Preprints
https://doi.org/10.5194/amt-2021-95
https://doi.org/10.5194/amt-2021-95

  12 Apr 2021

12 Apr 2021

Review status: this preprint is currently under review for the journal AMT.

Interpreting estimated Observation Error Statistics of Weather Radar Measurements using the ICON-LAM-KENDA System

Yuefei Zeng1, Tijana Janjic1, Yuxuan Feng1,2, Ulrich Blahak3, Alberto de Lozar3, Elisabeth Bauernschubert3, Klaus Stephan3, and Jinzhong Min2 Yuefei Zeng et al.
  • 1Meteorologisches Institut, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
  • 2Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China
  • 3Deutscher Wetterdienst, Offenbach, Germany

Abstract. Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations to assimilate and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

Yuefei Zeng et al.

Status: open (until 17 Jun 2021)

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Yuefei Zeng et al.

Yuefei Zeng et al.

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Short summary
Observation errors (OE) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, resulted statistics consists of contributions from different sources and are difficult to interpret. Here we use an approach that is based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.