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Zhiyong Meng and Yunji Zhang

Abstract

Based on a 3-yr (2007–09) mosaic of radar reflectivity and conventional surface and synoptic radiosonde observations, the general features of squall lines preceding landfalling tropical cyclones (TCs) (pre-TC) in China are examined and compared with their midlatitude and subtropical counterparts. The results show that about 40% of landfalling TCs are associated with pre-TC squall lines with high-occurring frequency in August and from late afternoon to midnight. Most pre-TC squall lines form in a broken-line mode with a trailing-stratiform organization. On average, they occur about 600 km from the TC center in the front-right quadrant with a maximum length of 220 km, a maximum radar reflectivity of 57–62 dBZ, a life span of 4 h, and a moving speed of 12.5 m s−1. Pre-TC squall lines are generally shorter in lifetime and length than typical midlatitude squall lines.

Pre-TC squall lines tend to form in the transition area between the parent TC and subtropical high in a moist environment and with a weaker cold pool than their midlatitude counterparts. The environmental 0–3-km vertical shear is around 10 m s−1 and generally normal to the orientation of the squall lines. This weak shear makes pre-TC squall lines in a suboptimal condition according to the Rottuno–Klemp–Weisman (RKW) theory. Convection is likely initiated by low-level mesoscale frontogenesis, convergence, and/or confluence instead of synoptic-scale forcing. The parent TC may contribute to (i) the development of convection by enhancing conditional instability and low-level moisture supply, and (ii) the linear organization of discrete convection through the interaction between the TC and the neighboring environmental system.

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Yunji Zhang, David J. Stensrud, and Fuqing Zhang

Abstract

This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when in-storm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones.

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Yunji Zhang, Fuqing Zhang, and David J. Stensrud

Abstract

This article presents the first practice of assimilating real-world all-sky GOES-16 ABI infrared brightness temperature (BT) observations using an ensemble-based data assimilation system coupled with the Weather Research and Forecasting (WRF) Model at a convection-allowing (1 km) horizontal resolution, focusing on the tornadic thunderstorm event across Wyoming and Nebraska on 12 June 2017. It is found that spurious clouds created before observed convection initiation are rapidly removed, and the analysis and forecasts of thunderstorms are significantly improved, when all-sky BT observations are assimilated with the adaptive observation error inflation (AOEI) and adaptive background error inflation (ABEI) techniques. Better forecasts of the timing and location of convection initiation can be achieved after only 30 min of assimilating BT observations; both deterministic and probabilistic WRF forecasts of midlevel mesocyclones and low-level vortices, started from the final analysis with 100 min of BT assimilation, closely coincide with the tornado reports. These improvements result not only from the effective suppression of spurious clouds, but also from the better estimations of hydrometeors owing to the frequent assimilation of all-sky BT observations that yield a more accurate analysis of the storm. Results show that BT observations generally have a greater impact on ice particles than liquid water species, which might provide guidance on how to better constrain modeled clouds using these spaceborne observations.

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Zhiyong Meng, Dachun Yan, and Yunji Zhang

Abstract

Based on mosaics of composite radar reflectivity patterns during the 2-yr period of 2008–09, a total of 96 squall lines were identified in east China with a maximum frequency of occurrence in north China near the boundaries between Shandong, Henan, Anhui, and Jiangsu Provinces. The squall lines form from March to October with a peak in July. Their diurnal variation shows a major peak in the early evening and two minor peaks in the early morning and early afternoon. The time between squall-line formation and the first echo is about 4.8 h. The squall lines have a dominant southwest–northeast orientation, an eastward motion at a speed of 14.4 m s−1, a maximum length of 243 km, a maximum intensity of 58–63 dBZ, and a duration of 4.7 h on average. The squall lines commonly form in a broken-line mode, display a trailing-stratiform pattern, and dissipate in a reversed broken-line mode. Composite rawinsonde analyses show that squall lines in midlatitude east China tend to form in a moister environment with comparable background instability, and weaker vertical shear relative to their U.S. counterparts. The rawinsondes were also composited with respect to different formation and organizational modes. The environmental flows of the squall lines in the area with high frequency of formation were classified into six synoptic weather patterns: pre–short trough, pre–long trough, cold vortex, subtropical high, tropical cyclone (TC), and posttrough. About one-third of the squall lines form in the dominant pre-short-trough pattern. Favorable conditions of various patterns were examined in terms of moisture supply, instability, vertical wind shear, low-level jet, etc.

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Yunji Zhang, Xingchao Chen, and Yinghui Lu

Abstract

There are ongoing efforts to establish an ensemble data assimilation and prediction system for tropical cyclones based on the finite-volume cubed-sphere (FV3) dynamic core with the capability to assimilate satellite all-sky infrared and microwave observations. To complement the system developments and improve our understanding of the assimilation of all-sky infrared and microwave observations, this study assesses their potential impacts on the analysis of Hurricane Harvey (2017) through examinations of the structure and dynamics of the ensemble-based correlations as well as single observation data assimilation experiments, using an ensemble forecast generated by a global-to-regional nested FV3-based model. It is found that different infrared and microwave channels are sensitive to different types of hydrometeors within different layers of the atmosphere, and the correlations vanish beyond 200 km in the region covered by cloud or abundant hydrometeors. The spatial correlations between brightness temperatures and model states will adjust the structure and intensity of the hurricane in the model so that the simulated hurricane will better fit the “observed” brightness temperatures. In general, these results show how assimilating infrared and microwave together can improve the analyses of tropical cyclone intensity and structure, which may lead to improved intensity forecasts.

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Yunji Zhang, Fuqing Zhang, David J. Stensrud, and Zhiyong Meng

Abstract

Using a high-resolution convection-allowing numerical weather prediction model, this study seeks to explore the intrinsic predictability of the severe tornadic thunderstorm event on 20 May 2013 in Oklahoma from its preinitiation environment to initiation, upscale organization, and interaction with other convective storms. This is accomplished through ensemble forecasts perturbed with minute initial condition uncertainties that were beyond detection capabilities of any current observational platforms. It was found that these small perturbations, too small to modify the initial mesoscale environmental instability and moisture fields, will be propagated and evolved via turbulence within the PBL and rapidly amplified in moist convective processes through positive feedbacks associated with updrafts, phase transitions of water species, and cold pools, thus greatly affecting the appearance, organization, and development of thunderstorms. The forecast errors remain nearly unchanged even when the initial perturbations (errors) were reduced by as much as 90%, which strongly suggests an inherently limited predictability for this thunderstorm event for lead times as short as 3–6 h. Further scale decomposition reveals rapid error growth and saturation in meso-γ scales (regardless of the magnitude of initial errors) and subsequent upscale growth into meso-β scales.

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Yunji Zhang, Zhiyong Meng, Fuqing Zhang, and Yonghui Weng
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Yunji Zhang, Fuqing Zhang, David J. Stensrud, and Zhiyong Meng

Abstract

The practical predictability of severe convective thunderstorms during the 20 May 2013 severe weather event that produced the catastrophic enhanced Fujita scale 5 (EF-5) tornado in Moore, Oklahoma, was explored using ensembles of convective-permitting model simulations. The sensitivity of initiation and the subsequent organization and intensity of the thunderstorms to small yet realistic uncertainties in boundary layer and topographical influence within a few hours preceding the thunderstorm event was examined. It was found that small shifts in either simulation time or terrain configuration led to considerable differences in the atmospheric conditions within the boundary layer. Small shifts in simulation time led to changes in low-level moisture and instability, primarily through the vertical distribution of moisture within the boundary layer due to vertical mixing during the diurnal cycle as well as advection by low-level jets, thereby influencing convection initiation. Small shifts in terrain led to changes in the wind field, low-level vertical wind shear, and storm-relative environmental helicity, altering locally enhanced convergence that may trigger convection. After initiation, an upscale growth of errors resulting from deep moist convection led to large forecast uncertainties in the timing, intensity, structure, and organization of the developing mesoscale convective system and its embedded supercells.

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Yunji Zhang, Zhiyong Meng, Fuqing Zhang, and Yonghui Weng

Abstract

The practical predictability of tropical cyclone (TC) intensity in terms of mean absolute forecast error with respect to different conditions at forecast initialization was explored through convection-permitting hindcasts of all Atlantic storms during the 2008–12 hurricane seasons using the Weather Research and Forecasting (WRF) Model. Averaged over a total of 2190 simulations, the day 1–5 performance of these WRF hindcasts was comparable to two operational regional-scale hurricane prediction models used by the National Hurricane Center (NHC) but was slightly inferior to the NHC official forecasts. It was found that the prediction accuracy of TC intensity, both at the initialization time and the targeted forecast hours, was strongly correlated with the TC intensity. On average, for both the WRF hindcasts and the NHC official forecasts, stronger intensities and larger intensity variations led to larger forecast errors. A number of synoptic-scale environmental parameters, such as vertical wind shear, sea surface temperature (SST), and the underlying surface condition (land vs sea), affected the intensity forecast errors of TCs, in part due to their influence on intensity changes, while other thermodynamic environmental parameters, such as moisture and instability, had relatively minor effects. The accuracy of the intensity prediction was also found to be sensitive to the translation speed of the TCs. A moderate TC translation speed of 11–15 knots (kt; 1 kt = 0.51 m s−1) corresponded to the largest intensity errors during forecast lead times less than 60 h, while the slowest translation speed (<7 kt) was associated with the largest errors after the 60-h forecast lead time.

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Yunji Zhang, David J. Stensrud, and Eugene E. Clothiaux

Abstract

Recent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) on board GOES-16 compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager—finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum—contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a by-product of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting.

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