A5102988736- Meteorological Phenomena and Simulations
- Climate variability and models
- Tropical and Extratropical Cyclones Research
- Geophysics and Gravity Measurements
- Ocean Waves and Remote Sensing
- Wind and Air Flow Studies
- Precipitation Measurement and Analysis
- Plant Water Relations and Carbon Dynamics
- Remote Sensing and Land Use
- Hydrological Forecasting Using AI
- Atmospheric and Environmental Gas Dynamics
- Forest, Soil, and Plant Ecology in China
- Environmental Changes in China
- Oceanographic and Atmospheric Processes
- Distributed and Parallel Computing Systems
- Flood Risk Assessment and Management
- Advanced Computational Techniques and Applications
China Meteorological Administration
2019-2024
Sheng Jing Hospital
2006
Since 1 July 2018, the GRAPES (Global/Regional Assimilation and PrEdiction System) global 4‐dimensional variational (4D‐Var) data assimilation system has been in operation at China Meteorological Administration (CMA). In this study, 4D‐Var is comprehensively introduced. This applies non‐hydrostatic tangent‐linear model (TLM) adjoint (ADM) for first time. The use of a digital filter as weak constraint achieved. A series linear physical processes developed, including vertical diffusion,...
Abstract. The spin-up refers to the dynamic and thermal adjustments made at initial stage of numerical integration in order reach a statistical equilibrium state. analyses on characteristics effects spin-ups are great significance for optimizing field model improving its forecast skills. In this paper, three different fields used experiments: analysis four-dimensional variational (4D-VAR) assimilation, 3 h prediction operational forecasting system, Final (FNL) Operational Global Analysis...
Abstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast of Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead using adjoint technique, this utilizes a dimension‐reduced projection (DRP) technique to minimize cost function standard variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) realizes explicit flow‐dependence BEC in configuration. An inflation linear...
Abstract Recent machine learning (ML)‐based weather forecasting models have improved the accuracy and efficiency of forecasts while minimizing computational resources, yet still depend on traditional data assimilation (DA) systems to generate analysis fields. Four dimensional variational (4DVar) enhances model states, relying prediction propagate observation initial field. Consequently, fields from DA are not optimal for ML‐based models, necessitating a customized system. This paper...
Minimization algorithms are singular components in four-dimensional variational data assimilation (4DVar). In this paper, the convergence and application of conjugate gradient algorithm (CGA), which is based on Lanczos iterative Hessian matrix derived from tangent linear adjoint models using a non-hydrostatic framework, investigated 4DVar minimization. First, influence Gram-Schmidt orthogonalization vector studied. The results show that without fails to converge after ninth iteration...
Abstract Spatial aspects of the physics–dynamics coupling in Global and Regional Assimilation Prediction System (GRAPES) for global medium‐range numerical weather prediction (GRAPES_GFS) are studied. As a Charney–Philips (CP) grid is used dynamics but all physical processes calculated on Lorenz GRAPES_GFS V2.2 its previous versions, interpolation has to be potential temperature moisture between full half levels coupling. Besides error, computational mode appears solutions vertical heat...
Abstract Initialization and the spin‐up effect are critical to model performance in four‐dimensional variational data assimilation (4DVar) system. The incremental analysis update (IAU) technique is able combat these problems. This study introduces IAU into 4DVar framework (IAU‐4DVar) of operational China Meteorological Administration Global Forecast System (CMA‐GFS) evaluates IAU‐4DVar. In this IAU‐4DVar framework, initial increments optimally estimated split up equally weighted forcing...
Abstract This study developed an ensemble four‐dimensional variational (En4DVar) hybrid data assimilation system. Different from most of the available En4DVar systems that adopted Kalman Filter class or approaches to produce covariances for their background error (BECs), it used a (4DEnVar) system obtain covariance. The localization scheme 4DEnVar applied orthogonal functions decompose correlation matrix so was implemented easily and rapidly. In terms analysis quality forecast skill,...
Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due insufficient ensemble members. Observation is simpler and more timesaving than model in implementation, but difficult directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations observations are undetermined transmission observational information thereby obstructed. To determine coordinates weighted average hypsometry...
Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation climatological component restricts prediction unusual tracks. should be a forcing quantity, not predictor integration all models. Anomaly-based variable overcome bottleneck forecast time length or one-week forecasting...
Abstract. We developed a strongly coupled chemistry meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, for investigating the feedbacks of chemical data on meteorological forecasts. This system was basis framework incremental analysis scheme China Meteorological Administration Global Forecasting System (CMA-GFS). 4D-Var includes three component models: forward, tangent linear, and adjoint models. forward model constructed by integrating an aerosol...
In this study, moist singular vector (MSV) was developed based on GRAPES-GEPS (Global/Regional Assimilation and Prediction System – Global Ensemble System), the adjoint model of large-scale condensation cumulus deep convection in GRAPES-4DVar (Four-dimensional variational assimilation). Five consecutive days numerical experiments were performed for a preliminary evaluation MSV. The values, horizontal distribution structure, spread MSVs perturbation its influence ensemble prediction compared...
The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China’s FengYun-4A geostationary satellite provides an unprecedented opportunity to observe the three-dimensional thermodynamic structure of typhoons in western North Pacific Ocean with high spatiotemporal resolutions. A series observing experiments based on GIIRS FY-4A were carried out for five typhoons: Chan-hom, Maysak, and Higos 2020 Chanthu Conson 2021 collected temporally continuous observations TCs their...
Spatial and temporal resolution of satellite precipitation products is high but they have errors in geographic distribution data accuracy, while rain gauge low accuracy. Therefore, the two observation can be fused to obtain accuracy products. In this paper, we propose a fusion method based on image registration warping processing for correction The essential element construct cost function containing term constrained field differences mapping domain. vector obtained by minimizing applied...
Considering the fact that computing resources and NWP model developers within framework of China Meteorological Administration (CMA) are distributed widespread across countries, systems very much depend on datasharing, remote collaborative research, heavy computation, massive data intensive services, Meso-scale Model Application Grid (MMAG) has been established based grid technology. Ultimately this platform aggregated software at CAMS, NMIC, STI, GRMC GXMB. The GRAPES runs routinely MMAG...