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Dongliang Wang, Xudong Liang, Ying Zhao, and Bin Wang

Abstract

The impact of two bogussing schemes on tropical cyclone (TC) forecasts is compared. One scheme for bogussing TCs into the initial conditions of the nonhydrostatic version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is proposed by NCAR and the Air Force Weather Agency (AFWA), and four-dimensional variational data assimilation technology is employed for the other bogus data assimilation (BDA) scheme. The initial vortex structure adjusted by the NCAR–AFWA (N–A) scheme is more physically realistic, while the BDA scheme produces an initial vortex structure that is more consistent with the model. The results from 41 forecasts of TCs occurring over the western North Pacific (WNP) in 2002 suggest that the adjustment of the initial structure in the BDA scheme produces a greater benefit to the subsequent track and intensity forecasts, and the improvements in the track and intensity forecasts are significant using the BDA scheme. It seems that when using a model with 45-km grid length, the N–A scheme has a negative impact on the track forecasts for the recurving TCs and on the intensity predictions after 24 h.

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Owen H. Shieh, Michael Fiorino, Matthew E. Kucas, and Bin Wang

Abstract

One of the primary challenges for both tropical cyclone (TC) research and forecasting is the problem of intensity change. Accurately forecasting TC rapid intensification (RI) is particularly important to interests along coastlines and shipping routes, which are vulnerable to storm surge and heavy seas induced by intense tropical cyclones. One particular RI event in the western North Pacific Ocean with important scientific implications is the explosive deepening of Typhoon Vicente (2012). Vicente underwent extreme RI in the northern South China Sea just prior to landfall west of Hong Kong, China, with maximum sustained winds increasing from 50 kt (1 kt = 0.51 m s−1) at 0000 UTC 23 July to 115 kt at 1500 UTC 23 July. This increase of 65 kt in 15 h far exceeds established thresholds for TC RI. Just prior to this RI episode, Vicente exhibited a near-90° poleward track shift. The relationship between the track and intensity change is described, and the authors speculate that the passage of an upper-tropospheric (UT) “inverted” trough was a significant influence. An analysis of real-time numerical model guidance is provided and is discussed from an operational perspective, and high-resolution global model analyses are evaluated. Numerical model forecasts of the UT trough interaction with the TC circulation were determined to be a shortcoming that contributed to the intensity prediction errors for Vicente. This case study discusses the importance of considering UT features in TC intensity forecasting and establishes current modeling capabilities for future research.

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Weiguo Wang, Bin Liu, Lin Zhu, Zhan Zhang, Avichal Mehra, and Vijay Tallapragada

Abstract

A new physically based horizontal mixing-length formulation is introduced and evaluated in the Hurricane Weather and Research Forecasting (HWRF) Model. Recent studies have shown that the structure and intensity of tropical cyclones (TCs) simulated by numerical models are sensitive to horizontal mixing length in the parameterization of horizontal diffusion. Currently, many numerical models including the operational HWRF Model formulate the horizontal mixing length as a fixed fraction of grid spacing or a constant value, which is not realistic. To improve the representation of the horizontal diffusion process, the new formulation relates the horizontal mixing length to local wind and its horizontal gradients. The resulting horizontal mixing length and diffusivity are much closer to those derived from field measurements. To understand the impact of different mixing-length formulations, we analyze the evolutions of an idealized TC simulated by the HWRF Model with the new formulation and with the current formulation (i.e., constant values) of horizontal mixing length. In two real-case tests, the HWRF Model with the new formulation produces the intensity and track forecasts of Hurricanes Harvey (2017) and Lane (2018) that are much closer to observations. Retrospective runs of hundreds of forecast cycles of multiple hurricanes show that the mean errors in intensity and track simulated by HWRF with the new formulation can be reduced approximately by 10%.

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Weiguo Wang, Jason A. Sippel, Sergio Abarca, Lin Zhu, Bin Liu, Zhan Zhang, Avichal Mehra, and Vijay Tallapragada

Abstract

This note describes a modification of the boundary layer parameterization scheme in the Hurricane Weather Research and Forecasting (HWRF) Model, which improves the simulations of low-level wind and surface inflow angle in the eyewall area and has been implemented in the HWRF system and used in the operational system since 2016. The modification is on an observation-based adjustment of eddy diffusivity previously implemented in the model. It is needed because the previous adjustment resulted in a discontinuity in the vertical distribution of eddy diffusivity near the surface-layer top, which increases the friction within the surface layer and compromises the surface-layer constant-flux assumption. The discontinuity affects the simulation of storm intensity and intensification, one of the main metrics of model performance, particularly in strong tropical cyclones. This issue is addressed by introducing a height-dependent adjustment so that the vertical profile of eddy diffusivity is continuous throughout the boundary layer. It is shown that the implementation of the modification results in low-level winds and surface inflow angles in the storm’s eyewall region closer to observations.

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Andrew Hazelton, Zhan Zhang, Bin Liu, Jili Dong, Ghassan Alaka, Weiguo Wang, Tim Marchok, Avichal Mehra, Sundararaman Gopalakrishnan, Xuejin Zhang, Morris Bender, Vijay Tallapragada, and Frank Marks

Abstract

NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

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