North Pacific Tropical Cyclone Research Articles

Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

Abstract This study details a two-method, machine learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), “D-MINT” and “D-PRINT.” D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-h lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, north Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally used RI guidances, but less skillful for yes–no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions. Significance Statement This study develops a method to predict current and short-term forecasts of tropical cyclone (TC) intensity using artificial intelligence. The resultant models use a convolutional neural network (CNN) that can identify two-dimensional features in satellite imagery that are indicative of TC intensity and future intensity change. The performance results indicate that in several TC basins, the CNN approach is generally more skillful than alternative satellite-based estimates of TC intensity as well as operational short-term forecasts of deterministic intensity change and of similar skill to probabilistic rapid intensification forecasts.

A Deep-Learning Reconstruction of Tropical Cyclone Size Metrics 1981–2017: Examining Trends

Abstract Observed climate records of length, homogeneity, and reliability are the basis of climatological studies on tropical cyclones (TCs). However, such data are scarce for TC size in terms of wind field, particularly over the western North Pacific (WNP). This study demonstrates that deep learning can practically bridge this data gap when applied to satellite data. Using transfer learning, deep learning algorithms were developed to estimate reliable TC sizes from infrared imagery for the WNP TCs. The algorithms were then applied to a homogeneous satellite database to reconstruct a new historical dataset of TC sizes, named DeepTCSize, which covers 37 years (1981–2017) over the WNP. DeepTCSize includes multiple TC size quantities, such as wind radii of 17, 26, and 33 m s−1 and maximum winds (i.e., R17, R26, R33, and RMW), which have high correlations (R = 0.85, 0.84, 0.79, and 0.76, respectively) with postseason quality-controlled best track data. Comparisons with ocean wind observations were made and this further revealed that DeepTCSize has good quality and is free from spurious error trends, providing an advantage over the historical “best estimates” of TC sizes currently available in the best track archives for the WNP. The new reconstructed TC sizes dataset for the WNP TCs shows significant expanding trends in the annual-mean outer circulations (at a rate of 2% decade−1 for R17 and a rate of 2% decade−1 for R26), which are mainly associated with weaker storms, as well as a weak contracting trend in the annual-mean inner-core size (RMW). Significance Statement Tropical cyclone (TC) size largely controls the TC-induced hazard and risk. If the size of TC can be determined more efficiently in observations spanning a long-enough period, the climatology and changes in TC can be better modeled and understood. This study applies deep learning methods to reconstruct a new dataset of multiple inner- to outer-core TC size metrics from infrared imagery of satellites for the western North Pacific TCs. The dataset spans 37 years. It is homogenous and has comparable accuracy with the existing “best estimates.” Using the dataset, a significant expanding trend was identified in the outer-core size, while the inner-core size exhibits a weak contracting trend. The dataset can be employed in several applications.

Recent increase in the potential threat of western North Pacific tropical cyclones

Conventionally, the threat of tropical cyclones (TCs) is often described by TC intensity. However, the damage caused by TCs is also strongly related to our forecasting ability, which is usually low for TCs with high intensification rates. Here, we challenge this intensity-only criterion and propose a concept of TC potential threat (PT) for the western North Pacific TCs by jointly clustering the TC lifetime maximum intensity and intensification rate. We show that TCs can be separated via an objective algorithm, and approximately 10% of all TCs pose a great PT and feature high forecast errors. Furthermore, the annual number of TCs with high PT has increased by 22% per decade over the past 41 years, and this trend is attributed to the rise in subsurface ocean temperatures. Our study provides a perspective on the TC threat and reveals its increase due to global warming and internal climate variability.

Predicting Rapid Intensification in North Atlantic and Eastern North Pacific Tropical Cyclones Using a Convolutional Neural Network

Abstract This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature. Significance Statement The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.

Quantifying tropical cyclone intensity change induced by sea surface temperature

AbstractThe relationship between sea surface temperature (SST) and tropical cyclone (TC) intensity change exhibits a strong dependence on the current TC intensity. Using western North Pacific TC observations from 1982 to 2018, a threshold SST (TSST) is identified as the SST required to maintain TC intensity. TSST increases with TC intensity, with TCs intensifying and weakening when SST is higher and lower than TSST, respectively. Across the dataset, mean TC intensity change is proportional to the difference between SST and TSST. This study also formulates an equation to quantify TC intensity change using SST and current TC intensity, which replicates 99.46% of the mean observed TC intensity changes. This equation could serve as an alternative to the linear regression‐based relationship between SST and TC intensity change that is widely used in statistical‐dynamical intensity models, thereby improving intensity forecasts.

Association between tropospheric temperature and tropical cyclone peak intensity over the North Pacific and North Atlantic

As natural weather disasters, tropical cyclones (TCs) are destructive in proportion to their peak intensity. This study investigates the association of North Atlantic, western North Pacific, and eastern North Pacific TC peak intensities with tropospheric air temperature, respectively, by applying NCEP–NCAR and MERRA reanalysis data. Both the correlation between TC peak intensity and air temperature and the difference in air temperature between strong and weak TC peak intensity conditions reveal that significant cooling of the tropopause and upper-tropospheric warming are accompanied by strengthening TC peak intensity for North Atlantic TCs, suggesting an important effect of upper-tropospheric static stability on TC peak intensity. However, warming in the lower troposphere is associated with strong TC peak intensity for eastern North Pacific TCs, indicating a major effect of lower-tropospheric static stability on TC peak intensity. The peak intensity of western North Pacific TCs is mainly affected by vertical wind shear, not the atmospheric temperature.摘要台风作为一种灾害性天气, 其破坏性大小与自身强度有很大的关系. 因此, 本项研究利用NCEP–NCAR和MERRA再分析数据, 考查了北大西洋, 西北太平洋, 东北太平洋台风强度峰值与对流层温度的关系. 台风强度峰值与大气温度的相关系数, 以及极大和极小台风强度峰值下大气温度的差值, 共同显示: 北大西洋台风强度峰值受到对流层顶低温和对流高层增温的正向作用, 表明了对流高层稳定性对北大西洋台风强度峰值的重要作用. 但是, 东北太平洋台风强度峰值却受到对流低层增温的正向作用, 表明了对流低层稳定性对东北太平洋台风强度峰值的重要作用. 西北太平洋台风强度峰值较少受大气温度影响, 主要受到垂直风切变的作用.

Relationship of the Southeast Asian summer monsoon and Mascarene High to the tropical cyclone activity in the western North Pacific

AbstractIn the present study, a highly positive correlation between the western North Pacific tropical cyclone (TC) genesis frequency and the Southeast Asian summer monsoon (SASM) was found during the June–August period. In order to examine the cause of the positive correlation between the two variables, the 14 years with the highest SASM index (positive SASM years) and the 14 years with the lowest SASM index (negative SASM years) were selected from a total of 39 years (1977–2015) and the mean difference between the two periods was analysed. In the positive SASM years, more TCs were generated in most of the western North Pacific region and tended to move in a northerly direction from the far northeastern sea of the Philippines towards Korea via the East China Sea. In the negative SASM years, TCs moved westward from the eastern sea of the Philippine to the southern coast of China and the Indochinese Peninsula via the South China Sea. A similar result is found in further comparisons in which years with strong El Niño‐Southern Oscillations are removed. Anomalous westerlies from the Mascarene High moved eastward, thereby forming anomalous cyclonic circulations in the western North Pacific. Thus, the Mascarene High played a decisive role in strengthening anomalous cyclonic circulations in the western North Pacific. This finding was verified by examining various environmental factors that affected TC genesis and the correlation analyses between the 850‐hPa geopotential height and the western North Pacific TC genesis frequency during the June–August period in addition to an analysis of the velocity potential and divergent winds.

Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS: evaluation for 2015–2018 seasons

The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running realtime since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015–2018, and compared against the NCEP GEFS. TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.

Tropical cyclone damages in Mainland China over 2005–2016: losses analysis and implications

This study analyzed the annual variation and provincial distribution of the number of landfalling tropical cyclones (TCs) and the associated losses in respect of direct economic losses, collapsed buildings, casualties, evacuated population, affected population, and the affected agricultural area in mainland China during 2005–2016. The numbers of western North Pacific TCs and landfall TCs were 24 and 7.5, respectively. The annual mean losses of TC disasters included 36.7 million affected people, 69.5 billion Yuan direct economic losses, and 254 deaths. For an average landfalling TC, the numbers were 4.9 million people, 9.3 billion Yuan, and 33.9 deaths, respectively. Most of the damages were caused by the small numbers of destructive TCs, and the top 10 TCs contributed to 48% of direct economic losses, 71% of deaths, and 66% of building damages. Among the provinces affected by TC disasters, Zhejiang, Guangdong, and Fujian took the majority of the losses. Nevertheless, the casualties per landfalling TC were highest in Hunan (63.3 deaths), while mortalities (the rate of casualties to the evacuated population) in Henan (200.0 per 105 persons) and Yunnan (116.7 per 105 persons) were significantly higher than the other provinces (below 30 per 105 persons), indicating more population needed to be evacuated in future TC disasters in these provinces. The larger the number of landfalling TCs in a year or higher the wind force scale of a landfalling TC did not necessary lead to larger losses. However, stronger rainfall and/or a northeast-recurving track played a role in increasing the TC disaster losses.

High-Resolution Seeded Simulations of Western North Pacific Ocean Tropical Cyclones in Two Future Extreme Climates

ABSTRACTA variant of downscaling is devised to explore the properties of tropical cyclones (TCs) that originate in the open ocean of the western North Pacific Ocean (WestPac) region under extreme climates. This variant applies a seeding strategy in large-scale environments simulated by phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate-model integrations together with embedded integrations of Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), an operational, high-resolution, nonhydrostatic, convection-permitting numerical weather prediction (NWP) model. Test periods for the present day and late twenty-first century are sampled from two different integrations for the representative concentration pathway (RCP) 8.5 forcing scenario. Then seeded simulations for the present-day period are contrasted with similar seeded simulations for the future period. Reinforcing other downscaling studies, the seeding results suggest that the future environments are notably more conducive to high-intensity TC activity in the WestPac. Specifically, the future simulations yield considerably more TCs that exceed 96-kt (1 kt ≈ 0.5144 m s−1) intensity, and these TCs exhibit notably greater average life cycle maximum intensity and tend to spend more time above the 96-kt intensity threshold. Also, the future simulations yield more TCs that make landfall at >64-kt intensity, and the average landfall intensity of these storms is appreciably greater. These findings are supported by statistical bootstrap analysis as well as by a supplemental sensitivity analysis. Accounting for COAMPS-TC intensity forecast bias using a quantile-matching approach, the seeded simulations suggest that the potential maximum western North Pacific TC intensities in the future extreme climate may be approximately 190 kt.

The impact of climate model sea surface temperature biases on tropical cyclone simulations

Sea surface temperature (SST) patterns both local to and remote from tropical cyclone (TC) development regions are important drivers of the variability of TC activity. Therefore, reliable simulations and predictions of TC activity depend on a realistic representation of tropical SST. Nevertheless, severe SST biases are common to the current generation of global climate models, especially in the tropical Pacific and Atlantic. These biases are strongly positive in the southeastern tropical basins, and negative, but weaker, in the northwestern tropical basins. To investigate the impact of the tropical SST biases on simulated TC activity, an atmospheric-only tropical channel model was used to conduct several sets of ensemble simulations. The simulations suggest an underrepresentation in Atlantic TC activity caused by the Atlantic cold bias alone, and an overrepresentation in Eastern North Pacific (ENP) TC activity due to the Atlantic cold bias and Pacific warm bias jointly. While the local impact of SST biases on TC activity is generally induced by the local anomalous SST and the associated changes in atmospheric conditions, the remote impact of the Atlantic bias on the ENP TCs is strongly driven by the change in topographically forced regional circulation. Moreover, an eastward shift in Western North Pacific TCs was generated by the Pacific SST biases, even though basin-wide TC activity indicators change insignificantly. The results indicate the importance of considering SST bias effects on simulated TC activity in climate model studies and highlight key regions where reducing SST biases could potentially improve TC representation in climate models.

Effects of Low-Level Flow Orientation and Vertical Shear on the Structure and Intensity of Tropical Cyclones

Abstract This article explores the simultaneous effect of vertical wind shear (VWS) and low-level mean flow (LMF) on tropical cyclone (TC) structure evolution. The structural evolution of 180 western North Pacific TCs from 2002 to 2014 was measured by a new parameter, the RV ratio, which is defined as the ratio of a TC’s radius of 34-kt (17.5 m s−1) wind to its maximum wind speed at the ending point of the intensification period. Whereas TCs with RV ratios in the lowest quartile of all 180 samples favored intensification over expansion, and 82% of these TCs experienced rapid intensification, TCs with RV ratios in the topmost quartile favored size expansion over intensification. A novel result of this study is that TC RV ratios were found to correlate with the LMF orientation relative to the deep-layer VWS vector. Specifically, whereas an LMF directed toward the left-of-shear orientation favors TC intensification, a right-of-shear LMF favors TC size expansion. This study further analyzed the TC rainfall asymmetry and asymmetric surface flow using satellite observations. Results show that for a TC affected by an LMF with right-of-shear orientation, the positive surface flux anomaly in the upshear outer region promotes convection in the downshear rainband region. On the other hand, a left-of-shear LMF induces a positive surface flux anomaly in the downshear outer region, thus promoting convection in the upshear inner core. Enhancement of the symmetric inner-core convection favors intensification, whereas enhancement of the downshear rainband favors expansion.

The Influence of ENSO Flavors on Western North Pacific Tropical Cyclone Activity

El Niño–Southern Oscillation (ENSO) is a major source of seasonal western North Pacific (WNP) tropical cyclone (TC) predictability. However, the spatial characteristics of ENSO have changed in recent decades, from warming more typically in the eastern equatorial Pacific during canonical or cold tongue El Niño to warming more typically in the central equatorial Pacific during noncanonical or warm pool El Niño. We investigated the response in basinwide WNP TC activity and spatial clustering of TC tracks to the location and magnitude of El Niño using observations, TC-permitting tropical channel model simulations, and a TC track clustering methodology. We found that simulated western North Pacific TC activity, including accumulated cyclone energy (ACE) and the number of typhoons and intense typhoons, is more effectively enhanced by sea surface temperature warming of the central, compared to the eastern, equatorial Pacific. El Niño also considerably influenced simulated TC tracks regionally, with a decrease in TCs that were generated near the Asian continent and an increase in clusters that were dominated by TC genesis in the southeastern WNP. This response corresponds with the spatial pattern of reduced vertical wind shear and is most effectively driven by central Pacific SST warming. Finally, internal atmospheric variability generated a substantial range in the simulated season total ACE (±25% of the median). However, extremely active WNP seasons were linked with El Niño, rather than internal atmospheric variability, in both observations and climate model simulations.

Concurrent Changes to Hadley Circulation and the Meridional Distribution of Tropical Cyclones

AbstractPoleward trends in seasonal-mean latitudes of tropical cyclones (TCs) have been identified in direct observations from 1980 to the present. Paleoclimate reconstructions also indicate poleward–equatorward migrations over centennial–millennial time scales. Hadley circulation (HC) is often both implicitly and explicitly invoked to provide dynamical linkages to these shifts, although no direct analysis of concurrent changes in the recent period has been presented. Here, the observational TC record (1981–2016) and ERA-Interim, JRA-55, and MERRA-2 are studied to examine potential relationships between the two. A zonally asymmetric HC is defined by employing Helmholtz theory for vector decomposition, and this permits the derivation of novel HC diagnostics local to TC basins.Coherent variations in both long-term linear trends and detrended interannual variability are found. TC genesis and lifetime maximum intensity latitudes share trend sign and magnitude with shifts in local HC extent, with rates being approximately 0.25° ± 0.1° lat decade−1. Both these life cycle stages in hemispheric means and all Pacific TC basins, as well as poleward-extreme North Atlantic lysis latitudes, shared approximately 35% of their interannual variability with HC extent. Local HC intensity is linked only to eastern North Pacific TC latitudes, where strong local overturning corresponds to equatorward TC shifts. Examination of potential dynamical linkages implicates La Niña–like sea surface temperature gradients to poleward HC termini. This corresponds to increased tropical and reduced subtropical vertical wind shear everywhere except in the North Atlantic and western North Pacific, where the opposite is true. These results quantify a long-hypothesized link between TCs and the large-scale oceanic–atmospheric state.

Response of the North Pacific Tropical Cyclone Climatology to Global Warming: Application of Dynamical Downscaling to CMIP5 Models

Abstract A downscaling approach is applied to future projection simulations from four CMIP5 global climate models to investigate the response of the tropical cyclone (TC) climatology over the North Pacific basin to global warming. Under the influence of the anthropogenic rise in greenhouse gases, TC-track density, power dissipation, and TC genesis exhibit robust increasing trends over the North Pacific, especially over the central subtropical Pacific region. The increase in North Pacific TCs is primarily manifested as increases in the intense and relatively weak TCs. Examination of storm duration also reveals that TCs over the North Pacific have longer lifetimes under global warming. Through a genesis potential index, the mechanistic contributions of various physical climate factors to the simulated change in TC genesis are explored. More frequent TC genesis under global warming is mostly attributable to the smaller vertical wind shear and greater potential intensity (primarily due to higher sea surface temperature). In contrast, the effect of the saturation deficit of the free troposphere tends to suppress TC genesis, and the change in large-scale vorticity plays a negligible role.

A New Satellite-Based Indicator for Estimation of the Western North Pacific Tropical Cyclone Current Intensity

A technique to estimate tropical cyclone (TC) current intensity based on geostationary satellite infrared window (IRW) and water vapor (WV) imagery is explored in this paper. First, to combine the advantages of the IRW imagery and the WV minus IRW (WV-IRW) imagery, a WV-IRW-to-IRW ratio (WIRa)-based indicator is proposed. This indicator not only can display the inner-core convection's symmetrization level and vigor but also is able to screen out thin cirrus, stratospheric WV anomaly, and overshooting tops from average deep convection. It is highly correlated with the best track minimum sea-level pressure and thus used to estimate the western North Pacific TC current intensity. Detailed analyses have demonstrated that the WIRa-based indicator can further improve the estimation of TC current intensity alongside the existing algorithms. The WIRa-based indicator is designed based on the hypothesis that “overshooting top is more useful for forecasting than initial estimation,” and the satisfying results of the WIRa-based method perhaps provide indirect evidence to support this hypothesis in turn.

New Possibilities for Geophysical Parameter Retrievals Opened by GCOM-W1 AMSR2

A new approach to retrieve sea surface wind speed (SWS) in tropical cyclones (TCs) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data is presented. Analysis of all six AMSR2 C- and X-band channel measurements over TCs is shown to efficiently help to separate the rain contribution. Corrected measurements at 6.9 and 10.65 GHz are then used to retrieve the SWS. Spatial and temporal collocation of AMSR2 and tropical rain measurement mission (TRMM) microwave instrument (TMI) data is then further used to empirically relate TMI rain rate (RR) product to RR estimates from AMSR2 in hurricanes. SWS estimates are validated with measurements from the stepped frequency microwave radiometer (SFMR). As further tested, more than 100 North Atlantic and North Pacific TCs are analyzed for the 2012–2014 period. Despite few particular cases, most SWS fields are in a very good agreement with TC center data on maximum wind speeds, radii of storm, and hurricane winds. As also compared, very high consistency between AMSR2 and L-band SMOS wind speed estimates are obtained, especially for the super typhoon Haiyan, to prove the high potential of AMSR2 measurements in TCs.

A 40-Year Climatology of Extratropical Transition in the Eastern North Pacific

Abstract A 42-yr study of eastern North Pacific tropical cyclones (TCs) undergoing extratropical transition (ET) is presented using the Japanese 55-yr Reanalysis dataset. By using cyclone phase space (CPS) to differentiate those TCs that undergo ET from those that do not, it is found that only 9% of eastern North Pacific TCs that developed from 1971 to 2012 complete ET, compared with 40% in the North Atlantic. Using a combination of CPS, empirical orthogonal function (EOF) analysis, and composite analysis, it is found that the evolution of ET in this basin differs from that observed in the North Atlantic and western North Pacific, possibly as a result of the rapidly decreasing sea surface temperatures north of the main genesis region. The presence of a strong, deep subtropical ridge extending westward from North America into the eastern North Pacific is a major factor inhibiting ET in this basin. Similar to other basins, eastern North Pacific ET generally occurs in conjunction with an approaching midlatitude trough, which helps to weaken the ridge and allow northward passage of the TC. The frequency of ET appears to increase during developing El Niño events but is not significantly affected by the Pacific decadal oscillation.

On tropical cyclone size and intensity changes associated with two types of long-lasting rainbands in monsoonal environments

Tropical cyclones (TCs) in a monsoonal environment may have heavy rain events separate from the eyewall rainfall. Two types of long-lasting rainbands in western North Pacific TCs interacting with the East Asia summer monsoon during 1999–2009 are identified and the effects of these rainbands on TC size and intensity changes are examined. For all of the south-type Outer Mesoscale Convective Systems as defined in our previous study, the TC intensification rate is decreased but the rate of size change is not modified. Long-lasting south-type Enhanced Rainbands (ERBs) that develop between 100 and 300 km radii and move cyclonically are associated with significant TC size increases. Seventy percent of very large typhoons had an ERB during the period when they intensified from tropical storms to typhoons.

An Objective Satellite-Based Tropical Cyclone Size Climatology

AbstractStorm-centered infrared (IR) imagery of tropical cyclones (TCs) is related to the 850-hPa mean tangential wind at a radius of 500 km (V500) calculated from 6-hourly global numerical analyses for North Atlantic and eastern North Pacific TCs for 1995–2011. V500 estimates are scaled using the climatological vortex decay rate beyond 500 km to estimate the radius of 5 kt (1 kt = 0.514 m s−1) winds (R5) or TC size. A much larger historical record of TC-centered IR imagery (1978–2011) is then used to estimate TC sizes and form a global TC size climatology. The basin-specific distributions of TC size reveal that, among other things, the eastern North Pacific TC basins have the smallest while western North Pacific have the largest TC size distributions. The life cycle of TC sizes with respect to maximum intensity shows that TC growth characteristics are different among the individual TC basins, with the North Atlantic composites showing continued growth after maximum intensity. Small TCs are generally located at lower latitudes, westward steering, and preferred in seasons when environmental low-level vorticity is suppressed. Large TCs are generally located at higher latitudes, poleward steering, and preferred in enhanced low-level vorticity environments. Postmaximum intensity growth of TCs occurs in regions associated with enhanced baroclinicity and TC recurvature, while those that do not grow much are associated with west movement, erratic storm tracks, and landfall at or near the time of maximum intensity. With respect to climate change, no significant long-term trends are found in the dataset of TC size.