Generalized background error covariance matrix model  (GEN_BE v2.0)

Descombes, Gael; Auligné, Thomas; Vandenberghe, François; Barker, Dale; Barré, Jérôme

doi:10.5194/gmd-8-669-2015

Cited by 72 publications

(52 citation statements)

References 46 publications

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“…The settings of GSI are similar to Pagowski et al (2014), in which the background error and length scales have vertical variance. Figure 1 shows that the vertical profiles of background errors and length scales for PM 2.5 (for PM 2.5 assimilation) and accumulation-mode sulfate (ASO4J, for AOD assimilation), which can be calculated using the GSI NMC (National Meteorological Center) method (Parrish and Derber, 1992) through the use of a tool called GEN_BE (Descombes et al, 2015). ASO4J is one of the CMAQ aerosol species used in AOD data assimilation.…”

Section: Settings For Data Assimilationsmentioning

confidence: 99%

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

et al. 2017

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Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM 2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM 2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM 2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM 2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM 2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM 2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

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Section: Settings For Data Assimilationsmentioning

confidence: 99%

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

et al. 2017

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“…Length‐scale is simply defined as the fourth root of the ratio of the variance of a field ( φ ) and the variance of its Laplacian (calculated using a second‐order finite difference approximation) (Descombes et al . ); that is

boldL = {((), \frac{8 \cdot v a r i a n c e false(φ false)}{v a r i a n c e false(\nabla^{2} φ false)})}^{1 false/ 4} .

…”

Section: Initial Resultsmentioning

confidence: 99%

A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds

Simonin

Waller

Ballard

et al. 2019

Quart J Royal Meteoro Soc

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Recent research has shown that high-resolution observations, such as Doppler radar radial winds, exhibit spatial correlations. High-resolution observations are routinely assimilated into convection-permitting numerical weather prediction models assuming their errors are uncorrelated. To avoid violating this assumption, observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of full, correlated, error statistics. Some operational centres have introduced satellite inter-channel observation-error correlations and obtained improved analysis accuracy and forecast skill scores. Here we present a strategy for implementing spatially correlated observation errors in an operational system. We then provide the first demonstration of the practical feasibility of incorporating spatially correlated Doppler radial wind error statistics in the Met Office numerical weather prediction system. Inclusion of correlated Doppler radial winds error statistics has little impact on the computation cost of the data assimilation system, even with a fourfold increase in the number of Doppler radial winds observations assimilated. Using the correlated observation-error statistics with denser observations produces increments with shorter length-scales than the control. Initial forecast trials show a neutral to positive impact on forecast skill overall, notably for quantitative precipitation forecasts.There is potential to improve forecast skill by optimizing the use of Doppler radial winds and applying the technique to other observation types. K E Y W O R D SDoppler radial wind, observation error, operational implementation, parallelization, spatial correlations

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“…To perform the variational DA with this newly developed radar operator, it is necessary to generate the background error covariance matrix with q r , q s , and q g as part of the control variables. The generalized software package for the background error covariance statistics (GEN_BE) developed by Descombes et al (2015) was used. The GEN_BE package can generate the univariate background error statistics for 11 variables, including these three hydrometeors.…”

Section: Generation Of the Background Error Covariancementioning

confidence: 99%

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

Wang

Liu

2019

Geosci. Model Dev.

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A reflectivity forward operator and its associated tangent linear and adjoint operators (together named Radar-Var) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50-100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using Radar-Var also improved the short-term (2-5 h) precipitation forecasts compared to those of the experiment without DA.

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Generalized background error covariance matrix model (GEN_BE v2.0)

Cited by 72 publications

References 46 publications

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

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