Pacific Tropical Cyclones Research Articles

The 2023 Eastern Pacific Hurricane Season: Otis Causes Major Destruction in Acapulco

The 2023 Eastern Pacific Hurricane Season: Otis Causes Major Destruction in Acapulco

Short-Term Intensity Prediction of Tropical Cyclones Based on Multi-Source Data Fusion with Adaptive Weight Learning

Tropical cyclones (TCs) can cause significant economic damage and loss of life in coastal areas. Therefore, TC prediction has become a crucial topic in current research. In recent years, TC track prediction has progressed considerably, and intensity prediction remains a challenge due to the complex mechanism of TC structure. In this study, we propose a model for short-term intensity prediction based on adaptive weight learning (AWL-Net) for the evolution of the TC’s structure as well as intensity changes, exploring the multidimensional fusion of features including TC morphology, structure, and scale. Furthermore, in addition to using satellite imageries, we construct a dataset that can more comprehensively explore the degree of TC cloud organization and structure evolution. Considering the information difference between multi-source data, a multi-branch structure is constructed and adaptive weight learning (AWL) is designed. In addition, according to the three-dimensional dynamic features of TC, 3D Convolutional Gated Recurrent (3D ConvGRU) is used to achieve feature enhancement, and then 3D Convolutional Neural Network (CNN) is used to capture and learn TC temporal and spatial features. Experiments on a sample of northwest Pacific TCs and official agency TC intensity prediction records are used to validate the effectiveness of our proposed model, and the results show that our model is able to focus well on the spatial and temporal features associated with TC intensity changes, with a root mean square error (RMSE) of 10.62 kt for the TC 24 h intensity forecast.

Influence of Eastern Pacific Hurricanes on the Southwest US Wildfire Environment

AbstractWhile some previous studies examined the contribution of Eastern Pacific (EP) hurricanes toward precipitation in the arid Southwest US (SWUS), their potential to influence wildfires in that region has not been explored. Here we show, using observations and simulations from the Energy Exascale Earth System Model (E3SM), that recurving EP hurricanes modulate the wildfire environment in the SWUS by increasing precipitation and soil moisture, and reducing the vapor pressure deficit. This is especially the case during late season months of September–October when the likelihood of storms to recurve and make landfall increases. Further, analysis of burnt area observations reveals that for the months of September–October, recurving EP hurricanes may significantly reduce the prevalence of wildfires in the SWUS. Finally, E3SM simulations indicate that late season EP hurricanes have been on the decline, with important implications for wildfires in the SWUS.

Development of Interpretable Probability Ellipse in Tropical Cyclone Track Forecasts Using Multiple Operational Ensemble Prediction Systems

AbstractMost tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions.

Tropical cyclone intensity forecasting using model knowledge guided deep learning model

This paper developed a deep learning (DL) model for forecasting tropical cyclone (TC) intensity in the Northwest Pacific. A dataset containing 20 533 synchronized and collocated samples was assembled, which included ERA5 reanalysis data as well as satellite infrared (IR) imagery, covering the period from 1979 to 2021. The u-, v- and w-components of wind, sea surface temperature, IR satellite imagery, and historical TC information were selected as the model inputs. Then, a TC-intensity-forecast-fusion (TCIF-fusion) model was developed, in which two special branches were designed to learn multi-factor information to forecast 24 h TC intensity. Finally, heatmaps capturing the model’s insights are generated and applied to the original input data, creating an enhanced input set that results in more accurate forecasting. Employing this refined input, the heatmaps (model knowledge) were used to guide TCIF-fusion model modeling, and the model-knowledge-guided TCIF-fusion model achieved a 24 h forecast error of 3.56 m s−1 for Northwest Pacific TCs spanning 2020–2021. The results show that the performance of our method is significantly better than the official subjective prediction and advanced DL methods in forecasting TC intensity by 4% to 22%. Additionally, compared to operational approaches, model-guided knowledge methods can better forecast the intensity of landfalling TCs.

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.

Stronger Tropical Cyclone–Induced Ocean Cooling in Near-Coastal Regions Compared to the Open Ocean

Abstract Tropical cyclones (TC) often induce strong mixing in the upper ocean that generates a trail of cooler sea surface temperature (Twake) in their wakes. The Twake can affect TC intensity, so its prediction is important, especially in coastal regions where TCs can make landfall. Coastal Twakes are often more complex than those in the open ocean due to the influences of coastline geometry, highly variable water depth, continental runoff, and shelf processes. Using observational data since 2002, here we show a significantly stronger global mean Twake in coastal regions compared to offshore regions. Temperature stratification is the main driver of stronger coastal Twakes in the North Atlantic and east Pacific. In the northwest Pacific and north Indian Ocean, the differences between coastal and offshore Twakes are smaller due to compensation between TC forcings and ocean stratification. The north Indian Ocean is unique in the Northern Hemisphere because salinity stratification plays a major role on the spatial distribution of Twake. In the South Pacific Ocean, TC intensity and translation speed are crucial for explaining coastal–offshore Twake differences, while ocean stratification and mixed layer depth are more important for the coastal–offshore Twake differences in the south Indian Ocean. These findings suggest that coastal–offshore differences in ocean stratification need to be properly represented in models in order to capture changes in TC-induced ocean cooling as storms approach landfall. Significance Statement Landfalling tropical cyclones (TCs) often cause considerable damage in coastal regions with dense human populations. Understanding TC–ocean interaction and how it differs between coastal and offshore regions can help predict TC intensity prior to landfall. Sea surface cooling after TC passage is an important proxy for TC–ocean interaction. A global evaluation of coastal TC-induced cooling has not been conducted. Using data covering two decades, we show significantly stronger TC-induced surface cooling in coastal regions compared to offshore regions at the global scale and in all basins except the northwest Pacific and north Indian Ocean. The difference is driven mainly by upper-ocean conditions in the North Atlantic, east Pacific, and south Indian Ocean, and by TC characteristics in the South Pacific.

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.

Interannual Variability in the Boreal Summer Intraseasonal Oscillation Modulates the Meridional Migration of Western North Pacific Tropical Cyclone Genesis

Abstract Prior studies have emphasized the influence of the boreal summer intraseasonal oscillation (BSISO) on basin-scale and global-scale tropical cyclones (TCs). An improved understanding of the BSISO’s impact on TCs at various climate time scales will likely lead to improved subseasonal-to-seasonal prediction. This study explores the impact of BSISO interannual variability on western North Pacific (WNP) TCs. We find that interannual meridional variability in the BSISO modulates the meridional migration of WNP TC genesis and is related to changes in BSISO phase structure. These structural changes are characterized by occurrence frequency changes for individual BSISO phases between the equatorial region over the eastern Indian Ocean (EIO)–Maritime Continent (MC) and the subtropical region over the South China Sea (SCS)–western Pacific. This interannual north–south change of the BSISO appears to be associated with changes in sea surface temperature over the MC region and WNP mid-to-low-tropospheric moisture advection, mainly via remote forcing of a Gill-type Rossby response to BSISO convection and a large meridional asymmetry of the low-level background moisture distribution. During years with increased BSISO residence time over the subtropical SCS–western Pacific, more TCs occur over the northern WNP due to TC-favorable BSISO-associated convection and circulation. In contrast, more TCs occur over the southern WNP in years with increased BSISO convection residence time over the equatorial EIO–MC. We find that the increase in frequency of BSISO phase occurrence over the subtropical SCS–western Pacific since the late 1990s has potentially contributed to a recent poleward shift of WNP TC genesis.

The 2022 Eastern Pacific Hurricane Season An Above Average Season with Five Landfalls

Click to increase image sizeClick to decrease image size AcknowledgementsThe cyclone summaries are based on Tropical Cyclone Reports prepared by NHC hurricane specialists Robbie Berg, Jack Beven, Eric Blake, Daniel Brown, Lisa Bucci, John Cangialosi, Andrew Latto, Philippe Papin, Richard Pasch, and Brad Reinhart. These reports are available on the Internet at: https://www.nhc.noaa.gov/data/tcr/index.php?season=2022&basin=epac.Additional informationNotes on contributorsEric BlakeERIC S. BLAKE is a senior hurricane specialist at NOAA's National Hurricane Center (NHC) in Miami, Florida

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.

Contrasting Responses of Atlantic and Pacific Tropical Cyclone Activity to Atlantic Multidecadal Variability

AbstractThis research assesses the influences of Atlantic Multidecadal Variability (AMV) on global tropical cyclones (TCs) using two large ensembles of idealized global climate model simulations with opposite signs of AMV forcings superimposed (i.e., AMV+ and AMV–). We first detect TCs and then compare TC activity by basin in the two AMV experiments. We find contrasting responses of Atlantic and Pacific TC frequency to the AMV anomalies. Compared to AMV–, AMV+ significantly increases TC frequency in the North Atlantic, including those making landfalls. The increase is explained by warmer sea surface temperature, higher relative humidity, increased relative vorticity, and weaker vertical wind shear under AMV+. By contrast, AMV+ decreases TC occurrence over the western North Pacific and South Pacific, which is tied to stronger vertical wind shear and lower relative humidity. The opposite responses of TC activity to AMV+ are attributed to strengthened Walker Circulation between the Atlantic and Pacific.

Effects of the Topography of Sumatra on Tropical Cyclone Formation over the Indian Ocean

One-quarter of the world’s tropical cyclones (TCs) occur in the Indian Ocean (IO) basin.The mechanisms for TC initiation in the IO are varied, but one recently discovered process involves the flow around the steep topography of Sumatra. When the low-level flow impinges on Sumatra, it is blocked and the flow splits under typical environmental stratification. As a result, wake vortices commonly develop at northern and southern island tips of the island. For the case of easterly flow, these circulationssubsequently move downstream over the IO. The wake vortices emanating from the island tips are counter-rotating, but since Sumatra straddles the equator, the circulations are cyclonic in both hemispheres and thus have the potential for TC development. Using data from2.5yearsof observations from DYNAMO and YOTC, it is found that approximately 25% of the TCsthat occurred overIO basin during that periodwere initiated by Sumatra-induced wake vortices.Additional analysis of vortex statistics for the period 2008-17 has found that vortex counts are highest near Madden-Julian Oscillation (MJO) phase 1 when low-level easterlies are strongest across southern Sumatra. A secondary peak in vortex formation occurs during MJO phase 4 when low-level westerlies exist near the equator west of Sumatra. The latter finding suggests that MJO-related, low-level westerly surges on the equator impinging on Sumatracontribute to an increase in wake vortex development. Numerical simulations have shown that circulations farther upstream such aswestern Pacific remnant TCs and the Borneo vortex can influence the development of Sumatra wake vortices and their growth into TCs over the IO.

Phase Shifts of the PDO and AMO Alter the Translation Distance of Global Tropical Cyclones

AbstractRecent decadal changes in tropical cyclone (TC) frequency since the mid‐1990s have been widely reported; however, it is unclear whether there have also been any changes in TC translation distance. Here, we show that long‐term decrease in global TC translation distance during 1975–2020 is caused by an abrupt change point around the year 1997. This change point marks a switch between an increasing translation distance during 1975–1997 and decreasing translation distance during 1998–2020. The shift in TC translation distance is attributed to changes in the distance between the location of TC genesis and land, and the percentage of landfalling TCs to all TCs, which is driven by the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) phase switch in the mid‐1990s. In the last 20 years, the cool, La Niña‐like sea surface temperatures (SST) during the PDO negative phase and the warm SST pattern during the AMO positive phase have enhanced the genesis potential index and the potential intensity in offshore areas, resulting in greater TC genesis landward. Phase shifts of PDO and AMO modulate environmental conditions, regulating TC genesis location and landfall frequency, and their combined effects on the translation distance of Pacific TCs. The warm SST anomalies during the AMO positive phase enhance these circulation patterns in two possible ways: via the Indian Ocean and the subtropical eastern Pacific relaying effects at a multidecadal timescale. Our findings suggest that the PDO and AMO act as key pacemakers for decadal changes in global TC translation distance.

Extreme South Pacific Phytoplankton Blooms Induced by Tropical Cyclones

AbstractWind‐driven mixing and Ekman pumping driven by slow‐moving tropical cyclones (TCs) can bring nutrients to the euphotic zone, promoting phytoplankton blooms (TC‐PBs) observable by satellite remote sensing. We examine an exceptional (z‐score = 18–48) TC‐PB induced by category‐1 Cyclone Oma near the South Pacific island of Vanuatu in February 2019, the most extreme event in the observed satellite record of South Pacific surface Chlorophyll‐a (Chl‐a). Examining all 156 South Pacific TC since 1997, we identify a “hover” parameter derivable from storm track data correlated with post‐TC surface Chl‐a (r = 0.84). Using a data set of synthetic storm tracks, we show revisit times for South Pacific TC‐PBs are O(250) years and O(1,500) years for Oma‐scale TC‐PBs. The episodic, extreme, but consistent nature of such events means they may imprint on sediment records. If so, we show their signature could be used to reconstruct past TC variability assuming near‐stationarity of TC statistics.

Mechanisms of tropical cyclone response under climate change in the community earth system model

Climate change induces a myriad of effects which influences the global tropical cyclone (TC) genesis frequency. Here we explore how North Atlantic and Western Pacific TCs are affected under climate change using a present-day and a future (1% pCO2 scenario) ensemble of high resolution simulations. We find that the number of TCs decreases (-45%) in the North Atlantic but increases (+15%) in the Western Pacific. Part of these opposing variations are linked to differences in the ocean’s meridional overturning circulation, which gives rise to a different sea surface temperature response and air-sea fluxes between the two basins. The results show the important role of oceanic climate change on TC response.

A deep learning ensemble approach for predicting tropical cyclone rapid intensification

AbstractPredicting rapid intensification (RI) of tropical cyclones (TCs) is critical in operational forecasting. Statistical schemes rely on human‐driven feature extraction and predictor correlation to predict TC intensities. Deep learning provides an opportunity to further improve the prediction if data, including satellite images of TC convection and conventional environmental predictors, can be properly integrated by deep neural networks. This study shows that deep learning yields enhanced intensity and RI prediction performance by simultaneously handling the human‐defined environmental/TC‐related parameters and information extracted from satellite images. From operational and practical perspectives, we use an ensemble of 20 deep‐learning models with different neural network designs and input combinations to predict intensity distributions at +24 h. With the intensity distribution based on the ensemble forecast, forecasters can easily predict a deterministic intensity value demanded in operations and be aware of the chance of RI and the prediction uncertainty. Compared with the operational forecasts provided for western Pacific TCs, the results of the deep learning ensemble achieve higher RI detection probabilities and lower false‐alarm rates.

Probability of induced extreme precipitation events in Central America due to tropical cyclone positions in the surrounding oceans

The preparedness of national and local authorities for extreme hydrometeorological events could alleviate the impacts in many socioeconomical sectors. A statistical tool for the prediction or assessment of extreme precipitation probabilities caused by the presence of Tropical Cyclones (TCs) in the surrounding oceans of Central America is presented. The model is based in fitting precipitation probability distributions associated with the location of the TCs. The probabilities of medium, high, and very high levels of extreme rain and associated with the observed precipitation of the 60, 75, and 90 percentiles, are displayed in a map which can be used (with other tools) to issue alerts by emergency and response authorities. Impacts related to TCs can be classified in direct or indirect. In the case when the TCs are located in the Caribbean/Atlantic basin, there is a critical configuration near the Gulf of Honduras that drives both high probabilities of direct (in the northern countries) and indirect (in the southern countries) extreme precipitation. In the Eastern Tropical Pacific TC locations, probabilities of indirect impacts are usually lower than for the Caribbean/Atlantic. This is related to the usual trajectories in this former basin, that move away from the continent. Both, in Caribbean/Atlantic and Eastern Tropical Pacific’s TCs, the probabilities of indirect effects usually are higher in the Pacific slope of the isthmus than in the Caribbean. Here we present one tool that can be used with others analyses by emergency officials to determine the locations where alerts of extreme weather must be issued to prevent human life’s lost.

Promoting seasonal prediction capability of the early autumn tropical cyclone formation frequency over the western North Pacific: effect of Arctic sea ice

Seasonal prediction of tropical cyclone (TC) activity has been a hot research theme in the past decades. Usually, the tropical sea surface temperatures (SSTs) provide considerable predictability sources for the western North Pacific (WNP) TC activity. Here, we emphasized that the Chukchi-Beaufort (C-B) and Greenland (GL) sea ice variability is closely linked to the year-to-year variations of the early autumn WNP TC formation frequency (TCF). Observational and numerical evidence proved that the excessive C-B and GL sea ice sustains from August to the following early autumn and triggers the southeastward propagation of the Rossby wave trains originating from the Arctic across Western Eurasia (Okhotsk Sea) to the WNP. The resultant anomalous low pressure over WNP provides suitable environmental conditions for TC formation―the enhancement of the lower-level relative vorticity and water moisture, and the decrease of vertical wind shear. For the reduced sea ice, an opposite situation tends to emerge. The persistent combined sea ice signal makes it a physically meaningful precursor for TCF prediction. The cross-validated hindcast and independent forecast based on both the tropical SST and the Arctic sea ice precursors present that the TCF index is predicted with much higher correlation coefficients than those of the empirical models with only the tropical SST predictors. The results demonstrate that the Arctic sea ice truly promotes the seasonal prediction capability of the WNP TCF.

Why Do Eastern North Pacific Hurricanes Intensify More and Faster than Their Western-Counterpart Typhoons with Less Ocean Energy?

Abstract Tropical cyclones operate as heat engines, deriving energy from the thermodynamic disequilibrium between ocean surfaces and atmosphere. Available energy for the cyclones comes primarily from upper-ocean heat content. Here, we show that eastern North Pacific hurricanes reach a given intensity 15% faster on average than western North Pacific typhoons despite having half the available ocean heat content. Eastern North Pacific hurricanes also intensify on average 16% more with a given ocean energy (i.e., air–sea enthalpy flux) than western North Pacific typhoons. As efficient intensifiers, eastern Pacific hurricanes remain small during their intensification period, tend to stay at lower latitudes, and are affected by relatively lower vertical wind shear, a colder troposphere, and a drier boundary layer. Despite a shallower warm upper-ocean layer in the eastern North Pacific, average hurricane-induced sea surface cooling there is only slightly larger than in the western North Pacific due to the opposing influences of stronger density stratification, smaller size, and related wave-interaction effects. In contrast, western North Pacific typhoons encounter a more favorable oceanic environment for development, but several factors cause typhoons to greatly increase their size during intensification, resulting in a slow and inefficient intensification process. These findings on tropical cyclones’ basin-dependent characteristics contribute toward a better understanding of TC intensification.