Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques

BackgroundFluctuation in sex hormones and the occurrence of sleep disturbance are 2 major health challenges among midlife women. However, the temporal relationship between them remains unclear.MethodsThis study included 2488 females (mean ± SD age, 49.0 ± 2.70 years) with an average follow-up of 6.95 years. We constructed a composite score by summing items related to sleep problems to reflect the comprehensive sleep status of the participants in the Study of Women’s Health Across the Nation. Cross-lagged path analysis was used to examine the temporal relationship between sex hormones and sleep status. Sensitivity analyses were conducted in nonoverweight and overweight groups and adjusted for vasomotor symptoms in the main model.AimIn this study, we aimed to examine the temporal relationship between sex hormones and sleep status in midlife women using cross-lagged path analysis.OutcomesThe primary outcomes included results of the cross-lagged path analysis between sex hormones and sleep status.ResultsAfter adjusting for age, race, income, menopausal status, body mass index, hormone therapy use, smoking, and drinking, the cross-lagged path coefficients from baseline follicle-stimulating hormone (FSH) and estradiol (E2) to follow-up sleep status were 0.054 (P = .017) and –0.054 (P = .016), respectively. The path coefficient from baseline sleep to follow-up dehydroepiandrosterone sulfate (DHAS) was 0.042 (P = .017). The path coefficients between testosterone and sleep were not statistically significant. In the nonoverweight group, the patterns of the temporal relationship between sex hormones and sleep were the same as the total sample, and the point estimates were larger. However, the temporal relationships in the overweight group were nonsignificant. After adjustment for vasomotor symptoms in the main model, results were basically consistent.Clinical ImplicationsGiven the temporal relationship between sex hormones and sleep, our findings will provide scientific perspectives to benefit health management in the transition of menopause.Strengths and LimitationsThis study used a longitudinal theoretical model to distinguish the temporal relationship between sex hormones and sleep status in midlife women. Limitations include limited causal evidence in observational studies, unknown confounders, and careful extrapolation.ConclusionThere were distinct patterns in the unidirectional temporal relationship between (1) FSH, E2, and DHAS and (2) sleep. Changes in FSH and E2 occurred earlier than the change of sleep, while the change of DHAS was later. In contrast, there was no temporal relationship between testosterone and sleep.

In recent years, an increase in the frequency and intensity of cyclones with tropical characteristics has been observed in areas of the Mediterranean Sea (De la Vara et al., 2021). The rise in this type of cyclones has triggered extreme weather conditions in many Mediterranean coastal regions (Romero & Emanuel, 2013). With the increasing focus on the study and characterisation of these types of cyclones (Gutiérrez-Fernández et al., 2024), numerous research efforts have employed different methodologies to investigate the favourable conditions for the development of cyclones with tropical characteristics in the Mediterranean (Romero & Emanuel, 2017). Among the methods used to study the favourable conditions for the development of tropical cyclones, one of the most widely applied is the Potential Intensity (PI; Holland, 1997). This index measures the theoretical maximum intensity a cyclone with tropical characteristics can achieve under specific atmospheric conditions. This method has been investigated in numerous studies on tropical cyclones and demonstrates that high values of this index are associated with a higher likelihood of intense tropical cyclone development (Emanuel, 2005; Wing & Camargo, 2007).Therefore, the main motivation for this study is to analyse the evolution of the Potential Intensity (PI) over the past 70 years in the Mediterranean region. For this purpose, ERA5 reanalysis data (Hersbach et al., 2023) covering the period from 1950 to 2023 over the Mediterranean basin were used. The results indicate an increase in the values of this index, particularly during the autumn months, when most cyclones with stronger tropical characteristics occur. Finally, an individual analysis of this index was conducted under pre-development conditions for recent cyclones with tropical characteristics in the Mediterranean.de la Vara, A., Gutiérrez‐Fernández, J., González‐Alemán, J. J., & Gaertner, M. A. (2021). Characterization of medicanes with a minimal number of geopotential levels. International Journal of Climatology, 41(5), 3300-3316.Emanuel, K. (2005). Genesis and maintenance of" Mediterranean hurricanes". Advances in Geosciences, 2, 217-220.Gutiérrez‐Fernández, J., Miglietta, M. M., González‐Alemán, J. J., & Gaertner, M. A. (2024). A new refinement of Mediterranean tropical‐like cyclones characteristics. Geophysical Research Letters, 51(8), e2023GL106429.Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.bd0915c6 (Accessed on 29/10/2024)Holland, G. J. (1997). The maximum potential intensity of tropical cyclones. Journal of the atmospheric sciences, 54(21), 2519-2541.Romero, R., & Emanuel, K. (2013). Medicane risk in a changing climate. Journal of Geophysical Research: Atmospheres, 118(12), 5992-6001.Romero, R., & Emanuel, K. (2017). Climate change and hurricane-like extratropical cyclones: Projections for North Atlantic polar lows and medicanes based on CMIP5 models. Journal of Climate, 30(1), 279-299Wing, A. A., Sobel, A. H., & Camargo, S. J. (2007). Relationship between the potential and actual intensities of tropical cyclones on interannual time scales. Geophysical research letters, 34(8).

share a focus on annual or multi-decadal mean SST estimates averaged over large ocean areas, such as the NAO Main Development Region (MDR), also referred to as the "Atlantic cyclogenic region," in which most hurricanes develop (here assumed to denote the region within 6° to 18° N latitude by 20° to 60° W longitude). Relatively elevated MDR SST during 1995 was hypothesized to increase the frequency of hurricane formation by transferring a relatively greater amount of heat to the usual number of easterly waves of wind (typically ~60) that enter the MDR each year between May and November after leaving the west coast of Africa, each acting to stimulate shallow cumulus convection that may evolve into cyclonic behavior (Saunders & Harris, 1997).The SST hypothesis has extended this frequency-specific observation, based on NAO data gathered in a single year, to pertain generally to cyclone intensity.However, the physical basis for this hypothesized extension involving local SST effects is dubious, in view of the great distances typically traversed by these storms.Over the last century, local NAO SST has varied in a large and predictable way along individual hurricane tracks, which typically extend many thousands of kilometers (km) from their points of origin (Fig. 1).The magnitude of this variation is sufficient to allow historical hurricane-activity records to serve as "natural experiments" that can be used to perform a direct test of the SST hypothesis, by examining the historical correlation between estimated hurricane intensities, and corresponding estimated historical changes in local SST (Smith & Reynolds, 2003, 2004), along individual hurricane tracks.To perform this test, we examined paired differences (Δv max ) in estimated local hurricane intensity v max , and corresponding differences (ΔSST) in local (i.e., year-, month-, latitude-, and longitudespecific) SST, over various non-overlapping intervals Δt = t i+1t i over time t for adjacent positions (indexed by i) along each hurricane track.Over all such intervals that occurred prior to attainment of each peak hurricane intensity, Max(v max ), the causal SST hypothesis requires that any detectable correlation between Δv max and ΔSST be positive.Below we describe methods and historical data on individual hurricane tracks that we used to implement this direct test of the SST hypothesis, and results we obtained that indicate that variation in SST is not causally linked to variation in hurricane intensities.If SST variations do not explain the historically observed pattern of hurricane intensities, what other factor(s) might explain this pattern?Statistical and trend analysis for hurricane activity traditionally has focused on annual frequency, landfall likelihood, storm duration, storm intensity (maximum sustained storm-specific wind speed v max ), or Saffir-Simpsonscale wind-intensity category (Fisher,

A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI andAquaAMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

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

Several key issues in the simple time-dependent theories of tropical cyclone (TC) intensification developed in recent years remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature (SST) under the eyewall and the definition of environmental conditions, such as the boundary layer enthalpy in TC environment and the TC outflow-layer temperature. In this study, some refinements to the most recent time-dependent theory of TC intensification have been accomplished to resolve those issues. The first is the construction of a functional relationship between the surface pressure under the eyewall and the TC intensity, which is derived using the cyclostrophic wind balance and calibrated using full-physics axisymmetric model simulations. The second is the definition of TC environment that explicitly includes the air–sea temperature difference. The third is the TC outflow-layer temperature parameterized as a linear function of SST based on global reanalysis data. With these refinements, the updated time-dependent theory becomes self-contained and can give both the intensity-dependent TC intensification rate (IR) and the maximum potential intensity (MPI) under given environmental thermodynamic conditions. It is shown that the pressure dependence of saturation enthalpy at SST can lead to an increase in the TC MPI and IR by about half of that induced by dissipative heating due to surface friction. Results also show that both MPI and IR increase with increasing SST, surface enthalpy exchange coefficient, environmental air–sea temperature difference, and decreasing environmental boundary layer relative humidity, but the maximum IR is insensitive to surface drag coefficient. Significance Statement A new advancement in the recent decade is the development of simple time-dependent theories of tropical cyclone (TC) intensification, which can provide quantitative understanding of TC intensity change. However, several key issues in these simple time-dependent theories remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature under the eyewall and the definition of environmental conditions. These are resolved in this study with several refinements, which make the most recent time-dependent theory of TC intensification self-contained and practical.

India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) intensity forecast valid for next 24 h over the north Indian Ocean (NIO) in 2003 and extended up to 72 h in 2009. In this study, an attempt is made to evaluate the TC intensity forecast issued by IMD during 2005–2011 (7 years) by calculating the absolute error (AE), root mean square error (RMSE) and skill in intensity forecast in terms of maximum sustained surface wind (MSW). The accuracy of TC intensity forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea and NIO as whole), season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm and severe cyclonic storm or higher intensities) and type of track of TCs (climatological/straight moving and recurving/looping type). The study shows that the average AE (RMSE) in intensity forecast is about 11(14), 14(19) and 20(26) knots, respectively, for 24-, 48- and 72-h forecasts over the NIO as a whole during 2009–2011. The skill of intensity forecast is about 44 %(48 %), 60 %(58 %) and 60 %(65 %) for 24-, 48- and 72-h forecasts during 2009–2011 with respect to AE (RMSE). There is no significant improvement in terms of reduction in AE and RMSE of MSW forecast over the NIO like that over the northwest Pacific and northern Atlantic Oceans during 2005–2011. However, the skill in intensity forecast compared to persistence method has significantly improved by about 6 %(10 %) and 9 %(8 %) per year, respectively, for 12- and 24-h forecasts considering the AE (RMSE) during 2005–2011. There is also significant increasing trend in percentage of 24-h intensity forecasts with error of 10 knots or less during 2005–2011.

In this paper, a regression neural network pGCNN-TC is designed for estimating tropical cyclone (TC) intensity using satellite images, which is an essential step for TC forecasting. The proposed model employs features derived from convective cores near TC centers, group-equivariant convolution layers that learn rotation equivariant representations from data, and an adjusted MSE loss function to alleviate the underestimation tendency on high intensity TCs. We evaluate our proposed model on a benchmark dataset TCIR, where TCs during 20152016 are used as testing cases. Our proposed model outperforms all other start-of-the-art models including NN-based models and objective operational techniques in overall performance. Case studies also suggest that convective core features are especially helpful for high intensity TCs when eye structures are not clear in satellite images.* This work was supported by the National Key Research and Development Program of China (2022YFC3004102) and Qinghai Kunlun Talents Program. Ying Zhao ([email protected]) is the corresponding author.

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, to fully exploit the potential of this signal, the robustness and stability of the SIF-GPP relationship across vegetation types and climates must be assessed. For this purpose, current studies have been limited by the availability of SIF datasets with sufficient spatial resolution to disentangle the signal between different vegetation cover types. To overcome this limitation our analysis uses GOME-2 SIF retrievals, downscaled to a resolution of 0.05° (∼5 km) to explore the relationship between SIF and FLUXCOM GPP (GPPFX), a data-driven dataset of primary productivity obtained by upscaling flux-tower measurements. The high resolution of the downscaled SIF (SIFDS) dataset allows the relationships to be broken down by vegetation cover for separate climate zones, thus enabling a confrontation between GPP and SIF at fine granularity,and the comparison and categorisation of vegetation covers based on the SIFDS-GPPFX response. This analysis first investigates the spatial and temporal relationships between FLUXCOM GPP and downscaled SIF at a global scale. Divergences and convergences between the two datasets are explored to see where high-resolution SIF can enhance our understanding of GPP. A reasonably strong linear relationship is generally observed between SIFDS and GPPFX in all vegetation categories, and an analysis of covariance (ANCOVA) shows that the spatial response is similar between certain plant traits, with some exceptions, such as equatorial broadleaf forests, and continental needleleaf forests, and distinction between woody and herbaceous vegetation. The temporal relationship between SIFDS and GPPFX, within a growing season, is shown to be the strongest, followed by the spatial relationship and finally the SIFDS-GPPFX trend between growing seasons. Whilst a linear spatial relationship is generally observed between SIFDS and GPPFX in all vegetation categories, geographical regions of non-linearity suggest where SIFDS could potentially provide information about ecosystem dynamics that are not represented in the FLUXCOM GPP dataset. With the demonstration of downscaled SIF as a proxy for GPP, the response of SIFDS to short-term fluctuations in several meteorological variables is analysed, and the utility of SIFDS as a measure of environmental stress explored. For a broad range of land cover categories, the most significant environmental driving and limiting meteorological variables are determined and vegetation groupings of similar SIF-meteo response reinforce the vegetation categorisations suggested by the SIF-GPP ANCOVA. This comparative exploration of two of the most recent products in carbon productivity estimation shows the value in downscaling SIF data, provides an independent probe of the FLUXCOM GPP model, enhances our understanding of the global SIF-GPP spatio-temporal relationship with a particular focus on the role of vegetation cover, and demonstrates the utility of SIF as a measure of environmental stress.

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.

A spatial model for predicting North Indian Ocean tropical cyclone intensity: Role of sea surface temperature and tropical cyclone heat potential

Recently, various artificial intelligence (AI)-based techniques have been developed to estimate tropical cyclone (TC) intensity by analyzing patterns in satellite imagery. Most of these models rely solely on currently available satellite data. However, traditional methods used in operational TC forecasting, such as the advanced Dvorak technique (ADT), as well as forecasters’ practices in TC intensity estimation, consider not only the current information but also historical data (e.g., previous TC intensities and their trends). Additionally, forecasters compare TC intensity from ADT and analysis track data, and if a bias is identified, they take this into account when estimating the current TC intensity. This study investigates how much the performance of current AI-based TC intensity estimation can be improved by incorporating historical information and bias estimates. Three experiments were conducted: (1) control (CTRL), which uses only current satellite images; (2) Experiment 1 (Exp 1), which includes current data along with satellite imagery and intensity from 6 hours earlier; and (3) Experiment 2 (Exp 2), which further incorporates bias estimates from the past 18 hours into a self-diagnosis algorithm. The results showed that Exp 1 reduced intensity estimation errors by 64.7% compared to CTRL, while Exp 2 further decreased errors by 32.3% compared to Exp 1. This suggests that utilizing historical TC information and a self-diagnosis approach in operational systems is crucial for improving the accuracy of AI-based TC intensity estimation.

Tropical cyclones (TCs) are an extreme weather hazard that can have major impacts on coastal and inland populations. Surveillance by meteorological satellites has essentially mitigated the problem of TC detection; however, estimating TC intensity via remote sensing techniques is more challenging. Exploring different sensor frequencies and developing novel ways to interrogate that data are essential steps toward improving the intensity analysis of TCs. This study employs a convolutional neural network (CNN) machine learning method trained on previously unexplored 183-GHz microwave imagery to estimate TC intensity. A CNN can identify two-dimensional features in satellite imagery that are indicative of a TC’s current intensity. The novel approach, coined “D-MINT183,” is similar to an earlier version [Deep Multispectral Intensity of TCs estimator (D-MINT)] in that it uses infrared imagery and environmental scalar predictors, but D-MINT183 uses 183-GHz microwave imagery, while D-MINT uses legacy 37 and 89 GHz. The D-MINT183 model intensity estimates are calculated for different types of low-Earth-orbiting (LEO) meteorological satellites: conical scanning and cross-track scanning, and the experimental TROPICS CubeSats. Results show that D-MINT183 TC intensity estimates from conical scanning satellites are generally as skillful as D-MINT. For cross-track scanning satellites and TROPICS, when D-MINT is not available, TC intensity estimates from D-MINT183 are more skillful than Deep infrared (IR) Intensity of TCs estimator (D-PRINT), a CNN which does not use microwave imagery. Thus, D-MINT183 can increase the frequency of more accurate, microwave-based estimates of TC intensity. This work also demonstrates the importance of explainable artificial intelligence, with examples highlighting the situationally dependent impacts of 183-GHz imagery on the D-MINT183 intensity estimates. Significance Statement This study develops a machine learning method to estimate tropical cyclone (TC) intensity using novel satellite observations. The resultant model uses a convolutional neural network (CNN) which can identify two-dimensional features in 183-GHz microwave imagery along with geostationary infrared imagery that are indicative of the current TC intensity. Model performance shows that in several TC basins, the inclusion of the previously unexplored (but operationally available) 183-GHz water vapor channels on current and future low-Earth-orbiting (LEO) satellites can provide information that can complement alternative satellite-based estimates of TC intensity. This is significant since G-band frequencies can provide suitable spatial resolutions with much smaller antenna size requirements and lower costs, meaning the data are likely to be more common on emerging and planned LEO CubeSat constellations.

Surface flux parameterization schemes used in current dynamic models are primarily based upon measurements at low and moderate wind speeds. Recent studies show that these parameterization schemes may be incorrect at high wind speeds (e.g., tropical cyclone forecasts). Five high-resolution numerical model experiments are designed to assess the sensitivity of tropical cyclone intensity forecasts to changes in the surface flux parameterization. The sensitivity experiments are conducted by running 48 h forecasts of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) for six selected tropical cyclones with individual modifications to surface flux calculation that include: (1) limiting the surface stress for wind speeds greater than 33 m s−1, or 64 knots (kt); (2) computing the stress at the top of the model bottom grid layer (MBGL) by averaging results from surface layer similarity and turbulence mixing parameterization for wind speeds greater than 33 m s−1; (3) increasing the roughness lengths for heat and moisture transfer by a factor of ten; (4) setting the roughness lengths for heat and moisture transfer to 1/10 of the momentum roughness length; and (5) cooling the sea surface temperature (SST) by a prescribed rate at high winds. Averaged responses for the six storms to these sensitivity tests show that: (i) the limit on surface stress at high winds significantly increases the cyclone intensity in 48 h forecasts; (ii) the averaged surface layer stress at high winds increases the cyclone intensity but to a much lesser degree than limiting the surface stress; (iii) large increases in the roughness lengths for heat and moisture transfer are needed to significantly impact the intensity forecast; (iv) the different roughness length formula for surface transfer coefficients notably increases Ch/Cd ratio from 0.59 to 0.79 for 25 m s−1 and 0.41 to 0.75 for 50 m s−1 that significantly increases the predicted cyclone intensity; and (v) cooling of the SST by −5.8°C in 48 h reduces the maximum surface wind speed by −32 kt, or 16.5 m s−1, at 48 h forecast. These results suggest that a surface flux parameterization scheme suitable for tropical cyclone intensity forecast must correctly model the leveling-off character of surface stress and Ch/Cd ratio at high winds. All modifications to surface flux calculation have little influence on 48 h track forecasts, even though they may significantly impact the intensity forecasts.

The Dvorak satellite intensity estimation technique (DSIET), a technique using enhanced infrared (EIR) satellite imagery to estimate the intensity of tropical cyclones (TCs), has been used by TC warning centers around the world since 1984. Although there are several subtechniques of the DSIET, the most reliable and objective is the “eye pattern” technique. However, because higher (∼2 km) resolution satellite imagery can resolve warmer brightness temperatures in some TC eyes than the imagery used to create the DSIET (∼8-km resolution), it can potentially produce artificially higher DSIET estimates. To test this postulate, satellite data during the 2020–24 North Atlantic and eastern North Pacific hurricane seasons were used to analyze 34 major hurricanes that possessed well-defined eye features. The DSIET estimates were then compared to reconnaissance aircraft flight-level observations. Of the 237 images analyzed, 69% exhibited the same current intensity (CI) number using 2- and 8-km resolution imagery, though a considerable portion of cases (25%) produced higher CI numbers using 2-km resolution. The Wilcoxon signed-rank test shows these differences to be statistically significant ( p < 0.01), suggesting that the higher resolution of current GOES satellite imagery yields higher intensity estimates for some hurricanes with well-developed eyes. Comparison with flight-level observations shows that estimates based on 2-km EIR imagery have slightly higher error than those based on 8-km imagery for hurricanes with intensities less than ∼63 m s −1 . This has implications for operational hurricane intensity estimates and TC postanalysis, which are often guided by subjective and objective estimates of TC intensity.