The Met Office global four‐dimensional variational data assimilation scheme

By F. Rawlins, Susan P. Ballard, K. Bovis, Adam Clayton, D. Li, G. Inverarity, A. Lorenc, T. Payne. 2007

529 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/564502f88dcc6910d4ab79eabbba9b46087715c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. R. Bannister
2. D. Barker
3. Xuguang Wang
4. Q. Xiao
5. A. Lorenc
6. Adam Clayton
7. Juanzhen Sun
8. Chengsi Liu

In Global and Planetary Change

1. R. Bannister
2. D. Barker
3. A. Lorenc
4. Xuguang Wang
5. Adam Clayton
6. Q. Xiao
7. Juanzhen Sun
8. Chengsi Liu

Citations in the corpus

• Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, unknown): “Thebackground-error covariancesin4D-VarweregeneratedusingaNMCmethodas describedinRawlins et al. (2007).”
• A coupled data assimilation system for climate reanalysis (2016, methodology): “A 4D-Var technique is used for the atmosphere (Rawlins et al., 2007), with a nudging scheme for the land surface analysis (Dharssi et al., 2011), while a 3D-Var technique is used in the ocean (Mogensen et al., 2009).”
• A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “Var has been the workhorse of DA for many years (Le Dimet and Talagrand, 1986; Talagrand and Courtier, 1987; Thepaut and Courtier, 1991; Zupanski, 1997; Rabier et al., 2000; Rawlins et al., 2007).”
• Introduction of the GSI into the NCEP Global Data Assimilation System (2009, background): “Many operational centers currently utilize variational schemes (e.g., Derber et al. 1991; Lorenc et al. 2000; Rabier et al. 2000; Rawlins et al. 2007).”
• Conditioning and preconditioning of the variational data assimilation problem (2011, methodology): “The operational configuration of the Met Office data assimilation system already includes a preconditioning by the square root of the background error covariance matrix and so the objective function is of the form given by (11), with Hessian of the form (12).”
• An economical approach to four-dimensional variational data assimilation (2010, methodology): “This assimilation approach has to date been adopted by five operational NWP centers for global analysis (Rabier et al., 2000; Rawlins et al., 2007 ).”
• On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “Data assimilation has proven to be essential for accurate weather forecasting by providing the initial conditions for numerical weather prediction (NWP) (Rabier et al. , 2000; Rawlins et al. , 2007).”
• Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method (2014, background): “Stream function and unbalanced velocity potential are widely used as control variables in global data assimilation systems and some regional data assimilation systems (e.g., Ingleby 2001; Barker et al. 2004, 2012; Zupanski et al. 2005; Rawlins et al. 2007; Huang et al. 2009; Wang et al. 2013a).”
• Accounting for correlated error in the assimilation of high‐resolution sounder data (2014, methodology): “The Met Office currently employ an incremental four-dimensional variational (4D-Var) assimilation scheme (Rawlins et al., 2007).”
• Satellite cloud and precipitation assimilation at operational NWP centres (2011, unknown): “(Rawlins et al., 2007).”
• A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, background): “filter, where larger values of n give better approximations of the Gaussian filter. Lorenc (1992) discusses a firstorder recursive filter of this type and states that a large number of passes with a first-order filter equivalently gives a Gaussian filter.”
• Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results (2009, background): “…several operational centers implemented 4D-Var in their operational applications, including Météo-France (Gauthier and Thépaut 2001), the Met Office (Lorenc and Rawlins 2005; Rawlins et al. 2007), the Japan Meteorological Agency (Honda et al. 2005), and Environment Canada (Gauthier et al. 2007).”
• Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “The plots also show changes to the NWP index itself, which is a function combining the individual skill scores, as explained in Appendix A of Rawlins et al. (2007).”