Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5735-2021
https://doi.org/10.5194/amt-14-5735-2021
Research article
 | 
20 Aug 2021
Research article |  | 20 Aug 2021

Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system

Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min

Related authors

Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024,https://doi.org/10.5194/amt-17-6659-2024, 2024
Short summary
Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021,https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation for the COSMO model (v5.07)
Yuefei Zeng, Alberto de Lozar, Tijana Janjic, and Axel Seifert
Geosci. Model Dev., 14, 1295–1307, https://doi.org/10.5194/gmd-14-1295-2021,https://doi.org/10.5194/gmd-14-1295-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Turbulence kinetic energy dissipation rate estimated from a WindCube Doppler lidar and the LQ7 1.3 GHz radar wind profiler in the convective boundary layer
Hubert Luce and Masanori Yabuki
Atmos. Meas. Tech., 18, 1193–1208, https://doi.org/10.5194/amt-18-1193-2025,https://doi.org/10.5194/amt-18-1193-2025, 2025
Short summary
Comparison of temperature and wind profiles between ground-based remote sensing observations and numerical weather prediction model in complex Alpine topography: the Meiringen campaign
Alexandre Bugnard, Martine Collaud Coen, Maxime Hervo, Daniel Leuenberger, Marco Arpagaus, and Samuel Monhart
Atmos. Meas. Tech., 18, 1039–1061, https://doi.org/10.5194/amt-18-1039-2025,https://doi.org/10.5194/amt-18-1039-2025, 2025
Short summary
Cluster analysis of vertical polarimetric radio occultation profiles and corresponding liquid and ice water paths from Global Precipitation Measurement (GPM) microwave data
Jonas E. Katona, Manuel de la Torre Juárez, Terence L. Kubar, F. Joseph Turk, Kuo-Nung Wang, and Ramon Padullés
Atmos. Meas. Tech., 18, 953–970, https://doi.org/10.5194/amt-18-953-2025,https://doi.org/10.5194/amt-18-953-2025, 2025
Short summary
Enhanced quantitative precipitation estimation through the opportunistic use of Ku TV-SAT links via a dual-channel procedure
Louise Gelbart, Laurent Barthès, François Mercier-Tigrine, Aymeric Chazottes, and Cécile Mallet
Atmos. Meas. Tech., 18, 351–370, https://doi.org/10.5194/amt-18-351-2025,https://doi.org/10.5194/amt-18-351-2025, 2025
Short summary
The added value and potential of long-term radio occultation data for climatological wind field monitoring
Irena Nimac, Julia Danzer, and Gottfried Kirchengast
Atmos. Meas. Tech., 18, 265–286, https://doi.org/10.5194/amt-18-265-2025,https://doi.org/10.5194/amt-18-265-2025, 2025
Short summary

Cited articles

Aksoy, A., Dowell, D. C., and Snyder, C.: A multiscale comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses, Mon. Weather Rev., 137, 1805–1824, 2009. a
Baldlauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediciton with the COSMO model: description and sensitivities, Mon. Weather Rev., 139, 3887–3905, 2011. a
Bick, T., Simmer, C., Trömel, S., Wapler, K., Stephan, K., Blahak, U., Zeng, Y., and Potthast, R.: Assimilation of 3D-Radar Reflectivities with an Ensemble Kalman Filter on the Convective Scale, Q. J. Roy. Meteor. Soc., 142, 1490–1504, 2016. a
Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M., and McNally, A.: Enhancing the impact of IASI observations through an updated observation-error covariance matrix, Q. J. Roy. Meteor. Soc., 142, 1767–1780, 2016. a, b, c
Caumont, O., Ducrocq, V., Wattrelot, E., Jaubert, G., and Pradier-Vabre, S.: 1D + 3DVar assimilation of radar reflectivity data: a proof of concept, Tellus, 62, 173–187, 2010. a
Download
Short summary
Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach 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.
Share