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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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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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Meng Zhang
and
Fuqing Zhang

Abstract

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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Juan Fang
and
Fuqing Zhang

Abstract

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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Yonghui Lin
and
Fuqing Zhang

Abstract

Using a two-dimensional Fourier decomposition and a four-dimensional ray-tracing technique, the propagating characteristics and source mechanisms of mesoscale gravity waves simulated in idealized baroclinic jet-front systems are investigated. The Fourier decomposition successfully separates the simulated gravity waves from a complex background flow in the troposphere. Four groups of gravity waves in the lower stratosphere are identified from the spectral decomposition. One is a northward-propagating short-scale wave packet with horizontal wavelength of ∼150 km, and another is a northeastward-propagating medium-scale wave packet with horizontal wavelength of ∼350 km. Both of these are most pronounced in the exit region of the upper-tropospheric jet. A third group exists in the deep trough region above (and nearly perpendicular to) the jet, and a fourth group far to the south of the jet right above the surface cold front, both of which are short-scale waves and have a horizontal wavelength of ∼100–150 km.

Ray-tracing analysis suggests that the medium-scale gravity waves originate from the upper-tropospheric jet-front system where there is maximum imbalance, though contributions from the surface fronts cannot be completely ruled out. The shorter-scale, northward-propagating gravity waves in the jet-exit region, on the other hand, may originate from both the upper-tropospheric jet-front system and the surface frontal system. The shorter-scale gravity waves in the deep trough region across the jet (and those right above the surface cold fronts) are almost certain to initiate from the surface frontal system. Ray-tracing analysis also reveals a very strong influence of the spatial and temporal variability of the complex background flow on the characteristics of gravity waves as they propagate.

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Masashi Minamide
and
Fuqing Zhang

Abstract

This study explores the impacts of assimilating all-sky infrared satellite radiances from Himawari-8, a new-generation geostationary satellite that shares similar remote sensing technology with the U.S. geostationary satellite GOES-16, for convection-permitting initialization and prediction of tropical cyclones with an ensemble Kalman filter (EnKF). This case studies the rapid intensification stages of Supertyphoon Soudelor (2015), one of the most intense tropical cyclones ever observed by Himawari-8. It is found that hourly cycling assimilation of the infrared radiance improves not only the estimate of the initial intensity, but also the spatial distribution of essential convective activity associated with the incipient tropical cyclone vortex. Deterministic convection-permitting forecasts initialized from the EnKF analyses are capable of simulating the early development of Soudelor, which demonstrates encouraging prospects for future improvement in tropical cyclone prediction through assimilating all-sky radiances from geostationary satellites such as Himawari-8 and GOES-16. A series of forecast sensitivity experiments are designed to systematically explore the impacts of moisture updates in the data assimilation cycles on the development and prediction of Soudelor. It is found that the assimilation of the brightness temperatures contributes not only to better constraining moist convection within the inner-core region, but also to developing a more resilient initial vortex, both of which are necessary to properly capture the rapid intensification process of tropical cyclones.

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Christopher Melhauser
and
Fuqing Zhang

Abstract

This study explores both the practical and intrinsic predictability of severe convective weather at the mesoscales using convection-permitting ensemble simulations of a squall line and bow echo event during the Bow Echo and Mesoscale Convective Vortex (MCV) Experiment (BAMEX) on 9–10 June 2003. Although most ensemble members—initialized with realistic initial condition uncertainties smaller than the NCEP Global Forecast System Final Analysis (GFS FNL) using an ensemble Kalman filter—forecast broad areas of severe convection, there is a large variability of forecast performance among different members, highlighting the limit of practical predictability. In general, the best-performing members tend to have a stronger upper-level trough and associated surface low, producing a more conducive environment for strong long-lived squall lines and bow echoes, once triggered. The divergence in development is a combination of a dislocation of the upper-level trough, surface low with corresponding marginal environmental differences between developing and nondeveloping members, and cold pool evolution by deep convection prior to squall line formation. To further explore the intrinsic predictability of the storm, a sequence of sensitivity experiments was performed with the initial condition differences decreased to nearly an order of magnitude smaller than typical analysis and observation errors. The ensemble forecast and additional sensitivity experiments demonstrate that this storm has a limited practical predictability, which may be further improved with more accurate initial conditions. However, it is possible that the true storm could be near the point of bifurcation, where predictability is intrinsically limited. The limits of both practical and intrinsic predictability highlight the need for probabilistic and ensemble forecasts for severe weather prediction.

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Zhiyong Meng
and
Fuqing Zhang

Abstract

Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

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Xinghua Bao
and
Fuqing Zhang

Abstract

The NCEP–NCAR reanalysis, NCEP Climate Forecast System Reanalysis (CFSR), 40-yr ECMWF Re-Analysis (ERA-40), and interim ECMWF Re-Analysis (ERA-Interim) products are evaluated with sounding observations from an enhanced radiosonde network available every 6 h during the Tibetan Plateau Experiment (TIPEX) conducted from 10 May to 9 August 1998. This study uses more than 3000 high-quality, independent rawinsondes at 11 stations (which were not assimilated in any of the reanalyses), which represents the first time that such a comprehensive evaluation is performed to assess the quality of these four most widely used reanalysis products over this region, which is highest in the world and crucial to the global climate and weather.

Averaging over the entire three-month period, it is found that each reanalysis dataset produces mean values of temperature and horizontal winds consistent with the verifying soundings (indicating relatively small mean bias); however, there are considerable differences (biases) in the mean relative humidity. On average, except for temperature at higher levels, both newer-generation reanalyses (CFSR and ERA-Interim) have smaller root-mean-square (RMS) error and bias than their predecessors (NCEP–NCAR and ERA-40). With some exceptions, the RMS errors of all variables for both CFSR and ERA-Interim (verifying with soundings) are similar in magnitude to the RMS difference between these two reanalyses, all of which are approximately twice as large as the corresponding observation errors. It is also found that there are strong diurnal variations in both RMS error and mean bias that differ greatly among different reanalyses and at different pressure levels.

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Chris Snyder
and
Fuqing Zhang

Abstract

Assimilation of Doppler radar data into cloud models is an important obstacle to routine numerical weather prediction for convective-scale motions; the difficulty lies in initializing fields of wind, temperature, moisture, and condensate given only observations of radial velocity and reflectivity from the radar. This paper investigates the potential of the ensemble Kalman filter (EnKF), which estimates the covariances between observed variables and the state through an ensemble of forecasts, to assimilate radar observations at convective scales. In the basic experiment, simulated observations are extracted from a reference simulation of a splitting supercell and assimilated using the EnKF and the same numerical model that produced the reference simulation. The EnKF produces accurate analyses, including the unobserved variables, after roughly 30 min (or six scans) of radial velocity observations. Additional experiments, in which forecasts are made from the ensemble-mean analysis, reveal that forecast errors grow significantly in this simple system, so that the ability of the EnKF to track the reference solution is not simply because of stable system dynamics. It is also found that the covariances between radial velocity and temperature, moisture, and condensate are important to the quality of the analyses, as is the initialization chosen for the ensemble members prior to assimilating the first observations. These results are promising, especially given the ease of implementing the EnKF. A number of important issues remain, however, including the initialization of the ensemble prior to the first observation, the treatment of uncertainty in the environmental sounding, the role of error in the forecast model (particularly the microphysical parameterizations), and the treatment of lateral boundary conditions.

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