EGU2020-4039
https://doi.org/10.5194/egusphere-egu2020-4039
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast

Zhaolu Hou1, Bin Zuo1, Shaoqing Zhang1,2,5, Fei Huang1, Ruiqiang Ding3, Wansuo Duan4, and Jianping Li1,2
Zhaolu Hou et al.
  • 1Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies/Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao 266100, China.
  • 2Laboratory for Ocean Dynamics and Climate, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China.
  • 3State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875 Beijing, China.
  • 4State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China.
  • 5International Laboratory for High-Resolution Earth System Model and Prediction (iHESP), College Station, Taxes, USA.

Numerical forecasts always have associated errors. Analogue correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogues remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogues and correct model forecast errors. As an example, an ENSO model forecast error correction experiment confirms that the LDA method locates more dynamical analogues of states of interest and better corrects forecast errors than do other methods. This is because the LDA method ensures similarity of the initial states and the evolution of both states. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. Model forecast error correction using the LDA method provides a new approach to correcting state-dependent model errors and can be readily integrated with other advanced models.

How to cite: Hou, Z., Zuo, B., Zhang, S., Huang, F., Ding, R., Duan, W., and Li, J.: Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4039, https://doi.org/10.5194/egusphere-egu2020-4039, 2020