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Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range, as well as significant and prolonged hazards. In this work, we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts, global models and regional models with lead times varying from 1 to 5 days. The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration (CMA). The typhoon also caused significant wind and rainfall impacts, with precipitation at several stations reaching historical extremes, ranking eighth in terms of total rainfall impact during the event. The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method (SSTC) and Fengwu Model are the most effective for short-term track forecasts. Meanwhile, the forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and United Kingdom Meteorological Office (UKMO) are optimal for long-term predictions. It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity. The objective forecast is terribly poor when there is a sudden change in intensity. CMA-National Digital Forecast System (CMA-NDFS) provides a better reference value for typhoon accumulated rainfall forecasts, and regional models perform well in forecasting extreme rainfall. The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model. This improves the effective application of model products.

ABSTRACT: The forecasts of tropical cyclones (TC) in 2016 from 5 official guidances, 5 global models, 3 regional models and 6 ensemble systems were assessed to study the current capabilities of track and intensity forecasts for the western North Pacific. In 2016, the position errors for each official agency were under 85, 150 and 250 km at the lead times of 24, 48, and 72 h, respectively, indicating the performance of track forecasts was a little worse than that in 2015. For each lead time, decreases were seen for each quantile value of the global models from 2010 to 2015; however, this progress in forecasts was stagnated or was reversed in 2016, especially for long lead times. A new error tracking tool, called a “Track Error Rose”, was used to visualize the spatial distributions of the track forecast error relative to the observed TC center. The results show that as lead time increases, the moving speed of most global model TC forecasts becomes slower than those of the observations, and the largest track error often appears to the south of the observation position. In 2016, JMA-GSM, NCEP-GFS, STI-GRAPES and UKMO-MetUM made considerable progress in their intensity forecasts at lead times of 24 and 48 h, and the EPS intensity forecasts made significant progress compared to those of 2015. Keywords: tropical cyclone, track, intensity, forecast verification

ABSTRACT: The operational track and intensity forecast errors of tropical cyclones (TCs) over the western North Pacific in 2015 were evaluated on the basis of RSMC-Tokyo’s “best-track” dataset. The results showed that position errors for each official agency were under 80 km, 130 km, 180 km, 260 km and 370 km at 24, 48, 72, 96 and 120 hr lead time. Stepped decreases in the values of each quantile were made at every lead times and have been made by global models from 2010 to 2015, especially for long lead time. The results of the Track Forecast Integral Deviation (TFID) show a clearly decreasing trend for most global models, indicating that the TC forecast tracks became increasingly similar to the observations. In 2015, the intensity forecast skill scores for both global and regional models were almost negative. However, the skill of EPSs’ intensity forecasting has made significant progress in the past year. Keywords: tropical cyclone, track, intensity, forecast verification, skill scores

This study reveals a connection of summer–fall (JJASO) tropical cyclone (TC) genesis over the western North Pacific (WNP) to preceding boreal spring (MAM) North America snow cover (NASC). Sea surface temperature (SST) anomalies in the tropical central Pacific and subtropical eastern Pacific play a crucial role in relaying influence of the MAM NASC on the following JJASO WNP TC genesis frequency. The increased NASC leads to a decrease in upward sensible heat flux and the atmospheric cooling over the North America. The atmospheric cooling enhances the meridional thermal contrast and geopotential height gradient, which is favorable for the occurrence of lower-level westerly wind anomalies and positive precipitation anomalies over the tropical eastern Pacific. The lower-level northeasterly wind anomalies over the subtropical northeastern Pacific as a Gill-type atmospheric response to positive precipitation anomalies induce ocean surface cooling via the enhanced wind speed. A positive feedback between the northeasterly wind anomalies and negative SST anomalies leads to a westward extension of the easterly flows to the western Pacific. The easterly wind anomalies along with the negative specific humidity anomalies and negative lower-level vorticity anomalies, and enhanced vertical wind shear suppress the TC genesis over the WNP during JJASO.

ABSTRACT: The track forecasts of five ensemble prediction systems (JMA-WEPS, CMA-GEFS, ECMWF-EPS, NCEP-GEFS and MSC-CENS, respectively) in 2014 are evaluated in this paper. First, on the assumption of bivariate normal distribution, a probability ellipse based on ensemble spread is proposed to represent forecast uncertainties, and then the ellipse and ensemble mean are used to evaluate the five different systems. It is found that the probability ellipse of ECMWF-EPS is smaller, with a high hit ratio, and its mean track errors are just a little larger than those of NCEP-GEFS within 48 h, meaning overall it outperforms the other four systems. Meanwhile, the performance of CMA-GEFS is found to be the poorest. Keywords: tropical cyclone, ensemble forecast, track verification, probability ellipse