The statistical structure of forecast errors and its representation in The Met. Office Global 3‐D Variational Data Assimilation Scheme

By N. Ingleby. 2001

149 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5e286f583582b150cfac09059dbb6cb83e7b0cbe

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. L. Berre
3. Xuguang Wang
4. G. Desroziers
5. Margarida Pereira
6. Juanzhen Sun
7. Xiangyu Huang
8. Hongli Wang

In Global and Planetary Change

1. R. Bannister
2. L. Berre
3. Xuguang Wang
4. G. Desroziers
5. Margarida Pereira
6. Martin Schlather
7. Xiangyu Huang
8. Hongli Wang

Citations in the corpus

• A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, methodology): “This is a useful way of looking at the properties of a given CVT model and is used in studies (e.g. Ingleby, 2001; Bannister, 2007).”
• A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “(23) Cost function (21) is minimized with respect to (δχ var, δγ var), which is an easier and better conditioned problem than minimizing (10) with respect to (δx, δη) (Lorenc et al., 2000; Haben et al., 2011) and useful physical constraints can be built into U (Ingleby, 2001; Bannister, 2008).”
• Background‐error correlation length‐scale estimates and their sampling statistics (2008, background): “As illustrated by several authors (Hollingsworth, 1987; Bouttier, 1993; Rabier et al., 1998; Ingleby 2001, Belo Pereira and Berre, 2006; Deckmyn and Berre, 2005), this length-scale diagnosis also gives information about atmospheric dynamic (Ingleby, 2001) and data density effects (Bouttier, 1993)…”
• The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model (2006, background): “The corresponding estimates provided by the ensemble method show that the length scale is largest in the Tropics and smallest in the data-rich regions, as it would be expected from other studies (e.g., Lindzen and Fox-Rabinovitz 1989; Ingleby 2001; Bouttier 1994).”
• Improved variational analyses using a nonlinear humidity control variable (2013, methodology): “For large-scale models, errors in humidity are shorter-scale than variables such as temperature or pressure (e.g. Table 1 of Ingleby, 2001).”
• Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances (2002, background): “(1998) and by Ingleby (2001). It has been recognized that objective analysis using the Gaussian shape to model the covariance severely hampers the ability of the analysis to assimilate the smallest scales.”
• 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).”
• Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, background): “Furthermore, temperature and pressure errors are strongly negatively correlated (Rabier et al. 1998; Ingleby 2001).”