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Yuanlong Li, Yuqing Wang, Yanluan Lin, and Rong Fei


This study revisits the superintensity of tropical cyclones (TCs), which is defined as the excess maximum surface wind speed normalized by the corresponding theoretical maximum potential intensity (MPI), based on ensemble axisymmetric numerical simulations, with the focus on the dependence of superintensity on the prescribed sea surface temperature (SST) and the initial environmental atmospheric sounding. Results show a robust decrease of superintensity with increasing SST regardless of being in experiments with an SST-independent initial atmospheric sounding or in those with the SST-dependent initial atmospheric soundings as in nature sorted for the western North Pacific and the North Atlantic. It is found that the increase in either convective activity (and thus diabatic heating) in the TC outer region or theoretical MPI or both with increasing SST could reduce the superintensity. For a given SST-independent initial atmospheric sounding, the strength of convective activity in the TC outer region increases rapidly with increasing SST due to the rapidly increasing air–sea thermodynamic disequilibrium (and thus potential convective instability) with increasing SST. As a result, the decrease of superintensity with increasing SST in the SST-independent sounding experiments is dominated by the increasing convective activity in the TC outer region and is much larger than that in the SST-dependent sounding experiments, and the TC intensity becomes sub-MPI at relatively high SSTs in the former. Due to the marginal increasing tendency of convective activity in the TC outer region, the decrease of superintensity in the latter is dominated by the increase in theoretical MPI with increasing SST.

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Rong Fei, Jing Xu, Yuqing Wang, and Chi Yang


In this study, based on the 6-hourly tropical cyclone (TC) best track data and the ERA-Interim reanalysis data, statistical analyses as well as a machine learning approach, XGBoost, are used to identify and quantify factors that affect the overwater weakening rate (WR) of TCs over the western North Pacific (WNP) during 1980–2017. Statistical analyses show that the TC rapid weakening events usually occur when intense TCs cross regions with a sharp decrease in sea surface temperature (DSST) with relatively faster eastward or northward translational speeds, and move into regions with large environmental vertical wind shear (VWS) and dry conditions in the upshear-left quadrant. Results from XGBoost indicate that the relative intensity of TC (TC intensity normalized by its maximum potential intensity), DSST, and VWS are dominant factors determining TC WR, contributing 26.0%, 18.3%, and 14.9% to TC WR, and 9, 5, and 5 m s−1 day−1 to the variability of TC WR, respectively. Relative humidity in the upshear-left quadrant of VWS, zonal translational speed, divergence at 200 hPa, and meridional translational speed contribute 12.1%, 11.8%, 8.8%, and 8.1% to TC WR, respectively, but only contribute 2–3 m s−1 day−1 to the variability of TC WR individually. These findings suggest that the improved accurate analysis and prediction of the dominant factors may lead to substantial improvements in the prediction of TC WR.

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Yubao Liu, Thomas T. Warner, James F. Bowers, Laurie P. Carson, Fei Chen, Charles A. Clough, Christopher A. Davis, Craig H. Egeland, Scott F. Halvorson, Terrence W. Huck Jr., Leo Lachapelle, Robert E. Malone, Daran L. Rife, Rong-Shyang Sheu, Scott P. Swerdlin, and Dean S. Weingarten


Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogamma-scale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.

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