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Should we conserve entropy or energy when computing CAPE with mixed-phase precipitation physics?

Should we conserve entropy or energy when computing CAPE with mixed-phase precipitation physics? AbstractThe rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from non-equilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1-3 % as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 m and 250 m using explicit first-order and second-order integration schemes respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast with energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution. Whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Atmospheric Sciences American Meteorological Society

Should we conserve entropy or energy when computing CAPE with mixed-phase precipitation physics?

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0469
eISSN
1520-0469
DOI
10.1175/jas-d-24-0027.1
Publisher site
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Abstract

AbstractThe rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from non-equilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1-3 % as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 m and 250 m using explicit first-order and second-order integration schemes respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast with energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution. Whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency.

Journal

Journal of the Atmospheric Sciences – American Meteorological Society

Published: Jul 15, 2024

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