EGU2020-6596
https://doi.org/10.5194/egusphere-egu2020-6596
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Flow-dependent Targeted Observation Method

Youmin Tang1,2 and Yaling Wu1
Youmin Tang and Yaling Wu
  • 1Second Institution of Oceanography,Ministry of Natural Resources
  • 2University of Northern British Columbia,Canada (ytang@unbc.ca)

In this study, we developed a flow-dependent ensemble-based targeted observation method, by minimizing the analysis error variance under the framework of Ensemble Kalman filter (EnKF) data assimilation system. This method estimates the background error statistics as a flow dependent function. The covariance localization is also introduced for computing efficiency and alleviating the spurious observations.  As a test bed, an  optimal observation array of sea level anomalies (SLA) is designed for its seasonal prediction over the tropical Indian Ocean (TIO) region.  Furthermore, the observing system simulation experiments (OSSEs) is used to verify the resultant optimal observational array using our recently developed coupled data assimilation system. A comparison between this flow-dependent method and the traditional method is also given. ​

How to cite: Tang, Y. and Wu, Y.: A Flow-dependent Targeted Observation Method , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6596, https://doi.org/10.5194/egusphere-egu2020-6596, 2020