Joint Typhoon Warning Center Research Articles

A Markov model approach for statistical tropical cyclone wind radii baseline forecasts

Abstract A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.

ECMWF Ensemble Forecasts of Six Tropical Cyclones That Formed during a Long-Lasting Rossby Wave Breaking Event in the Western North Pacific

The ECMWF‘s ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and 25° N, and between 136° E and 160° E. All five typhoons recurved north of 30° N, and the three typhoons that did not make landfall had long tracks to 50° N and beyond. The ECEPS weighted mean vector motion track forecasts from pre-formation onward are quite accurate, with track forecast spreads that are primarily related to initial position uncertainties. The ECEPS intensity forecasts have been validated relative to the Joint Typhoon Warning Center (JTWC) Working Best Track (WBT) intensities (when available). The key results for Tokage (11 W) were the ECEPS forecasts of the intensification to a peak intensity of 100 kt, and then a rapid decay as a cold-core cyclone. For Hinnamnor (12 W), the key result was the ECEPS intensity forecasts during the post-extratropical transition period when Hinnamnor was rapidly translating poleward through the Japan Sea. For Muifa (14 W), the key advantage of the ECEPS was that intensity guidance was provided for longer periods than the JTWC 5-day forecast. The most intriguing aspect of the ECEPS forecasts for post-Merbok (15 W) was its prediction of a transition to an intense, warm-core vortex after Merbok had moved beyond 50° N and was headed toward the Aleutian Islands. The most disappointing result was that the ECEPS over-predicted the slow intensification rate of Nanmadol (16 W) until the time-to-typhoon (T2TY), but then failed to predict the large rapid intensification (RI) following the T2TY. The tentative conclusion is that the ECEPS model‘s physics are not capable of predicting the inner-core spin-up rates when a small inner-core vortex is undergoing large RI.

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.

On the Size Discrepancies between Datasets from China Meteorological Administration and Joint Typhoon Warning Center for the Northwestern Pacific Tropical Cyclones

This study analyzes the Northwestern Pacific tropical cyclone (TC) size difference between the China Meteorological Administration (CMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. The TC size is defined by the near-surface 34-knot wind radius (R34). Although there is a high correlation (correlation coefficient of 0.71) between CMA and JTWC R34 values, significant discrepancies are still found between them. The JTWC tends to report larger R34 values than the CMA for large-sized TCs, while the trend is reversed for compact TCs. Despite spatial distribution discrepancies, both datasets exhibit significant similarity (spatial correlation coefficient of 0.61), particularly in latitudinal distribution; higher R34 values are observed near 25° N. An investigation of key parameters affecting R34 estimations shows that the discrepancies in R34 values between the two agencies’ estimates of TC size are primarily influenced by the size itself and latitude. There is a high correlation between R34 difference and R34 values, with a high correlation of up to 0.58 with the JTWC’s R34 values. There is also a significant correlation between R34 difference and latitude, with a correlation coefficient of 0.26 in both the CMA and JTWC datasets. Case studies of Typhoons “Danas” and “Maysak” confirm distinct characteristics in R34 estimations during different development stages, with the JTWC capturing TC intensification better, while the CMA underestimates TC size during rapid growth phases. During the weakening stage of the TC, both agencies accurately estimate the R34 values. These findings contribute valuable insights into the discrepancies and characteristics of R34 datasets, informing the selection and utilization of data for typhoon research and forecasting.

Effects of jetstream on seasonal variations of tropical cyclone tracks in the East Sea

Tropical cyclones (TCs) frequently occurs and result in substantial socio-economic consequences for the countries around the East Sea. In this study, the Joint Typhoon Warning Center (JTWC) best tracks and the NCEP/NCAR reanalysis data are employed to investigate the effect of jetstream on the seasonal variations of the TC tracks. The results reveal four primary directions of the tracks: northeastward, northwestward, westward and southwestward. A low-level anomalous cyclone moving from the Western North Pacific (WNP) to the Indochina Peninsula (IP) plays a significant role in guiding the movement of the TCs. This anomalous cyclone is strongly modulated by the development of a wavetrain along the subtropical jetstream. In May, the wavetrain induces strong anomalous divergence to the east of China, leading to the northeastward expansion of the low-level anomalous cyclones, thereby directing the TCs to the northeast. From June to August, the jetstream shifts to higher latitudes, reducing its impact on the TC tracks. From September to December, the jetstream moves back to the south; however, its effect on the TC tracks is opposite to that in May. During this time, the wavetrain accelerates an anomalous anticyclone in Southeast China and the Western North Pacific, which in turn pushes the anomalous cyclone to the south and promotes westward and southwestward movement of the TCs in the East Sea.

A new surge index with the incorporation of cyclone track approach angle information for the Bay of Bengal

A novel tropical cyclone surge potential index (TCSPI) for estimating tropical cyclone (TC) induced potential peak surge levels near the landfall locations for the entire coast of the Bay of Bengal (BoB) is presented. The proposed TCSPI incorporates parameters that are often readily available in real-time for BoB: maximum sustained wind speed (Vmax), the radius of given wind (R), translation speed (S), approach angle (θ), coastal geometry (a, b), and bathymetry information (L30). The inclusion of approach angle and associated coastal geometry information led to improvements of the TCSPI to other existing surge indices. A retrospective analysis of TCSPI using the Indian Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) tropical cyclone (TC) best track data from 1990 to 2021 for BoB suggests that the index captures historical events reasonably well. In particular, it explains up to ∼90% of observed surge variance and up to ∼92% of those peak surge values obtained from physics-based numerical simulation using the Advanced Circulation (ADCIRC) model. Further, we utilized renowned regression-based supervised machine learning (ML) models on small data samples. The Linear Regression (LR), Regression Tree (TR), Support Vector Machine (SVM), Ensembles of Trees (EnTR), and Gaussian Process Regression (GPR) models are incorporated that can rapidly predict peak surge height across an extensive coastal region of BoB, using the same input parameter information (i.e., Vmax, R, S,L30, and θ) that have been considered in the TCSPI at the landfall time frame. Finally, the observed peak surge values associated with historical TC events in the BoB are compared with those estimated by TCSPI, ADCIRC, and ML models. The errors associated with TCSPI are comparatively less. The consideration of approach angle information in the TCSPI has improved the accuracy of the overall prediction of peak surge height associated with historical TCs by up to 17% and 19% for observed and ADCIRC-simulated peak surges, respectively. The development of such a prediction methodology can reduce the numerical computation requirements to improve the surge hazard maps, and hence, proper implementation based on surge risk is possible.

Tropical cyclone genesis prediction based on support vector machine considering effects of multiple meteorological parameters

Most coastal regions worldwide face great challenges from tropical cyclones (TCs). Accurate prediction of TC genesis is critical for conducting risk assessments and disaster mitigation. Compared to conventional statistical models, this study introduces a machine-learning-based approach utilizing a support vector machine (SVM) to predict TC genesis. Historical TC genesis data from the Japan Meteorological Agency (JMA), China Meteorological Administration (CMA), and Joint Typhoon Warning Center (JTWC) are utilized. Five meteorological parameters are extracted from climate reanalysis datasets: absolute vorticity, relative humidity, vertical velocity relative sea surface temperature, and vertical wind shear. A high-dimensional mapping between meteorological parameters and TC genesis labels is performed before classification training. The effects of proportions of positive/negative samples and training/testing sets on model performance are examined, aiming to identify the optimal solution. Different meteorological parameter combinations tested to achieve the highest classification accuracy. Temporal and spatial outcomes of TC predictions are compared with historical data to verify model accuracy. The proposed approach enables simultaneous determination of TC genesis locations and counts, significantly enhancing computational efficiency. It also uses meteorological parameters more comprehensively considering the underlying physical mechanisms. In future climate change scenarios, forecasted meteorological data can be directly incorporated to derive TC genesis patterns.

The extraordinarily large vortex structure of Typhoon In-fa (2021), observed by spaceborne microwave radiometer and synthetic aperture radar

Tropical cyclone (TC) vortex structure can be estimated from wind radii (34, 50, and 64 kt). Accurate wind radii are essential for assessing the impact range of TCs. Combining observations from active and passive microwave remote sensing instruments can provide long-time series data for monitoring changes in TC wind structure. Here, the evolution of the wind radii for Typhoon In-fa (2021), from its genesis to its first landfall, is evaluated using data from spaceborne microwave radiometers and synthetic aperture radars (SARs). Our results show that the retrieved wind radii are relatively close to those from the Joint Typhoon Warning Center (JTWC), the coastal automatic weather stations in China, and the advanced scatterometer. The time series for the wind radii show significant increasing trends during the lifecycle of In-fa and a massive vortex structure before In-fa made landfall. The average wind radii observed before landfall are all likely in the 90th percentile of the 2001–2021 JTWC historical wind radii records, indicating that the vortex structure is extraordinarily large before In-fa made landfall and represents a historical extreme. The extremely large vortex structure may be attributable to the interaction between In-fa and an extremely strong monsoon gyre.

Simulating Meteorological and Water Wave Characteristics of Cyclone Shaheen

The Bay of Bengal and Arabian Sea are annually exposed to severe tropical cyclones, which impose massive infrastructure damages and cause the loss of life in coastal regions. Cyclone Shaheen originally generated in the Bay of Bengal in 2021 and translated a rare east-to-west path toward the Arabian Sea. Although the cyclone’s wind field can be obtained from reanalysis datasets such as ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts), the wind speed cannot be reproduced with realistic details in the regions close to the center of cyclone due to spatial resolution. In this study, to address this problem, the high-resolution advanced Weather Research and Forecasting (WRF) model is used for simulation of Shaheen’s wind field. As a critical part of the study, the sensitivity of the results to the planetary boundary layer (PBL) parameterization in terms of the track, intensity, strength and structure of the cyclone Shaheen is investigated. Five experiments are considered with five PBL schemes: Yonsei University (YSU); Mellor–Yamada–Janjić (MYJ); Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN); Asymmetric Convective Model version 2 (ACM2); Quasi-Normal Scale Elimination (QNSE). The track, intensity, and strength of the experiments are compared with the wind fields obtained from the Joint Typhoon Warning Centre (JTWC) dataset. The results imply the high dependency of the track, intensity, and strength of the cyclone to the PBL parameterization. Simulated tracks with non-local PBL schemes (YSU and ACM2) outperformed those of the local PBL schemes (MYJ, MYNN, and QNSE), especially during the rapid intensification phase of Shaheen before landfall. The YSU produced highly intensified storm, while the ACM2 results are in better agreement with the JTWC data. The most accurate track was obtained from the ERA5 data; however, this dataset overestimated the spatial size and underestimated the wind speed. The WRF model using either YSU or ACM2 overestimated the wind speed compared to that of the altimeter data. The YSU and ACM2 schemes were able to reproduce the observed increase in wind speed and pressure drop at in situ stations. The wind data from EAR5 and cyclone parametric model were applied to the SWAN model to simulate the wave regime in the Arabian Sea during the time that Shaheen was translating across the region. Janssen formulation for wind input and whitecapping dissipation source terms in combination with both ERA5 and hybrid wind were used and the minimum combined error in the prediction of significant wave height (Hs) and zero up-crossing wave period (Tz) was examined. The maximum significant wave height for hybrid wind is higher than that of ERA5, while the cyclone development was successfully inferred from the wave field of the hybrid data.

Application of Microwave Space-Based Environmental Monitoring (SBEM) Data for Operational Tropical Cyclone Intensity Estimation at the Joint Typhoon Warning Center

Abstract The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) radiometers, and synthetic aperture radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the poststorm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the poststorm BT process, and provide examples of how these data influenced forecaster decision-making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new datasets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data-sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.

Interagency discrepancies in tropical cyclone intensity estimates over the western North Pacific in recent years

AbstractThis study investigates interagency discrepancies among best‐track estimates of tropical cyclone (TC) intensity in the western North Pacific, provided by the Joint Typhoon Warning Center (JTWC), the China Meteorological Administration (CMA), and the Regional Specialized Meteorological Center (RSMC) Tokyo during 2013 to 2019. The results reveal evident differences in maximum wind speed (MSW) estimates, where linear systematic differences are significant. However, the Dvorak parameter (CI) numbers derived from the MSWs reported by the three agencies are internally consistent. Further analysis suggests that the remained CI discrepancies are related to differences in the estimation of intensity trends, initial intensities, and TC positions among these datasets. In addition, the CI estimates provided by the JTWC for TCs over the open ocean are generally higher than those reported by the CMA and RSMC. However, the estimates from CMA and RSMC tend to give higher TC intensities for the TCs in the mainland and coastal areas of China and Japan, respectively, than those over the open ocean with the same intensity in JTWC dataset. This pattern potentially reflects the extensive use of surface observations by these two agencies for landfalling and offshore TCs. These results may help the research community to get more accurate details about the TCs in WNP from the best track datasets of different agencies.

Changes of Tropical Cyclones Landfalling in China From 1979 to 2018

AbstractThis study investigates the long‐term changes of tropical cyclones (TCs) that originated over the western North Pacific (WNP) and made landfall in China from 1979 to 2018. The TC best track data sets from the three primary weather agencies, the China Meteorological Administration (CMA), the Japan Meteorological Agency (JMA), and the Joint Typhoon Warning Center (JTWC), and the TCs landfall data from the CMA are used in this study. Trend analysis of the mean locations of the TCs' lifetime maximum intensity (LMI) suggests that the locations of the LMI of TCs have moved closer to the coast gradually. This shift may be associated with a landward migration of TC genesis. The changes in TC LMI possibly contribute to the slight increase in the TCs' landfall intensity and the significant growth of TCs' overland duration. Further analyses indicate that all these changes may be mainly due to both dynamic and thermodynamic conditions in the WNP, which tend to be more favorable for the formation and development of TCs, and some of the favorable conditions have shifted toward the land during the 40 years.

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

As an artificial intelligence method, machine learning (ML) has been widely used in prediction models of high-dimensional datasets. This study proposes an ML method, the Gradient Boosted Regression Tree (GBRT), to predict the intensity changes of tropical cyclones (TCs) in the Western North Pacific at 12-, 24-, 36-, 48-, 60-, and 72-h (hr) forecasting lead time and the model is optimized by the Bayesian Optimization algorithm. The model predictands are the TCs intensity changes at different forecasting lead times, obtained from the best track data of the Shanghai Typhoon Institute (STI) and the Joint Typhoon Warning Center (JTWC) from 2000 to 2019. The model predictors are the synoptic variables, climatological and persistent variables derived from the reanalysis data obtained from the National Centers for Environmental Prediction (NCEP), and the sea surface temperature (SST) data obtained from the National Oceanic and Atmospheric Administration (NOAA). The results show that the GBRT model can capture the TCs intensity changes well for the succeeding 12-h, 24-h, 36-h, and 72-h. Compared with the traditional multiple linear regression (MLR) model, the GBRT model has better performance in predicting TCs intensity changes. Compared with the MLR model, R2 of the GBRT model for TCs intensity forecast increases by an average of 8.47% and 4.45% for STI data and JTWC data. MAE (RMSE) drops by 26.24% (25.14%) and 10.51% (4.68%) for the two datasets, respectively. The potential future intensity change (POT), the intensity changes during the previous 12 h (Dvmax), Initial storm maximum wind speed (Vmax), SST, and the Sea-Land ratio are the most significant predictors for the GBRT model in predicting TCs intensity change over the Western North Pacific.

Sensitivity of typhoon wind hazard in coastal region to the track modelling and the considered historical best track database

The tropical cyclone (TC) wind hazard is often assessed using the autoregressive type of model and the beta-advection model. In the present study, the beta-advection models are developed using the best track datasets from the China Meteorological Administration (CMA) and the Joint Typhoon Warning Center (JTWC). A comparison of statistics of the TC track parameters along the coastline of mainland China is presented using historical tracks and simulated tracks from the developed models. The comparison is extended by considering an existing autoregressive type of track model. Results show that the number of genesis per year based on the dataset in CMA is about 7% greater than that in JTWC. A comparison of the estimated T-year return period value of the annual maximum TC wind speed, vA-T, is presented using the three different track models. vA-T obtained based on the beta-advection model developed using the dataset from CMA is greater than that by using the dataset from JTWC by about 5%. vA-T estimated using the autoregressive type of model and the beta-advection model indicates that their differences are up to 10% and 12% for T equals 50 and 100 years if the dataset from CMA is considered.

Comparison of Tropical Cyclone Wind Radius Estimates between the KMA, RSMC Tokyo, and JTWC

This study compared estimates of gale-force wind radii (R30 or R34) and storm-force wind radii (R50) of tropical cyclones (TC) by three agencies—the Korea Meteorological Administration (KMA), the Regional Specialized Meteorological Center (RSMC) Tokyo, and the Joint Typhoon Warning Center (JTWC)—in the western North Pacific during 2015–2018 and investigated the characteristics of these estimates. The results showed that the KMA’s R30 and R50 estimates were smaller (38% and 29%, respectively) than those of the RSMC Tokyo, and larger (11%) for R30 and smaller (12%) for R50 than those of the JTWC. The differences between these agencies seem to be largely determined by whether the agency estimates wind radii based only on a TC’s own winds or on TC winds combined with other mid-latitude synoptic systems to make TC warnings more comprehensive. The former is mainly the practice of the KMA and JTWC, whereas the latter is mainly the practice of the RSMC Tokyo. The factors considered for estimating wind radii also differ between the agencies: the KMA heavily relies on TC intensity—the higher the intensity, the larger the radius—while the RSMC Tokyo and JTWC rely less on TC intensity than the KMA but additionally consider the latitude and storm translation speed in their estimations. In particular, the TC translation speed considered by the RSMC Tokyo and JTWC explains why their estimated wind radii exhibit, on average, greater asymmetries (i.e., greater differences between the longest and shortest radii) than those estimated by the KMA. The findings of agency-dependent characteristics of TC wind radius data help to better determining and understanding the TC impact areas for TC risk reduction and management.

A numerical estimate of water level elevation due to a cyclone associated with a different landfall angle

<p>Bangladesh is a disaster-prone riverine country in South Asia, most of them are cyclone-related. That's why research on cyclones in this region is very important. This study investigates the surge height associated with the changes of landfall angle due to climate change. The deflected angle of landfall was investigated from the data analysis of Bangladesh Meteorological Department (BMD), Joint typhoon warning center (JTWC), and Meteorological Research Institute- Atmospheric global circulation model (MRI-AGCM). A cyclone of future climate has been investigated from the Database for Policy Decision-Making for Future Climate Change (d4PDF) data under present and future climate conditions. To find the surge height, a vertically shallow water Cartesian coordinate model has been used to simulate the surge height. The shallow water model equations were discretized through finite difference technique with the Arakawa C grid system and solved by a conditionally stable semi-implicit manner. The fluctuated striking angle due to climate change was then applied to the known cyclone BOB 01 and the associated surge height was then investigated. We found that our simulated result and the observed result make a good agreement. We have also seen that different types of cyclones have a significant effect on the water level elevation due to their landfall angle</p>

A 50‐Year Tropical Cyclone Exposure Climatology in Southeast Asia

AbstractIn this study, the landfalling tropical cyclone (TC) exposure in Southeast Asia for a 50‐year period from 1970 to 2019 is investigated relative to the total western North Pacific (WNP) climatology taking disparities in historical records into account. Long‐term trends in landfalling TCs are analyzed and intercompared among the Regional Specialized Meteorological Center of Tokyo (TOKYO), China Meteorological Administration (CMA), Hong Kong Observatory (HKO), and Joint Typhoon Warning Center (JTWC) best track datasets. Interannual and intra‐seasonal variations are further examined by sub‐region and nation using JTWC records. Approximately half of the WNP TCs make landfall in Southeast Asia representing over 75% of the total WNP landfalls in all datasets. Over the study period, there is a slight upward trend in landfalling TC frequency in both the WNP and Southeast Asia in the JTWC dataset, while the number of landfall events has decreased in the CMA and TOKYO datasets. A consistent northward shift in landfalling locations over the 50‐year period is found in all datasets such that landfalls have decreased in the Philippines, Vietnam, but increased in some South China areas. The TOKYO dataset alone suggests that landfalling TCs in South China have slowed down over the study period, which would increase rainfall and wind risks in their path if substantiated. Less TC landfalls occur in El Niño years with landfalling locations shifting northwestward over the Asian mainland, while landfalls are higher and more distributed in La Niña, and highest in Neutral years.

Numerical Simulation of Storm Waves during Typhoon Maysak and Haishen in the Korean Peninsula

In this study, we simulated storm waves induced by typhoon Maysak and Haishen in the Korean peninsula by using the third-generation numerical wave model SWAN(Simulating WAves Nearshore). We used the sea-surface wind data of KMA(Korea Meteorological Agency)’s operational meteorological model RDAPS(Regional Data Assimilation Prediction System), and JMA(Japan Meteorological Agency)’s weather forecast model JMA-MSM(Meso-Scale Model) as input forcing of the numerical wave model. In addition, the wind field of the tropical cyclone was established with JTWC(Joint Typhoon Warning Center)’s best track data for the storm wave simulation. We used two different source term options, which consider other physics of energy generation by wind and dissipation by whitecapping. Validation of the simulation result was conducted over statistical analysis with the observation data from KMA and KHOA(Korea Hydrographic and Oceanographic Agency)’s buoys. As a result, the whole scenario tended to overestimate significant wave height. The simulation result by using RDAPS as input forcing outperformed the result by using JMA-MSM. It denoted that the performance of a wave model depends on the accuracy of input forcing wind. The wind field by JTWC’s best track data was not suitable compared with the meteorological numerical model. ST6 simulated the wave height higher than Komen’s source term. However, Komen's source term simulated storm waves better than ST6 in this study, and further researches under various conditions are required to evaluate ST6 for the storm waves simulation. In the simulation of wave period parameters, it was hard to estimate the accuracy of the model, and other approaches are necessary for the evaluation.

A New Type of Red-Green-Blue Composite and Its Application in Tropical Cyclone Center Positioning

Weak tropical cyclone (TC) center positioning is difficult work in operational forecasting. In the present study, a TC-red-green-blue (TC-RGB) composite was designed by using satellite multichannel observations (reflectance, brightness temperature, and brightness temperature differences). Compared with single channel images, TC-RGB composites can clearly show the exposed low-level circulation (LLC) of weak TCs under large vertical wind shear. Based on the guidelines of TC-RGB composites for TC center positioning, we repositioned 83 western North Pacific (WNP) TC cases during 2017–2019. Then, the comparisons of TC center positions were made between the TC-RGB composite and the Regional Specialized Meteorological Centre-Tokyo (RSMC-Tokyo), the Joint Typhoon Warning Center (JTWC) and the China Meteorological Administration-Shanghai Typhoon Institute (CMA-STI). Via case analysis of TC Kalmaegi (2019), it was found that the best-track data from the RSMC-Tokyo, JTWC and CMA-STI would have over 100 km biases at the early stage of TC life history. Taking all the 83 TC cases into account, the results show that the average center position biases and standard deviations for weak TCs under small vertical wind shear in the daytime are 5 km larger than those under large vertical wind shear at nighttime. When considering the 83 TC cases with clear LLC centers, the difference of these two biases is 10 km. The average biases are mostly above 20 km in the areas south of 18° N and north of 36° N over the WNP. Conversely, in the areas between 18° N and 36° N over the WNP, they are mostly below 20 km.

A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific

Abstract Tropical cyclones are among the most powerful and destructive meteorological systems on Earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by a bidirectional gate recurrent unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data experiments are conducted on tropical cyclone best-track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the northwestern Pacific Ocean. Results show that our model performs well for tracks of 6, 12, 24, 48, and 72 h in the future. The prediction results show that our proposed combined model is superior to state-of-the-art deep learning models, including a recurrent neural network (RNN), long short-term memory neural network (LSTM), gate recurrent unit network (GRU), and BiGRU without the use of attention mechanism. In comparison with the methods used by the China Meteorological Administration, Japan Meteorological Agency, and the JTWC, our method has obvious advantages in the mid- to long-term track forecasting, especially in the next 72 h.