As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, a...

The problem of Bayesian state estimation of a dynamical system given a stream of noisy and incomplete measurements is important in many applications involving real-time prediction and control. Time-sequential data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given all past observations, is updated from the prior mode...

The advantages of using Doppler weather radar to track and forecast mesoscale severe weather events are widely known to both meteorologists and the public. With the use of Doppler radar, meteorologists can provide better information to the public, ultimately saving lives and property, by remotely observing the internal structure of thunderstorms at high resolution. Despite its highly advanced c...

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that comb...

Abstract In this study, we assess tentatively the predictive capability of Weather Research and Forecasting (WRF) model its Three-Dimensional Variational (3DVAR) system. We study impact LiDAR data assimilation on predictions wind speed direction. The simulation domain covers German energy research platform FINO1 in Southern North Sea, at which were recorded. results demonstrate significance app...

have been conducted. Here I is the identity operator and v is the diffusion tensor, whose spatial variability is derived from the forecast field and m is the spline approximation order. Performance of these BEC representations are compared in the framework of numerical experiments with real 3dVar data assimilation into the Navy Coastal Ocean model (NCOM) in the Western TVopical Pacific. It is s...

This study developed a hybrid four-dimensional (4D) ensemble–variational (4DEnVar) radar data assimilation (DA) system for the Weather Research and Forecasting model. The 4DEnVar approach incorporated ensemble covariances at multiple time levels to assimilate observations distributed in windows. By approximating evolution of background error using 4D covariance, use tangent linear adjoint model...

Abstract The assimilation of reflectivity ( Z ) within 3DVar or hybrid ensemble-3DVar (En3DVar) requires the adjoint observation operator. With En3DVar method, previous studies often use operators consistent with a single-moment microphysics scheme even when forecast model uses double-moment scheme. As such, only mixing ratios hydrometeors are directly updated by data (DA) system, leading to in...

An experimental ENSO prediction system is presented, based on an ocean general circulation model (GCM) coupled to a statistical atmosphere and the adjoint method of 4D variational data assimilation. The adjoint method is used to initialize the coupled model, and predictions are performed for the period 1980–99. The coupled model is also initialized using two simpler assimilation techniques: for...