Inclusion of Linearized Moist Physics in NASA’s Goddard Earth Observing System Data Assimilation Tools
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Ronald M. Errico | Daniel Holdaway | R. Errico | R. Gelaro | D. Holdaway | Jong G. Kim | Ronaldo Gelaro
[1] Adrian M. Tompkins,et al. A cloud scheme for data assimilation: Description and initial tests , 2004 .
[2] Yannick Trémolet,et al. Computation of observation sensitivity and observation impact in incremental variational data assimilation , 2008 .
[3] X. Zou,et al. Tests of an Adjoint Mesoscale Model with Explicit Moist Physics on the Cloud Scale , 2008 .
[4] Ronald M. Errico,et al. Mesoscale Predictability and the Spectrum of Optimal Perturbations , 1995 .
[5] Dynamical Sensitivity Analysis of Tropical Cyclone Steering Using an Adjoint Model , 2011 .
[6] Ricardo Todling,et al. The THORPEX Observation Impact Intercomparison Experiment , 2010 .
[7] Ronald M. Errico,et al. Sensitivity Analysis Using an Adjoint of the PSU-NCAR Mesoseale Model , 1992 .
[8] S. P. Ballard,et al. Efficient moist physics schemes for data assimilation. I: Large‐scale clouds and condensation , 2009 .
[9] J. Thepaut,et al. Impact of a simplified physical package in 4D‐var analyses of fastex situations , 1999 .
[10] A. Arakawa,et al. Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I , 1974 .
[11] T. Vukicevic,et al. Mesoscale Adjoint Modeling System: Version 1 , 1994 .
[12] Ronald M. Errico,et al. Issues Regarding the Assimilation of Cloud and Precipitation Data , 2007 .
[13] Christian D. Kummerow,et al. Global Precipitation Measurement , 2008 .
[14] A. Simmons,et al. The ECMWF operational implementation of four‐dimensional variational assimilation. I: Experimental results with simplified physics , 2007 .
[15] P. Courtier,et al. A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .
[16] Ronald M. Errico,et al. Interpretations of the total energy and rotational energy norms applied to determination of singular vectors , 2000 .
[17] Ronald M. Errico,et al. Singular-Vector Perturbation Growth in a Primitive Equation Model with Moist Physics , 1999 .
[18] Thomas Koop,et al. Review of the vapour pressures of ice and supercooled water for atmospheric applications , 2005 .
[19] Lawrence L. Takacs,et al. Data Assimilation Using Incremental Analysis Updates , 1996 .
[20] S. Moorthi,et al. Relaxed Arakawa-Schubert - A parameterization of moist convection for general circulation models , 1992 .
[21] Ronald M. Errico,et al. Examination of the accuracy of a tangent linear model , 1993 .
[22] Olaf Stiller,et al. Efficient moist physics schemes for data assimilation. II: Deep convection , 2009 .
[23] William Putman,et al. The finite-volume dynamical core on the cubed-sphere , 2006, SC.
[24] Rolf H. Langland,et al. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system , 2004 .
[25] R. Errico,et al. Using Jacobian sensitivities to assess a linearization of the relaxed Arakawa–Schubert convection scheme , 2014 .
[26] Philippe Lopez,et al. A convection scheme for data assimilation: Description and initial tests , 2005 .
[27] Julio T. Bacmeister,et al. Rain Reevaporation, Boundary Layer Convection Interactions, and Pacific Rainfall Patterns in an AGCM , 2006 .
[28] Philippe Lopez,et al. Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review , 2007 .
[29] H. Kim,et al. Moist adjoint-based forecast sensitivities for a heavy snowfall event over the Korean Peninsula on 4-5 March 2004 , 2009 .
[30] Ronald M. Errico,et al. An examination of the accuracy of the linearization of a mesoscale model with moist physics , 1999 .