Tropical Cyclone Intensity Forecasts Research Articles

The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.

Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models

Tropical cyclone (TC) “Fani” (April 26–May 04, 2019) over the Bay of Bengal (BoB) is the first extreme severe cyclonic storm (ESCS) of the season that struck Puri coast with a maximum sustained wind speed of 110 knots. In the past 130 years, TC Fani is the first of its kind with the peculiarity of being persistent with the intensity of severe cyclonic storm (SCS) over land for more than 24 h, thus causing substantial casualties in the major cities of Odisha. Two high-resolution mesoscale modeling systems, (1) state-of-the-art advanced research version of weather research and forecasting (WRF-ARW) model with a single fixed domain (9 km horizontal resolution) and (2) TC specific hurricane WRF (HWRF) with three domains (27/9/3 km horizontal resolution) are used for real-time prediction of Fani in quasi-operational setup. Storms surge prediction is also carried out using IIT Delhi dynamical storm surge model. Both the models, WRF and HWRF, were able to predict the system from its genesis stage (5 days in advance of landfall) with reasonable accuracy in position and strength. The tracks from both models are in good agreement with the observed track. The average track errors for WRF and HWRF are 101 km and 85 km, respectively. The landfall time and position are well predicted, with 60 h of lead time. HWRF model performance in predicting landfall time, position, and intensity is significant. Along with the statistical analysis, structural and vertical analysis of wind, enthalpy flux, and thermal distribution are carried out by considering two represented initial conditions from both WRF and HWRF models. Maximum reflectivity and precipitable water diagnostic studies depict that both the models show asymmetricity at the peak intensity of ESCS. The rainfall during the landfall day from both the models is comparable with the gauge-satellite merged observed rainfall datasets. Along with TC track, intensity, and structural forecasts associated with storm surge at different vulnerable coastal cities are attempted to predict with reasonable accuracy.

Satellite-Based Observations of Nonlinear Relationships between Vertical Wind Shear and Intensity Changes during the Life Cycle of Hurricane Joaquin (2015)

Abstract A CIMSS vertical wind shear (VWS-C) dataset based on reprocessed GOES-East atmospheric motion vectors (AMVs) at 15-min intervals has a −0.36 correlation with the CIMSS Satellite Consensus (SATCON) intensity changes at 30-min intervals over the life cycle of Hurricane Joaquin (2015). Correlations are then calculated for four intensity change events including two rapid intensifications (RIs) and two decays, and four intensity change segments immediately before or after these events. During the first RI, the peak intensity increase of 16 kt (6 h)−1 (1 kt ≈ 0.51 m s−1) follows a small VWS-C decrease to a moderate 8 m s−1 value (negative correlation). A 30-h period of continued RI following the first peak RI occurred under moderate magnitude VWS-C (negative correlation), but with a rotation of the VWS-C direction to become more aligned with the southwestward heading of Joaquin. During the second RI, the peak intensity increase of 15 kt (6 h)−1 leads the rapid VWS-C increase (positive correlation), which the horizontal plots of VWS-C vectors demonstrate is related to an upper-tropospheric cyclone to the northeast of Joaquin. A conceptual model of ocean cooling within the anticyclonic track loop is proposed to explain a counterintuitive decreasing intensity when the VWS-C was also decreasing (positive correlation) during the Joaquin track reversal. These alternating negative and positive correlations during the four events and four segments of intensity change demonstrate the nonlinear relationships between the VWS-C and intensity changes during the life cycle of Joaquin that must be understood, analyzed, and modeled to improve tropical cyclone intensity forecasts, and especially RI events.

A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF

The present study uses the nonlinear singular vector (NFSV) approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting (WRF) model for tropical cyclone (TC) intensity forecasts. For nine selected TC cases, the NFSV-tendency perturbations of the WRF model, including components of potential temperature and/or moisture, are calculated when TC intensities are forecasted with a 24-hour lead time, and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts. The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time, and their dominant energies concentrate in the middle layers of the atmosphere. Moreover, such structures do not depend on TC intensities and subsequent development of the TC. The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs, makes larger contributions to the TC intensity forecast uncertainty. Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model, even if using a WRF with coarse resolution.

Short-Term Tropical Cyclone Intensity Forecasting from Satellite Imagery Based on the Deviation Angle Variance Technique

Abstract The deviation angle variance (DAV) is a parameter that characterizes the level of organization of a cloud cluster compared with a perfectly axisymmetric tropical cyclone (TC) using satellite infrared (IR) imagery, and can be used to estimate the intensity of the TC. In this study, the DAV technique is further used to analyze the relationship between satellite imagery and TC future intensity over the North Atlantic basin. The results show that the DAV of the TC changes ahead of the TC intensity change, and this can be used to predict short-term TC intensity. The DAV-IR 24-h forecast is close to the National Hurricane Center (NHC) 24-h forecast, and the bias is lower than NHC and other methods during weakening periods. Furthermore, an improved TC intensity forecast is obtained by incorporating all four satellite bands. Using SST and TC latitude as the other two predictors in a linear regression model, the RMSE and MAE of the DAV 24-h forecast are 13.7 and 10.9 kt (1 kt ≈ 0.51 m s−1), respectively, and the skill space of the DAV is about 5.5% relative to the Statistical Hurricane Intensity Forecast model with inland decay (Decay-SHIFOR) during TC weakening periods. Considering the DAV is an independent intensity technique, it could potentially add value as a member of the suite of operational intensity forecast techniques, especially during TC weakening periods.

The Uncertainty of Tropical Cyclone Intensity and Structure Based on Different Parameterization Schemes of Planetary Boundary Layer

Based on different parameterization schemes of planetary boundary layer (PBL), the uncertainty of intensity and structure of the Super-strong Typhoon Rammasun (1409) is investigated using the WRF model (v3.4) with six PBL parameterization schemes. Results indicate that PBL uncertainty leads to the uncertainty in tropical cyclone (TC) prediction, which increases with forecast time. The uncertainty in TC prediction is mainly reflected in the uncertainty in TC intensity, with significant differences in the TC intensity forecasts using various PBL schemes. The uncertainty in TC prediction is also reflected in the uncertainty in TC structures. Greater intensity is accompanied by smaller vortex width, tighter vortex structure, stronger wind in the near-surface layer and middle and lower troposphere, stronger inflow (outflow) wind at the lower (upper) levels, stronger vertical upward wind, smaller thickness of the eye wall, smaller outward extension of the eye wall, and warmer warm core at the upper levels of eye. PBL height, surface upward heat flux and water vapor flux are important factors that cause the uncertainty in TC intensity and structure. The more surface upward heat flux and water vapor flux and the lower PBL height, the faster TC development and the stronger TC intensity.

WMO Typhoon Landfall Forecast Demonstration Project (WMO-TLFDP) Progress and Future Plans

ABSTRACT The “WMO Typhoon Landfall Forecast Demonstration Project (TLFDP)” was started in May 2010 in conjunction with the start of the Shanghai World Expo 2010. The project was successful in terms of assisting the local forecasters in providing an efficient tropical cyclone (TC) forecast service for World Expo 2010, demonstrating the performance of the most up-to-date techniques in TC forecasting, and enhancing the ability of forecasters to effectively use products based on advanced TC forecasting techniques. The first phase was completed in December 2012 and the second phase was completed in December 2015. The third phase (TLFDP-III) started in 2016. During its first (2010-2012) and second (2013-2015) phases, TLFDP collected real-time TC forecast products from 15 Typhoon Forecast Product Providers (TFPPs). The products include deterministic track and intensity forecasts, ensemble track and intensity forecasts, deterministic wind radii forecasts, wind probability forecasts, and gridded model outputs. The products were disseminated through the project’s website and the operational website of the Shanghai Typhoon Warning Center. TLFDP has made significant progress in TC forecast verification, including setting up the tools for both realtime and post-season TC forecast verification, developing and integrating several new verification techniques, carrying out a survey on the operational status of TC forecast verification in the western North Pacific region, and contributing to the WMO document “Verification of tropical cyclone forecasts”. The post-season forecast verification was reported to the Session of the UNESCAP/WMO Typhoon Committee since 2013. New consensus methods for TC track and intensity forecasts were also proposed either based on Ensemble Prediction Systems (EPSs) products or multiple deterministic NWP model products. Two training workshops on TC forecasting were organized by the TLFDP and held in Shanghai, China, in May 2010 and June 2012, respectively, and seven research fellowship projects were implemented as jointly supported by the UNESCAP/WMO Typhoon Committee, Shanghai Typhoon Institute and Hong Kong Observatory. In its third phase (TLFDP-III, 2016-2018), the project will continue its effort on demonstration and evaluation of the newly developed forecasting techniques for TCs, and developing and documenting advanced guidance for the verification of TC forecasts, with special attention to TC intensity and precipitation.

Relationship between sea surface temperature and the maximum intensity of tropical cyclones affecting Vietnam's coastline

AbstractEstimating an upper bound for tropical cyclone (TC) intensity in a given environment is important for TC intensity forecasts and risk management. While theoretical limits for the TC maximum potential intensity have been well examined in previous studies, the actual maximum intensity ( Vmax) that a TC attains in real atmospheric conditions varies from basin to basin and is subject to much more uncertainties. This study examines different empirical relationships between sea surface temperature (SST) and Vmax in the South China Sea that affect Vietnam's coastal region. Using different SST datasets and the TC intensity record from 1982 to 2016, it is shown that Vmax in this area increases slower than the typical linear or exponential function form found in previous studies. Such different dependence of Vmax on SST in the South China Sea is due to the narrow range of SST variation in this region, which is between 24 and 30°C as compared to the range of 15 and 30°C used in previous studies. An alternative functional form based on the natural logarithm of SST is then proposed, which captures better the characteristics of the actual TC maximum intensity in Vietnam's coastal region. Examination of the variability of Vmax also indicates an uptrend of the TC maximum intensity over last 30 years in the South China Sea, which has a potentially large effect on Vietnam's coastal region. This increase of Vmax is consistent with the warmer trend of the SST as projected in the Fifth Assessment by the Intergovernmental Panel on Climate Change (IPCC).

Application of Vertical Grid-nesting to the Tropical Cyclone Track and Intensity Forecast

The impact of vertical grid-nesting on the tropical cyclone intensity and track forecast was investigated using the Weather Research and Forecast (WRF) version 3.8 and the initialization method of the Structure Adjustable Balanced Bogus Vortex (SABV). For a better resolution in the central part of the numerical domain, where the tropical cyclone of interest is located, a horizontal and vertical nesting technique was employed. Simulations of the tropical cyclone Sanba (16th in 2012) indicated that the vertical nesting had a weak impact on the cyclone intensity and little impact on the track forecast. Further experiments revealed that the performance of forecast was quite sensitive to the horizontal resolution, which is in agreement with previous studies. The improvement is due to the fact that horizontal resolution can improve forecasts not only on the tropical cyclone-scale but also for large-scale disturbances.

Recent Advances in Research and Forecasting of Tropical Cyclone Track, Intensity, and Structure at Landfall

This review prepared for the fourth International Workshop on Tropical Cyclone Landfall Processes (IWTCLP-4) summarizes the most recent (2015-2017) theoretical and practical knowledge in the field of tropical cyclone (TC) track, intensity, and structure rapid changes at or near landfall. Although the focus of IWTCLP-IV was on landfall, this summary necessarily embraces the characteristics of storms during their course over the ocean prior to and leading up to landfall. In the past few years, extremely valuable observational datasets have been collected using both aircraft reconnaissance and new geostationary and low-earth orbiting satellites at high temporal and spatial resolution for TC forecasting guidance and research studies. Track deflections for systems near complex topography such as that of Taiwan and La Reunion have been further investigated, and advanced numerical models with high spatial resolution necessary to predict the interaction of the TC circulation with steep island topography have been developed. An analog technique has been designed to meet the need for longer range landfall intensity forecast guidance that will provide more time for emergency preparedness. Probabilistic track and intensity forecasts were also developed to better communicate on forecast uncertainty. Operational practices of several TC forecast centers are described herein and some challenges regarding forecasts and warnings for TCs making landfall are identified. This review concludes with insights from both researchers and forecasters regarding future directions to improve predictions of TC track, intensity, and structure at landfall.

Assimilation of Data Derived from Optimal-member Products of TREPS for Convection-Permitting TC Forecasting over Southern China

To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014–2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae.

Comparison of Performance of Various Multiple-model Ensemble Techniques in Forecasting Intensity of Tropical Cyclone

ABSTRACT the objective of this study was to explore an optimal multiple-model ensemble technique to aid the forecasting of tropical cyclone (TC) intensity. The maximum winds of TCs as forecast by the models of the european Centre for Medium-Range Weather Forecasts, the Japan Meteorological Agency and the national Centers for environmental Prediction for the period from July 2010 to October 2011 were studied. Performance of various multiple-model ensemble techniques, including equally weighted ensemble, weighted ensemble based on initial forecast error, weighted ensemble based on 12-hour forecast error, bias-corrected equally weighted ensemble and bias-corrected weighted ensemble based on initial forecast error, was verified against the TC intensities post-analysed by the hong Kong Observatory. Results showed that the equally weighted ensemble technique generally outperformed the best of the individual models and other multiple-model ensemble techniques. The mean absolute errors of the equally weighted ensemble technique were the lowest at 12, 24 and 36-hour forecasts, and the error spreads were generally the smallest from 12 to 72-hour forecasts.

A Multi-Model Consensus Forecast Technique for Tropical Cyclone Intensity Based on Model Output Calibration

ABSTRACT We analyzed the errors associated with forecasts of tropical cyclone (TC) intensity from 2010-2012 in the western North Pacific region made by seven operational numerical weather prediction models. The results show that the forecast error is significantly related to the initial error as well as the initial TC intensity, size, and translation speed. Other factors highly related to the forecast error include the environmental sea surface pressure, vertical wind shear and maximum potential intensity. We used stepwise regression to set up model forecast error estimation equations, which were used to calibrate the model output. Independent experiments showed that the calibrated model forecasts have significant skill compared to the original model output. Finally, a multimodel consensus forecast technique for TC intensity was developed based on the calibrated model output; this technique has 28% (15-20%) skill at 12 h (24-72 h) compared to the climatology and persistence forecasts of TC intensity. This consensus technique has greater skill than the consensus forecast based on the original model output and therefore it has the potential to be applied in operation.

Promises in Air‐Sea Fully Coupled Data Assimilation for Future Hurricane Prediction

AbstractThe recent article by Li and Toumi (2018, https://doi.org/10.1029/2018GL079677) published in Geophysical Research Letters explored the potential for improving tropical cyclone intensity forecasts by assimilating synthetic coastal surface currents from high‐frequency radar observations. Although it is an idealized study using simulated observations, this may signal the beginning of a new frontier in future hurricane prediction through ingesting in situ and remotely sensed observations of oceanic currents into fully coupled systems. Assimilation of oceanic observations can improve not only the state estimation of both oceanic and atmospheric variables but it also has the potential to better estimate uncertain model physics' parameters such as the air‐sea exchange coefficients.

Impact of Spectral Nudging on Real‐Time Tropical Cyclone Forecast

AbstractRegional models often face a chronic problem caused by inconsistencies between simulated model fields and large‐scale forcing data, which could limit predictability of regional models. Spectral nudging is a technique which can ameliorate this issue by providing large‐scale forcing in the regional model interior. In this study, spectral nudging was applied to tropical cyclone (TC) track and intensity forecasts, and its effects were investigated using a high‐resolution regional model. Case studies on Typhoon Neoguri and Vongfong showed that spectral nudging was effective at enhancing track forecast by improving large‐scale winds and the western North Pacific subtropical high. However, simulated TC intensity was underestimated without the optimization of spectral nudging. Sensitivity tests on spectral nudging options were performed to improve TC intensity while maintaining forecast track improvements. By reducing the nudging coefficient, intensity errors were reduced with a more reasonably developed TC structure. A total of 51 experiments for 18 TCs were conducted with revised spectral nudging options to investigate the general effects of spectral nudging on TC forecasting. The track forecast was generally improved by spectral nudging, especially after 96 hr, and forecast intensity was also improved as compared to experiments without nudging. Furthermore, spectral nudging was especially effective for TCs which occurred to the east of the western North Pacific and turned near Northeast Asia. Therefore, the impact of spectral nudging can be increased by selectively applying spectral nudging based on TC characteristics.

Improving Multi-Model Ensemble Forecasts of Tropical Cyclone Intensity Using Bayesian Model Averaging

This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (Pmin). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of Pmin was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting.

Improved Tropical Cyclone Intensity Forecasts by Assimilating Coastal Surface Currents in an Idealized Study

AbstractHigh‐frequency (HF) radars can provide high‐resolution and frequent ocean surface currents observations during tropical cyclone (TC) landfall. We describe the first assimilation of such potential observations using idealized twin experiments with and without these observations. The data assimilation system consists of the Ensemble Adjustment Kalman Filter and a coupled ocean‐atmosphere model. In this system, synthetic HF radar‐observed coastal currents are assimilated, and the 24‐, 48‐ and 72‐hr forecast performances are examined for TCs with various intensities, sizes, and translation speeds. Assimilating coastal surface currents improves the intensity forecast. The errors of the maximum wind speed reduce by 2.7 (33%) and 1.9 m/s (60%) in the 72‐hr forecast and 2.8 (40%) and 1.4 m/s (62%) in the 48‐hr forecast, for Category 4 and 2 cyclones, respectively. These improvements are similar to the current operational TC forecast errors, so that assimilating HF radar observations could be a substantial benefit.

Spontaneous Emission of Spiral Inertia–Gravity Waves and Formation of Elliptical Eyewalls in Tropical Cyclone–Like Vortices: Three-Dimensional Nonlinear Simulations

Abstract Although intense tropical cyclones (TCs) are considered to be axisymmetric vortices, observations reveal that they are often highly asymmetric. Better understanding of the underlying asymmetric dynamics is a critical step toward advancing TC intensity forecasting. In this paper, we revisit the mechanisms behind one of the most frequent asymmetric patterns: the deformation of the core into an elliptical shape. Previously, elliptical eyewalls were primarily thought to be an outcome of barotropic instability, a mechanism that involves the coupling and mutual growth of counterpropagating vortex Rossby (VR) waves. These results were largely based on simplified numerical models that filter out inertia–gravity (IG) waves. Consideration of IG waves introduces the possibility of an additional instability mechanism, one that involves a VR wave that spontaneously emits a spiral IG wave into the environment. We provide evidence that elliptical eyewalls, which may form within a three-dimensional primitive-equation nonlinear model that supports both instability types, can solely originate by the mechanism of spontaneous radiative imbalance. These evidences are supported by a number of nonlinear simulations, supplemental linear eigenmode analysis, and a linear simulation. The potential role of a multimechanistic instability is also briefly addressed.

Dynamics and Predictability of the Rapid Intensification of Super Typhoon Usagi (2013)

AbstractThis study explores the dynamics and predictability of the rapid intensification (RI) of Super Typhoon Usagi (2013) through a 60‐member convection‐permitting ensemble using the Weather Research and Forecasting (WRF) model and an ensemble Kalman filter (EnKF) data assimilation method. The surface maximum wind speed of Usagi, which was an intense category 4 western North Pacific tropical cyclone (TC), increased by 33 m s−1 over a 24‐hr period. The RI process was captured by the WRF simulation initialized with the global analysis but with a unique forecast challenge of early prediction. We improved the intensity forecasts by assimilating satellite‐derived atmospheric motion vectors into the WRF‐EnKF, which primarily reduced the strength of both the primary and secondary circulations in the TC vortex. Nevertheless, our ensemble forecasts initialized with the EnKF analysis ensemble predicted a significant spread in the intensity with considerable differences in the RI onset timing among individual members. Our analyses show that variation in the RI timing is most sensitive to differences in the initial TC vortex intensity and inner‐core moisture. Ensemble members with similar initial intensities but greater tropospheric moisture content exhibited earlier vortex axisymmetrization and consequently earlier RI. Further sensitivity experiments showed that variations in the inner‐core moisture content have an immediate impact on the structure and strength of inner‐core convection. These variations in inner‐core convection gradually caused differences in intensity between the TC vortices. In this study, we highlight the importance of accurate estimates of the inner‐core moisture content in the modeling and forecasting of TC intensity.