EGU2020-273, updated on 17 Mar 2021
https://doi.org/10.5194/egusphere-egu2020-273
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Sampling errors on convective scales: What can we learn from a 1000-member ensemble?

Tobias Necker1, Martin Weissmann1,2, Yvonne Ruckstuhl1, Juan Ruiz3, Takemasa Miyoshi4, and Jeffrey Anderson5
Tobias Necker et al.
  • 1Meteorologisches Institut München, LMU München, Munich, Germany
  • 2Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
  • 3CIME/CONICET-UBA, Buenos Aires, Argentina
  • 4RIKEN Center for Computational Science, Kobe, Japan
  • 5NCAR/Data Assimilation Research Section, Boulder, Colorado, US

Current regional forecasting systems particularly aim at the forecast of convective events and related hazards. Most weather centers apply high-resolution ensemble forecasts that resolve convection explicitly but can only afford a limited ensemble size of less than 100 members. Given that the degrees of freedom of atmospheric models are several magnitudes higher implies sampling errors. Sampling errors and fast error growth on convective scales in turn lead to a low predictability. Consequently, improving initial conditions and subsequent forecasts requires a better understanding of error correlations in both space and time.
For this purpose, we conducted the first convective-scale 1000-member ensemble simulation over central Europe. Several 1000-member ensemble forecasts are investigated during a high impact weather period in summer 2016 using ensemble sensitivity analysis. Spatial and spatiotemporal correlations are used to quantify sampling errors on convective scales. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of different localization approaches. Those approaches include a standard distance-based localization technique and a statistical sampling error correction method as proposed by Anderson (2012). Our study highlights advantages and disadvantages of existing methods and emphasises the need of different localization approaches for different scales and variables. Several results are published in Necker et al (2020a) and (2020b).

How to cite: Necker, T., Weissmann, M., Ruckstuhl, Y., Ruiz, J., Miyoshi, T., and Anderson, J.: Sampling errors on convective scales: What can we learn from a 1000-member ensemble?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-273, https://doi.org/10.5194/egusphere-egu2020-273, 2019.

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