This study examines a number of very intense tropical cyclones (TCs) in the western North Pacific (WNP) since 1978 as depicted by the best track data from the four main weather agencies in the WNP, namely the Hong Kong Observatory, China Meteorological Administration, Regional Specialized Meteorological Centre Tokyo and Joint Typhoon Warning Centre, and the Advanced Dvorak Technique–Hurricane Satellite dataset prepared by the University of Wisconsin/NOAA National Climatic Data Center to identify the most intense TCs in the western North Pacific. Comparison analysis reveals that there are very large differences in the ranking of maximum sustained wind speed (MSW) among these datasets, probably due to data inhomogeneity issues and the discrepancies in TC intensity assessment among these centres. Re-assessment of the MSW of potential candidates suggests that, within uncertainty range of the analysis, Tip (1979) and Haiyan (2013) are the most intense TCs in the WNP during the period. Among the potential candidates, eight of them made landfall during their lifetime. Satellite pattern comparison and the best track dataset analysis of these typhoons show that Haiyan (2013) has the highest estimated MSW right before landfalling, making it the most intense TC making landfall in the WNP during the period 1978-2013.

Using daily precipitation data from 110 stations in Southeast China from 1960 to 2012, the extreme precipitation (EP) events associated with monsoon and tropical cyclones were examined using the Objective Synoptic Analysis Technique. In Southeast China, the extreme precipitation associated with tropical cyclones (TEP), which mainly occur in the summer (July–September), accounted for 27.9% of the total extreme precipitation amount, with 40–50% in the
coastal regions. While the regional mean TEP amount showed an inconspicuous trend, total EP and monsoon EP (MEP) both showed an increasing trend, with the MEP trend being statistically significant. Although there was little change in the frequency of tropical cyclones affecting Southeast China, the TEP contribution to frequency increased with increasing EP threshold and the frequency of TEP with daily precipitation of more than 300 mm showed an increasing trend in the background of global climate change. The upward trend in the highest-threshold TEP events presents a challenge for mitigation of the damage associated with tropical cyclones.

Tropical cyclones (TCs) forecasts from seven global models in 2013 were assessed to study the current capability of track and intensity forecast guidance over the western North Pacific. Analysis of along- and cross-track error revealed stepped decreases in the values of each quantile at each lead time level by showing the annual track error distribution from 2010 to 2013, particularly in the ECMWF-IFS, NCEP-GFS and UKMO-MetUM models. The TC propagation direction was much easier to handle for most of the global models; however, the propagation speed seemed to be more closely linked to the inner-core dynamics and thus processes that take place at smaller spatial scales. A new model evaluation tool, ‘track error rose’, was used to analyze the models’
systematic error in the track forecast using the same concepts as the ‘wind rose’. The results showed that as the lead time increased, most of the global models forecast a TC moving speed that was slower than observations and the largest track error often appeared around the rear direction of the observation position. Another new model evaluation tool, the Taylor diagram, was used to evaluate the intensity predictions from the global models. A Taylor diagram provides a way of plotting standard deviation, centered root mean square, and the correlation coefficient on a two-dimensional graph, indicating how closely a predicted TC intensity matches observations. This made it easy to distinguish the intensity forecast performance of the seven global models and determine which models were in relatively good agreement with observations. Furthermore, it also provided a statistical measure of the correlation between modeled and observed TC intensity, offering a practical way of assessing and summarizing model capability.

The track of tropical cyclone ‘Madi’ showed almost 180o turn around, while immediately after recurvature it decayed rapidly in the following 24 hours. Sea surface temperature (SST) analyses rule out confrontation with low SST as cause of weakening. At 0000 UTC of 9 December 2013 diabatic heating (DH), potential vorticity (PV) and vertical wind shear (VWS) were favourable for intensification. However, a dry area close to the system centre at mid-troposphere subsequently engulfed the middle strata and the mid-tropospheric relative humidity (MTH) drastically reduced on 0000 UTC of 10 December 2013. The MTH further reduced on 0000 UTC of 11 December 2013. Lack of moisture in mid-troposphere apparently caused a mutation within the structure causing a sudden decrease in elevation. As the system dried up, reduction in convection above 400 hPa hindered latent heat release. PV above 400 hPa decreased significantly. Eventually, there was a spurt of VWS at 1200 UTC of the 10 December 2013. The opposite wind flow between lower level and upper level whisked away the top of the system to northeast. Under the influence of the net northeasterly flow in lower to middle troposphere, the miniature vortex recurved southwestward and the vertical distortion due to shear weakened the system into a depression.

Knowledge regarding how people obtain, understand, and use warning information is critical to saving lives and protecting property, yet scientifically valid and reliable research to support this part of the tropical cyclone (TC) warning process has been limited. This is due in part to misconceptions about the social sciences and the theories, knowledge and methodologies they offer. To help fill this gap, we highlight some topics and issues where social science research offers valuable contributions to the TC forecast and warning process, and present findings from some recent TC-related research projects. A range of studies (albeit limited in number) includes social science research on how individuals interpret risk, the importance and impact of delivering clear messages, the communication of uncertainty; and, more specifically, on the TC forecast and warning process, including the effective communicating of storm surge risk. We also discuss some findings related to promoting appropriate protective actions, and understanding economic and societal impacts. We conclude by identifying some shortcomings in empirical research in this area, and offer recommendations for improved integration of social sciences
into the TC forecast and warning process.