Institute of Data Assimilation and Prediction
Brief Introduction：
High-impact air-sea environmental events refer to the oceanic, weather, or climate events that may cause weather or climate anomalies over a large scale or a long time. These events, e.g., tropical cyclones, El-Nino Southern Oscillation (ENSO), India Ocean Dipole mode (IOD), Variation in Kuroshio Extension, Atlantic Meridional Overturning Circulation (AMOC), and Atlantic Multidecadal Variability (AMV), will often induce aggressively large economic and societal loss on regional or global scales. Thus, understanding and predicting these events are a focus of the ocean and atmosphere research community.
The Institute of Data Assimilation and Prediction (IDAP) was formed in May of 2020. The main task of the institute is to use observations, reanalysis products, and global/regional coupled models to study the mechanism, simulation, and prediction of these events. In recent years, the members of the institute have made a series of innovative achievements in the fields of mechanism analysis, predictability diagnosis, key technologies of coupled data assimilation, target observation, and ensemble prediction of these high-impact air-sea environmental events, reaching the international advanced level.
IDAP will make full use of the multi-discipline advantages and research expertise of its members, with the predictability assessment method and coupled model data assimilation system developed by them. It will be combined with the new theories and applications of artificial intelligence and big data to promote the comprehensive development of the research on the mechanism, simulation, and prediction of high-impact air-sea environmental events. IDAP will also contribute to the cultivation of graduate students and postdoctoral students, making them competitive both in theory and in practice.
Currently, the institute has two professors, four associate professors, one lecturer, and two post-doctors. Research in IDAP includes:
1. Mechanisms and impacts of the high-impact air-sea environmental events
2. Simulation and predictability study of the high-impact air-sea environmental events
3. Ensemble prediction and coupled data assimilation
4. Oceanic target observations
Members:
Dr. Qiang Wang, Professor
His research interest includes the atmospheric and oceanic dynamics, predictability and targeted observation. Currently, he has undertaken several national scientific research projects and published 37 peer-reviewed papers including 30 first- or corresponding author papers. These papers were mainly published in Journal of Climate, Journal of Geophysical Research-Oceans, Journal of Physical Oceanography and so on. In these papers, he made two important progresses in recent years: (1) proposing a new method for predictability study that overcomes the limitations of previous study approaches; (2) revealing the predictability feature of the Kuroshio and its extension and identifying the sensitive area of targeted observation for the prediction of the Kuroshio variations. These works will provide important scientific support for improving the prediction of the ocean and climate.
Dr. Xiaoqin Yan
Dr. Yan's research interests include the detection & attribution of cliamte change as well as the climate variability and predictability on seasonal to decadal time scale. Dr. Yan has published papers on popular climate journals such as Nature Communications, Journal of Climate and Geophysical Research Letters, etc. Dr. Yan's publications are mainly in following aspects: 1). propose a systematic method to detect the response of climate system to anthropogenic aerosols forcing; 2). identify the relationship between the changes of North Atlantic major hurricane frequncy and the AMOC on decadal time scale and provides guidances for decadal predictaion of North Atlantic major hurricane frequncy; 3). confirm the close linkage between AMV and AMOC, and reveal the fact that most climate models at present underestimate the low frequency variability of AMOC, therefore show the needs to improve model simulations of low frequency AMOC variability; 4).define an multivariate AMV index and use it to confirm that the primary driving mechanism of AMV is related to the oceanic processes such as AMOC, therefore provide a new angle to resolve the hot debate on AMV driving mechanisms.
Dr. Zheqi Shen, Associate Professor
Dr. Shen's research interests include the study of new data assimilation methods and the development of coupled data assimilation systems. He has accumulated a lot of work experience on nonlinear data assimilation methods such as particle filters and developing coupled data assimiation systems on coupled earth system models. His publications are on the following aspects: 1) He has developed the hybrid Ensemble Kalman and Particle filter and the localied particle filter for the nonlinear/non-Gaussian data assimilation, which provides a potential solution to the filter degeneracy problem in particle filters with limited computational resources; 2) He takes a major part in developing a weakly coupled data assimilation system for NCAR-CESM model with multiple-ocean observations; 3) He developed some new target observation methods with data assimilation systems. He has published over ten research papers, hosted one youth project of the National Natural Science Foundation of China (NSFC), participated in two National Key Research & Development projects, one major projects of NSFC. He was nominated as the Youth Talent of the Second Institute of Oceanography, MNR when he used to work there.
Dr. Meiyi Hou， PDRA
Dr Hou's research interests include the predictability of the weather and the climate, especially ENSO events. She has accumulated experience in the research of the predictability of ENSO and the development of a predictability method by using model off-line data. She has published three papers and the publications are on the following aspects: (1) propose a theoretical framework of the data analysis method for the predictability dynamics of the concerned weather and climate events from the perspective of error growth; (2) reveal that the EP-El Nino prediction is more affected by the summer predictability barrier while the CP-El Nino prediction is more influenced by the spring predictability barrier; (3) suggest that to improve the ENSO prediction, the initial conditions in the sensitive areas of the North Pacific and South Pacific should also be attended expect for that of the Tropical Pacific.
Dr Yi Li PDRA
I'm a postdoctor working with Prof. Yongmin Tang. I received my PhD degree from Imperial College London in 2018 and moved to Hohai University in 2020. I'm now focusing on tropical cyclone prediction with strongly coupled data assimilation. The effects on typhoon intensity forecast have been demonstrated using an idealised case. I have also worked on the physical balance issue of ocean ensemble data assimilation, as well as global climate prediction. I have published over ten papers, including one selected as editors' highlights by AGU.
