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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

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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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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Zhiyong Meng
and
Fuqing Zhang

Abstract

In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.

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Zhiyong Meng
and
Fuqing Zhang

Abstract

The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors’ recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. The current study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event on 10–12 June 2003. Direct comparison between the EnKF and a three-dimensional variational data assimilation (3DVAR) system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF consistently performs better than the 3DVAR method by assimilating either individual or multiple data sources (i.e., sounding, surface, and wind profiler) for this MCV event. Background error covariance plays an important role in the performance of both the EnKF and the 3DVAR system. Proper covariance inflation and the use of different combinations of physical parameterization schemes in different ensemble members (the so-called multischeme ensemble) can significantly improve the EnKF performance. The 3DVAR system can benefit substantially from using short-term ensembles to improve the prior estimate (with the ensemble mean). Noticeable improvement is also achieved by including some flow dependence in the background error covariance of 3DVAR.

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Yonghui Weng
and
Fuqing Zhang

Abstract

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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Baoguo Xie
and
Fuqing Zhang

Abstract

Cloud-resolving ensemble simulations and sensitivity experiments utilizing the Weather Research and Forecasting model (WRF) are performed to investigate the dynamics and predictability of the record-breaking rainfall and flooding event in Taiwan induced by Typhoon Morakot (2009). It is found that a good rainfall forecast foremost requires a good track forecast during Morakot’s landfall. Given a good track forecast, interaction of the typhoon circulation with complex topography in southern Taiwan plays a dominant role in producing the observed heavy rainfalls. The terrain slope, strength of the horizontal winds, and mid–lower-tropospheric moisture content in the southwesterly upslope flow are the primary factors that determine the rainfall location and intensity, as elucidated by the idealized one-dimensional precipitation-rate forecast model. The typhoon circulation and the southwesterly monsoon flow transport abundant moisture into southern Taiwan, which produces the heavy rainfall through interactions with the complex high terrain in the area. In the meantime, as part of the south China monsoon, the southwesterly flow may be substantially enhanced by the typhoon circulation.

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Huizhong He
and
Fuqing Zhang

Abstract

This study examines the diurnal variations of the warm-season precipitation over northern China using the high-resolution precipitation products obtained from the Climate Prediction Center’s morphing technique (CMORPH) during May–August of 2003–09. The areas of focus are the Yanshan–Taihangshan Mountain ranges along the east peripheries of the Loess and Inner Mongolian Plateaus and the adjacent North China Plains. It is found that the averaged peak in local precipitation begins early in the afternoon near the top of the mountain ranges and propagates downslope and southeastward at a speed of ∼13 m s−1. The peak reaches the central North China Plains around midnight and the early morning hours resulting in a broad area of nocturnal precipitation maxima over the plains. The diurnal precipitation peak (minimum) is closely collocated with the upward (downward) branch of a mountain–plains solenoid (MPS) circulation. Both the MPS and a low-level southwesterly nocturnal jet are likely to be jointly responsible for the nighttime precipitation maxima over the plains.

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Shuguang Wang
and
Fuqing Zhang

Abstract

This study investigates the sensitivity of mesoscale gravity waves to the baroclinicity of the background jet-front systems by simulating different life cycles of baroclinic waves with a high-resolution mesoscale model. Four simulations are made starting from two-dimensional baroclinic jets having different static stability and wind shear in order to obtain baroclinic waves with significantly different growth rates. In all experiments, vertically propagating mesoscale gravity waves are simulated in the exit region of upper-tropospheric jet streaks. A two-dimensional spectral analysis demonstrates that these gravity waves have multiple components with different wave characteristics. The short-scale wave components that are preserved by a high-pass filter with a cutoff wavelength of 200 km have horizontal wavelengths of 85–161 km and intrinsic frequencies of 3–11 times the Coriolis parameter. The medium-scale waves that are preserved by a bandpass filter (with 200- and 600-km cutoff wavelengths) have horizontal wavelengths of 250–350 km and intrinsic frequencies less than 3 times the Coriolis parameter. The intrinsic frequencies of these gravity waves tend to increase with the growth rate of the baroclinic waves; gravity waves with similar frequency are found in the experiments with similar average baroclinic wave growth rate but with significantly different initial tropospheric static stability and tropopause geometry. The residuals of the nonlinear balance equation are used to assess the flow imbalance. In all experiments, the developing background baroclinic waves evolve from an initially balanced state to the strongly unbalanced state especially near the exit region of upper-level jet fronts before mature mesoscale gravity waves are generated. It is found that the growth rate of flow imbalance also correlates well to the growth rate of baroclinic waves and thus correlates to the frequency of gravity waves.

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