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Fuqing Zhang

Abstract

Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

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Fuqing Zhang

Abstract

Multiply nested mesoscale numerical simulations with horizontal resolution up to 3.3 km are performed to study the generation of mesoscale gravity waves during the life cycle of idealized baroclinic jet–front systems. Long-lived vertically propagating mesoscale gravity waves with horizontal wavelengths ∼100–200 km are simulated originating from the exit region of the upper-tropospheric jet streak, in a manner consistent with past observational studies. The residual of the nonlinear balance equation is found to be a useful index in diagnosing flow imbalance and predicting wave generation. The imbalance diagnosis and model simulations suggest that balance adjustment, as a generalization of geostrophic adjustment, is likely responsible for generating these mesoscale gravity waves. It is hypothesized that, through balance adjustment, the continuous generation of flow imbalance from the developing baroclinic wave will lead to the continuous radiation of gravity waves.

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Meng Zhang and Fuqing Zhang

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A hybrid data assimilation approach that couples the ensemble Kalman filter (EnKF) and four-dimensional variational (4DVar) methods is implemented for the first time in a limited-area weather prediction model. In this coupled system, denoted E4DVar, the EnKF and 4DVar systems run in parallel while feeding into each other. The multivariate, flow-dependent background error covariance estimated from the EnKF ensemble is used in the 4DVar minimization and the ensemble mean in the EnKF analysis is replaced by the 4DVar analysis, while updating the analysis perturbations for the next cycle of ensemble forecasts with the EnKF. Therefore, the E4DVar can obtain flow-dependent information from both the explicit covariance matrix derived from ensemble forecasts, as well as implicitly from the 4DVar trajectory. The performance of an E4DVar system is compared with the uncoupled 4DVar and EnKF for a limited-area model by assimilating various conventional observations over the contiguous United States for June 2003. After verifying the forecasts from each analysis against standard sounding observations, it is found that the E4DVar substantially outperforms both the EnKF and 4DVar during this active summer month, which featured several episodes of severe convective weather. On average, the forecasts produced from E4DVar analyses have considerably smaller errors than both of the stand-alone EnKF and 4DVar systems for forecast lead times up to 60 h.

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Masashi Minamide and Fuqing Zhang

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An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures through observing system simulation experiments with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiances, even if the forecast model and the observation operator (the radiative transfer model) are perfect. The promising performance of this newly proposed AOEI method is demonstrated through observing system simulation experiments assimilating all-sky brightness temperatures from GOES-R (now GOES-16) in comparison with experiments using an alternative empirical observation error inflation method proposed by Geer and Bauer. It is found that both inflation methods perform similarly in the accuracy of the analysis and in the containment of potential representativeness errors; both outperform experiments using a constant observation error without inflation. Besides being easier to implement, the empirical AOEI method proposed here also shows some advantage over the Geer–Bauer method in better updating variables at large scales. Large representative errors are likely to be compounded by unavoidable uncertainties in the forecast system and/or nonlinear observation operator (as for the radiative transfer model), in particular in the areas of moist processes, as will be the case for real-data cloudy radiances, which will be further investigated in future studies.

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Juan Fang and Fuqing Zhang

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As a follow-up to a previously published article on the initial development and genesis of Hurricane Dolly (2008), this study further examines the evolution of, and interactions among, multiscale vortices ranging from the system-scale main vortex (L > 150 km) to the intermediate-scale cloud clusters (50 km < L < 150 km) and individual vorticity-rich convective cells (L < 50 km). It is found that there are apparent self-similarities among these vortices at different scales, each of which may undergo several cycles of alternating accumulation and release of convective available potential energy. Enhanced surface fluxes below individual cyclonic vortices at each scale contribute to the sustainment and reinvigoration of moist convection that in turn contributes to the maintenance and upscale growth of these vortices.

Spectral analysis of horizontal divergence and relative vorticity further suggests that the cloud-cluster-scale and system-scale vortices are predominantly balanced while the individual convective vortices are largely unbalanced. The vorticity and energy produced by these individual vorticity-rich convective cells first saturate at convective scales that are subsequently transferred to larger scales. The sum of the diabatic heating released from these convective cells may be regarded as a persistent forcing on the quasi-balanced system-scale vortex. The secondary circulation induced by such forcing converges the cluster- and convective-scale vorticity anomalies into the storm center region. Convergence and projections of the smaller-scale vorticity to the larger scales eventually produce the spinup of the system-scale vortex. Meanwhile, convectively induced negative vorticity anomalies also converge toward the storm center, which are weaker and shorter lived, and thus are absorbed rather than expelled.

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Juan Fang and Fuqing Zhang

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Through observational analysis and numerical simulations, this study examines the roles of the Madden–Julian oscillation (MJO) and tropical waves in the three-stage formation of Supertyphoon Megi (2010) including 1) convective bursts followed by vorticity aggregation, 2) vortex rearrangement during decaying convection, and 3) convective reinvigoration and vortex intensification. The MJO was responsible for preconditioning the large-scale circulation and low-level moisture favorable for convection during all stages, while the counterpropagating Kelvin and equatorial Rossby (ER) waves brought low-level convergence and cyclonic vorticity anomalies to enhance massive convection in the western tropical Pacific in stage 1. Convection strengthened the vorticity anomalies nearby, which subsequently developed into Megi’s embryo by the end of stage 1 through merging with the positive vorticity anomaly carried by a westward-propagating mixed Rossby–gravity and tropical depression (MRG–TD)-type wave. The ER- and MRG–TD-type waves might also contribute to Megi’s formation through increasing low-level southwesterlies to the southwest of the precursor during stages 2 and 3. These tropical waves also indirectly affect Megi’s genesis through modulating surroundings near the precursor. Without the MJO, the low-level vorticity anomaly to the near west of the precursor would intensify more effectively and develop into a tropical cyclone instead of the observed Megi. Removing the Kelvin or ER wave would enhance convection to the far west of Megi’s precursor, which was less favorable for low-level convergence in the region of the precursor, and thus the formation of Megi.

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Yonghui Weng and Fuqing Zhang

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Through a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA’s operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity.

Also documented here are the details of a series of data thinning and quality control procedures that were developed to generate superobservations from large volumes of airborne radial velocity measurements. These procedures have since been implemented operationally on the NOAA hurricane reconnaissance aircraft that allows for more efficient real-time transmission of airborne radar observations to the ground.

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Zhiyong Meng and Fuqing Zhang

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In Part I of this two-part work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24–26 January 2000. The current study seeks to explore the performance of the EnKF for the same event in the presence of significant model errors due to physical parameterizations by assimilating synthetic sounding and surface observations with typical temporal and spatial resolutions. The EnKF performance with imperfect models is also examined for a warm-season mesoscale convective vortex (MCV) event that occurred on 10–13 June 2003. The significance of model error in both warm- and cold-season events is demonstrated when the use of different cumulus parameterization schemes within different ensembles results in significantly different forecasts in terms of both ensemble mean and spread. Nevertheless, the EnKF performed reasonably well in most experiments with the imperfect model assumption (though its performance can sometimes be significantly degraded). As in Part I, where the perfect model assumption was utilized, most analysis error reduction comes from larger scales. Results show that using a combination of different physical parameterization schemes in the ensemble forecast can significantly improve filter performance. A multischeme ensemble has the potential to provide better background error covariance estimation and a smaller ensemble bias. There are noticeable differences in the performance of the EnKF for different flow regimes. In the imperfect scenarios considered, the improvement over the reference ensembles (pure ensemble forecasts without data assimilation) after 24 h of assimilation for the winter snowstorm event ranges from 36% to 67%. This is higher than the 26%–45% improvement noted after 36 h of assimilation for the warm-season MCV event. Scale- and flow-dependent error growth dynamics and predictability are possible causes for the differences in improvement. Compared to the power spectrum analyses for the snowstorm, it is found that forecast errors and ensemble spreads in the warm-season MCV event have relatively smaller power at larger scales and an overall smaller growth rate.

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Dandan Tao and Fuqing Zhang

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This study explores the spatial and temporal changes in tropical cyclone (TC) thermodynamic and dynamic structures before, near, and during rapid intensification (RI) under different vertical wind shear conditions through four sets of convection-permitting ensemble simulations. A composite analysis of TC structural evolution is performed by matching the RI onset time of each member. Without background flow, the axisymmetric TC undergoes a gradual strengthening of the inner-core vorticity and warm core throughout the simulation. In the presence of moderate environmental shear (5–6 m s−1), both the location and magnitude of the asymmetries in boundary layer radial flow, relative humidity, and vertical motion evolve with the tilt vector throughout the simulation. A budget analysis indicates that tilting is crucial to maintaining the midlevel vortex while stretching and vertical advection are responsible for the upper-level vorticity generation before RI when strong asymmetries arise. Two warm anomalies are observed before the RI onset when the vortex column is tilted. When approaching the RI onset, these two warm anomalies gradually merge into one. Overall, the most symmetric vortex structure is found near the RI onset. Moderately sheared TCs experience an adjustment period from a highly asymmetric structure with updrafts concentrated at the down-tilt side before RI to a more axisymmetric structure during RI as the eyewall updrafts develop. This adjustment period near the RI onset, however, is found to be the least active period for deep convection. TC development under a smaller environmental shear (2.5 m s−1) condition displays an intermediate evolution between ensemble experiments with no background flow and with moderate shear (5–6 m s−1).

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Fuqing Zhang and Dandan Tao

Abstract

Through cloud-resolving simulations, this study examines the effect of vertical wind shear and system-scale flow asymmetry on the predictability of tropical cyclone (TC) intensity during different stages of the TC life cycle. A series of ensemble experiments is performed with varying magnitudes of vertical wind shear, each initialized with an idealized weak TC-like vortex, with small-scale, small-amplitude random perturbations added to the initial conditions. It is found that the environmental shear can significantly affect the intrinsic predictability of tropical cyclones, especially during the formation and rapid intensification stage. The larger the vertical wind shear, the larger the uncertainty in the intensity forecast, primarily owing to the difference in the timing of rapid intensification.

In the presence of environmental shear, initial random noise may result in changes in the timing of rapid intensification by as much as 1–2 days through the randomness (and chaotic nature) of moist convection. Upscale error growth from differences in moist convection first alters the tilt amplitude and angle of the incipient tropical storms, which leads to significant differences in the timing of precession and vortex alignment. During the precession process, both the vertical tilt of the storm and the effective (local) vertical wind shear are considerably decreased after the tilt angle reaches 90° to the left of the environmental shear. The tropical cyclone intensifies immediately after the tilt and the effective local shear reach their minima. In some instances, small-scale, small-amplitude random noise may also limit the intensity predictability through altering the timing and strength of the eyewall replacement cycle.

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