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Yi-Fan Wang and Zhe-Min Tan

Abstract

Secondary eyewall formation (SEF) could be considered as the aggregation of a convective-ring coupling with a tangential wind maximum outside the primary eyewall of a tropical cyclone (TC). The dynamics of SEF are investigated using idealized simulations based on a set of triplet experiments, whose differences are only in the initial outer-core wind speed. The triplet experiments indicate that the unbalanced boundary layer (BL) process driven by outer rainbands (ORBs) is essential for the canonical SEF. The developments of a secondary tangential wind maximum and a secondary convective ring are governed by two different pathways, which are well coupled in the canonical SEF. Compared with inner/suppressed rainbands, the downwind stratiform sectors of ORBs drive significant stronger BL convergence at its radially inward side, which fastens up the SEF region and links the two pathways. In the wind-maximum formation pathway, the positive feedback among the BL convergence, supergradient force, and relative vorticity within the BL dominates the spinup of a secondary tangential wind maximum. In the convective-ring formation pathway, the BL convergence contributes to the ascending motion through the frictional-forced updraft and accelerated outflow associated with the supergradient force above the BL. Driven only by inner rainbands, the simulated vortex develops a fake SEF with only the secondary convective ring since the rainband-driven BL convergence is less enhanced and thus fails to maintain the BL positive feedback in the wind-maximum pathway. Therefore, only ORBs can promote the canonical SEF. It also infers that any environmental/physical conditions favorable for the development of ORBs will ultimately contribute to SEF.

Open access
Jing Xu, Yuqing Wang, and Zhe-Min Tan

Abstract

An empirical relationship between sea surface temperature (SST) and the maximum potential intensification rate (MPIR) of tropical cyclones (TCs) over the North Atlantic has been developed based on the best-track TC data and the observed SST during 1988–2014. Similar to the empirical relationship between SST and the maximum potential intensity of TCs previously documented, results from this study show a nonlinear increasing trend of the MPIR with increasing SST, with a more rapid increasing trend when SST is higher than 27°C. Further analyses indicate that about 28% of intensifying TCs over the North Atlantic reached 50% of their MPIR and only 7% reached 80% of their MPIR at the time when they were at their lifetime maximum intensification rates. Moreover, a TC tended to have a larger intensification rate when it was located in regions with higher SST and lower vertical wind shear (VWS). This indicates that although the MPIR–SST relationship is much stronger than that for the IR rate versus SST for most TCs, the actual intensification rate of a TC is determined by not only the SST but also other environmental effects, such as VWS. Additional results from a simplified dynamical system previously developed for TC intensity prediction suggest an SST-dependent TC MPIR, similar to that fitted from observations. However, the MPIR obtained from the observational fitting seems to underestimate the MPIR in regions with low SST at higher latitudes where VWS is often large. Nevertheless, this study provides the observational evidence for the existence of the MPIR for TCs.

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Ruifen Zhan, Yuqing Wang, and Min Wen

Abstract

The sea surface temperature gradient (SSTG) between the southwestern Pacific Ocean (40°–20°S, 160°E–170°W) and the western Pacific warm pool (0°–16°N, 125°–165°E) in boreal spring has been identified as a new factor that controls the interannual variability of tropical cyclone (TC) frequency over the western North Pacific Ocean (WNP). This SSTG can explain 53% of the total variance of the WNP TC genesis frequency during the typhoon season for the period 1980–2011. The positive SSTG anomaly produces an anomalous cross-equatorial pressure gradient and thus anomalies in low-level southward cross-equatorial flow and tropical easterlies over the central-western Pacific. The anomalous easterlies further produce local equatorial upwelling and seasonal cooling in the central Pacific, which in turn maintains the easterly anomalies throughout the typhoon season. These dynamical/thermodynamical effects induced by the positive SSTG anomaly lead to a reduced low-level cyclonic shear, increased vertical wind shear, and weakened monsoon trough over the WNP, greatly suppressing WNP TC genesis during the typhoon season. This implies that the spring SSTG could be a good predictor for WNP TC genesis frequency.

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Min Deng, Gerald. G. Mace, and Zhien Wang

Abstract

The anvil productivities of tropical deep convection are investigated and compared among eight climatological regions using 4 yr of collocated and combined CloudSat and CALIPSO data. For all regions, the convective clusters become deeper while they become wider and tend to be composed of multiple rainy cores. Two strong detrainment layers from deep convection are observed at 6–8 km and above 10 km, which is consistent with the trimodal characteristics of tropical convection that are associated with different divergence, cloud detrainment, and fractional cloudiness. The anvil productivity of tropical deep convection depends on the convection scale, convective life stage or intensity, and large-scale environment. Anvil ice mass ratio related to the whole cluster starts to level off or decrease when the cluster effective scales W eff (the dimension of an equivalent rectangular with the same volume and height as the original cluster) increase to about 200 km wide, while the ratios of anvil scale and volume keep increasing from 0.4 to 0.6 and 0.15 to 0.4, respectively. The anvil clouds above 12 km can count for more than 20% of cluster volume, or more than 50% of total anvil volume, but they only count less than about 2% of total ice mass in the cluster. Anvil production of younger convection of the same W eff is higher than that of the decaying convection. The regional difference in the composite anvil productivities of tropical convective clusters sorted by W eff is subtle, while the occurrence frequencies of different scales of convection vary substantially.

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Juan Li, Bin Wang, and Young-Min Yang

Abstract

The distinctive monsoon climate over East Asia, which is affected by the vast Eurasian continent and Pacific Ocean basin and the high-altitude Tibetan Plateau, provides arguably the best testbed for evaluating the competence of Earth system climate models. Here, a set of diagnostic metrics, consisting of 14 items and 7 variables, is specifically developed. This physically intuitive set of metrics focuses on the essential features of the East Asian summer monsoon (EASM) and East Asian winter monsoon (EAWM), and includes fields that depict the climatology, the major modes of variability, and unique characteristics of the EASM. The metrics are applied to multimodel historical simulations derived from 20 models that participated in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively), along with the newly developed Nanjing University of Information Science and Technology Earth System Model, version 3. The CMIP5 models show significant improvements over the CMIP3 models in terms of the simulated East Asian monsoon circulation systems on a regional scale, major modes of EAWM variability, the monsoon domain and precipitation intensity, and teleconnection associated with the heat source over the Philippine Sea. Clear deficiencies persist from CMIP3 to CMIP5 with respect to capturing the major modes of EASM variability, as well as the relationship between the EASM and ENSO during El Niño developing and decay phases. The possible origins that affect models’ performance are also discussed. The metrics provide a tool for evaluating the performance of Earth system climate models, and facilitating the assessment of past and projected future changes of the East Asian monsoon.

Open access
Cheng Wang, Min Chen, and Yaodeng Chen

Abstract

The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts.

Significance Statement

Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.

Open access
Xiaoshi Qiao, Shizhang Wang, and Jinzhong Min

Abstract

The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH.

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Yuanlong Li, Yuqing Wang, and Zhe-Min Tan

Abstract

The phenomenon that rapid contraction (RC) of the radius of maximum wind (RMW) could precede rapid intensification (RI) in tropical cyclones (TCs) has been found in several previous studies, but it is still unclear how frequently and to what extent RC precedes RI in rapidly intensifying and contracting TCs in observations. In this study, the statistical relationship between RMW RC and TC RI is examined based on the extended best track dataset for the North Atlantic and eastern North Pacific during 1999–2019. Results show that for more than ∼65% of available TCs, the time of the peak contraction rate precedes the time of the peak intensification rate, on average, by ∼10–15 h. With the quantitatively defined RC and RI, results show that ∼50% TCs with RC experience RI, and TCs with larger intensity and smaller RMW and embedded in more favorable environmental conditions tend to experience RI more readily following an RC. Among those TCs with RC and RI, more than ∼65% involve the onset of RC preceding the onset of RI, on average, by ∼15–25 h. The preceding time tends to be longer with lower TC intensity and larger RMW and shows weak correlations with environmental conditions. The qualitative results are insensitive to the time interval for the calculation of intensification/contraction rates and the definition of RI. The results from this study can improve our understanding of TC structure and intensity changes.

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Xiaoshi Qiao, Shizhang Wang, and Jinzhong Min

Abstract

Diffusion plays an important role in supercell simulations. A stochastically perturbed parameterization tendency scheme for diffusion (SPPTD) is developed to incorporate diffusive uncertainties in ensemble forecasts. This scheme follows the same procedure as the previously published stochastically perturbed parameterization tendencies (SPPT) scheme but uses a recursive filter to generate smooth perturbations. It also employs horizontal and vertical localization to retain the impact of perturbation in areas with strong shear. Three additional restrictions are added for the sake of integration stability; these restrictions determine the area and amplitude of the perturbation and the situation to suspend SPPTD.

The performance of this scheme is examined by using an idealized supercell storm. The model errors are simulated using different resolutions in the truth run (1 km) and ensemble forecasts (2 km). The results indicate that the ensemble forecasts using SPPTD encompass the intensity and displacement of maximum updraft helicity in the truth run. This scheme yields better results than can be obtained using initial perturbations or larger computational mixing coefficients.

The sensitivity of SPPTD to each of its parameters is also examined. The results indicate that the optimal horizontal and temporal scales for SPPTD are 40 km and 30 min, respectively. Moderately adjusting the spatiotemporal scale by 10 km or 10 min does not significantly change the SPPTD performance. In this case study, an ensemble size of 20 is sufficient. Perturbing the diffusion terms of all variables using the same approach does not provide additional benefits other than that of selected variables and thus requires further study.

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Lili Lei, Zhongrui Wang, and Zhe-Min Tan

Abstract

Hybrid ensemble–variational assimilation methods that combine static and flow-dependent background error covariances have been widely applied for numerical weather predictions. The commonly used hybrid assimilation methods compute the analysis increment using a variational framework and update the ensemble perturbations by an ensemble Kalman filter (EnKF). To avoid the inconsistencies that result from performing separate variational and EnKF systems, two integrated hybrid EnKFs that update both the ensemble mean and ensemble perturbations by a hybrid background error covariance in the framework of EnKF are proposed here. The integrated hybrid EnKFs approximate the static background error covariance by use of climatological perturbations through augmentation or additive approaches. The integrated hybrid EnKFs are tested in the Lorenz05 model given different magnitudes of model errors. Results show that the static background error covariance can be sufficiently estimated by climatological perturbations with an order of hundreds. The integrated hybrid EnKFs are superior to the traditional hybrid assimilation methods, which demonstrates the benefit to update ensemble perturbations by the hybrid background error covariance. Sensitivity results reveal that the advantages of the integrated hybrid EnKFs over traditional hybrid assimilation methods are maintained with varying ensemble sizes, inflation values, and localization length scales.

Significance Statement

Data assimilation is critical for providing the best possible initial condition for forecast and improving the numerical weather predictions. The hybrid ensemble–variational data assimilation method has been widely adopted and developed by many operational centers. The hybrid ensemble–variational assimilation method combines the advantages of ensemble and variational methods and minimizes the weaknesses of the two methods, and thus it outperforms the stand-alone variational and ensemble assimilation methods. The hybrid ensemble–variational assimilation method often computes the control analysis using a variational solver with hybrid background error covariances, but generates the ensemble perturbations by an ensemble Kalman filter (EnKF) system with pure flow-dependent background error covariances. The inconsistencies that result from performing separate variational and EnKF systems can lead to suboptimality in the hybrid ensemble–variational assimilation method. Therefore, integrated hybrid EnKF methods that utilize the framework of an EnKF to update both the ensemble mean and ensemble perturbations by the hybrid background error covariance, are proposed. The integrated hybrid EnKFs use climatological ensemble perturbations to approximate the static background error covariance. The integrated hybrid EnKFs are superior to the traditional hybrid ensemble–variational assimilation methods by producing smaller errors, and the advantages are persistent with varying assimilation parameters.

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