Short biography:
Dr. Yiguo Wang works as post-doc at NERSC and the Bjerknes Centre since 2014. His research interest is in applying data assimilation into Climate systems to enhance the prediction skill of the systems and improve the quality of the reanalysis. He has a background of applied mathematics and graduated in China and France. He got a PhD degree on data assimilation and air quality forecast in France in 2013.
Abstract:
The Norwegian Climate Prediction Model (NorCPM) combines the Norwegian Earth System Model (NorESM) with an advanced data assimilation method - the Ensemble Kalman Filter (EnKF) - for the purpose of seasonal-to-decadal climate predictions. In this talk, I will start by giving an introduction of data assimilation and in particular of the Ensemble Kalman Filter. Practical implementation of data assimilation in NorCPM will also be discussed. We test the skill of the system for seasonal time scales (up to 1 year). The skill of NorCPM is estimated from the period of 1985-2010 with 4 hindcast starts per year (and 9 members) and compared to the North American Multi-Model Ensemble (NMME). Over the upper ocean, NorCPM achieves highly competitive skill compared to NMME both in correlation and RMSE. NorCPM performs the best in multiple regions including in the NA and the Nordic Seas. The prediction skill in the atmosphere (Z500, precipitation and SLP) is found to be related to ENSO and its teleconnection patterns. Seasonal variability of the prediction is discussed.
Arranged date for the seminar talk: Mar 06, 2017