This work was supported by the Laboratory Directed Research and Development Program of the Los Alamos National Laboratory, which is under the auspices of the National Nuclear Security Administration of the U.S. Department of Energy under DOE Contracts W-7405-ENG-36 and LA-UR-10-04291. Computer resources were provided both by the Computing Division at Los Alamos and the Oak Ridge National Laboratory Cray clusters; approved for public release, LA-UR-11-10121.
Aksoy, A., F. Zhang, and J. Nielsen-Gammon, 2006a: Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon. Wea. Rev., 134, 2951–2970.
Aksoy, A., F. Zhang, and J. Nielsen-Gammon, 2006b: Ensemble-based simultaneous state and parameter estimation with MM5. Geophys. Res. Lett., 33, L12801, doi:10.1029/2006GL026186.
Andrejczuk, M., J. Reisner, B. Henson, M. Dubey, and C. Jeffery, 2008: The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type? J. Geophys. Res., 113, D19204, doi:10.1029/2007JD009445.
Annan, J. D., J. C. Hargreaves, N. R. Edwards, and R. Marsh, 2005: Parameter estimation in an intermediate complexity Earth system model using an ensemble Kalman filter. Ocean Modell., 8, 135–154.
Arakawa, A., and V. Lamb, 1977: Computational design of the basic dynamical processes of the UCLA general circulation model. Methods Comput. Phys., 17, 173–265.
Bryan, G. H., 2012: Effects of surface exchange coefficients and turbulence length scales on the intensity and structure of numerically simulated hurricanes. Mon. Wea. Rev., 140, 1125–1143.
Emanuel, K., and R. Rotunno, 2011: Self-stratification of tropical cyclone outflow. Part I: Implications for storm structure. J. Atmos. Sci., 68, 2236–2249.
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143–10 162.
Evensen, G., and P. van Leeuwen, 1996: Assimilation of Geosat altimeter data for the Agulhas Current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev., 124, 85–96.
Gao, J., M. Xue, A. Shapiro, and K. Droegemeier, 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127, 2128–2142.
Godinez, H., and J. Moulton, 2012: An efficient matrix-free algorithm for the ensemble Kalman filter. Comput. Geosci., 16, 565–575, doi:10.1007/s10596-011-9268-9.
Guimond, S., M. Bourassa, and P. Reasor, 2011: A latent heat retrieval and its effects on the intensity and structure change of Hurricane Guillermo (1997). Part I: The algorithm and observations. J. Atmos. Sci., 68, 1549–1567.
Hacker, J. P., and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL. Mon. Wea. Rev., 133, 3260–3275.
Houtekamer, P., and H. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811.
Hu, X.-M., F. Zhang, and J. W. Nielsen-Gammon, 2010: Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study. Geophys. Res. Lett., 37, L08802, doi:10.1029/2010GL043017.
Leonard, B., and J. Drummond, 1995: Why you should not use ‘hybrid’, ‘power-law’ or related exponential schemes for convective modeling—There are better alternatives. Int. J. Numer. Methods Fluids, 20, 421–442.
McFarquhar, G., and R. Black, 2004: Observations of particle size and phase in tropical cyclones: Implications for mesoscale modeling of microphysical processes. J. Atmos. Sci., 61, 777–794.
Nielsen-Gammon, J., X. Hu, F. Zhang, and J. Pleim, 2010: Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon. Wea. Rev., 138, 3400–3417.
Reasor, P., M. Eastin, and J. Gamache, 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603–631.
Reisner, J., A. Mousseau, A. Wyszogrodzki, and D. Knoll, 2005: An implicitly balanced hurricane model with physics-based preconditioning. Mon. Wea. Rev., 133, 1003–1022.
Sitkowski, M., and G. Barnes, 2009: Low-level thermodynamic, kinematic, and reflectivity fields of Hurricane Guillermo (1997) during rapid intensification. Mon. Wea. Rev., 137, 645–663.
Thompson, G., R. Rasmussen, and K. Manning, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.
Tong, M., and M. Xue, 2008: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part II: Parameter estimation experiments. Mon. Wea. Rev., 136, 1649–1668.
Torn, R. D., and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817–2829.
Yussouf, N., and D. Stensrud, 2012: Comparison of single-parameter and multiparameter ensembles for assimilation of radar observations using the ensemble Kalman filter. Mon. Wea. Rev., 140, 562–586.
Zhang, F., Y. Weng, J. Sippel, Z. Meng, and C. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105–2125.
Zou, X., Y. Wu, and P. S. Ray, 2010: Verification of a high-resolution model forecast using airborne Doppler radar analysis during the rapid intensification of Hurricane Guillermo. J. Appl. Meteor. Climatol., 49, 807–820.