A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVar-AUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive observations, is used here to assimilate the fixed observations that are found in the region of local ...

Monsoon depressions form over the sea, which is a typical data-sparse region for conventional observations. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides for very high-horizontal resolution temperature and humidity soundings. Such high-resolution satellite data can improve the poorly analyzed depressions. The objective of this study is to investigate the impact of ingesting...

46 47 A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and 48 ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System 49 (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the 50 static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational 51 analysis is performed usi...

A limited-area three-dimensional variational data assimilation (3DVAR) system applicable to both synoptic and mesoscale numerical weather prediction is described. The system is designed for use in time-critical realtime applications and is freely available to the data assimilation community for general research. The unique features of this implementation of 3DVAR include (a) an analysis space r...

Hybrid ensemble–three-dimensional variational analysis schemes incorporate flow-dependent, ensembleestimated background-error covariances into the three-dimensional variational data assimilation (3DVAR) framework. Typically the 3DVAR background-error covariance estimate is assumed to be stationary, nearly homogeneous, and isotropic. A hybrid scheme can be achieved by 1) directly replacing the b...

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), ...

2. COMPARISON BETWEEN THE ECMWF 3DVAR AND 4DVAR SYSTEMS The ECMWF 4dVar system is, from the technical point of view, very similar to the 3dVar system. Both systems use the same unix scripts, and share much of the Fortran code. The same background error covariance matrix and observation operators are used, and most of the peripheral tasks such as fetching and archiving of fields and observations...

The impact of observations in a data assimilation (DA) may depend on various factors, and one aspect that can affect the is specification background error covariance matrix. present study compares INSAT-3D atmospheric motion vector (AMV) traditional three-dimensional variational (3DVAR) DA system hybrid ensemble transform Kalman filter (ETKF)-3DVAR (HYBRID) available Weather Research Forecast (...

An enhanced version of the hybrid ensemble–three-dimensional variational data assimilation (3DVAR) system for the Weather Research and Forecasting Model (WRF) is applied to the assimilation of radial velocity (Vr) data from two coastal Weather Surveillance Radar-1988 Doppler (WSR-88D) radars for the prediction of Hurricane Ike (2008) before and during its landfall. In this hybrid system, flow-d...

In late 2006, the NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA, McLaughlin et al. 2005) began its first integrated project (IP1, Brotzge et al. 2007). CASA-IP1 consists of a network of four x-band dual-polarization Doppler radars in southwest Oklahoma. During the spring of 2007, this radar network collected data from a number of severe con...