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Afa, siccità e caldo torrido non accennano a diminuire, nonostante l'estate volga al termine. I ragazzi della Quercia Storta decidono di dare il loro contributo alla lotta contro il cambiamento climatico: piantare un albero a testa nel podere dove trascorrono le vacanze estive. Ma l'annuncio di una perturbazione atmosferica della potenza di un uragano minaccia le giovani piante... «Il bosco si ritrovò solo. Vuoto e silenzioso. I suoi abitanti se ne erano andati. Gli alberi si strinsero tra di loro, intrecciarono i rami e chinarono le fronde a proteggere i cespugli con cui condividevano il nutrimento. Ma non chiusero gli occhi. Li tennero ben aperti per affrontare l'uragano che, lo sentivano bene, si sarebbe avventato senza pietà».

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.

The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting.

The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts, and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity Forecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a gridpoint spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%–15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB’s vertical weights shows that the lower troposphere’s environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere’s, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER, and TAB models relative to their earlier counterparts are discussed.

The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.

The combination of moderate vertical wind shear (VWS) and dry environments can produce the most uncertain scenarios for tropical cyclone (TC) genesis and intensification. We investigated the sources of increased uncertainty of TC development under moderate VWS and dry environments using a set of Weather Research and Forecasting (WRF) ensemble simulations. Statistical analysis of ensemble members for precursor events and time-lagged correlations indicates that successful TC development is dependent on a specific set of precursor events. A deficiency in any of these precursor events leads to a failure of TC intensification. The uncertainty of TC intensification can be largely attributed to the probabilistic characteristics of precursor events lining up together before TC intensification. The critical bifurcation point between successful and failed trials in these idealized simulations is the sustained vortex alignment process. Even for the failed intensification cases, most simulations showed deep organized convection, which reformed a midlevel vortex. However, for the failed cycles, the new midlevel vortex could not sustain vertical alignment with the low-level center and was carried away by VWS shortly. Under the most uncertain setup (VWS = 7.5 m s−1 and 50% moisture), the latest-developing ensemble member had seven events of tilt decreasing and increasing again that occurred during the 8 days before genesis. Some unsuccessful precursor events looked very close to the successful ones, implying limits on the intrinsic predictability for TC genesis and intensification in moderately sheared and dry environments.

We evaluate the ability of Navy's Earth System Prediction Capability (Navy ESPC) to forecast tropical cyclone (TC) activity in the Western North Pacific basin in the subseasonal timeframe. Navy ESPC forecasts of TCs were added to a logistic regression model that also incorporated the Madden‐Julian Oscillation (MJO) and El Niño Southern Oscillation to forecast basin‐wide TC activity. The skill of the statistical‐dynamical model at 14‐day lead times doubled when forecasted accumulated cyclone energy from Navy ESPC was incorporated into the model. This suggests that Navy ESPC TC tracks contain information that is independent from the MJO. An analysis of forecasted TC positions also suggests that the model has skill in forecasting TC track out to 21 days. These results suggest that combining TC tracks and large‐scale environmental information in a statistical‐dynamical model offers a promising path forward for predictions of basin‐scale or even sub‐basin‐scale subseasonal TC activity.

The authors demonstrate how and why cloud–radiative forcing (CRF), the interaction of hydrometeors with longwave and shortwave radiation, can influence tropical cyclone structure through “semi idealized” integrations of the Hurricane Weather Research and Forecasting model (HWRF) and an axisymmetric cloud model. Averaged through a diurnal cycle, CRF consists of pronounced cooling along the anvil top and weak warming through the cloudy air, which locally reverses the large net cooling that occurs in the troposphere under clear-sky conditions. CRF itself depends on the microphysics parameterization and represents one of the major reasons why simulations can be sensitive to microphysical assumptions. By itself, CRF enhances convective activity in the tropical cyclone’s outer core, leading to a wider eye, a broader tangential wind field, and a stronger secondary circulation. This forcing also functions as a positive feedback, assisting in the development of a thicker and more radially extensive anvil than would otherwise have formed. These simulations clearly show that the weak (primarily longwave) warming within the cloud anvil is the major component of CRF, directly forcing stronger upper-tropospheric radial outflow as well as slow, yet sustained, ascent throughout the outer core. In particular, this ascent leads to enhanced convective heating, which in turn broadens the wind field, as demonstrated with dry simulations using realistic heat sources. As a consequence, improved tropical cyclone forecasting in operational models may depend on proper representation of cloud–radiative processes, as they can strongly modulate the size and strength of the outer wind field that can potentially influence cyclone track as well as the magnitude of the storm surge.

This paper introduces a new tool for verifying tropical cyclone (TC) forecasts. Tropical cyclone forecasts made by operational centers and by numerical weather prediction (NWP) models have been objectively verified for decades. Typically, the mean absolute error (MAE) and/or MAE skill are calculated relative to values within the operational center’s best track. Yet, the MAE can be strongly influenced by outliers and yield misleading results. Thus, this paper introduces an assessment of consistency between the MAE skill as well as two other measures of forecast performance. This “consistency metric” objectively evaluates the forecast-error evolution as a function of lead time based on thresholds applied to the 1) MAE skill; 2) the frequency of superior performance (FSP), which indicates how often one forecast outperforms another; and 3) median absolute error (MDAE) skill. The utility and applicability of the consistency metric is validated by applying it to four research and forecasting applications. Overall, this consistency metric is a helpful tool to guide analysis and increase confidence in results in a straightforward way. By augmenting the commonly used MAE and MAE skill with this consistency metric and creating a single scorecard with consistency metric results for TC track, intensity, and significant-wind-radii forecasts, the impact of observing systems, new modeling systems, or model upgrades on TC-forecast performance can be evaluated both holistically and succinctly. This could in turn help forecasters learn from challenging cases and accelerate and optimize developments and upgrades in NWP models.