A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes involved.Here,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural network.In time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical efficiency.The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–2017.The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–21.The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity.The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data.The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h forecast.Particularly at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.

A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland(PRITC dataset V1.0)

A dataset entitled“A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland”(PRITC dataset V1.0)is described in this paper,as are some basic statistical analyses.Estimating the severity of the impacts of tropical cyclones(TCs)that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study,including an index combining TC-induced precipitation and wind(IPWT)and further information,such as the corresponding category level(CAT_IPWT),an index of TC-induced wind(IWT),and an index of TC-induced precipitation(IPT).The current version of the dataset includes TCs that made landfall from 1949-2018;the dataset will be extended each year.Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased,as embodied by the annual mean IPWT values,and increases in TC-induced precipitation are the main contributor to this increase.TC Winnie(1997)and TC Bilis(2006)were the two TCs with the highest IPWT and IPT values,respectively.The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones

Accurate prediction of tropical cyclone(TC)intensity remains a challenge due to the complex physical processes involved in TC intensity changes.A seven-day TC intensity prediction scheme based on the logistic growth equation(LGE)for the western North Pacific(WNP)has been developed using the observed and reanalysis data.In the LGE,TC intensity change is determined by a growth term and a decay term.These two terms are comprised of four free parameters which include a time-dependent growth rate,a maximum potential intensity(MPI),and two constants.Using 33 years of training samples,optimal predictors are selected first,and then the two constants are determined based on the least square method,forcing the regressed growth rate from the optimal predictors to be as close to the observed as possible.The estimation of the growth rate is further refined based on a step-wise regression(SWR)method and a machine learning(ML)method for the period 1982−2014.Using the LGE-based scheme,a total of 80 TCs during 2015−17 are used to make independent forecasts.Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration(CMA),especially for TCs in the coastal regions of East Asia.Moreover,the scheme based on ML demonstrates better forecast skill than that based on SWR.The new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts.

Recent advances in landfalling tropical cyclone asymmetric rainfall mechanism and forecast verification over China

Held every four years,the International Workshop on Tropical Cyclone(IWTC)organized by the World Meteorological Organization has been a global leading conference in thefield of tropical cyclone.In preparation for the 10th IWTC(IWTC-10)in December 2022,a summary of research advances of landfalling tropical cyclone(LTC)rainfall during past four years of 2019–2022 has been prepared.Some of the latest research advances has been summarized in Lamers et al.(2023),which reviewed the latest forecast and disaster prevention methods related to TC precipitation.As a supplement,this article mainly focuses on the recent advances in LTC asymmetric rainfall evolution mechanisms and forecast verification results over China.Some newfindings have been made in the LTC inner-core size relationship with the asymmetric rainfall distri-bution.Some major advances focused on asymmetric microphysical characteristics in the TC rainbands.Current simulation and forecast per-formances of LTC precipitation have been analyzed,and different forecast error sources for rainfall during different landfall stages of TC were compared.To estimate the risk of TC rainfall hazards in China,a parameterized Tropical Cyclone Precipitation Model was reviewed as well in this article.

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 simplified index to assess the combined impact of tropical cyclone precipitation and wind on China

Relationships between tropical cyclone(TC)precipitation,wind,and storm damage are analyzed for China based on TCs over the period from 1984 to 2013.The analysis shows that the maximum daily areal precipitation from stations with daily precipitation of ≥50 mm and the sum of wind gusts of ≥13.9 m/s can be used to estimate the main damage caused by TCs,and an index combining the precipitation and wind gust of a TC(IPWT)is defined to assess the severity of the combined impact of precipitation and wind.The correlation coefficient between IPWT and the damage index for affecting TCs is 0.80,which is higher than that for only precipitation or wind.All TCs with precipitation and wind affecting China are divided intofive categories,Category 0 to Category 4,based on IPWT,where higher categories refer to higher combined impacts of precipitation and wind.The combined impact category is closely related to damage category and it can be used to estimate the potential damage category in operational work.There are 87.7%,72.9%,69.8%,and 73.4%of cases that have the same or one category difference between damage category and combined impact category for Categories 1,2,3,and 4,respectively.IPWT and its classification can be used to assess the severity of the TC impact and of combined precipitation and wind conveniently and accurately,and the potential damage caused by TCs.The result will be a good supplementary data for TC intensity,precipitation,wind,and damage.In addition,IPWT can be used as an index to judge the reliability of damage data.Further analysis of the annual frequency of combined precipitation-wind impact categories reveals no significant increasing or decreasing trend in impact over China over the past 30 years.

INTRODUCTION AND APPILICATION OF A NEW COMPREHENSIVE ASSESSMENT INDEX FOR DAMAGE CAUSED BY TROPICAL CYCLONES

Using principal component analysis,a new comprehensive assessment index for damage caused by tropical cyclones in China's Mainland is developed based on data from 1984 to 2008.it is a weighted average of four kinds of damage data:including the deaths and missing,affected crop area,destroyed houses,and rate of direct economic loss.The weighting coefficients are set by principal component analysis.Two indices are derived,which differ in the importance of the deaths and missing in severity assessment according to the sign of the second principal component of damage data.Trends in the damage caused by individual tropical cyclones and in the annual frequencies of the various levels of severity of damage caused by tropical cyclones are analyzed.no clear trend in damage from individual tropical cyclones is found.The annual frequency of tropical cyclones causing heavy and catastrophic damage shows a clear decrease from 1984 to 2008 with no trend in the total number of damaging tropical cyclones.

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Impact of physical representations in CALMET on the simulated wind field over land during Super Typhoon Meranti(2016)

A WRF(Weather Research and Forecasting Model)/CALMET(California Meteorological Model)coupled system is used to investigate the impact of physical representations in CALMET on simulations of the near-surface wind field of Super Typhoon Meranti(2016).The coupled system is configured with a horizontal grid spacing of 3 km in WRF and 500 m in CALMET,respectively.The model performance of the coupled WRF/CALMET system is evaluated by comparing the results of simulations with observational data from 981 automatic surface stations in Fujian Province.The root mean square error(RMSE)of the wind speed at 10 m in all CALMET simulations is significantly less than the WRF simulation by 20%^30%,suggesting that the coupled WRF/CALMET system is capable of representing more realistic simulated wind speed than the mesoscale model only.The impacts of three physical representations including blocking effects,kinematic effects of terrain and slope flows in CALMET are examined in a specified local region called Shishe Mountain.The results show that before the typhoon landfall in Xiamen,a net downslope flow that is tangent to the terrain is generated in the west of Shishe Mountain due to blocking effects with magnitude exceeding 10 m/s.However,the blocking effects seem to take no effect in the strong wind area after typhoon landfall.Whether being affected by the typhoon strong wind or not,the slope flows move downslope at night and upslope in the daytime due to the diurnal variability of the local heat flux with magnitude smaller than 3 m/s.The kinematic effects of terrain,which are speculated to play a significant role in the typhoon strong wind area,can only be applied to atmospheric flows in stable conditions when the wind field is quasinondivergent.

The impact of Typhoon Lekima (2019) on East China: a postevent survey in Wenzhou City and Taizhou City

Typhoon Lekima(2019)struck Zhejiang Province on 10 August 2019 as a supertyphoon,which severely impacted Zhejiang Province.The typhoon killed 45 people and left three others missing,and the total economic loss reached 40.71 billion yuan.This paper reports a postdisaster survey that focuses on the storm precipitation,flooding,landslides,and weather services associated with Typhoon Lekima(2019)along the southeastern coastline of Zhejiang Province.The survey was conducted by a joint survey team from the Shanghai Typhoon Institute and local meteorological bureaus from 26 to 28 August,2019,approximately two weeks after the disaster.Based on this survey and subsequent analyses of the results,we hope to develop countermeasures to prevent future tragedies.

ON TROPICAL CYCLONE ACTIVITY OVER THE WESTERN NORTH PACIFIC IN 2012

Tropical cyclone(TC) activity over the western North Pacific(WNP) in 2012 is summarized and the associated large-scale environmental conditions are discussed. In total 25 named storms formed in the WNP basin in 2012, among them were 3 tropical storms(TSs), 7 severe TSs, 4 typhoons, 6 severe typhoons, and 5 super typhoons. TC activity was close to a 30-year average but above the average active level of recent years since 2005. Total number of TCs formed in the South China Sea(SCS) in 2012 was below normal, with only 40% of the climatological mean. Overall, TC genesis over the WNP was characterized by four active periods. During each period TCs took distinct prevailing tracks. The periodic characteristics in TC genesis were attributed to the activity of the intraseasonal oscillation(ISO), while those in TC tracks were related to the large-scale dynamical and thermodynamic conditions induced by the enhanced WNP monsoon activity and the weak El Ni?o conditions.

Analysis of characteristics and evaluation of forecast accuracy for Super Typhoon Doksuri(2023)

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.