A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings

Gao, Jidong; Smith, Travis; Stensrud, David J.; Fu, Chenghao; Calhoun, Kristin M.; Manross, Kevin L.; Brogden, Jeffrey; Lakshmanan, Valliappa; Wang, Yunheng; Thomas, Kevin W.; Brewster, Keith; Xue, Ming

doi:10.1175/waf-d-12-00093.1

Cited by 57 publications

(45 citation statements)

References 39 publications

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“…They noted that the background error covariance should change with different steps to incorporate scale-dependent information (like the Barnes successive correction scheme) but left this issue to future studies for further improvements. Gao et al (2013) adopted a real-time variational data assimilation system in which a two-step approach was employed to analyse observations of different spatial resolutions. In their two-step approach, a reduced background error de-correlation length was used in the second step, but the background error de-correlation length and error variances for different model variables were specified empirically in each step.…”

Section: Introductionmentioning

confidence: 99%

Multistep variational data assimilation: important issues and a spectral approach

Xu¹,

Gao³

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T In this paper, two important issues are raised for multistep variational data assimilation in which broadly distributed coarse-resolution observations are analysed in the first step, and then locally distributed highresolution observations are analysed in the second step (and subsequent steps if any). The first one concerns how to objectively estimate or efficiently compute the analysis error covariance for the analysed field obtained in the first step and used to update the background field in the next step. To attack this issue, spectral formulations are derived for efficiently calculating the analysis error covariance functions. The calculated analysis error covariance functions are verified against their respective benchmarks for one-and twodimensional cases and shown to be very (or fairly) good approximations for uniformly (or non-uniformly) distributed coarse-resolution observations. The second issue concerns whether and under what conditions the above calculated analysis error covariance can make the two-step analysis more accurate than the conventional single-step analysis. To answer this question, idealised numerical experiments are performed to compare the two-step analyses with their respective counterpart single-step analyses while the background error covariance is assumed to be exactly known in the first step but the number of iterations performed by the minimisation algorithm is limited (to mimic the computationally constrained situations in operational data assimilation). The results show that the two-step analysis is significantly more accurate than the single-step analysis until the iteration number becomes so large that the single-step analysis can reach the final convergence or nearly so. The two-step analysis converges much faster and thus is more efficient than the single-step analysis to reach the same accuracy. Its computational efficiency can be further enhanced by properly coarsening the grid resolution in the first step with the high-resolution grid used only over the nested domain in the second step.

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Section: Introductionmentioning

confidence: 99%

Multistep variational data assimilation: important issues and a spectral approach

Xu¹,

Gao³

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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“…These formulations will be extended together with the spectral formulations of Xu et al [8] for real-data applications in three-dimensional space with the variational data assimilation system of Gao et al [5], in which the analyses are univariate and performed in two steps. Such an extension is currently being developed.…”

Section: Discussionmentioning

confidence: 99%

“…This problem is common for the widely adopted singlestep approach in operational variational data assimilation, especially when patchy high-resolution observations, such as those remotely sensed from radars and satellites, are assimilated together with coarse-resolution observations into a high-resolution model. To solve this problem, multiscale and multistep approaches were explored and proposed by several authors (Xie et al [4], Gao et al [5], Li et al [6], and Xu et al [7,8]). For a two-step approach (or the first two steps of a multistep approach) in which broadly distributed coarse-resolution observations are analyzed first and then locally distributed high-resolution observations are analyzed in the second step, an important issue is how to objectively estimate or efficiently compute the analysis error covariance for the analyzed field that is obtained in the first step and used to update the background field in the second step.…”

Section: Introductionmentioning

confidence: 99%

Formulations for Estimating Spatial Variations of Analysis Error Variance to Improve Multiscale and Multistep Variational Data Assimilation

2018

Advances in Meteorology

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When the coarse-resolution observations used in the first step of multiscale and multistep variational data assimilation become increasingly nonuniform and/or sparse, the error variance of the first-step analysis tends to have increasingly large spatial variations. However, the analysis error variance computed from the previously developed spectral formulations is constant and thus limited to represent only the spatially averaged error variance. To overcome this limitation, analytic formulations are constructed to efficiently estimate the spatial variation of analysis error variance and associated spatial variation in analysis error covariance. First, a suite of formulations is constructed to efficiently estimate the error variance reduction produced by analyzing the coarseresolution observations in one-and two-dimensional spaces with increased complexity and generality (from uniformly distributed observations with periodic extension to nonuniformly distributed observations without periodic extension). Then, three different formulations are constructed for using the estimated analysis error variance to modify the analysis error covariance computed from the spectral formulations. The successively improved accuracies of these three formulations and their increasingly positive impacts on the two-step variational analysis (or multistep variational analysis in first two steps) are demonstrated by idealized experiments.

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“…The data assimilation system used to assimilate radar velocity and reflectivity at the convective scale was the 3DVAR system with the WRF model (version 3.4.1) interface that includes a complex cloud analysis package (Gao et al, 2002(Gao et al, , 2004Brewster et al, 2005;Hu et al, 2006a). The system was computationally very efficient, and therefore relatively large model domains could be used on available computers to reduce the effects of lateral boundaries on the convective storms of interest (Stensrud and Gao, 2010).…”

Section: Dvar Scheme and Cloud Analysis Systemmentioning

confidence: 99%

“…However, a limitation of the convective-scale EnKF based approach is the rapid error growth in forecasts due to the lack of balance in the model dynamics (Lange and 545 Craig, 2014). The 3D variational data assimilation scheme (3DVAR) can improve the balance among model variables by using weak constraints in the cost function (Gao et al, 1999(Gao et al, , 2002(Gao et al, , 2004Hu et al, 2006a, b;Stensrud and Gao, 2010;Ge et al, 2013a, b). More advanced techniques, such as the 4D variational method (4DVAR; Sun and Crook, 1998) can also be used to assimilate radar observations with a much more balanced analysis, but it is computationally quite expensive in high-resolution storm-scale NWP.…”

Section: Introductionmentioning

confidence: 99%

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

2016

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As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed-and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

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A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings

Cited by 57 publications

References 39 publications

Multistep variational data assimilation: important issues and a spectral approach

Multistep variational data assimilation: important issues and a spectral approach

Formulations for Estimating Spatial Variations of Analysis Error Variance to Improve Multiscale and Multistep Variational Data Assimilation

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

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