In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-c...

An ensemble Kalman filter–variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winte...

A hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation (ETKF– 3DVAR) system for the Weather Research and Forecasting (WRF) Model is introduced. The system is based on the existing WRF 3DVAR. Unlike WRF 3DVAR, which utilizes a simple, static covariance model to estimate the forecast-error statistics, the hybrid system combines ensemble covariances with the stat...

In recent years techniques have been developed to obtain integrated water vapor along slant paths between ground-based Global Positioning System (GPS) receivers and the GPS satellites. Results are presented of an observing system simulation (OSS) to determine whether three-dimensional water vapor fields could be recovered from a high-resolution network (e.g., with 40-km spacing) of GPS receiver...

Monsoon depressions form during the Southwest Indian Monsoon over the Bay of Bengal and provide copious rainfall over the eastern and central parts of the country. Since these depressions form over sea, a region of data scarcity, satellite data provides only source of information of the meteorological system. Furthermore, for short-range prediction, it is extremely important to have accurate in...

A hybrid ensemble adjustment Kalman filter—three-dimensional ensemble—variational (EAKF-En3DVar) system is developed to assimilate conventional and radar data, and is applied to a convective case in Colorado and Kansas, USA. The system is based on the framework of the Weather Research and Forecasting model’s three-dimensional variational (3DVar) and Data Assimilation Research Testbed. A two-ste...

The problem of effectively combining data with a mathematical model constitutes a major challenge in applied mathematics. It is particular challenging for high-dimensional dynamical systems where data is received sequentially in time and the objective is to estimate the system state in an on-line fashion; this situation arises, for example, in weather forecasting. The sequential particle filter...

Abstract In this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure for the of radar in real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic (CVlogq) as control variables are variational DA framework. To address lack radial velocity impact issues when CVq, procedure that assimilates reflectivity two separate analysis pa...

We discuss the analysis impact of ensemble-based assimilation differential absorption lidar observed water vapour and Raman temperature profiles into Weather Research Forecasting model at convection-permitting scale. The flow-dependent background error covariance in data (DA) system that uses hybrid three-dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3D...

Gridpoint statistical interpolation (GSI), a three-dimensional variational data assimilation method (3DVAR) has been widely used in operations and research in numerical weather prediction. The operational GSI uses a static background error covariance, which does not reflect the flow-dependent error statistics. Incorporating ensemble covariance in GSI provides a natural way to estimate the backg...