Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble
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| Pubblicato in: | Monthly Weather Review vol. 148, no. 3 (Mar 2020), p. 1229 |
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| Autore principale: | |
| Altri autori: | , , , |
| Pubblicazione: |
American Meteorological Society
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| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.1175/MWR-D-19-0154.1 |2 doi | |
| 035 | |a 2396869358 | ||
| 045 | 2 | |b d20200301 |b d20200331 | |
| 084 | |a 32489 |2 nlm | ||
| 100 | 1 | |a Necker, Tobias | |
| 245 | 1 | |a Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble | |
| 260 | |b American Meteorological Society |c Mar 2020 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Sensitivity analysis | ||
| 653 | |a Estimates | ||
| 653 | |a Error correction | ||
| 653 | |a Atmospheric models | ||
| 653 | |a Data assimilation | ||
| 653 | |a Ensemble forecasting | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Weather forecasting | ||
| 653 | |a Localization | ||
| 653 | |a Random sampling | ||
| 653 | |a Correlation | ||
| 653 | |a Computer simulation | ||
| 653 | |a Errors | ||
| 653 | |a Systematic errors | ||
| 653 | |a Simulation | ||
| 653 | |a Precipitation | ||
| 653 | |a Spatial data | ||
| 653 | |a Error correction & detection | ||
| 653 | |a Lookup tables | ||
| 653 | |a Sample variance | ||
| 653 | |a Studies | ||
| 653 | |a Statistical sampling | ||
| 653 | |a Data collection | ||
| 653 | |a Covariance | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Error reduction | ||
| 653 | |a Correlation analysis | ||
| 653 | |a Sampling error | ||
| 653 | |a Performance assessment | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Weissmann, Martin | |
| 700 | 1 | |a Ruckstuhl, Yvonne | |
| 700 | 1 | |a Anderson, Jeffrey | |
| 700 | 1 | |a Miyoshi, Takemasa | |
| 773 | 0 | |t Monthly Weather Review |g vol. 148, no. 3 (Mar 2020), p. 1229 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2396869358/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2396869358/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2396869358/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |