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 | 4D-Var | Tangent Linear and Adjoint Models
Data assimilation (DA) What Are Tangent Linear and Adjoint Models? A mathematical explanation of the tangent linear and adjoint models in the context of variational data assimilation such as WRFDA:
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
MPAS | FDDA
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
