The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

Putnam, Bryan J.; Xue, Ming; Jung, Youngsun; Snook, Nathan; Zhang, Guifu

doi:10.1175/mwr-d-13-00042.1

Cited by 62 publications

(39 citation statements)

References 51 publications

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“…Double-moment schemes contain prognostic equations for both mixing ratios and number concentrations of certain hydrometeor species, unlike single-moment schemes, which only contain prognostic equations for mixing ratios. Recent work indicates that double-moment schemes are necessary for reproducing dual-polarization signatures in ensemble Kalman filter analyses of supercells (Jung et al 2012) and MCSs (Putnam et al 2014). Additionally, Dawson et al (2010) obtained more accurate simulations of a tornadic supercell with two-and three-moment schemes, especially with the simulated cold pool.…”

Section: Methodsmentioning

confidence: 99%

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Clark

Bullock²,

Jensen³

et al. 2014

Weather and Forecasting

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Meaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach-a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]-was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold used to define objects [8 km and 0.10 in. (1 in. 5 2.54 cm), respectively], the most common object duration was 3 h in both models and observations. With an increased smoothing radius and rainfall threshold, the most common duration became shorter. The simulations depicted the diurnal cycle of object frequencies well, but overpredicted object frequencies uniformly across all forecast hours. The simulations had spurious maxima in initiating objects at the beginning of the forecast and a corresponding spurious maximum in dissipating objects slightly later. Examining average object velocities, a slow bias was found in the simulations, which was most pronounced in the Thompson member. These findings should aid users and developers of convection-allowing models and motivate future work utilizing time-domain methods for verifying high-resolution forecasts.

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Section: Methodsmentioning

confidence: 99%

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Clark

Bullock²,

Jensen³

et al. 2014

Weather and Forecasting

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“…Using this system, they analyzed the storm-scale features of the 27 April 2011 Alabama severe weather outbreak. Through assimilating radar and surface observations with an EnKF in combination with a double-moment microphysics scheme, Putnam et al (2014) analyzed the microphysical states and precipitation structures within a mesoscale convective system that passed over western Oklahoma during 8-9 May 2007. Schenkman et al (2011b) examined the impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) through a three-dimensional variational data assimilation (3DVAR) system.…”

Section: Introductionmentioning

confidence: 99%

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

et al. 2016

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This paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System (VDRAS). Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational (4DVAR) data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments-assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function-are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall lineincluding the surface warm inflow, cold pool, gust front, and low-level wind-are much closer to the observations after assimilating the surface data in VDRAS.Key words: VDRAS, 4-D data assimilation, radar data, surface data, squall line Citation: Chen, X. C., K. Zhao, J. Z. Sun, B. W. Zhou, and W.-C. Lee, 2016: Assimilating surface observations in a fourdimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case. Adv.

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“…The ensemble filters have been used in operational global forecast systems to provide ensemble-based BEC (e.g., Raynaud et al 2011;Bonavita et al 2012;Hamill et al 2011b; as well as initial conditions for ensemble forecasts (e.g., Houtekamer et al 2005;Whitaker et al 2008;Hamill et al 2011a). The application of EnKF to mesoscale models has also enjoyed encouraging successes (e.g., Fujita et al 2007;Meng and Zhang 2007;Bonavita et al 2008) while for the convective scale, EnKF has shown great ability in dealing with complex, nonlinear physical processes (e.g., Tong and Xue 2005) that may even involve two-moment microphysics parameterization (e.g., Xue et al 2010;Jung et al 2012;Putnam et al 2014). Accurate representation of microphysical processes is especially important at the convective scale.…”

Section: Introductionmentioning

confidence: 99%

A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

Pan

Zhu

Xue

et al. 2014

Monthly Weather Review

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A coupled ensemble square root filter-three-dimensional ensemble-variational hybrid (EnSRF-En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at 1 /3 of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

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The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

Cited by 62 publications

References 51 publications

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

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