A5079264770- Climate variability and models
- Solar and Space Plasma Dynamics
- Oceanographic and Atmospheric Processes
- Tropical and Extratropical Cyclones Research
- Meteorological Phenomena and Simulations
- Economic and Technological Innovation
- Anomaly Detection Techniques and Applications
- Cryospheric studies and observations
- Environmental Impact and Sustainability
- Computational Physics and Python Applications
- Environmental Changes in China
- Geophysics and Gravity Measurements
- Energy, Environment, Economic Growth
- Ocean Waves and Remote Sensing
Fudan University
2022-2024
Nanjing University of Information Science and Technology
2021-2022
Jiangsu Institute of Meteorological Sciences
2021
Abstract The 2014–2015 “Monster”/“Super” El Niño failed to be predicted one year earlier due the growing importance of a new type Niño, Modoki, which reportedly has much lower forecast skill with classical models. In this study, we show that, so far as today, actually can mostly at lead time more than 10 years. This is achieved through tracing predictability source an information flow-based causality analysis, been rigorously established from first principles during past 16 years (e.g.,...
Abstract The relative importance of oceanic modes to global surface air temperature (SAT) and terrestrial precipitation during 1934–2020 was investigated using a variety statistical dynamical system methods. Through singular spectrum analysis, we present the distribution decadal (10–20‐year), multidecadal (20–50‐year), secular (>50‐year) variabilities SAT anomalies. Three sea were identified by value decomposition that affect low‐frequency anomalies—namely, warming (GW), Interdecadal...
The advent of satellite altimetry datasets sea surface height (SSH) is a major advance in oceanography and other Earth system sciences. However, while the along-track data coverage dense, relatively poor resolution between tracks poses challenge to reconstruction those processes such as mesoscale submesoscale eddies. This study proposes machine learning algorithm based on causal inference tool, i.e., Liang–Kleeman information flow (L-K IF) analysis, address challenge. For region South China...
Panel data, which consist of observations on many individual units over two or more instances time, have gradually become an important type scientific data. Subsequently causal inference for panel data has attracted enormous interest from fields as well statistics. In this study, the rigorously formulated information flow analysis time series, is very concise in form and been successfully applied different disciplines, generalized to identify causality homogeneous independent identically...
The 2014-2015 "Monster"/"Super" El Niño failed to be predicted one year earlier due the growing importance of a new type Niño, Modoki, which reportedly has much lower forecast skill with classical models. In this study, we show that, so far as today, actually can mostly at lead time more than 10 years. This is achieved through tracing predictability source an information flow-based causality analysis, rigorously established from first principles in past decade. We that flowing solar activity...