Abstract

The development of the Earth system science is impossible without the coordination between model and observation. Model is an integration of formal knowledge as to the cognition of the Earth system science, but it is only an approximation of the truth, but far from perfection; observation tends to be more resourceful and abundant, the big data of the earth is flooding, but all observations have spatial-temporal representations, and its representative errors are often difficult to estimate, which is considered as one of the main reasons for the inconsistency between model and observation. However, is this inconsistency a representation or truth? It is often hard to identify whether the model is more credible or the observation is more credible. Model and observation can play beautiful chords. From Gauss, Wiener to Lorenz, great scientists have followed the same vein, developed estimation theory, cybernetics, and chaos theory, and provided a solid methodological foundation for the integration of model and observation. The data assimilation method is rooted in these theories, it stands on the giants’ shoulders, grows deep roots and luxuriant leaves, and has been a key methodology for the Earth system science. Its underlying notion is to integrate direct and indirect observations with different sources and resolutions within the dynamic framework of the model, thereby enhancing the predictability and observability of the system. However, the accurate estimation of model and observation error is the key to modulating the model-observation chord, but this is also the biggest challenge in the area of data assimilation. To realize the physical accordance of model and observation, we must cross over this major challenge. Chinese scholars have made innovative accomplishments in nonlinear non-Gauss Bayesian recursive filter, representative error estimation, variational and ensemble filtering methods, and developed systems for high-resolution and multi-source remote sensing data assimilation.

Presenter Profile

Xin Li, Ph.D., a research fellow of Institute of Tibetan Plateau Research, Chinese Academy of Sciences, the winner of The National Science Fund for Distinguished Young Scholars. In 1992, graduated from School of Geography and Ocean Science of Nanjing University, in 1998, was granted the Ph.D. degree by Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences. Made innovative scientific research achievements for the remote sensing and information system in the cryosphere, land surface data assimilation, and comprehensive observation and integration research of inland basin, including the development of China's large-scale land surface data assimilation system and high-resolution basin scale land hydrological data assimilation system, and the implementation of “Integrated Remote Sensing Combined Test in Heihe” and “Ecological Hydrological Remote Sensing Test in Heihe”.