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Title: A Computationally Efficient Linear Semi-Lagrangian Scheme for the Advection of Microphysical Variables in Cloud-Resolving Models

Journal Article · · Monthly Weather Review
 [1];  [1];  [1]
  1. Hebrew Univ. of Jerusalem (Israel)
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An efficient semi-Lagrangian (SL) numerical scheme for the advection of positive-definite microphysical variables in cloud-resolving models is proposed. The scheme is a linear combination of SL schemes of the first and second order. The proposed scheme is compared with two high-order, monotonic, nonlinear Eulerian schemes, in two 2D tests. In the first test, advection of a positive scalar field with large gradients is performed within complicated idealized steering flows. In the second test, a hail storm is simulated using the 2D Hebrew University Cloud Model, describing the interactions between 15 size distributions of different microphysical quantities (each distribution is described by 43 mass bins). The proposed scheme is computationally efficient, has a low numerical diffusion and a high level of mass conservation accuracy, and preserves the sum of multiple advected variables as well as the linear relationships between the advected scalar variables. Although the proposed SL scheme is not exactly positive definite, the negative values and their number, which may appear only in the case of very strong gradients, are negligible. The scheme produces results similar to those of nonlinear Eulerian advection schemes while reducing computation time by approximately a factor of 10.

Research Organization:
Univ. of Oklahoma, Norman, OK (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0014295
OSTI ID:
1802603
Journal Information:
Monthly Weather Review, Vol. 148, Issue 8; ISSN 0027-0644
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Meteorology & Atmospheric Sciences
Cloud resolving models
Numerical analysis/modeling
Semi-Lagrangian models