Objective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data

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 cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

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.

Several numerical experiments were performed to investigate the dynamic and thermodynamic effects of sea surface temperature (SST) on tropical cyclone (TC) intensity. The results reveal that the relative SST within a radius of 2–3 times the radius of maximum wind contributes positively and greatly to TC intensity, while the remote SST far away from the TC center could reduce storm intensity. The change of air-sea temperature and moisture differences may be the reason why TC intensity is more sensitive to the relative rather than the absolute SST. As the inflow air moves toward the eyewall, warmer (colder) remote SST can gradually increase (decrease) the underlying surface air temperature and moisture, and thus decrease (increase) the air-sea temperature and moisture differences, which lead to less (more) energy fluxes entering the eyewall and then decrease (increase) the TC intensity and make it less sensitive to the absolute SST change. Finally, with all the related dynamic and thermodynamic processes being taken into account, a schematic diagram for the effects of relative SST and absolute SST on TC intensity is proposed.

This study examines the potential limit in predicting tropical cyclone (TC) intensity under idealized conditions. Using the phase-space reconstruction method for TC intensity time series obtained from the CM1 idealized simulations, it is found that CM1 axisymmetric dynamics contain low-dimensional chaos at the maximum intensity equilibrium. Examination of several attractor invariants including the largest Lyapunov exponent, the Sugihara–May correlation, and the correlation dimension captures a consistent range of the chaotic attractor dimension between 4 and 5 for TC intensity at the maximum intensity equilibrium. In addition, the intensity error doubling time estimated from the largest Lyapunov exponent is roughly 1–3 h, which accords with the decay time obtained from the Sugihara–May correlation. Furthermore, the findings in this study reveal a relatively short TC intensity predictability limit for CM1, which is ∼3–9 h based on the maximum tangential wind but noticeably longer for the minimum central pressure (∼12–18 h) after reaching the mature stage. So long as the traditional metrics for TC intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that TC intensity forecast errors will not be reduced indefinitely in any model, even in the absence of all model and observational errors. As such, the future improvement of TC intensity forecast should be based on different metrics beyond the absolute intensity errors that are currently used in real-time intensity verification. Significance Statement Using the phase-space reconstruction method for tropical cyclone (TC) intensity time series obtained from idealized axisymmetric simulations, we show that TC axisymmetric dynamics in CM1 possesses low-dimensional chaos at the maximum intensity equilibrium. This low-dimensional dynamics explains the long tradition of representing TC intensity by a few measures as in the current practice. The chaotic property of CM1 axisymmetric dynamics also suggests a relatively short predictability range for TC intensity at the maximum intensity equilibrium. The potential existence of low-dimensional chaos for TC intensity in CM1 idealized simulations as found in this study supports the use of different intensity verification metrics beyond the traditional absolute intensity errors currently used in operational model evaluation.

Using primitive equations with adiabatic, non‐friction non‐environmental steering, the dynamical equilibrium features of tropical cyclones (TC) are investigated. The parameter, dI/dt, was obtained and its dynamical balance formulation describing TC intensity change. The results of the we discussed the characteristics of TC motion and intensity change in barotropic and baroclinic model showed that the stationary motion of TC possess “equivalent‐geostrophic equilibrium” and isolines of potential vorticity and E(E = φ + (u2 + v2)/2) coincide with streamlines. The TC intensity will be weakened by “ventilation flow” in “beta (β) gyres” and “shear gyres” from the positive (negative) wind vertical shear at high‐ (low‐) level. The positive (negative) vorticity vertical convection of high‐ (low‐) level strengthens TC intensity. Generally, the TC center has the tendency to drift poleward and there is a gradient (ascendent) direction of vorticity vertical convection at high‐ (low‐) level. The TC motion is connected with the asymmetry structure of vertical shear and vertical motion, the “γ effect” (γ = ∂β/∂y) that is one of the dynamical mechanisms of “ventilation flow” steering TC center motion.

Tropical cyclones (TCs) are among the most severe and destructive natural events, and they have a major detrimental impact on both the economy and society. This study used Geo-KOMPSAT-2A (GK2A) satellite data to estimate TC intensity in the western North Pacific based on a convolutional neural network (CNN) model. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from six hours earlier, transfer learning reduced the RMSE by 5.5%. Because continuous long-term data are not always available for TC intensity estimation based on geostationary satellite images, these results imply that transfer learning may constitute a new advance in this area. Acknowledgement. This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Korea Coast Guard (RS-2023-00238652, Integrated Satellite-based Applications Development for Korea Coast Guard).  

In this study, the relationship between tropical cyclone (TC) intensity and rain rate over the ocean is investigated using a full-physics numerical model (WRF) and a physics-based TC rainfall model (TCR). TC intensity is found to be nearly linearly correlated with the average rain rate in the inner core [∼0.97 (mm h−1 m−2)/(m s−1)], while the correlation is weak at outer radii. This difference is induced because TC intensity is significantly correlated with both the vertical velocity and specific humidity in the inner core but is not significantly correlated with the vertical velocity in the outer radii. Further investigation shows that the intensity–rain-rate relationship at the outer radii is influenced by the TC evolution stage. The rain rate for the outer radii is positively correlated with TC intensity for nondecaying TCs, while this correlation is reduced for decaying TCs due to systematic downdrafts in the outer radii. In the context of climate change, the sensitivity of the TC rain rate to sea surface temperature (SST) is found to be +9% per 1 K increase of SST, roughly the product of the sensitivity of TC intensity to SST (+3%) and the Clausius-Clapeyron scaling (+7%). Coupled with synthetic storms, evolution of the TC rain rate over the twenty-first century under the SSP5-8.5 scenario is projected by the TCR (calibrated with the WRF simulations). The annual increase rates of averaged TC rain rate are 0.17% and 0.20% for the inner core and outer radii, respectively, larger than the annual increase rate of TC intensity (0.046%) but comparable to that of cube of intensity (0.18%).

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness.

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.

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

A tropical cyclone intensity prediction model using conditional generative adversarial network

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.

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

The study of the impact of global warming on tropical cyclone (TC) intensity is subject to uncertainty in historical datasets, especially in the western North Pacific (WNP) basin, where conflicting results have been found with the TC datasets archived in different organizations. In this study the basinwide TC intensity in the WNP basin is derived dynamically with a TC intensity model, based on the track data from the Joint Typhoon Warning Center (JTWC), the Regional Specialized Meteorological Center (RSMC) of Tokyo, and the Shanghai Typhoon Institute (STI) of the China Meteorological Administration. The dynamically derived TC intensity is compared to the three datasets and used to investigate trends in TC intensity. The associated contributions of changes in SST, vertical wind shear, and prevailing tracks are also examined. The evolution of the basinwide TC intensity in the JTWC best-track dataset can be generally reproduced over the period 1975–2007. Dynamically derived data based on the JTWC, RSMC, and STI track datasets all show an increasing trend in the peak intensity and frequency of intense typhoons, mainly because of the combined effect of changes in SST and vertical wind shear. This study suggests that the increasing intensity trend in the JTWC dataset is real, but that it may be overestimated. In contrast, the TC intensity trends in the RSMC and STI intensity datasets are dynamically inconsistent. Numerical simulations also suggest that the frequency of intense typhoons is more sensitive to changes in SST and vertical wind shear than the peak and average intensities defined in previous studies.