EGU25-14710, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-14710
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Sensitivity analysis of severe weather events to different background error covariances in meteorological aircraft data assimilation
Seung-Beom Han, Tae-Young Goo, Sueng-Pil Jung, Min-Seong Kim, Deok-Du Kang, and Chulkyu Lee
Seung-Beom Han et al.
  • National Institute of Meteorological Sciences, Observation Research Department, Seogwipo, Republic of Korea (cats916@korea.kr)

Aircraft data are considered one of the best platforms for obtaining atmospheric spatial information in the observation gap over the ocean. The National Institute of Meteorological Sciences (NIMS) has operated an atmospheric research aircraft to mitigate this observation gap. In particular, the dropsonde and AIMMS-20 systems installed on the aircraft generate vertical distributions of meteorological variables over the ocean, and these specialized observation data enhance the accuracy of the initial model fields. These aircraft observation data provide continuous distributions of meteorological variables and significantly contribute to improving the performance of numerical predictions. In this study, we evaluated the effectiveness of data assimilation (DA) on the prediction of severe meteorological phenomena affecting the Korean Peninsula using high-resolution numerical modeling using atmospheric research aircraft observation data. To analyze the sensitivity of the difference in the background error covariance in the data assimilation method, three sets of simulation experiments were performed. First, an experiment was conducted using the background error covariance option CV3 based on the NMC method, which is suitable for simple settings or when the computational resources are limited. Second, an experiment using option CV5 is suitable for studying more complex situations or high-accuracy forecasts. This option generates a covariance structure that adapts to atmospheric conditions by using an ensemble-based method. The last is an experiment using the CV7 option, which is a hybrid background error covariance option that combines static methods (such as CV3) and ensemble-based methods (such as CV5), and has the advantage of combining climate statistics and flow-dependent features to improve model prediction performance.

How to cite: Han, S.-B., Goo, T.-Y., Jung, S.-P., Kim, M.-S., Kang, D.-D., and Lee, C.: Sensitivity analysis of severe weather events to different background error covariances in meteorological aircraft data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14710, https://doi.org/10.5194/egusphere-egu25-14710, 2025.