4DVARFour-Dimensional Variational Data Assimilation
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The basic principle of 3DVAR and 4DVAR is to avoid the explicit calculation of the gain matrix and make its inversion by using a minimization procedure of the cost function.
Four-dimensional variational data assimilation (4DVar) is one of the most promising methods for providing optimal analyses of numerical weather predictions.
2001, 2004), to constrain the assimilation of cloud-affected infrared sounder channels in 4DVAR. In a similar vein, McNally (2009) has introduced an operational analysis scheme using cloud-affected radiances to good effect by extending the 4DVAR analysis control to include parameters that describe the cloud conditions and simultaneously to estimate these parameters together with temperature and humidity.
Based on the RTP derived by the extending algorithm of Argo temperature profile, more temperature information of the surroundings is obtained and would benefit the T-S assimilation with more "observations." To validate this, an experiment focusing on the T-S assimilation is conducted based on the ROMS and 4DVAR. Respectively, the Argo profile and RTP are assimilated, and then the results are evaluated by EN4.
The use of the 3DVAR technique to study this case was chosen over the EnKF or 4DVAR techniques because the 3DVAR technique is not computationally costly and has shown good results in other related studies [18, 20, 21].
Zheng, "Dynamical and microphysical retrieval from simulated Doppler radar observations using the 4DVAR assimilation technique," Acta Meteorologica Sinica, vol.
* Data assimilation schemes vary among ocean forecasting groups, ranging from Ensemble Optimum Interpolation (EnOI) and Ensemble Kalman Filter (EnKF) to three--and four-dimensional variational methods (3DVar and 4DVar).
Prior to 2012, the operational data assimilation system at NOAA was dominated by the three-dimensional variational data assimilation (3DVAR) technique, while other operational centers in the world had transitioned to more advanced techniques [e.g., 4DVAR for the European Centre for Medium-Range Weather Forecasts (ECMWF; Mahfouf and Rabier 2000); hybrid ensemble-variational (EnVar) technique for the Met Office (Clayton et al.
Observations used by the VDRAS four-dimensional variational data assimilation (4DVar) system include radar radial velocity and reflectivity from NWS radars located in Denver (KFTG) and in Cheyenne, Wyoming (KCYS) and surface data from surface meteorological stations in the Rocky Mountain Front Range region.
We investigate the possibility that model error can be wrongly interpreted as an error in the forward model for satellite radiance using the four-dimensional variational data assimilation (4DVar) of the European Centre for Medium-Range Weather Forecasts (ECMWF).