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Lu Liu
,
Yuqing Wang
,
Ruifen Zhan
,
Jing Xu
, and
Yihong Duan

Abstract

This study investigates the trend in destructive potential of landfalling tropical cyclones (TCs) in terms of power dissipation index (PDI) over mainland China in the period of 1980–2018. Results show that both the accumulated PDI and averaged PDI after landfall show significant increasing trends. The increasing trends are found to be contributed primarily by the increasing mean duration of TCs over land and the increasing TC intensity at landfall. Further analyses indicate that the increase in landfalling TC intensity prior to and at landfall, the decrease in intensity weakening rate after landfall, and the northward shift of landfalling TC track density all contribute to the longer duration of TCs after landfall. Moreover, the conducive large-scale conditions, such as the increases in coastal sea surface temperature and land surface temperature and soil moisture, the decrease in low-level vertical wind shear, and the increase in upper-level divergence, are all favorable for intense landfalling TCs and their survival after landfall, thus contributing to the increasing destructive potential of landfalling TCs over China.

Free access
Xinyan Lu
,
Kevin K. W. Cheung
, and
Yihong Duan

Abstract

The effects of multiple mesoscale convective systems (MCSs) on the formation of Typhoon Ketsana (2003) are analyzed in this study. Numerical simulations using the Weather Research and Forecasting (WRF) model with assimilation of Quick Scatterometer (QuikSCAT) and Special Sensor Microwave Imager (SSM/I) oceanic winds and total precipitable water are performed. The WRF model simulates well the large-scale features, the convective episodes associated with the MCSs and their periods of development, and the formation time and location of Ketsana. With the successive occurrence of MCSs, midlevel average relative vorticity is strengthened through generation of mesoscale convective vortices (MCVs) mainly via the vertical stretching mechanism. Scale separation shows that the activity of the vortical hot tower (VHT)-type meso-γ-scale vortices correlated well with the development of the MCSs. These VHTs have large values of positive relative vorticity induced by intense low-level convergence, and thus play an important role in the low-level vortex enhancement with aggregation of VHTs as one of the possible mechanisms.

Four sensitivity experiments are performed to analyze the possible different roles of the MCSs during the formation of Ketsana by modifying the vertical relative humidity profile in each MCS and consequently the strength of convection within. The results show that the development of an MCS depends substantially on that of the prior ones through remoistening of the midtroposphere, and thus leading to different scenarios of system intensification during the tropical cyclone (TC) formation. The earlier MCSs are responsible for the first stage vortex enhancement, and depending on the location can affect quite largely the simulated formation location. The extreme convection within the last MCS before formation largely determines the formation time.

Full access
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.

Full access
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.

Full access
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.

Full access
Dan Wu
,
Kun Zhao
,
Matthew R. Kumjian
,
Xiaomin Chen
,
Hao Huang
,
Mingjun Wang
,
Anthony C. Didlake Jr.
,
Yihong Duan
, and
Fuqing Zhang

Abstract

This study analyzes the microphysics of convective cells in an outer rainband of Typhoon Nida (2016) using data collected by a newly upgraded operational polarimetric radar in China. The life cycle of these convective cells is divided into three stages: developing, mature, and decaying according to the intensity of the corresponding updraft. Composite analysis shows that deep columns of Z DR and K DP collocate well with the enhanced updraft as the cells develop to their mature stage. A layered microphysical structure is observed in the ice region with riming near the −5°C level within the updraft, aggregation around the −15°C level, and deposition anywhere above the 0°C level. These ice-phase microphysical processes are important pathways of particle growth in the outer rainbands. In particular, riming contributes significantly to surface heavy rainfall. These contrast to previously documented inner rainbands, where warm-rain processes are the predominant pathway of particle growth.

Full access
Yihong Duan
,
Jiandong Gong
,
Jun Du
,
Martin Charron
,
Jing Chen
,
Guo Deng
,
Geoff DiMego
,
Masahiro Hara
,
Masaru Kunii
,
Xiaoli Li
,
Yinglin Li
,
Kazuo Saito
,
Hiromu Seko
,
Yong Wang
, and
Christoph Wittmann

The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.

The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.

The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).

Full access
Yihong Duan
,
Qilin Wan
,
Jian Huang
,
Kun Zhao
,
Hui Yu
,
Yuqing Wang
,
Dajun Zhao
,
Jianing Feng
,
Jie Tang
,
Peiyan Chen
,
Xiaoqin Lu
,
Yuan Wang
,
Jianyin Liang
,
Liguang Wu
,
Xiaopeng Cui
,
Jing Xu
, and
Pak-Wai Chan

Abstract

Landfalling tropical cyclones (TCs) often experience drastic changes in their motion, intensity, and structure due to complex multiscale interactions among atmospheric processes and among the coastal ocean, land, and atmosphere. Because of the lack of comprehensive data and low capability of numerical models, understanding of and ability to predict landfalling TCs are still limited. A 10-yr key research project on landfalling TCs was initiated and launched in 2009 in China. The project has been jointly supported by the China Ministry of Science and Technology, China Meteorological Administration (CMA), Ministry of Education, and Chinese Academy of Sciences. Its mission is to enhance understanding of landfalling TC processes and improve forecasting skills on track, intensity, and distributions of strong winds and precipitation in landfalling TCs. This article provides an overview of the project, together with highlights of some new findings and new technical developments, as well as planned future efforts.

Full access
Mark Govett
,
Bubacar Bah
,
Peter Bauer
,
Dominique Berod
,
Veronique Bouchet
,
Susanna Corti
,
Chris Davis
,
Yihong Duan
,
Tim Graham
,
Yuki Honda
,
Adrian Hines
,
Michel Jean
,
Junishi Ishida
,
Bryan Lawrence
,
Jian Li
,
Juerg Luterbacher
,
Chiasi Muroi
,
Kris Rowe
,
Martin Schultz
,
Martin Visbeck
, and
Keith Williams

Abstract

The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A recent WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities. Further, the explosive growth in data from observations, model and ensemble output, and post processing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision making. AI offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical, and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations.

Open access