Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system

By R. Langland, N. Baker. 2004

394 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f1e321fd493d3d705df03f1639cc6e0a565f2e13

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Citations in the corpus

The impact of Aeolus wind retrievals on ECMWF global weather forecasts (2021, methodology): “FSOI measures the impact of observations in the context of all the other observations assimilated (Langland and Baker, 2004).”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, background): “This has recently attracted considerable attention in meteorology for routine monitoring of observing systems (Langland and Baker, 2004; Cardinali et al., 2004; Zhu and Gelaro, 2008; Daescu, 2008).”
Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting (2007, methodology): “Such use of ensemble sensitivities requires no adjoint model as in Langland and Baker (2004), because the adjoint sensitivity field is implicit in the ensemble sensitivity values.”
Ensemble-Based Sensitivity Analysis (2008, result): “We note that the objective here is similar to that of Langland and Baker (2004), who use an adjoint-based technique to estimate the impact of observations on the error in global forecasts.”
Issues Regarding the Assimilation of Cloud and Precipitation Data (2007, methodology): “At least one forecast center includes metrics obtained using adjoints of the assimilation and forecast model systems (Langland and Baker 2004).”
On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, methodology): “The approach of Langland and Baker (2004) provides a higher-order accuracy in the estimation of the observation impact on the forecast error by combining adjoint sensitivity gradients from two trajectories (background and analysis).”
Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System (2008, methodology): “This approach has been used to diagnose the effectiveness of specific targeted observations (Doerenbecher and Bergot 2001; Fourrie et al. 2002), as well as to perform comprehensive assessments of observing system impacts on short-range forecast errors (Langland and Baker 2004).”
Data Assimilation for Numerical Weather Prediction: A Review (2009, methodology): “Le Dimet (1982), Lewis and Derber (1985), Courtier (1985), Le Dimet and Talagrand (1986) were the first to work on adjoint data assimilation.”