NWP | CMA | CMA_GFS real time data
中國氣象局（China Meteorological Administration，縮寫：CMA），是中華人民共和國國務院負責氣象行政管理與科學研究的副部級直屬事業單位。 CMA-GFS (原名为GRAPES-GFS) 作为我国自主研发的新一代全球数值预报模式，从初始场、变分同化、模式框架等核心技术均实现了自主化。CMA-GFS全球模式从2001年开始组织数值预报团队启动研发，2016年实现业务化
NWP | DA | Kalman Filter
Optimal Interpolation (OI), 3D-Variational Assimilation (3DVar), and the Kalman Filter (KF) share foundational mathematical objectives but differ in implementation and error covariance treatment. Belo
NWP | DA | Optimal weight matrix W
Data assimilation (DA) NWP | DA | Kalman Filter NWP | DA | 4D-Var To derive the optimal weight matrix $ $in DA, we follow a minimum variance approach to combine background (model) and observation d
NWP | DA | 4D-Var
Data assimilation (DA) 4D-Var for Dummies | 2009 Linearised physics: the heart of ECMWF’s 4D-Var | 2023 WRF 多重解析度四維變分資料同化方法之研究 | 劉志權 | 2019 25 years of 4D-Var: how machine learning can improve the
NWP | Spectral Method
Spectral methods Spectral methods in numerical weather prediction (NWP) are advanced computational techniques that use global basis functions to solve the atmospheric governing equations with high acc
Precipitation minus Evaporation (P-E)
Precipitation minus Evaporation (P-E) To calculate Precipitation minus Evaporation (P-E) in numerical weather simulations using mixing ratio, the process involves analyzing atmospheric moisture fluxes
NWP | AI | NeuralGCM
NWP | AI for Numerical Weather Prediction (NWP) NeuralGCM is trained through a sophisticated process that combines differentiable physical modeling with machine learning optimization techniques. He
MPAS | LBC
Links https://mpas-dev.github.io/ /MPAS-Model/releases MPAS-Atmosphere Users' Guide V4.0 MPAS-Atmosphere Users' Guide V5.3 MPAS-Atmosphere Users' Guide V6.3 MPAS-Atmosphere Users' Guide V7.0 MPAS-Atm
