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Fumin Ren, Chenchen Ding, Da-Lin Zhang, Deliang Chen, Hong-li Ren, and Wenyu Qiu

Abstract

Combining dynamical models with statistical algorithms is an important way to improve weather and climate prediction. In this study, a concept of a perfect model, whose solutions are from observations, is introduced, and a dynamical-statistical-analog ensemble forecast (DSAEF) model is developed as an initial-value problem of the perfect model. This new analog-based forecast model consists of the following three steps: (i) construct generalized initial value (GIV), (ii) identify analogs from historical observations, and (iii) produce an ensemble of predictands. The first step includes all appropriate variables, not only at an instant state but also during their temporal evolution, that play an important role in determining the accuracy of each predictand. An application of the DSAEF model is illustrated through the prediction of accumulated rainfall associated with 21 landfalling typhoons occurring over South China during the years of 2012–16. Assuming a reliable forecast of landfalling typhoon track, two different experiments are conducted, in which the GIV is constructed by including (i) typhoon track only; and (ii) both typhoon track and landfall season. Results show overall better performance of the second experiment than the first one in predicting heavy accumulated rainfall in the training sample tests. In addition, the forecast performance of both experiments is comparable to the operational numerical weather prediction models currently used in China, the United States, and Europe. Some limitations and future improvements as well as comparisons with some existing analog ensemble models are also discussed.

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Xinrong Wu, Wei Li, Guijun Han, Lianxin Zhang, Caixia Shao, Chunjian Sun, and Lili Xuan

Abstract

Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.

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Na Wei, Ying Li, Da-Lin Zhang, Zi Mai, and Shi-Qi Yang

Abstract

The geographical and temporal characteristics of upper-tropospheric cold low (UTCL) and their relationship to tropical cyclone (TC) track and intensity change over the western North Pacific (WNP) during 2000–12 are examined using the TC best track and global meteorological reanalysis data. An analysis of the two datasets shows that 73% of 346 TCs coexist with 345 UTCLs, and 21% of the latter coexist with TCs within an initial cutoff distance of 15°. By selecting those coexisted systems within this distance, the possible influences of UTCL on TC track and intensity change are found, depending on their relative distance and on the sectors of UTCLs where TCs are located. Results show that the impact of UTCLs on TC directional changes are statistically insignificant when averaged within the 15° radius. However, left-turning TCs within 5° distance from the UTCL center exhibit large deviated directional changes from the WNP climatology, due to the presence of highly frequent abrupt left turnings in the eastern semicircle of UTCL. The abrupt turnings of TCs are often accompanied by their slow-down movements. Results also show that TCs seem more (less) prone to intensify at early (late) development stages when interacting with UTCLs compared to the WNP climatology. Intensifying (weakening) TCs are more distributed in the southern (northern) sectors of UTCLs, with less hostile conditions for weakening within 9°–13° radial range. In addition, rapid intensifying TCs take place in the south-southwest and east-southeast sectors of UTCLs, whereas rapid weakening cases appear in the western semicircle of UTCLs due to their frequent proximity to mainland coastal regions.

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Xiaoyan Sun, Yali Luo, Xiaoyu Gao, Mengwen Wu, Mingxin Li, Ling Huang, Da-Lin Zhang, and Haiming Xu

Abstract

In this study, high-resolution surface and radar observations are used to analyze 24 localized extreme hourly rainfall (EXHR; >60 mm h−1) events with strong urban heat island (UHI) effects over the Great Bay Area (GBA) in South China during the 2011–16 warm seasons. Quasi-idealized, convection-permitting ensemble simulations driven by diurnally varying lateral boundary conditions, which are extracted from the composite global analysis of 3–5 June 2013, are then conducted with a multilayer urban canopy model to unravel the influences of the UHI and various surface properties nearby on the EXHR generation in a complex geographical environment with sea–land contrast, topography, and vegetation variation. Results show that EXHR is mostly distributed over the urban agglomeration and within about 40 km on its downwind side, and produced during the afternoon-to-evening hours by short-lived meso-γ- to meso-β-scale storms. On the EXHR days, the GBA is featured by a weak gradient environment with abundant moisture, and a weak southwesterly flow prevailing in the boundary layer (BL). The UHI effects lead to the development of a deep mixed layer with “warm bubbles” over the urban agglomeration, in which the lower-BL convergence and BL-top divergence is developed, assisting in convective initiation. Such urban BL processes and associated convective development with moisture supply by the synoptic low-level southwesterly flow are enhanced by orographically increased horizontal winds and sea breezes under the influence of the herringbone coastline, thereby increasing the inhomogeneity and intensity of rainfall production over the “Π-shaped” urban clusters. Vegetation variations are not found to be an important factor in determining the EXHR production over the region.

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Baoqiang Xiang, Shian-Jiann Lin, Ming Zhao, Shaoqing Zhang, Gabriel Vecchi, Tim Li, Xianan Jiang, Lucas Harris, and Jan-Huey Chen

Abstract

While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness and mitigation. Using a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs: Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results demonstrate that the geneses of these two TCs are highly predictable with the maximum prediction lead time reaching 11 days. The “beyond weather time scale” predictability of tropical cyclogenesis is primarily attributed to the model’s skillful prediction of the intraseasonal Madden–Julian oscillation (MJO) and the westward propagation of easterly waves. Meanwhile, the landfall location and time can be predicted one week ahead for Sandy’s U.S landfall, and two weeks ahead for Haiyan’s landing in the Philippines. The success in predicting Sandy and Haiyan, together with low false alarms, indicates the potential of using the GFDL coupled model for extended-range predictions of TCs.

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