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| Titel |
Identifying non-normal and lognormal characteristics of temperature, mixing ratio, surface pressure, and wind for data assimilation systems |
| VerfasserIn |
A. J. Kliewer, S. J. Fletcher, A. S. Jones, J. M. Forsythe |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 5 ; Nr. 2, no. 5 (2015-09-04), S.1363-1405 |
| Datensatznummer |
250115194
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| Publikation (Nr.) |
 copernicus.org/npgd-2-1363-2015.pdf |
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| Zusammenfassung |
| Data assimilation systems and retrieval systems that are based upon
a maximum likelihood estimation, many of which are in operational
use, rely on the assumption that all of the errors and variables
involved follow a normal distribution. This work develops a series
of statistical tests to show that mixing ratio, temperature, wind
and surface pressure follow non-normal, or in fact, lognormal
distributions thus impacting the design-basis of many operational
data assimilation and retrieval systems. For this study one year of
Global Forecast System 00:00 UTC 6 h forecast were analyzed
using statistical hypothesis tests. The motivation of this work is
to identify the need to resolve whether or not the assumption of
normality is valid and to give guidance for where and when a data
assimilation system or a retrieval system needs to adapt its cost
function to the mixed normal-lognormal distribution-based Bayesian
model. The statistical methods of detection are based upon
Shapiro–Wilk, Jarque–Bera and a χ2 test, and a new composite
indicator using all three measures. Another method of detection
fits distributions to the temporal-based histograms of temperature,
mixing ratio, and wind. The conclusion of this work is that there
are persistent areas, times, and vertical levels where the normal
assumption is not valid, and that the lognormal distribution-based
Bayesian model is observationally justified to minimize the error
for these conditions. The results herein suggest that comprehensive
statistical climatologies may need to be developed to capture the
non-normal traits of the 6 h forecast. |
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