Search

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region.

The trends in annual precipitation and wind induced by tropical cyclones (TCs) over Shanghai during the last 40 years are estimated. Results indicate that there is a significant increasing trend in the annual total precipitation induced by TCs, which is related to the significant positive trends in daily precipitation and annual torrential rain days. Meanwhile, a significant decreasing trend shows in maximum sustained wind, which seems to be related to the downward trend in the intensity of TCs when affecting Shanghai. The annual frequencies of affected TCs, TC translation speed and distance from Shanghai when affecting Shanghai have no obvious tendency. The different trends in precipitation and wind suggested that a more comprehensive metric for assessing TCs' influence on society is necessary.

Abstract Persistent heavy rainfall produced by western North Pacific (WNP) tropical cyclones (TCs) can lead to widespread flooding and landslides in Asian countries. On July 2021, unprecedent rainfall amount occurred when Typhoon In‐fa passed through the highly populated eastern China. While the associated synoptic features have been analyzed, the extreme characteristics and return periods of rainfall induced by In‐fa remain unexplored. Analyses of rainfall data from a WNP TC database of the China Meteorological Administration (CMA) show that Typhoon In‐fa not only produces record‐breaking rainfall accumulations at individual surface stations, but generates unprecedent rainfall amounts for the whole area of eastern China. Quantitatively, 2, 4, 11, 24 and 55 stations are exposed to once in 200‐, 100‐, 50‐, 20‐ and 10‐year extreme TC rainfall accumulations, respectively, and total rainfall at 75 stations reaches a record high since 1980. Overall, the return period is up to ~481 years for the total rainfall amount accumulated in eastern China during the 1980–2019 baseline. The extremely long rainfall duration is identified as key to the torrential rains in the Yangtze River Delta before In‐fa changes its direction of movement from northwestward to northeastward, while the extreme rain rate plays a dominant role in the northern areas afterwards. Probabilities of occurrence of such an unprecedented TC rainfall event have increased in most (~75%) of the eastern China during the period of 2000–2019 compared with those during 1980–1999. Our study highlights the likely increase in risk of extreme TC‐induced rainfall accumulations which should be considered in disaster risk mitigation.

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

ABSTRACT: We analyzed the errors associated with forecasts of tropical cyclone (TC) intensity from 2010-2012 in the western North Pacific region made by seven operational numerical weather prediction models. The results show that the forecast error is significantly related to the initial error as well as the initial TC intensity, size, and translation speed. Other factors highly related to the forecast error include the environmental sea surface pressure, vertical wind shear and maximum potential intensity. We used stepwise regression to set up model forecast error estimation equations, which were used to calibrate the model output. Independent experiments showed that the calibrated model forecasts have significant skill compared to the original model output. Finally, a multimodel consensus forecast technique for TC intensity was developed based on the calibrated model output; this technique has 28% (15-20%) skill at 12 h (24-72 h) compared to the climatology and persistence forecasts of TC intensity. This consensus technique has greater skill than the consensus forecast based on the original model output and therefore it has the potential to be applied in operation. Keywords: tropical cyclone, intensity, numerical weather prediction model, error calibration, consensus forecast