. V. Gupta, and, W. J. Shuttleworth, (2006), Parameter sensitivity analysis for different complexity land surface models using multicriteria methods, J. Geophys. Res., 111, D20101, doi: 10.1029/2005JD006377.
3. Bellprat, O., S. Kotlarski, D. Lüthi, and, C. Schär, (2012), Exploring perturbed physics ensembles in a regional climate model, J. Clim., 25, 4582-4599, doi: 10.1175/JCLI-D-11-00275.1.
4. Bernardet, L. R., L. D. Grasso, J. E. Nachamkin, and, C. A. Finley, (2000), Simulating convective events using a high-resolution mesoscale model, J. Geophys. Res., 105 (D11), 14,963-14,982, doi: 10.1029/2000JD900100.
5. Campolongo, F., J. Cariboni, and, A. Saltelli, (2007), An effective screening design for sensitivity analysis of large models, Environ. Model. Software, 22, 1509-1518, doi: 10.1016/j.envsoft.2006.10.004.
6. Chen, F., and, J. Dudhia, (2001), 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, doi: 10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2.
7. Dudhia, J., (1989), Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077-3107, doi: 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.
8. Dudhia, J., (1993), A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front, Mon. Weather Rev., 121, 1493-1513, doi: 10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2.
9. Dudhia, J., (2014), A history of mesoscale model development, Asia-Pac. J. Atmos. Sci., 50, 121-131, doi: 10.1007/s13143-014-0031-8.
10. Dudhia, J., G. Dave, K. Manning, W. Wang, and, C. Bruyere, (2003), PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and User's Guide: MM5 Modeling System Version 3, NCAR, Boulder, Colo.
11. Ebert, E., and, J. L. McBride, (2000), Verification of precipitation in weather systems: Determination of systematic errors, J. Hydrol., 239, 179-202, doi: 10.1016/S0022-1694(00)00343-7.
12. Evensen, G., (1997), Advanced data assimilation in strongly nonlinear dynamics, Mon. Weather Rev., 125, 1342-1354, doi: 10.1175/1520-0493(1997)125<1342:ADAFSN>2.0.CO;2.
13. Gan, Y., Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, A. Ye, C. Miao, and, Z. Di, (2014), A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model, Environ. Model. Software, 51, 269-285, doi: 10.1016/j.envsoft.2013.09.031.
14. Gilliam, R. C., and, J. E. Pleim, (2010), Performance assessment of new land surface and planetary boundary layer physics in the WRF-ARW, J. Appl. Meteor. Climatol., 49, 760-774, doi: 10.1175/2009JAMC2126.1.
15. Grell, G. A., J. Dudhia, and, D. R. Stauffer, (1995), A description of the fifth-generation penn state/NCAR mesoscale model (MM5), NCAR Technical Note, NCAR/TN-398-+-STR,122 pp., Natl. Cent. for Atmos. Res., Boulder, Colo.
16. Herman, J. D., J. B. Kollat, P. M. Reed, and, T. Wagener, (2013), Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893-2903, doi: 10.5194/hess-17-2893-2013.
17. Hong, S. Y., and, J. O. J. Lim, (2006), The WRF single-moment 6-class microphysics scheme (WSM6), J. Korean Meteor. Soc., 42, 129-151.
18. Hong, S. Y., J. Dudhia, and, S. H. Chen, (2004), A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103-120, doi: 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
19. Hong, S. Y., Y. Noh, and, J. Dudhia, (2006), A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318-2341, doi: 10.1175/MWR3199.1.
20. Hou, Z., M. Huang, L. R. Leung, G. Lin, and, D. M. Ricciuto, (2012), Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model, J. Geophys. Res., 117, D15108, doi: 10.1029/2012JD017521.
21. Huang, C., X. Zheng, A. Tait, Y. Dai, C. Yang, Z. Chen, T. Li, and, Z. Wang, (2014), On using smoothing spline and residual correction to fuse rain gauge observations and remote sensing data, J. Hydrol., 508, 410-417, doi: 10.1016/j.jhydrol.2013.11.022.
22. Huang, X., et al. (2009), Four-dimensional variational data assimilation for WRF: Formulation and preliminary results, Mon. Weather Rev., 137, 299-314, doi: 10.1175/2008MWR2577.1.
23. Janjic, Z. I., (1994), The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Mon. Weather Rev., 122, 927-945, doi: 10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.
24. Jiang, H., G. Feingold, and, A. Sorooshian, (2010), Effect of aerosol on the susceptibility and efficiency of precipitation in warm trade cumulus clouds, J. Atmos. Sci., 67, 3525-3540, doi: 10.1175/2010JAS3484.1.
25. Johannesson, G., D. Lucas, Y. Qian, L. P. Swile, and, T. M. Wildey, (2014), Sensitivity of precipitation to parameter values in the Community Atmosphere Model Version 5, Sandia Rep. SAND2014-0829, Sandia Natl. Lab., Albuquerque, N. M.
26. Kain, J. S., (2004), The Kain-Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170-181, doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
27. Kain, J. S., and, J. M. Fritsch, (1990), A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784-2802, doi: 10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.
28. Kalnay, E., (2003), Atmospheric Modelling, Data Assimilation and Predictability, Cambridge Univ. Press, U. K.
29. Kim, J. C., C. B. Lee, M. Belorid, and, P. Zhao, (2011), A study of sensitivity of WRF simulation to microphysics parameterizations, slope option and analysis nudging in Haean Basin, South Korea, Proceedings of 2011 TERRECO Science Conference, October 2-7, 2011; Karlsruhe Inst. of Tech., Garmisch-Partenkirchen, Germany.
30. Li, J., Q. Duan, W. Gong, A. Ye, Y. Dai, C. Miao, Z. Di, C. Tong, and, Y. Sun, (2013), Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis, Hydrol. Earth Syst. Sc., 10, 2243-2286, doi: 10.5194/hess-17-3279-2013.
31. Liang, X.-Z., et al. (2012), Regional Climate-Weather Research and Forecasting Model (CWRF), Bull. Am. Meteorol. Soc., doi: 10.1175/BAMS-D-11-00180.1.
32. Liu, J., M. Bray, and, D. Han, (2013), Exploring the effect of data assimilation by WRF-3DVar for numerical rainfall prediction with different types of storm events, Hydrol. Process., 27, 3627-3640, doi: 10.1002/hyp.9488.
33. Liu, Y., H. V. Gupta, S. Sorooshian, L. A. Bastidas, and, W. J. Shuttleworth, (2004), Exploring parameter sensitivities of the land surface using a locally coupled land-atmosphere model, J. Geophys. Res., 109, D21101, doi: 10.1029/2004JD004730.
34. Mlawer, E. J., S. J. Taubman, P. D. Brown, and, J. L. Lacono, (1997), Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, 16,663-16,682, doi: 10.1029/97JD00237.
35. Morris, M. D., (1991), Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161-174, doi: 10.1080/00401706.1991.10484804.
36. Nasrollahi, N., A. Aghakouchak, J. Li, X. Gao, K. Hsu, and, S. Sorooshian, (2012), Assessing the impacts of different WRF precipitation physics in hurricane simulations, Weather Forecasting, 27, 1003-1016, doi: 10.1175/WAF-D-10-05000.1.
37. Rabier, F., (2005), Overview of global data assimilation developments in numerical weather -prediction centres, Q. J. R. Meteorol. Soc., 131, 3215-3233, doi: 10.1256/qj.05.129.
38. Ruiz, J. J., L. Ferreira, and, A. C. Saulo, (2007), WRF-ARW sensitivity to different planetary boundary layer parameterization over South America,4-11 and 4-12 Research activities in atmospheric and oceanic modeling, in WGNE Blue Book, WMO, Geneva, Switzerland. [Available at http://collaboration.cmc.ec.gc.ca/science/wgne/BlueBook/index.html ].
39. Saltelli, A., S. Tarantola, F. Campolongo, and, M. Ratto, (2004), Sensitivity Analysis in Practice: A guide to Assessing Scientific Models, John Wiley, New York.
40. Sheng, P., J. Miao, J. Li, A. Zhang, J. Sang, and, N. Pan, (2003), Atmospheric Physics [in Chinese], pp. 106-107, Beijing Univ. Press, Beijing.
41. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X. Huang, W. Wang, and, J. G. Powers, (2008), A description of the Advanced Research WRF Version 3, NCAR Technical Note, NCAR/TN-475-+-STR, doi: 10.5065/D68S4MVH.
42. Stensrud, D. J., (2007), Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, Cambridge Univ. Press, Cambridge, U. K.
43. Stephens, G. L., (1984), The parameterization of radiation for numerical weather prediction and climate models, Mon. Weather Rev., 112, 826-867, doi: 10.1175/1520-0493(1984)112<0826:TPORFN>2.0.CO;2.
44. Tong, C., (2005), PSUADE User's Manual, pp. 109-110, Lawrence Livermore Natl. Lab. (LLNL), Calif.
45. Tong, C., and, F. Graziani, (2008), Computational methods in transport: verification and validation, in A Practical Global Sensitivity Analysis Methodology for Multi-Physics Applications, pp. 277-299, Springer, Berlin.
46. Tripoli, G. J., and, W. R. Cotton, (1982), The Colorado State University three-dimensional cloud/mesoscale model-1982. Part I: General theoretical framework and sensitivity experiments, J. de Rech. Atmos., 16, 185-220.
47. Wang, X., D. M. Barker, C. Snyder, and, T. M. Hamill, (2008), A Hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment, Mon. Weather Rev., 136, 5116-5131, doi: 10.1175/2008MWR2444.1.
48. Xiao, Q., and, J. Sun, (2007), Multiple radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP-2002, Mon. Weather Rev., 135, 3381-3404, doi: 10.1175/MWR3471.1.
49. Xiong, S., X. Zeng, J. Liu, and, Z. Wu, (2010), Numerical simulation on the sensitivity of a heavy rain case to the random disturbances of land surface parameters, Torrential Rain Disasters, 29, 117-121.
50. Yang, B., Y. Qian, G. Lin, R. Leung, and, Y. Zhang, (2012), Some issues in uncertainty quantification and parameter tuning: A case study of convective parameterization scheme in the WRF regional climate model, Atmos. Chem. Phys., 12, 2409-2427, doi: 10.5194/acp-12-2409-2012.
51. Zamora, R. J., E. G. Dutton, M. Trainer, S. A. Mckeen, J. M. Wilczak, and, Y. T. Hou, (2005), The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction, Mon. Weather Rev., 133, 783-792, doi: 10.1175/MWR2886.1.
52. Zepeda-Arce, J., E. Foufoula-Georgiou, and, K. K. Droegemeier, (2000), Space-time rainfall organization and its role in validating quantitative precipitation forecasts, J. Geophys. Res., 105 (D8), 10,129-10,146, doi: 10.1029/1999JD901087.
53. Zhang, D., and, A. R. Anthes, (1982), A high-resolution model of the planetary boundary layer-sensitivity tests and comparisons with SESAME-79 data, J. Appl. Meteorol., 21, 1594-1609, doi: 10.1175/1520-0450(1982)021<1594:AHRMOT>2.0.CO;2.