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Efficient approximation of the incomplete gamma function for use in cloud model applications

Blahak, U.

Abstract:
This paper describes an approximation to the lower incomplete gamma function γl(a,x) which has been obtained by nonlinear curve fitting. It comprises a fixed number of terms and yields moderate accuracy (the absolute approximation error of the corresponding normalized incomplete gamma function P is smaller than 0.02 in the range 0.9 ≤ α ≤ 45 and χ ≥ 0). Monotonicity and asymptotic behaviour of the original incomplete gamma function is preserved. While providing a slight to moderate performance gain on scalar machines (depending on whether a stays the same for subsequent function evaluations or not) compared to established and more accurate methods based on series- or continued fraction expansions with a variable number of terms, a big advantage over these more accurate methods is the applicability on vector CPUs. Here the fixed number of terms enables proper and efficient vectorization. The fixed number of terms might be also beneficial on massively parallel machines to avoid load imbalances, caused by a possibly vastly different number of terms in series expansions to reach convergence at different grid points. For many cloud microphysical applications, the provided moderate accuracy should be enough. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000020643
Veröffentlicht am 11.04.2018
Originalveröffentlichung
DOI: 10.5194/gmd-3-329-2010
Scopus
Zitationen: 6
Web of Science
Zitationen: 6
Coverbild
Zugehörige Institution(en) am KIT Fakultät für Physik (PHYSIK)
Institut für Meteorologie und Klimaforschung (IMK)
Institut für Meteorologie und Klimaforschung - Forschungsbereich Troposphäre (IMK-TRO)
Publikationstyp Zeitschriftenaufsatz
Jahr 2010
Sprache Englisch
Identifikator ISSN: 1991-959X, 1991-9603
urn:nbn:de:swb:90-206438
KITopen-ID: 1000020643
HGF-Programm 12.01.02 (POF II, LK 01)
Erschienen in Geoscientific model development
Band 3
Heft 2
Seiten 329 - 336
Externe Relationen Abstract/Volltext
Nachgewiesen in Web of Science
Scopus
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