Error Growth Rate Research Articles

Reasons for Different Predictability of Tropical Cyclone Tracks in the Western North Pacific and Atlantic Oceans

Abstract Recent several studies have focused on the predictability of tropical cyclone track forecast. As a response to the question issued by Landsea and Cangialosi (2018) about "the approaching limit of predictability for tropical cyclone (TC) track prediction is near or has already been reached", Zhou and Toth (2020) (short for ZT20) and Yu et al. (2022) (short for Y22) have found that the limit of predictability for TC track prediction has not been reached both in Atlantic (ATL) and Western North Pacific (WNP) basins. However, the predictabilities are different in two basins, as ZT20 found that 1 day's improvement can be obtained through 10 years in ATL, while Y22 found that 2 days' improvement can be obtained through 15 years in WNP. To reveal the causes of this difference, the predictability of TC track in WNP is first investigated under the same framework as ZT20. Then important parameters that determined the predictabilities are found and analyzed. Results suggested that the growth rate of true track forecast error in WNP is higher than that in ATL, indicating a lower predictability in WNP. Further explorations suggested that TCs in WNP basin have averagely larger sizes, stronger intensities, lower-latitude locations, and poorer forecast skills of their guided flows. All these factors contribute to the larger track forecast error growth rate. Moreover, it is pointed out that as the improvement of forecast skills over years mainly due to the reduction of initial analysis errors, although a lower predictability is found in WNP, the forecast skill improvement in WNP is faster than that in ATL.

SIMMER code two-fluid model well-posedness and stability for simulating liquid metal (lead–lithium eutectic), water vapor, and non-condensable gases multi-component multi-phase flows

Liquid metals are employed as a coolant in liquid metal fast reactors (LMFR) and are considered breeders and coolants for future power-producing fusion reactors. The interaction of liquid metals with other coolants, such as air and water, is one of the possible occurrences in liquid metal-cooled reactors. Although the SIMMER-III computer code is currently in the validation and verification phase, it is a prospective candidate code for correctly investigating possible accidents. This study aims to determine the stability and well-posedness of the SIMMER-III code Eulerian-Eulerian two-fluid model (TFM) under any such accident scenario. This work also considers the influence of the virtual mass force and diffusion forces in momentum on TFM stability in accelerated liquid metal, steam, and non-condensable gaseous flows throughout all flow regimes. The characteristics method is used to assess the ill-posed nature of TFM for all types of accidents and accident scenarios in a hypothetical simplified system model with several components (Lead–Lithium, non-condensable gases, and water vapor). It has been discovered that the analysis findings vary from the air and water two-component two-phase flows (characteristics roots spectrum and error growth rate patterns) and are particularly sensitive to the diffusion and virtual mass coefficients. This is because liquid metals have a higher density than liquid water and steam, resulting in strong virtual mass forces and weak diffusion forces in liquid-metal and gas two-phase flows. Because of the extensive range of possible interactions between fluxes of different components, producing an accurate representation of diffusion in multi-component mixtures is difficult. Because of this, it is strongly suggested that the virtual mass coefficient and diffusion coefficients be handled more accurately for these kinds of flows. The values of these coefficients significantly affect how accurate proposed TFM predictions are, but there hasn't been much research on how to estimate them. The study also sheds light on the model's accuracy and highlights the areas where the model's predictions will be mathematically trustworthy.

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

AbstractThe North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

Sensitivity of Intrinsic Error Growth to Large-Scale Uncertainty Structure in a Record-Breaking Summertime Rainfall Event

Abstract It is recognized that the atmosphere’s predictability is intrinsically limited by unobservably small uncertainties that are beyond our capability to eliminate. However, there have been discussions in recent years on whether forecast error grows upscale (small-scale error grows faster and transfers to progressively larger scales) or up-amplitude (grows at all scales at the same time) when unobservably small-amplitude initial uncertainties are imposed at the large scales and limit the intrinsic predictability. This study uses large-scale small-amplitude initial uncertainties of two different structures—one idealized, univariate, and isotropic, the other realistic, multivariate, and flow dependent—to examine the error growth characteristics in the intrinsic predictability regime associated with a record-breaking rainfall event that happened on 19–20 July 2021 in China. Results indicate upscale error growth characteristics regardless of the structure of the initial uncertainties: the errors at smaller scales grow fastest first; as the forecasts continue, the wavelengths of the fastest error growth gradually shift toward larger scales with reduced error growth rates. Therefore, error growth from smaller to larger scales was more important than the growth directly at the large scales of the initial errors. These upscale error growth characteristics also depend on the perturbed and examined quantities: if the examined quantity is perturbed, then its errors grow upscale; if there is no initial uncertainty in the examined quantity, then its errors grow at all scales at the same time, although its smaller-scale errors still grow faster for the first several hours, suggesting the existence of the upscale error growth. Significance Statement This study compared the error growth characteristics associated with the atmosphere’s intrinsic predictability under two different structures of unobservably small-amplitude, large-scale initial uncertainties: one idealized, univariate, and isotropic, the other realistic, multivariate, and flow dependent. The characteristics of the errors growing upscale rather than up-amplitude regardless of the initial uncertainties’ structure are apparent. The large-scale errors do not grow if their initial amplitudes are much bigger than the small-scale errors. This study also examined how the error growth characteristics will change when the quantity that is used to describe the error growth is inconsistent with the quantity that contains uncertainty, suggesting the importance of including multivariate, covariant uncertainties of state variables in atmospheric predictability studies.

Application of Recurrent Neural Networks to Model Bias Correction: Idealized Experiments With the Lorenz‐96 Model

AbstractSystematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation information incorporated through data assimilation can provide information for detecting and alleviating such biases. In this study, the application of machine learning to model bias correction is demonstrated, emphasizing the effectiveness of recurrent neural networks. Idealized experiments are performed using the two‐scale coupled Lorenz‐96 model as the true system and single Lorenz‐96 model as the imperfect forecast model, to compare the effectiveness of bias correction methods based on various architectures of neural networks and simple linear regression. The neural networks generally outperformed linear regression, and recurrent neural networks showed the best ability in finding the systematic bias component from the analysis increment data. Bias correction using the recurrent neural networks also gives the most significant improvement in reducing the error growth rate in extended range forecasts. The results suggest that including past time series of the forecast variables improve model bias correction when limited information of the observation is incorporated through data assimilation.

Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy‐duty truck control in the field

AbstractTo develop cooperative adaptive cruise control (CACC), the choice of control approach often influences and can limit the choice of model structure, and vice versa. For heavy‐duty trucks, practical application of CACC in the field is heavily influenced by the accuracy of the used model. Deep learning and deep reinforcement learning (deep‐RL) have recently been used to demonstrate improved modeling and control performance for vehicles such as cars and quadrotors compared to state‐of‐the‐art. The literature on the application of deep learning and deep‐RL for heavy‐duty trucks in the field, which are significantly more complex than cars, is still sparse, however. In this article, we develop a two‐layer gray‐box deep learning model to capture longitudinal dynamics of heavy‐duty trucks while abstracting their complexity and present an approach to properly break the nested feedback loops in the model for training. We compare this model with three other alternative models and show that it achieves better general performance compared to a standard artificial neural network and results in and slower steady‐state acceleration and speed error growth rates, respectively. We then present an architecture to utilize these deep learning models within the deep‐RL framework and use it to develop baseline CACC controllers that can be zero‐shot transferred to the field. To carry out the work, we present a setup of differently configured trucks along with their interface architecture and stochastic driving cycle generators for data collection. Numerical validation of the approach demonstrated stationary and bounded modeling error, and demonstrated transfer of CACC controllers with consistent overshoot bounds and a stable approximately‐zero steady‐state error. Validation from field experiments demonstrated similarly consistent results. Compared to a state‐of‐the‐art benchmark, the deep‐RL controller achieved lower speed and time‐gap error variance but higher time‐gap error offset.

Scale-dependent error growth in Navier-Stokes simulations.

We estimate the maximal Lyapunov exponent at different resolutions and Reynolds numbers in large eddy simulations (LES) and direct numerical simulations of sinusoidally driven Navier-Stokes equationsin three dimensions. Independent of the Reynolds number when nondimensionalized by Kolmogorov units, the LES Lyapunov exponent diverges as an inverse power of the effective grid spacing showing that the fine scale structures exhibit much faster error growth rates than the larger ones. Effectively, i.e., ignoring the cutoff of this phenomenon at the Kolmogorov scale, this behavior introduces an upper bound to the prediction horizon that can be achieved by improving the precision of initial conditions through refining of the measurement grid.

Model-Based Cooperative Navigation for a Group of Flying Robots

In this article, a model-based cooperative navigation system for a group of quadrotors, while no absolute position data is accessible, is proposed. The growth of position errors in a GNSS-denied condition is such that the navigation methods are not reliable. In the proposed method, instead of the conventional inertial navigation equations, a different dynamic equation based on drag forces is used. Together with the relative position measurements between agents, the acceleration measurements on the plane of the rotors of at least one of the agents are also used in the observation process. For evaluation of the proposed method, an algorithm-in-loop 6DOF simulation environment is developed. The results illustrate that the growth rate of navigation errors is significantly reduced; 97% in the horizontal plane and 40% in the vertical one. In short, integrating the proposed model-based and cooperative navigation methods is more effective than using each of them separately.

Improving Numerical Model Predicted Float Trajectories by Deep Learning

AbstractThe Lagrangian tracking approach is often used in numerical modeling (NM) to simulate and predict the movement of marine particles such as plastic, oil spills, and floating wreckage. The uncertainties in NM reduce prediction accuracy as a result of the coarse temporal and spatial resolution, along with waves, winds, and currents. From 29 August to 22 December 2020, the United States Defense Advanced Research Projects Agency's Ocean of Things program deployed 422 floating drifters in the Gulf of Mexico, providing an opportunity of using the observed trajectories as ground truth to train Artificial Intelligence (AI) models to correct NM float trajectory predictions. A Regional Ocean Model System (ROMS) and AI Hybrid model was developed to implement AI to correct the ROMS‐predicted 1‐day float trajectories. The AI model is built on a convolutional neural network and Gated Recurrent Unit. The results of the ROMS‐AI Hybrid model show that 82.0% of the trajectory predictions were improved at the 24 hr, with the corresponding overall mean separation error decreasing by 11.82 km, from 20.56 to 8.74 km, which is a 57% improvement, and the error growth rate decreasing from 5.06 km per 6 hr to 1.95 km per 6 hr. The evident improvement indicates that the ROMS‐AI Hybrid model can correct the ROMS simulation to improve the 1‐day prediction of the float trajectories and shows great potential to predict the cluster of the floats.

Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

AbstractModel error has a marked influence on forecast error, and integral correction of the initial and model errors in a novel four‐dimensional variational data assimilation (4DVar) method (i4DVar) has recently been developed. Unlike traditional 4DVar, in which the analysis increment focuses only on the initial time point, i4DVar introduces an averaged integral correction term to correct the error evolution at multiple times with the same time interval in the assimilation window; that is, the assimilation window is divided into several sub‐windows. In this study, we incorporated i4DVar into the System of Multigrid Nonlinear Least‐squares‐4DVar Data Assimilation for Numerical Weather Prediction (NWP) (SNAP), hereafter referred to as SN‐i4DVar, and explored its impacts on NWP. The effectiveness and performance of SN‐i4DVar were evaluated using two extreme rainstorm cases assimilating conventional and satellite observations, respectively. The results indicated that integral correction of the initial and model errors reduced analysis error and forecast error growth rates. SN‐i4DVar performed better in precipitation forecasting than the original SNAP and exhibited generally better forecasting capability in terms of the horizontal wind speed (u and v), temperature, and humidity variables, indicating that SN‐i4DVar simultaneously countered initial model errors to improve NWP capability. Meanwhile, the 4DEnVar method, which considers the optimal analysis in the ensemble space, benefits from the improved ensemble space generated according to the formulas of i4DVar, and the observation information can be more effectively absorbed and a more accurate analysis can be obtained by SN‐i4DVar.

Optimized relinearization algorithm of the multikey homomorphic encryption scheme

Multikey homomorphic encryption (MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers (e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user's public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm; thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.

Investigating decadal variations of the seasonal predictability limit of sea surface temperature in the tropical Pacific

El Niño and the Southern Oscillation (ENSO) have a worldwide impact on seasonal to yearly climate. However, there are decadal variations in the seasonal prediction skill of ENSO in dynamical and statistical models; in particular, ENSO prediction skill has declined since 2000. The shortcomings of models mean that it is very important to study ENSO seasonal predictability and its decadal variation using observational/reanalysis data. Here we quantitatively estimate the seasonal predictability limit (PL) of ENSO from 1900 to 2015 using Nonlinear local Lyapunov exponent (NLLE) theory with an observational/reanalysis dataset and explore its decadal variations. The mean PL of sea surface temperature (SST) is high in the central/eastern tropical Pacific and low in the western tropical Pacific, reaching 12–15 and 7–8 months, respectively. The PL in the tropical Pacific varies on a decadal timescale, with an interdecadal standard deviation of up to 2 months in the central tropical Pacific that has similar spatial structure to the mean PL. Taking the PL of SST in the Niño 3.4 region as representative of the PL in the central/eastern tropical Pacific, there are clearly higher values in the 1900s, mid-1930s, mid-1960s, and mid-1990s, and lower values in the 1920s, mid-1940s, and mid-2010s. Meanwhile, the PL of SST in the Niño 6 region—whose average value is 7 months—is in good agreement with the PL of most regions in the western tropical Pacific, with higher values in the 1910s, 1940s, and 1980s and lower values in the 1930s, 1950s, and mid-1990s. In the framework of NLLE theory, the PL is determined by the error growth rate (representing the dissipation rate of the predictable signal) and the saturation value of relative error (representing predictable signal intensity). We reveal that the spatial structure of the mean PL in the tropical Pacific is determined mainly by the error growth rate. The decadal variability of PL is affected more by the variation of the saturation value of relative error in the equatorial Pacific, whereas the error growth rate cannot be ignored in the PL of some regions. As an important source of predictability in ENSO dynamics, the relationship between warm water volume and SST in the Niño 3.4 region has a critical role in the decadal variability of PL in the tropical Pacific through the error growth rate and saturation value of relative error. This strong relationship reduces the error growth rate in the initial period and increases the saturated relative error, contributing to the high PL.

Are We Reaching the Limit of Tropical Cyclone Track Predictability in the Western North Pacific?

Abstract The annual-mean position errors (PE) of tropical cyclone (TC) track forecasts from three forecast agencies [WMO Regional Specialized Meteorological Center in Tokyo (RSMC-Tokyo), China Meteorological Administration (CMA), and Joint Typhoon Warning Center of the United States (JTWC)] are analyzed to document the past improvements and project future tendency in track forecast accuracy for TCs in the western North Pacific. An improvement of 48 h (2 days) in lead time has been achieved in the past 30 years, but with noticeable stepwise periods of improvements with superposed short-term fluctuations. The stepwise improvement features differ among the three forecast agencies, but are highly related to the development of objective forecast guidance and the application strategy. As demonstrated by an exponential model for the growth of PEs with lead time for TCs of tropical storm category and above, the improvements in the past 10 years have mainly been due to the reduction in analysis errors rather than the reduction in the error growth rate. If the current trend continues, a further 2-day improvement in TC track forecast lead times may be projected for the coming 15 years up to 2035, and we certainly have not reached yet the limit of TC track predictability in the western North Pacific.

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

Simulation of Dynamic Processes in Long Josephson Junctions. The Problem on Calculating the Current–Voltage Characteristics. Numerical Method for Estimating the Round-Off Error Growth Rate

Simulation of Dynamic Processes in Long Josephson Junctions. The Problem on Calculating the Current–Voltage Characteristics. Numerical Method for Estimating the Round-Off Error Growth Rate

Mesoscale Predictability in Moist Midlatitude Cyclones Is Not Sensitive to the Slope of the Background Kinetic Energy Spectrum

Abstract We investigate the sensitivity of mesoscale atmospheric predictability to the slope of the background kinetic energy spectrum E by adding initial errors to simulations of idealized moist midlatitude cyclones at several wavenumbers k for which the slope of E(k) is significantly different. These different slopes arise from 1) differences in the E(k) generated by cyclones growing in two different moist baroclinically unstable environments, and 2) differences in the horizontal scale at which initial perturbations are added, with E(k) having steeper slopes at larger scales. When small-amplitude potential temperature perturbations are added, the error growth through the subsequent 36-h simulation is not sensitive to the slope of E(k) nor to the horizontal scale of the initial error. In all cases with small-amplitude perturbations, the error growth in physical space is dominated by moist convection along frontal boundaries. As such, the error field is localized in physical space and broad in wavenumber (spectral) space. In moist midlatitude cyclones, these broadly distributed errors in wavenumber space limit mesoscale predictability by growing up-amplitude rather than by cascading upscale to progressively longer wavelengths. In contrast, the error distribution in homogeneous turbulence is broad in physical space and localized in wavenumber space, and dimensional analysis can be used to estimate the error growth rate at a specific wavenumber k from E(k). Predictability estimates derived in this manner, and from the numerical solutions of idealized models of homogeneous turbulence, depend on whether the slope of E(k) is shallower or steeper than k−3, which differs from the slope-insensitive behavior exhibited by moist midlatitude cyclones.

On the rate of error growth in time for numerical solutions of nonlinear dispersive wave equations

We study the numerical error in solitary wave solutions of nonlinear dispersive wave equations. A number of existing results for discretizations of solitary wave solutions of particular equations indicate that the error grows quadratically in time for numerical methods that do not conserve energy, but grows only linearly for conservative methods. We provide numerical experiments suggesting that this result extends to a very broad class of equations and numerical methods.

Observation impact metrics in NWP: A theoretical study. Part I: Optimal systems

AbstractTwo methods are widely used to assess the impact of observations in global numerical weather prediction (NWP): data denial experiments (DDEs) and the forecast sensitivity‐based observation impact (FSOI) method. A DDE measures the impact on forecast accuracy of removing an observation type from the system, whereas FSOI measures the amount by which an observation type reduces the short‐range forecast error (within a system containing all observation types). It is usually found, in global NWP experiments in which both methods are used, that the FSOI impact of a given observation type is greater than its DDE impact. The aim of this article is to present the theory behind the DDE and FSOI metrics that are commonly used and to explore factors that cause DDE and FSOI metrics to give different results. The general theory is presented for an optimal analysis and forecast system and then applied to a simple model with two state variables. To explain the commonly found result of FSOI impact greater than DDE impact, the following system properties are found to be important: mixing of information (and error) by the forecast model between state variables, the rate of forecast error growth, the presence of forecast model error, the way in which observational information is distributed between state variables and denied from them, and the presence of error in the data used for forecast verification. These results provide insight into why NWP systems are resilient to the removal of observational information; they are shown to be resilient if information is denied primarily from well‐observed variables but not when denied primarily from poorly observed variables.

A coupled finite volume and material point method for two-phase simulation of liquid–sediment and gas–sediment flows

Mixtures of fluids and granular sediments play an important role in many industrial, geotechnical, and aerospace engineering problems, from waste management and transportation (liquid–sediment mixtures) to dust kick-up below helicopter rotors (gas–sediment mixtures). These mixed flows often involve bulk motion of hundreds of billions of individual sediment particles and can contain both highly turbulent regions and static, non-flowing regions. This breadth of phenomena necessitates the use of continuum simulation methods, such as the material point method (MPM), which can accurately capture these large deformations while also tracking the Lagrangian features of the flow (e.g. the granular surface, elastic stress, etc.).Recent works using two-phase MPM frameworks to simulate these mixtures have shown substantial promise; however, these approaches are hindered by the numerical limitations of MPM when simulating pure fluids. In addition to the well-known particle ringing instability and difficulty defining inflow/outflow boundary conditions, MPM has a tendency to accumulate quadrature errors as materials deform, increasing the rate of overall error growth as simulations progress. In this work, we present an improved, two-phase continuum simulation framework that uses the finite volume method (FVM) to solve the fluid phase equations of motion and MPM to solve the solid phase equations of motion, substantially reducing the effect of these errors and providing better accuracy and stability for long-duration simulations of these mixtures.

Dependence of tropical cyclone intrinsic intensity variability on the large‐scale environment

AbstractThis study examines the dependence of tropical cyclone (TC) intrinsic intensity variability on the large‐scale environment. Using idealized simulations with the Hurricane Research and Forecasting (HWRF) model, the growth of TC intensity errors and the subsequent saturation of intensity errors at the mature stage are quantified for a range of environmental conditions. Among several factors, it is found that sea‐surface temperature (SST) and tropospheric static stability are the two main ones affecting the growth rate and saturation of TC intensity errors, with a larger error saturation at maximum intensity equilibrium and a faster error growth rate during rapid intensification for a warmer SST or less stable troposphere. Results from this study highlight the fundamental characteristics of TC intensity predictability, which appears to vary with environmental conditions, instead of being a universal measure for TCs in all ocean basins. These results also suggest a limit on TC intensity forecast accuracy that is governed by the large‐scale environment, which needs to be taken into consideration for future TC model development.