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Jianing Feng
,
Yihong Duan
,
Qilin Wan
,
Hao Hu
, and
Zhaoxia Pu

Abstract

This work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

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Dongliang Wang
,
Xudong Liang
,
Yihong Duan
, and
Johnny C. L. Chan

Abstract

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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Hao-Yan Liu
,
Yuqing Wang
,
Jing Xu
, and
Yihong Duan

Abstract

This study extends an earlier dynamical initialization (DI) scheme for tropical cyclones (TCs) to situations under the influence of terrain. When any terrain lower than 1 km exists between 150 and 450 km from the TC center, topographic variables are defined and a filtering algorithm is used to remove noise due to the presence of terrain before the vortex separation is conducted. When any terrain higher than 1 km exists between 150 and 300 km from the TC center, or the TC center is within 150 km of land, a semi-idealized integration without the terrain is conducted to spin up an axisymmetric TC vortex before the inclusion of the terrain and the merging of the TC vortex with the large-scale analysis field. In addition, a procedure for the vortex size/intensity adjustment is introduced to reduce the initial errors before the forecast run. Two sets of hindcasts, one without (CTRL run) and one with the new DI scheme (DI run), are conducted for nine TCs affected by terrain over the western North Pacific in 2015. Results show that the new DI scheme largely reduces the initial position and intensity errors. The 72-h position errors and the intensity errors up to the 36-h forecasts are smaller in DI runs than in CTRL runs and smaller than those from the HWRF forecasts for the same TCs as well. The new DI scheme is also shown to produce the TC inner-core structure and rainbands more consistent with satellite and radar observations.

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Fumin Ren
,
Wenyu Qiu
,
Chenchen Ding
,
Xianling Jiang
,
Liguang Wu
,
Yinglong Xu
, and
Yihong Duan

Abstract

Combining dynamical model output and statistical information in historical observations is an innovative approach to predicting severe or extreme weather. In this study, in order to examine a dynamical–statistical method for precipitation forecasting of landfalling tropical cyclones (TC), an objective TC track similarity area index (TSAI) is developed. TSAI represents an area of the enclosed scope surrounded by two TC tracks and two line segments connecting the initiating and ending points of the two tracks. The smaller the TSAI value, the greater the similarity of the two TC tracks, where a value of 0 indicates that the two tracks overlap completely. The TSAI is then preliminarily applied to a precipitation forecast test of landfalling TCs over South China. Given the considerable progress made in TC track forecasting over past few decades, TC track forecast products are also used. Through this test, a track-similarity-based landfalling TC precipitation dynamical–statistical ensemble forecast (LTP_DSEF) model is established, which consists of four steps: adopting the predicted TC track, determining the TC track similarity, checking the seasonal similarity, and making an ensemble prediction. Its application to the precipitation forecasts of landfalling TCs over South China reveals that the LTP_DSEF model is superior to three numerical weather prediction models (i.e., ECMWF, GFS, and T639/China), especially for intense precipitation at large thresholds (i.e., 100 or 250 mm) in both the training (2012–14) and independent (2015–16) samples.

Open access
Wei Na
,
John L. McBride
,
Xing-Hai Zhang
, and
Yi-Hong Duan

Abstract

The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

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