Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

Autor(en)

Tobias Necker, Martin Weissmann, Yvonne Ruckstuhl, Jeffrey Anderson, Takemasa Miyoshi

Abstrakt

State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

Organisation(en)

Institut für Meteorologie und Geophysik

Externe Organisation(en)

Hans Ertel Centre for Weather Research, Ludwig-Maximilians-Universität München, National Center for Atmospheric Research (NCAR), RIKEN

Journal

Monthly Weather Review

Band

148

Seiten

1229–1249

Anzahl der Seiten

21

ISSN

0027-0644

DOI

https://doi.org/10.1175/MWR-D-19-0154.1

Publikationsdatum

03-2020

Peer-reviewed

Ja

ÖFOS 2012

105206 Meteorologie

Schlagwörter

ASJC Scopus Sachgebiete

Atmospheric Science

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/sampling-error-correction-evaluated-using-a-convectivescale-1000member-ensemble(55fbd3cc-3061-40f8-abfd-3f640bfb70ac).html