Western North Pacific Tropical Cyclone Database Created by the China Meteorological Administration

This paper describes the access to, and the content, characteristics, and potential applications of the tropical cyclone(TC) database that is maintained and actively developed by the China Meteorological Administration, with the aim of facilitating its use in scientific research and operational services. This database records data relating to all TCs that have passed through the western North Pacific(WNP) and South China Sea(SCS) since 1949. TC data collection has expanded over recent decades via continuous TC monitoring using remote sensing and specialized field detection techniques,allowing collation of a multi-source TC database for the WNP and SCS that covers a long period, with wide coverage and many observational elements. This database now comprises a wide variety of information related to TCs, such as historical or real-time locations(i.e., best track and landfall), intensity, dynamic and thermal structures, wind strengths, precipitation amounts, and frequency. This database will support ongoing research into the processes and patterns associated with TC climatic activity and TC forecasting.

Tropical cyclone activities in the Western North Pacific in 2022

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration,the paper provides a compre-hensive 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.Thefindings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region.

PERFORMANCE OF TROPICAL CYCLONE FORECAST IN WESTERN NORTH PACIFIC IN 2016

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.

PERFORMANCE OF TROPICAL CYCLONE FORECAST IN WESTERN NORTH PACIFIC IN 2015

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.

A MULTI-MODEL CONSENSUS FORECAST TECHNIQUE FOR TROPICAL CYCLONE INTENSITY BASED ON MODEL OUTPUT CALIBRATION

We analyzed the errors associated with forecasts of tropical cyclone(TC) intensity from 2010-2012 in the western North Pacifi c region made by seven operational numerical weather prediction models. The results show that the forecast error is signifi cantly 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 signifi cant 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.

Climate trends in tropical cyclone-induced precipitation and wind over Shanghai

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.

A method to automatically ascertain the identities of tropical cyclones in tropical cyclone warning messages

In the operational forecasting of tropical cyclones(TCs),decoding TC warning messages from global centers,along with extracting,organizing,and storing useful track observations and forecasts,are fundamental tasks.The technical core lies in accurately identifying distinct TC individuals through automated programming methods.Based on the statistical characteristics of historical distances between TC individuals,this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages.This method accurately identifies each TC individual and assigns it a unique database number through a two-step process:initially,through the'Same Center same Number Comparison(SCNC)'identi-fication method,followed by the'Spatio-Temeporal Distance Comparison(STDC)'identification method.On this basis,we obtain a well-organized and comprehensive dataset that covers entire TC life time.Over the past decade,the operational practice has demonstrated that this method is accurate and efficient,providing solid data support for the TC forecasting operation,the assessment of TC forecasting accuracy,the compilation of TC yearbook,and TC-related research.