A5012834882- Climate variability and models
- Meteorological Phenomena and Simulations
- Oceanographic and Atmospheric Processes
- Hydrological Forecasting Using AI
- Tropical and Extratropical Cyclones Research
- Methane Hydrates and Related Phenomena
- Geological Studies and Exploration
- Geological and Geophysical Studies
- Target Tracking and Data Fusion in Sensor Networks
- Geophysics and Gravity Measurements
- Precipitation Measurement and Analysis
- Arctic and Antarctic ice dynamics
- Microwave Engineering and Waveguides
- Marine and environmental studies
- Clay minerals and soil interactions
- Advanced Data Storage Technologies
- Atmospheric and Environmental Gas Dynamics
- Wind and Air Flow Studies
- Electromagnetic Simulation and Numerical Methods
- Algorithms and Data Compression
- Soil and Unsaturated Flow
- Photonic and Optical Devices
- Religion and Sociopolitical Dynamics in Nigeria
- Digital Imaging for Blood Diseases
- Aluminum toxicity and tolerance in plants and animals
First People's Hospital of Yunnan Province
2025
Kunming University of Science and Technology
2025
Ministry of Natural Resources
2019-2024
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)
2020-2024
Hohai University
2007-2024
National Energy Research Scientific Computing Center
2023
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
2021-2023
Bjerknes Centre for Climate Research
2023
Second Institute of Oceanography
2014-2021
Zhejiang University
1994-2012
Abstract In situ observation of a buoys/moorings array and model simulation were used to study the modulation upper ocean thermal structure by Typhoon Kalmaegi in September 2014. The inertial period signals significant after forcing Kalmaegi, but they did not account for net heat change. Removing showed that response biased right Kalmaegi's track. Vertical mixing caused surface cooling with an inverted‐cone subsurface warming double‐wing structure. Net upwelling converted left wing cooling,...
Abstract In this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different time scales. To our knowledge, is first assessment of ENSO a long-term hindcast with complicated coupled general circulation model (CGCM). Our results indicate that both dispersion component (DC) signal (SC) contribute interannual variation (measured by relative entropy)....
Abstract The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian‐based algorithms, namely, the (EnKF) and sequential importance resampling (SIR‐PF). It was recently introduced to address non‐Gaussian features in data assimilation for highly nonlinear systems, by providing continuous interpolation between EnKF SIR‐PF analysis schemes. In this paper, we first extend EnKPF algorithm modifying formula computation covariance matrix, making it suitable measurement functions...
Abstract. Systematic errors in dynamical climate models remain a significant challenge to accurate predictions, particularly when modeling the nonlinear coupling between atmosphere and oceans. Despite notable advances that have improved our understanding of variability, these systematic can still degrade predictive skills. In this study, we adopt twin experiment framework with reduced-order coupled atmosphere-ocean model explore utility machine learning mitigating errors. Specifically, train...
Abstract. While dynamical models are essential for seasonal Arctic sea ice prediction, they often exhibit significant errors that challenging to correct. In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) improve predictions. We compare online and offline error correction approaches. approach, ML corrects in model’s instantaneous state during simulation, while post-processes calibrates predictions after...
Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial proper treatment planning. Studies have shown that in emergency settings, the misdiagnosis rate hand/wrist can reach up to 29%, particularly among non-specialist physicians due a high workload and limited experience. While existing AI methods detect they typically require large training datasets are fracture detection without type classification. Therefore, there is an urgent need...
Abstract In the construction of an ensemble‐based data assimilation system for a complex fully coupled general circulation model (CGCM), state errors at initial time have important influence on quality. this study, with Community Earth System Model (CESM) and Data Assimilation Research Testbed (DART), we found that states persists throughout vicious cycle cannot be automatically remedied via consequent assimilations. As such, two strategies were applied to alleviate errors, reliable was...
The study of Equatorial Undercurrent (EUC) has attracted a broad attention in recent years due to its strong response and feedback the Indian Ocean Dipole. In this paper, we first produce high-quality simulation three-dimensional temperature, salinity zonal current from 1982 2014, using high-resolution ocean general circulation model. On basis, with two sensitivity experiments, investigate role temperature anomalies driving enhancing EUC during positive IOD events by examining variation...
Abstract Here, we explored in depth the relationship among deterministic prediction skill, probabilistic skill and potential predictability. This was achieved by theoretical analyses and, particular, an analysis of long-term ensemble ENSO hindcast over 161 years from 1856 to 2016. First, a nonlinear monotonic between derived analysis, examined validated using hindcast. Further, co-variability predictability both perfect model assumption actual scenario. On these bases, investigated practice...
In real applications, one common issue of parameter estimation using ensemble-based data assimilation methods is the accumulation sampling errors when a large number observations are used to update single-value parameters. this article, new method which assimilates estimate states while adaptive parameters introduced. The resulting in maximum total variance reduction ensembles identified perform estimation. To validate method, two-scale Lorenz-96 model generate true states, parameterized...
For a nonhomogeneous waveguide, whose refractive index is not constant, the problem very complicated since nonlinear eigenvalue problems are unable to reduce algebraic equations yet. When varied, dispersion relation cannot be derived by using analytic expressions of solutions in each layer. In this paper, solved differential transfer matrix method, which introduced deduce relations leaky modes for TE and TM cases, respectively. Moreover, waveguide gradually can approximated some simpler...
Abstract This study provided an extension to the latest version of Lamont-Doherty Earth Observation (LDEO5) prediction system. First, ensemble coupled data assimilation (CDA) system, based on Ensemble Kalman Filter, was established. Both Kaplan sea surface temperature (SST) from January 1856 December 2018 and ECMWF twentieth century reanalysis (ERA-20C) wind 1900 February 2010 were assimilated for initialization. Second, (EP) system established using stochastic optimal perturbation that...
Abstract. In the data assimilation of coupled models, stongly (SCDA) is much more complicated than weakly (WCDA), since it involves cross-domain error covariances which could be very inaccurate when ensemble size small. this study, SCDA experiments are conducted using a two-scale Lorenz '96 model, system composed by two models in domains have different temporal and spatial scales. A localization strategy specially designed for adjustment Kalman filter (EAKF) used (CDA) experiments. The...
Abstract This study assesses the seamless predictability of subseasonal precipitation over Asian summer monsoon (ASM) region. The prediction skill 12 models from subseasonal‐to‐seasonal (S2S) hindcast database varies considerably, suggesting a large uncertainty in ASM precipitation. However, all show that is better predicted ocean than land, and less reliable subtropical areas tropical areas. results reveal significant spatial variations investigates factors controlling these by analyzing...
Particle filters (PFs) constitute a sequential data assimilation method based on the Monte Carlo approximation of Bayesian estimation theory. Standard PFs use scalar weights derived from likelihood approximate posterior probability density functions (PDFs) observations and resampling schemes to generate particles. However, approach interferes with localization algorithm often results in filter degeneracy. Recently, localized particle (LPF) was developed by extending vector weights, which...
Abstract In this study, we developed a flow‐dependent sequential assimilation‐based targeted observation method by minimizing the analysis error variance under framework of ensemble Kalman filter (EnKF). This approach considers variation in background statistics when identifying optimal observational sites through assimilation method. Covariance localization is also introduced method, enabling computational efficiency and eliminating impacts from spurious observations. By quantifying...
Abstract A new criterion was proposed recently to measure the influence of internal variations on secular trends in a time series. When magnitude trend is greater than theoretical threshold that scales from variations, sign estimated can be interpreted as underlying long‐term change. Otherwise, may depend period chosen. An improved least squares method developed here further reduce and applied eight sea surface temperature (SST) data sets covering 1881–2013 investigate whether there are...
The perfectly matched layer (PML) is a widely used tool to truncate the infinite domain in modal analysis for optical waveguides. Since PML mimics unbounded domain, propagation modes and leaky of original waveguide can be derived. However, presence will introduce series new modes, which depend on parameters PML, they are named as Berenger modes. For two-dimensional step-index waveguides, eigenmode problem usually transformed into an algebraic equation by transfer matrix method (TMM). When...