Editorial Type:
Article
Article Type:
Research Article

A Linearized Prognostic Cloud Scheme in NASA’s Goddard Earth Observing System Data Assimilation Tools

Daniel Holdaway Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Sciences, Technology and Research, Universities Space Research Association, Columbia, Maryland

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Ronald Errico Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Sciences, Technology and Research, Morgan State University, Baltimore, Maryland

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Ronald Gelaro Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Jong G. Kim Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Science Systems and Applications Inc., Lanham, Maryland

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Rahul Mahajan Oak Ridge Associated Universities, NASA Postdoctoral Program, Oak Ridge, Tennessee, and Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Oct 2015
DOI:
https://doi.org/10.1175/MWR-D-15-0037.1
Page(s):
4198–4219
Restricted access

Abstract

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA’s Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Corresponding author address: Daniel Holdaway, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Code 610.1, Greenbelt, MD 20771. E-mail: dan.holdaway@nasa.gov

Abstract

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA’s Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Corresponding author address: Daniel Holdaway, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Code 610.1, Greenbelt, MD 20771. E-mail: dan.holdaway@nasa.gov
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