Linear and non-linear response to parameter variations in a mesoscale model

References

• Aksoy, A., Zhang, F. and Nielsen-Gammon, J. W. 2006a. Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon. Wea. Rev. 134, 2951–2970.
   Google Scholar
• Aksoy, A., Zhang, F. and Nielsen-Gammon, J. W. 2006b. Ensemble-based simultaneous state and parameter estimation with MM5. Geo-phys. Res. Lett. 33, 10.1029/2006GL026186.
   Google Scholar
• Alapaty, K., Raman, S. and Niyogi, D. 1997. Uncertainty in the speci-fication of surface characteristics: a study on prediction errors in the boundary layer. Bound.-Layer Meteorol. 82, 473–500.
   Web of Science ®Google Scholar
• Annan, J. D., Hargreaves, J. C., Edwards, N. R. and Marsh, R. 2005a. Pa-rameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter. Ocean Modell. 8, 135–154.
   Web of Science ®Google Scholar
• Annan, J. D., Lunt, D. J. and Valdes, P. J. 2005b. Parameter estimation in an atmospheric GCM using the ensemble Kalman filter. Nonlinear Proc. Geophys. 12,363–371.
   Web of Science ®Google Scholar
• Ball, F. K. 1960. Control of inversion height by surface heating. Q. J. R. Meteorol. Soc. 44, 2823–2838.
   Google Scholar
• Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. StaL Soc. 57B, 289–300.
   Google Scholar
• Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 134, 703–722.
   Web of Science ®Google Scholar
• Clark, A., Gallus Jr., W. A. and Chen, T.-C. 2008. Contributions of mixed physics and perturbed lateral boundary conditions to the skill and spread of precipitation forecasts from a wrf ensemble. Mon. Wea. Rev. 136, 2140-2156.
   Google Scholar
• Cohn, S. E. 1997. An introduction to estimation theory. J. Meteorol. Soc. Jpn. 75, 257–288.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Eckel, F. A. and Mass, C. F. 2005. Aspects of effective mesoscale, short-range, ensemble forecasting. Wea. Forecast. 20, 328–350.
   Web of Science ®Google Scholar
• Fritsch, J. M. and Chappel, C. F. 1980. Numerical prediction of convec-tively driven mesoscale pressure systems. Part I: convective parame-terization. J. Atmos. Sci. 37, 1722–1733.
   Web of Science ®Google Scholar
• Grimit, E. P. and Mass, C. F. 2002. Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecast. 17, 192–205.
   Web of Science ®Google Scholar
• Hacker, J. P., Ha, S.-Y., Snyder, C., Berner, J., Eckel, F. A., and co-authors. 2011. The U.S. Air Force Weather Agency’s mesoscale en-semble: scientific description and performance results. Tellus 63A, this issue.
   Google Scholar
• Hong, S.-Y., Dudhia, J. and Chen, S.-H. 2004. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev. 132, 103–120.
   Web of Science ®Google Scholar
• Hong, S.-Y. and Pan, H.-L. 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev. 124, 2322–2339.
   Web of Science ®Google Scholar
• Hou, D., Kalnay, E. and Drogemeier, K. 2001. Objective verification of the SAMEX ’98 ensemble experiments. Mon. Wea. Rev. 129, 73–91.
   Google Scholar
• Jackson, C., Sen, M. K. and Stoffa, P. L. 2004. An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model prediction. J. Clim. 17, 2828–2841.
   Web of Science ®Google Scholar
• Kain, J. S. 2004. The Kain-Fritsch convective parameterization: an up-date. J. Appl. Meteorol. 43, 170–181.
   Google Scholar
• Kain, J. S. and Fritsch, J. M. 1990. A one-dimensional entrain-ing/detraining plume model and its application in convective param-eterization. J. Atmos. Sci. 47, 2784–2802.
   Web of Science ®Google Scholar
• Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W.-S. and co-authors. 2009. Introduction of the GSI into the NCEP Global Data Assimilation system. Wea. Forecast. 24, 1691–1705.
   Web of Science ®Google Scholar
• Livezey, R. and Chen, W. 1983. Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev. 111,46–59.
   Google Scholar
• Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141.
   Web of Science ®Google Scholar
• Marshall, J. S. and Palmer, W. M. 1948. The distribution of raindrops with size. J. Meteorol. 5, 165–166.
   Google Scholar
• Moeng, C. H. and Sullivan, P. P. 1994. A comparison of shear and buoyancy-driven planetary boundary layer flows. J. Atmos. Sci. 51, 999–1022.
   Google Scholar
• Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J. and co-authors. 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768-772.
   Google Scholar
• Nielsen-Gammon, J. W., Hu, X.-M., Zhang, E and Pleim, J. E. 2010. Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon. Wea. Rev. 138, 3400-3417.
   Google Scholar
• Noh, Y., Cheon, W. G., Hong, S. Y. and Raasch, S. 2003. Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteorol. 107, 401–427.
   Web of Science ®Google Scholar
• Posselt, D. J. and Vukeéevié, T. 2010. Robust characterization of model physics uncertainty for simulations of deep moist convection. Mon. Wea. Rev. 138, 1513–1535.
   Google Scholar
• Raftery, A. E., Gneiting, T., Blabdaoui, E and Polakowski, M. 2005. Us-ing Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev. 133, 1155-1174.
   Google Scholar
• Rodwell, M. J. and Palmer, T. N. 2007. Using numerical weather predic-tion to assess climate models. Q. J. R. Meteorol. Soc. 133, 129–146.
   Web of Science ®Google Scholar
• Sanderson, B. M., Knutti, R., Aina, T., Chirstensen, C., Fault, N. and co-authors. 2008. Constraints on model response to greenhouse gas forc-ing and the role of subgrid-scale processes. J. Clim. 21, 2384-2400.
   Google Scholar
• Santer, T. J. and Williams, B. J. 2003. Design and Analysis of Computer Experiments. Springer, New York.
   Google Scholar
• Sauvageot, H. and Lacaux, J.-P. 1995. The shape of averaged drop size distributions. J. Atmos. Sci. 52, 1070–1083.
   Web of Science ®Google Scholar
• Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M. and co-authors. 2008. A description of the advanced research WRF Version 3, Technical Report TN-475, National Center for Atmospheric Research.
   Google Scholar
• Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N. and co-authors. 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403-406.
   Google Scholar
• Stensrud, D., Bao, J.-W. and Warner, T. T. 2000. Using initial condi-tion and model physics perturbations in short-range ensemble simu-lations of mesoscale convective systems. Mon. Wea. Rev. 128, 2077–2107.
   Google Scholar
• Stensrud, D. J. and Yussouf, N. 2003. Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev. 131, 2510–2524.
   Google Scholar
• Tong, M. and Xue, M. 2008. Simultaneous estimation of microphysi-cal parameters and atmospheric state with simulated radar data and ensemble square-root Kalman filter. Mon. Wea. Rev. 136, 1630–1648.
   Google Scholar
• Torn, R. and Hakim, G. 2008. Ensemble-based sensitivity analysis. Mon. Wea. Rev. 136, 663–677.
   Google Scholar
• Troen, I. and Mahrt, L. 1986. A simple model of the atmospheric bound-ary layer: sensitivity to surface evaporation. Bound-Layer MeteoroL 37, 129–148.
   Web of Science ®Google Scholar
• Ventura, V., Paciorek, C. J. and Risbey, J. S. 2004. Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J. Climata 17,4343–4356.
   Web of Science ®Google Scholar
• Waldvogel, A. 1974. The No jump of raindrop spectra. J. Atmos. Sci. 31, 1067–1078.
   Google Scholar
• Wei, M., Toth, Z., Wobus, R. and Zhu, Y. 2008. Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus 60A, 62–79.
   Google Scholar
• Wilks, D. S. 2006. On “field significance’ and the false discovery rate. J. AppL MeteoroL Climata 45, 1181–1189.
   Web of Science ®Google Scholar
• Ziehmann, C. 2000. Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models. Tellus 52A, 280–299.
   Google Scholar