Tropical Cyclone Track Research Articles

Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: An Indian Ocean tropical cyclone case study

Tropical cyclones (TCs) predictions are crucial and of significant scientific and socio-economic interest. To improve the TC's simulation, this study aimed to use the Weather Research and Forecasting (WRF) model to assimilate satellite radiances and surface and upper-air conventional observations cyclically at the intervals of 6 h. The predictive skill of WRF with 4D variational (4DVar) and 3D variational (3DVar) data assimilation (DA) systems are investigated to examine the added value of 4DVar considering four cyclones namely Vardah, Titli, Bulbul, and Yaas over the North Indian Ocean (NIO). Each analysis was subjected to a short-range free forecast, and the estimates obtained from the 4DVar DA showed more realistic TC characteristics in terms of structure, intensity, rainfall, and track. The simulation of mean sea-level pressure and maximum sustained wind has demonstrated significant improvements in the 4DVar run for all the cases. Furthermore, the vector displacement error in cyclone Vardah was reduced by 54%–57% in the 4DVar simulation, whereas this improvement was found to be 9%–18% in the case of Titli. More or less similar results were found for Bulbul and Yaas also. Simulation of rainfall also showed marked improvement from the 4DVar scheme. Thus, the performance of the WRF model integrated with the 4DVar DA method enhanced the simulations of TC intensity, tracks, and structures over the NIO.

Effects of Tropical Cyclones on Ozone Pollution in the Pearl River Delta in Autumn

Based on the tropical cyclone track data in the northwest Pacific Ocean from 2015 to 2020, meteorological observation data, and ozone concentration monitoring data in the Pearl River Delta (PRD), the impacts of four tropical cyclones, namely the westbound tropical cyclone (type A), East China Sea tropical cyclone (type B), offshore tropical cyclone (type C), and offshore tropical cyclone (type D), on ozone concentration in the PRD were analyzed. The results showed that:under the influence of the type A tropical cyclone, the risk of regional ozone concentration exceeding the standard exhibited little change. Under the influence of the type B tropical cyclone, the risk of ozone exceeding the standard in the PRD was obviously increased. Under the influence of the type C tropical cyclone, the risk of regional ozone exceeding the standard obviously increased, but the increase was weaker than that of the type B tropical cyclone. The type D tropical cyclone was far away from the Chinese mainland and had little influence on ozone concentration in the PRD. When the type A or type C tropical cyclones occurred, the average daily maximum 8-hour average ozone concentration (MDA8) in the PRD region increased by approximately 5 μg·m-3, and the ozone MDA8 in some cities may have decreased. When the type B tropical cyclone occurred, the regional ozone MDA8 increased by 19 μg·m-3 on average, and the ozone concentration in all cities increased significantly. Among them, the average increase in ozone MDA8 in Zhuhai and Jiangmen was relatively large, with an increase of greater than 20 μg·m-3. Generally speaking, the ozone concentration in cities in the western PRD was more affected by tropical cyclones. When the type B tropical cyclone occurred, solar radiation increased, sunshine duration lengthened, cloud cover decreased, air temperature rose, and relative humidity decreased in the PRD, all of which were beneficial to photochemical reactions. Meanwhile, downward flow increased in the boundary layer, and downward flow transported high-concentration ozone to the ground, which promoted the increase in ozone concentration on the ground. When type A or type C tropical cyclones occurred, the change in meteorological conditions was not entirely conducive to the increase in ozone concentration, and in some cases, even adverse meteorological conditions such as rainfall occurred, which led to the risk of regional ozone exceeding the standard being less than that of the type B tropical cyclone. Affected by tropical cyclones, sunshine hours and air temperature in western cities of the PRD increased more than those in eastern cities, which was more conducive to ozone generation.

Generating reliable estimates of tropical-cyclone-induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

Abstract. Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among other things, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, thus resulting in poorly reliable estimates of the extremes, especially in regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open-source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation Tool) to create thousands of realizations representative of 1000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e., wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e., storm surge and waves) are computed that are generated by the first-order hazard of wind. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks shows that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge of up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modeled storm surge and wave heights was computed. As a proof of concept, an increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values and without accounting for uncertainties in future climate projection, was estimated to possibly result in an increase in storm surge and wave heights of +11 % and +9 %, respectively. This suggests that climate change could increase tropical-cyclone-induced coastal hazards more than just the actual increase in maximum wind speeds.

Tropical Cyclone Track Prediction with an Encoding-to-Forecasting Deep Learning Model

Abstract Recently, with the accumulation of remote sensing data, the traditional tropical cyclone (TC) track prediction methods (e.g., dynamic methods and statistical methods) have limitations in prediction efficiency and accuracy when dealing with a large amount of data. However, deep learning methods begin to show their advantages to capture the complex spatiotemporal features in high-dimensional data. The task of TC track prediction based on remote sensing images can be formulated as a spatiotemporal sequence-to-sequence problem. Therefore, a novel encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) was proposed to predict TC tracks in this paper, which can fully extract the long-term spatial and temporal features. The proposed model was evaluated on the real remote sensing images in the western North Pacific Ocean to forecast 24-h TC tracks. Compared with ECMWF-HRES model and NCEP-GFS model, the proposed method has a better prediction accuracy in the testing set with an average position error about 30 km less. Compared with the deep learning methods, the proposed method also has the best performance. Significance Statement Tropical cyclones have a great impact on human life and natural environment due to their high frequency of occurrence, heavy degree of harm, wide impact range, and long disaster chain. In this article, we propose an encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) to predict tropical cyclone tracks, in which the SAN module and the convolution module of ConvLSTM have the ability to extract spatial features, and the long short-term memory (LSTM) module of ConvLSTM can extract temporal features from remote sensing images and tropical cyclone tracks. The results show that the average position error (APE) of our proposed method has about 30% improvement in a 24-h forecast compared with prevailing numerical weather prediction models.

The Development of the NCEP Global Ensemble Forecast System Version 12

Abstract The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

Abstract This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting. Significance Statement Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society.

Modulation of Pacific Sea Surface Temperature on Two Types of Tropical Cyclone Tracks Affecting Northeast China

In this study, the northward-moving tropical cyclones (TCs) that influence Northeast China (NEC) in July–September are identified based on the distance between the TC center and the boundaries of NEC. Then, based on a machine learning algorithm named hierarchical agglomerative clustering, the tracks of northward-moving TCs that influence NEC are classified into the eastern-track type and the turning type. In NEC, the precipitation induced by eastern-track type TCs gradually decreases from east to west, and the precipitation induced by turning-type TCs gradually decreases from south to north. For eastern-track type TCs, negative Niño3 sea surface temperature (SST) anomalies in preceding January–March can induce cyclonic circulation anomalies and positive vorticity anomalies over the Philippine Sea during subsequent July–September, which favors the genesis of TCs. Moreover, the westerly anomaly in the subtropical western north Pacific and the strengthening of cyclonic steering flow over the Philippines jointly steer the TCs to move northward along the northerly airflow on the west of the western Pacific subtropical high, which favors the genesis of eastern-track type TCs. For turning-type TCs, the positive SST anomalies in the West Wind Drift area during preceding May–July cause positive vorticity anomalies from the northern Philippines to Taiwan from July to September. The cyclonic steering flow over the Philippines and the anticyclonic steering flow over the Sea of Japan lead the TCs to move northwestwards and then turn to northeast, which is conducive to the genesis of turning type northward-moving TCs. Finally, the results of numerical experiments have confirmed these findings.

Short-term prediction of the intensity and track of tropical cyclone via ConvLSTM model

As one of the most destructive natural disasters, Tropical Cyclone (TC) can cause severe casualties and economic losses almost every year in TC-prone areas. For the prevention and reduction of TC-induced disasters in wind engineering, it is of vital importance to forecast TC's intensity and track both accurately and efficiently. Although great achievements have been made in this field, it remains a challenge to balance accuracy and efficiency during the forecast. In recent years, deep learning techniques have gained fast development and demonstrated huge application potential in various fields. This article presents a study on the short-term prediction of TCs in Northwest Pacific basin via the newly developed Convolutional Long and Short Term Memory (ConvLSTM) network which is able to extract both time-related correlations (involved in the sequence of a single parameter) and parameter-related correlations (among different parameters) from input information of different feature parameters. Results demonstrate that the proposed ConvLSTM model has good performance in terms of prediction accuracy and working stability. Through comparison, it is also suggested the proposed model works better than the standard LSTM model. Meanwhile, unlike most dynamic methods which require expensive computational resources, the proposed model can be operated conveniently and economically. Thus, it can be readily applied for engineering practices.

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.

Interannual and Interdecadal Drivers of Meridional Migration of Western North Pacific Tropical Cyclone Lifetime Maximum Intensity Location

Abstract Recent studies have noted a poleward shift in tropical cyclone (TC) lifetime maximum intensity (LMI) location. Whether this observed shift is due to global warming, natural variability, or a combination of both factors remains inconclusive. The western North Pacific (WNP) has been shown in prior research to be the most robust contributor to the observed poleward LMI migration. This study explores interannual and interdecadal climate drivers of WNP LMI location from 1979 to 2018. On interannual time scales, there are more northward-moving TC tracks during El Niño years compared with La Niña years. However, there is substantially smaller variance in the latitudinal distance from TC genesis latitude to LMI latitude than the variance in the TC genesis latitude. Thus, TC genesis El Niño years tend to reach their LMI at a lower latitude given the increased likelihood that they undergo genesis at a lower latitude. On decadal or longer time scales, global warming has contributed to the recent poleward shift of LMI location by causing more northwestward-/northward-moving TC tracks, while the PDO also significantly modulates decadal variability in TC genesis latitude, thus also contributing to LMI latitude changes. Further analysis of the extended period from 1960 to 2018 suggests that the trends in TC LMI latitude and TC tracks are dominated by global warming, and the PDO phase change likely reinforces this trend during 1979–2018. These three leading modes of climate variability (e.g., ENSO, PDO, and global warming) offer a more complete picture of the meridional migration of WNP LMI location on various time scales.

Assessing Heavy Precipitation Risk Associated with Tropical Cyclones in China

Abstract Tropical cyclone precipitation (TCP), accounting for some of the most extreme rainfall events, can lead to severe flooding and landslides, which often occur together as compound natural hazards during a tropical cyclone landfall. The impact due to TCP is largely associated with its intensity and spatial extent as the storm approaches landfall. Yet it is not entirely clear how TCP intensity and spatial extent vary from one tropical cyclone to another. In this study, we employ an advanced geostatistical framework to determine the TCP intensity and spatial extent along cyclone tracks for different cyclone categories, defined using the wind speed and tropical cyclone lifetime maximum intensity (LMI) at each track point (“point intensity-LMI”). The results show that when a tropical cyclone with an LMI of a supertyphoon makes landfall and has weakened to tropical storm strength it usually produces the most intense rainfall and covers the largest spatial extent. The total TCP amount estimated using the varying spatial extent helps to determine more accurately the amount of seasonal rainfall that is from tropical cyclones in China. We also determined the rainfall trend from 1951 to 2019 for TCP and found that when compared with the inland stations the historical TCP rainfall trend in those stations near the coastline of China is significantly increasing. Significance Statement Heavy rainfall caused by tropical cyclones has caused huge direct or indirect economic losses in the coastal areas of China. This impact is particularly significant when the rainfall intensity is high and the area of heavy rainfall is extensive. Here we investigate the rainfall intensity and spatial extent by classifying and comparing the different types of tropical cyclones impacting China with varying intensities. To do this, we group the tropical cyclone tracks of the western North Pacific Ocean during the last seven decades according to the strength of wind speed across the cyclone tracks. We found that the largest areas and heaviest intensities of rainfall occur when a supertyphoon has weakened to a tropical storm at landfall. When considering all tropical cyclones and their rainfall contribution to rainfall over land stations, we found that tropical cyclone rainfall has become heavier in most coastal areas of China.

Ensemble forecast of tropical cyclone tracks based on deep neural networks

Ensemble forecast of tropical cyclone tracks based on deep neural networks

The decadal shift in TC-induced precipitation over China

This study examines changes in tropical cyclone (TC) rainfall and the associated contributing factors over China during 1960–2018. The dominant mode of TC rainfall decreases from south to north, with a maximum in Guangdong Province. The associated time series of principal components reveals clear interdecadal variation, and three change points can be identified based on Lepage analysis: the late 1970s, early 1990s, and early 2000s. Before the 2000s, TC rainfall patterns are dominated by different processes in different subregions of South China. The maximum TC-induced rainfall in South China is attributed mainly to the TC rainfall frequency effect and TC genesis effect. TC-induced rainfall in East China is modulated primarily by TC rainfall intensity and TC track. After the 2000s, TCs north of the Yangtze River increase, especially in Shandong Province. Further examination of TC passage frequency reveals that increasing TC rainfall in the north of southeastern China is contributed largely by a northwestward shift in TC tracks.

Anomaly Format of Atmospheric Governing Equations with Climate as a Reference Atmosphere

To reduce numerical instability and increase forecast accuracy of a numerical weather prediction (NWP) model, one approach is to subtract a reference atmosphere from atmospheric governing equations. In the past, scientists have proposed one-dimensional, two-dimensional, and three-dimensional static (in time) reference atmospheres with respect to temperature and pressure. These three reference atmospheres were first reviewed, and their corresponding perturbation equations were derived. Then, a new four-dimensional (space and time) all-variable (temperature, pressure, wind, moisture, etc.) reference atmosphere was defined using observed climatic states. Unlike the previous three approaches, the perturbations derived from this new method are actual anomalies relative to climate and directly a part of individual weather systems in both structure and strength. By subtracting climatic states, anomaly equations were derived and analyzed. Finally, the benefits and challenges of the anomaly-equation-based NWP model were discussed. Theoretically, an anomaly model should reduce model systematic errors (bias) and should avoid model climate drift to significantly enhance a model’s performance. An example of tropical cyclone track forecasts using the Beta advection model (vorticity) was demonstrated. The separation of model physics into climatic and anomalous physics is a significant challenge if a pure anomaly-equation-based NWP model is desired. Fortunately, a model including both anomaly and climatic equations should work with current full physics. In an anomaly climate mixed model, the anomaly part needs to be predicted and the climate parts are precalculated constants. It is hoped that this study will inspire model developers to explore the approach, which could be a possible new direction in developing next-generation NWP models. A high-resolution reanalysis is also key to the success of this new approach.

Opposite Changes in Tropical Cyclone Rain Rate During the Recent El Niño and La Niña Years

AbstractIn this paper, we investigate whether the relationship between the El Niño‐Southern Oscillation and tropical cyclone (TC) rain rate over the western North Pacific (WNP) changes between the global warming slowdown (2001–2012) and resumption (2013–2020) periods. Using high‐resolution satellite rainfall data, we show that the average TC rain rate increases by 9% but decreases by 14% during the recent El Niño and La Niña years (since 2013), respectively. During the El Niño years, the increase is related to higher TC track density at lower latitudes, where more water vapor and stronger convergence are present. On the other hand, the decrease in TC rain rate during the recent La Niña years is mainly related to an increase in track density at higher latitudes and a decrease in TC intensity. These changes in TC track density and intensity are both associated with the strengthening of the subtropical high since 2013.

The Impact of Intra-Seasonal Oscillation on Westward Track Deflection of Super Typhoon Fitow (2013)

Typhoon Fitow (2013) took an unusual westward track deflection after a lengthy northward movement over the western North Pacific (WNP). Based on observation and wave analysis, it is found that the track deflection of Fitow is attributed to the transition of environmental flow from meridional to zonal orientation, which is closely associated with a low-frequency intra-seasonal oscillation (ISO). Furthermore, the impact of ISO on tropical cyclone (TC) unusual movement is investigated using the Advanced Research version of Weather Research and Forecasting (WRF-ARW) model. The control simulation (CTL) reproduces well the synoptic pattern and track deflection of the TC. The TC moves straightly westward and northwestward without track deflection in the sensitivity experiments with the removal of total ISOs and the west-propagating ISO component, while keeping the recurving track with the removal of east-propagating ISO, which suggests that the west-propagating ISO plays a dominant role in the westward track deflection. In the experiment of removing west-propagating ISOs, an anomalous southeast–northwest-oriented wave train around the TC is modified, the mid-latitude trough decays, and the enhanced zonally elongated subtropical high is responsible for the straight northwestward motion of the TC. However, after removing a weaker convection anomaly associated with east-propagating ISOs in the form of a southwest–northeast oriented dipole circulation, the TC is affected by a sustained shallow mid-latitude trough and a west-extended ridge of subtropical high to keep the cyclonic track turning analogous to the counterpart in CTL. The piecewise potential vorticity inversion diagnosis further assesses the contribution of the different ISO components to TC track deflection.

On the causes of tropical cyclone driven floods in India

Landfalling tropical cyclones (TCs) cause severe damage to infrastructure and economic losses in India. Extreme rainfall from TCs can lead to flooding, which disrupts the lives of people and their socio-economic well-being. Despite the severe hazards caused by TCs in India, their impacts on river basin-scale flooding are not well examined. Here, we use cyclone tracks from India Meteorological Department (IMD) and ERA5-Land reanalysis to identify the drivers of TC induced floods in India. We select TCs that affected the Subarnarekha, Brahmani, Mahanadi, and Vamshadhara river basins during the 1981–2019 period. We estimated return periods of extreme daily and hourly rainfall and total runoff associated with TCs. We examined the role of antecedent soil moisture on floods caused by TCs. Our results show that the distribution of extreme rainfall over a basin from TCs depend on the translation speed, track length, and size of the TC. Severe flooding in the basins is strongly linked to antecedent soil moisture conditions. Regardless of extreme rainfall, flooding due to TCs during the pre-monsoon (April–May) is less likely because of dry antecedent soil moisture conditions. On the other hand, TCs can lead to severe flooding during the summer monsoon (June–September) period due to relatively wetter antecedent conditions. In the post-monsoon season (October–November), the severity of flooding caused by TCs is higher than the pre-monsoon season, and as we move further into the post-monsoon this severity reduces. Our findings highlight the need to monitor the land-surface characteristics and TC track prediction to identify the potential of flooding from landfalling TCs.

Comparative analyses of the heavy rainfall associated with landfalling tropical cyclones SOULIK (1307) and MARIA (1808) with similar routes

Different rainfall evolutions associated with landfalling tropical cyclones (TCs) SOULIK (1307) and MARIA (1808) are analyzed before and after they made landfalls in Fujian province based on reanalysis data of the ERA5 and CMORPH data. The two TCs shared similar tracks, motion speed as well as landing points, but showed different asymmetric features of rainfall intensities and locations relative to routes. The results primarily exhibited the discrepancy of the inland rainfall distributions between the two TCs and the related influence factors. The constant torrential rain over land with the maximum on the left of the storm track associated with SOULIK was attributed to favorable dynamical conditions. The northeasterly outflow of SOULIK was reinforced by the easterly wave in upper-level, which induced strong divergence along with the distinct ascending motions by suction effect. The strong ambient VWS broke the warm core structure of SOULIK, which pointed to south-southwest direction relevant to the strong northeasterly wind at 200 hPa, and further caused a more asymmetric rainfall pattern on the left of TC track. Besides, a cold intrusion was observed south of SOULIK in mid-levels, enhancing convective instability coincident with the alternating circumstance of cold and warm advections from the lower layer to the upper layer over south of SOULIK, which further intensify the precipitation on the left of TC track. Simultaneously, the meridional moisture convergence owing to the enhanced southerly wind at lower level played a key role in the maintenance of severe rainfall south of SOULIK. Comparatively, the 48-h accumulated rainfall over land associated with MARIA was much weaker than that associated with SOULIK, and the maximum is on the northern side. The strong convergence in the lower level induced by the predominant easterly winds interacting with coastal terrains, as well as the distinct divergence coincident with the upper level jet in the southern part of the South Asia High (SAH), provide favorable dynamical conditions for the maximum rainfall north of MARIA. Simultaneously, the strong westerlies over the South China Sea, together with the eastern cyclonic system over the Northwest Pacific, create favorable moisture conditions for the rainfall on the northern side of MARIA. Results presented in this study provides an improved predictive understanding of physical mechanism dictating storm rainfall evolution from landfalling TCs, especially those with similar tracks

The Decadal Variation of Eastward‐Moving Tropical Cyclones in the South China Sea During 1980–2020

AbstractThe track of tropical cyclones (TCs) formed in the South China Sea (SCS) can be divided into eastward and westward directions. Significant decadal variation during 1980–2020 only exists in the number of eastward‐moving TCs, especially during July–September, with 47% TCs moving eastward during 1994–2004 (Period II), 22% during 1980–1993 (Period I) and only 15% during 2005–2020 (Period III). This decadal change is related to the zonal shift of Western Pacific Subtropical High (WPSH). An eastward‐retreated WPSH during 1994–2004 leads to upward motion and westerly flow anomaly over the northern SCS, and therefore favors TC genesis and eastward motion. The eastward‐retreated WPSH is associated with a warm sea surface temperature anomaly over the tropical western‐central Pacific which induces a cyclonic flow and weakens the WPSH. With the weaker modulation of WPSH, stronger intraseasonal oscillation (ISO) in the SCS during Period II favors eastward‐moving TCs due to the westerly flow associated with the ISO.

Tropical Cyclone Impact and Forest Resilience in the Southwestern Pacific

Tropical cyclones (TCs) can have profound effects on the dynamics of forest vegetation that need to be better understood. Here, we analysed changes in forest vegetation induced by TCs using the normalized difference vegetation index (NDVI). We used an accurate historical database of TC tracks and intensities, together with the Willoughby cyclone model to reconstruct the 2D surface wind speed structure of TCs and analyse how TCs affect forest vegetation. We used segmented linear models to identify significant breakpoints in the relationship between the reconstructed maximum sustained wind speed (Wmax) and the observed changes in NDVI. We tested the hypothesis that the rate of change in damage caused by TCs to forest and recovery time would increase according to Wmax thresholds as defined in the widely used Saffir–Simpson hurricane wind scale (SSHWS). We showed that the most significant breakpoint was located at 50 m/s. This breakpoint corresponds to the transition between categories 2 and 3 TCs in the SSHWS. Below this breakpoint, damages caused to forest vegetation and the time needed to recover from these damages were negligable. We found a second breakpoint, with a sharp increase in damages for winds >75 m/s. This suggested that extremely intense tropical cyclones, which might be more frequent in the future, can cause extreme damages to forest vegetation. Nevertheless, we found high variation in the observed damages and time needed to recover for a given Wmax. Further studies are needed to integrate other factors that might affect the exposure and resistance to TCs as well as forests’ capacity to recover from these disturbances.