# Machine Learning in Tropical Cyclone Forecast Modeling: A Review

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## Abstract

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## 1. Introduction

**This review is organized as follows:**Section 1 is the introduction, which introduces the progress and problems of TC forecasts, machine learning; the application of machine learning in remote sensing, meteorology, and ocean fields, the prospect of machine learning in TC prediction, and the organization of this paper. Section 2 summarizes the practical process of machine learning, and an overview of machine learning in TC forecasts. In Section 3, the successful cases of machine learning in TC forecasts modeling in recent years are divided into five categories, and a detailed summary of them is described. Section 4 is our reflections on the above problems and progress, indicating the opportunities and challenges for machine learning in future tropical cyclone forecasting. Section 5 is our conclusions.

## 2. Machine Learning

#### 2.1. A Brief Introduction to Machine Learning

#### 2.2. An Overview of Machine Learning in TC Forecasts

## 3. Applications

#### 3.1. Genesis Forecasts

#### 3.1.1. Short-Term Forecasting

#### 3.1.2. Long-Term Forecasting

#### 3.2. Track Forecasts

#### 3.2.1. Path Prediction

#### 3.2.2. Predictors Mining and Similarity Search

#### 3.3. Intensity Forecasts

#### 3.3.1. Intensity Estimation

#### 3.3.2. Intensity Prediction

#### 3.3.3. Intensity Change Prediction

#### 3.4. TC Weather and the Disastrous Impact Forecasts

#### 3.4.1. TC Wind Field Forecasts

#### 3.4.2. TC Rainfall Forecasts

#### 3.4.3. Storm Surge Forecasts

#### 3.5. Improving Numerical Forecast Models

#### 3.5.1. Pre-Processing

#### 3.5.2. Improved Models

#### 3.5.3. Post-Processing

## 4. Discussion

#### 4.1. Opportunities

#### 4.2. Challenges

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

TC | Tropical cyclone |

ML | Machine learning |

AI | Artificial Intelligence |

SVM | Support vector machine |

DT | Decision tree |

ANN | Artificial neural network |

CNN | Convolutional neural network |

RNN | Recurrent neural network |

DL | Deep learning |

SAE | Sparse autoencoder |

SAR | Synthetic aperture radar |

ConvLSTM | Convolutional long short-term memory network |

TrajGRU | Trajectory gate recurrent unit |

SVR | Support vector regression |

RF | Random forest |

BPN | Back-propagation neural network |

MLP | Multi-layer perceptron |

LSTM | Long short-term memory neural network |

CISK | Convective instability of the second kind |

WHISE | Wind-induced surface heat exchange |

ECMWF | European Centre for Medium-Range Weather Forecasts |

GFS | Global Forecast System |

UKMET | United Kingdom Meteorological Forecast Model |

LDA | Linear discriminative analysis |

NB | Naïve bayes |

KNN | K-nearest neighbor |

QDA | Qualitative data analysis |

PCA | Principal component analysis |

LR | Logistic regression |

MCS | Mesoscale convective system |

NOGAPS | Navy Operational Global Atmospheric Prediction System |

OLR | Outgoing longwave radiation |

QBO | Quasi-Biennial Oscillation |

MAE | Mean absolute error |

RMSE | Root mean square error |

GPI | Genesis potential index |

SOM | Self-organizing map |

NOEGM | Neural oscillatory elastic graph matching model |

HRBF | Hybrid radial basis function network |

TDSL | Time difference and structural learning |

OTCM | One-way-interactive Tropical Cyclone Model |

JTWC | Joint Typhoon Warning Center |

TFS | Track Forecast System |

CLIPER | Climatology and Persistence |

LAM | Florida State University based Limited Area Model |

QLM | Quasi Lagrangian Model |

NHC | National Hurricane Center |

MNN | matrix neural network |

GRU | Gated Recurrent Unit |

ENSO | El Niño-Southern Oscillation |

MJO | Madden-Julian Oscillation |

CART | Classification and regression tree |

GAN | Generative adversarial networks |

DBN | Deep belief networks |

SSM/I | Special Sensor Microwave/Image |

MLR | Multiple logistic regression |

2D-CNN | Two-dimensional convolutional neural network |

3D-CNN | Three-dimensional convolutional neural network |

WNP | West Northern Pacific |

JMA-GSM | Japan Meteorological Agency-Global Spectral Model |

CNN-LSTM | Convolutional neural network-Long short-term memory neural network |

WP | West Pacific |

EA | Evoutionary algorithm |

PSO | Partical Swarm Optimization |

OC_PI | ocean coupling potential intensity index |

RI | Rapid intensification |

non-RI | No rapidly intensifying |

LSSVM | Least squares support vector machine |

WRF | Weather Research and Forecasting |

MRA | Multiple regression analysis |

NCEP | National Centers for Environmental Prediction |

NCAR | National Center for Atmospheric Research |

FNN | Fuzzy neural network |

SRM | Stepwise regression method |

LLE | Locally linear embedding |

FNN-LLE | Fuzzy neural network-Locally linear embedding |

ANFIS | Adaptive neuro-fuzzy inference system |

NWP | Numerical weather prediction |

ANN-SFM | Artificial neural network-based storm surge forecast model |

HWRF | Hurricane Weather and Research Forecasting Model |

EMX | European Center for Medium-Range Weather Forecasts Global Model |

FSSE | Florida State Super Ensemble |

MOS | Model output statistics |

SSTC | Sea surface temperature cooling |

NICAM | Nonhydrostatic Icosahedral Atmospheric Model |

GFS-FNL | Global Forecasting System-Final Analysis Dataset |

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**Figure 2.**An organization chart of cases involving machine learning in tropical cyclone forecasts. The abbreviations used in this figure are as follows: logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), support vector regression (SVR), multi-layer perceptron (MLP), convolutional neural network (CNN), generative adversarial network (GAN), recurrent neural network (RNN), hybrid radial basis function network (HRBF), self-organizing map (SOM), principal component analysis (PCA), convolutional long short-term memory network (ConvLSTM).

Tasks | Algorithms | Main Idea | Reference |
---|---|---|---|

Short-term forecasting | LR | Select the optimal predictors and modeling for genesis forecasts | Wijnands, J.S. (2016). [40] |

DT | Predict future tropical cyclogenesis based on tropical perturbations | Zhang, W. (2015). [41] | |

Detect the causes of TCs by using predictors from satellite data | Park, M.S. (2016). [42] | ||

RF | Predict the development of MCSs | Ahijevych, D. (2016). [43] | |

AdaBoost | Determine whether MCSs will evolve into TCs | Zhang, T. (2019). [44] | |

SVM | Predict TC formation from satellite image data | Kim, M. (2019). [45] | |

CNN | Detect TCs and their precursors based on the simulation of numerical models | Matsuoka, D. (2018). [46] | |

Long-term forecasting | SVR | Generate forecasts of the TC activity for an upcoming season | Richman, M.B. (2012). [47] |

Reduce TC seasonal prediction errors | Richman, M.B. (2017). [48] | ||

Improve the accuracy of seasonal TC predictions | Wijnands, J.S. (2014). [49] | ||

MLP | Provide seasonal prediction of TC activity | Nath, S. (2016). [50] | |

SOM, FNN | Define GPI for a ensemble of global climate models | Yip, Z.K. (2012). [51] |

Tasks | Algorithms | Main Idea | Reference |
---|---|---|---|

Path prediction | HRBF | Develop an automatic and integrated TC identification and track mining system | Lee, R.T. (2000). [54] |

MLP | Predict cyclone tracks based on MLP with BP algorithms | Ali, M.M. (2007). [55] | |

Wang, Y. (2011). [56] | |||

RNN | Propose a sparse RNN with flexible topology for trajectory prediction | Moradi, K.M. (2016). [57] | |

Propose a fully connected RNN to predict the trajectory of TCs | Alemany, S. (2019). [58] | ||

MNN | Develop a predictive model that preserves spatial information from the cyclone tracks | Wang, Y. (2018). [59] | |

GAN | Predict TC tracks using GAN with satellite images and meteorological data | Rüttgers, M. (2018,2019). [60,61] | |

ConvLSTM | Propose a ConvLSTM-based spatio-temporal model to track and predict TC trajectories | Kim, S. (2019). [62] | |

CNN | Design a model fusing multi-source data to predict TC tracks | Giffard, R.S. (2020). [63] | |

Predictors mining | DT | Discover predictors and rules governing TC landfall and recurvature | Zhang; Geng (2013, 2016). [64,65,66] |

Similarity search | DBN | Find the similar TCs in history and reference this data to improve TC forecasting | Wang, Y. (2018). [67] |

Clustering | Apply K-means, fuzzy c-mean, and SOM for clustering TC tracks | Jinhua; Kim (2011, 2016). [68,69,70] | |

Study TC properties and large-scale factors | Camargo; Ramsay; Zhang; Geng (2007, 2008, 2012, 2013, 2016) [66,71,72,73,74,75] |

Tasks | Algorithms | Main Idea | Reference |
---|---|---|---|

Intensity estimation | DT | Estimate intensity using microwave image data | Bankert, R.L. (2003). [83] |

SVM | Propose a machine learning framework in labelling TC intensity levels | Chen, Z. (2018). [84] | |

CNN | Design a network architecture for categorizing TCs based on intensity | Pradhan, R. (2018). [85] | |

Explore the possibilities of estimating TC intensity from satellite images | Wimmers, A. (2019). [86] | ||

Estimate TC intensity as a regression task | Chen, B.F. (2019). [87] | ||

Employ 2D-CNN and 3D-CNN to analyze the relationship between satellite images and TC intensity | Lee, J. (2020). [88] | ||

Intensity prediction | MLP | Predict TC intensity values directly | Jong-Jin, B. (2000). [89] |

Compare different network-based models to identify the best intensity forecasts model | Chaudhuri, S. (2013). [90] | ||

RNN | Design a pure data-driven intensity prediction model | Pan, B. (2019). [91] | |

CNN-LSTM | Design a spatio-temporal model based on a hybrid network of 2D-CNN, 3D-CNN, and LSTM | Chen, R. (2019). [92] | |

Transfer learning | Develop a robust prediction model with transfer learning and stacking | Deo, R.V. (2017). [93] | |

Intensity change prediction | EA, PSO | Apply EA or PSO to predict whether TCs will intensify or weaken | Geng, H. (2015,2017). [94,95] |

DT | Validate RI related predictors and predict intensity change | Zhang; Gao (2013, 2016). [96,97] | |

RNN | Employ RNN with cooperative coevolution for RI prediction. | Zhang, W. (2013). [98] | |

SVM | Apply ML techniques to classify storms as RI or non-RI | Mercer, A. (2015). [99] | |

SVM, ANN, RF | Quantify the RI predictability using an ensemble of AI methods | Mercer, A. (2017). [100] | |

K-means | Explore TC-troughs configurations that are favorable for RI | Fischer, M.S. (2019). [101] |

Tasks | Algorithms | Main Idea | Reference |
---|---|---|---|

TC wind field forecasts | SVR | Develop a highly reliable surface wind speed prediction technique | Wei, C.C. (2015). [113] |

LSSVM | Estimate TC innercore 2D surface wind field structure | Zhang, C. (2017). [114] | |

MLP | Simulate the wind field inside the TC boundary layer | Snaiki, R. (2019). [115] | |

Construct a wind velocity simulation model | Wei, C.C. (2017). [116] | ||

Optimize TC winds from satellite data | Stiles, B.W. (2014). [117] | ||

TC rainfall forecasts | SVM | Design SVM-based models for the forecasts of hourly TC rainfall | Lin, G.F. (2009, 2013). [118,119] |

Design a two-stage TC-induced flood forecasting model | Lin, G.F. (2013). [120] | ||

MLP | Develop a shallow MLP to forecast TC rainfall | Lin, G.F. (2005). [121] | |

SOM, MLP | Develop a hybrid neural network model to forecast TC rainfall | Lin, G.F. (2009). [122] | |

ANN-MRA | Build a model for forecasting the total rainfall and the groundwater level | Hsieh, P.C. (2019). [123] | |

FNN-LLE | Design a TC precipitation prediction scheme | Huang, Y. (2018). [124] | |

Storm surge forecasts | MLP | Predict the short-term surge and surge deviation | Lee, T.L. (2007, 2009). [125,126] |

SVR | Rajasekaran, S. (2008). [127] | ||

BPN-ANFIS | Quantify the RI predictability using an ensemble of AI methods | Chen, W.B. (2012). [128] | |

MLP | Develop a time-dependent storm surge model for quick prediction | Kim, S.W. (2015). [129] | |

Predict the peak values of storm surge using the tropical storm parameters | Lewis, M.R.H. (2016). [130] | ||

ANN-SFM | Descibe a storm surge forecast model and an objective selection procedure | Kim, S. (2016, 2019). [131,132] |

Tasks | Algorithms | Main Idea | Reference |
---|---|---|---|

Pre-processing | SVM | Produce the probability distribution of the TC regions in the data to be assimilated | Lee, Y.J. (2019). [144] |

Improve model | RF, PCA | Replace simple TC winds parametric formulations with ML alogrithms | Loridan, T. (2017). [145] |

MLP, CNN | Parameterize SSTC induced by TCs to improve TC numerial models | Wei, J. (2017, 2018). [146,147] | |

Post-processing | CNN | Predict TC genesis using simulated OLR data from NICAM | Matsuoka, D. (2018). [46] |

Dectect TCs in the outputs dataset from climate models | Liu, Y. (2016). [148] | ||

Racah, E. (2017). [149] | |||

Kim, S. (2017). [150] | |||

ConvLSTM | Predict TC paths based on large-scale data generated by climate models | Kim, S. (2019). [62] | |

DT | Mine predictors of TC recurvature and landfall using the GFS-FNL dataset | Zhang, W. (2013). [64,65] | |

SVM, MLP, RF | Predict RI using the output dataset of GFS | Mercer, A. (2017). [100] |

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Chen, R.; Zhang, W.; Wang, X.
Machine Learning in Tropical Cyclone Forecast Modeling: A Review. *Atmosphere* **2020**, *11*, 676.
https://doi.org/10.3390/atmos11070676

**AMA Style**

Chen R, Zhang W, Wang X.
Machine Learning in Tropical Cyclone Forecast Modeling: A Review. *Atmosphere*. 2020; 11(7):676.
https://doi.org/10.3390/atmos11070676

**Chicago/Turabian Style**

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2020. "Machine Learning in Tropical Cyclone Forecast Modeling: A Review" *Atmosphere* 11, no. 7: 676.
https://doi.org/10.3390/atmos11070676