# Atlantic Hurricane Activity Prediction: A Machine Learning Approach

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## Abstract

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## 1. Introduction

- Short term predictions: These are generally made 6 h to 7 days in advance. The predictands are spatio-temporal quantities, such as track and intensity of an individual cyclone.
- Long term predictions: These are seasonal forecasts generally made at least a month in advance. The predictands are one-dimensional time series like the total number of cyclones and Accumulated Cyclone Energy (ACE) in a basin (see Section 4 for details). Generally, certain handcrafted climatological indices are used as predictors.

## 2. Related Work

## 3. Hurricane Dynamics

- A preexisting low-pressure disturbance
- A sufficient Coriolis force to provide rotational force to oncoming winds.
- Sufficiently warm sea surface temperatures.
- High humidity in the lower to middle levels of the troposphere.
- Atmospheric instability to sustain a vertical movement of air parcels.
- Low vertical wind shear.

## 4. Predictors and Predictands

#### 4.1. NINO Indices for ENSO

**monthly**average SST at location $(x,y)$ at month ${t}_{m}$.

**daily**average SST at $(x,y)$ at day ${t}_{d}$ and ${N}_{d}=30\times {N}_{m}$.

#### 4.2. Accumulated Cyclone Energy (ACE)

## 5. Problem Formulation

## 6. Proposed Solution

#### Dimensionality Reduction/Information Extraction

- Approximately 10–20 s-values hold above 5% relative significance. Most of these s-values have relatively similar significance.
- 50–100 s-values hold above 1% relative significance.
- No single s-value holds above 15% significance.

## 7. Experiments

## 8. Results

#### 8.1. Prediction Accuracy

#### 8.2. Prediction Reliability

## 9. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Column vectors, ${\mathbf{u}}_{SLP}^{0}$, ${\mathbf{u}}_{SLP}^{1}$, ${\mathbf{u}}_{SLP}^{2}$ and ${\mathbf{u}}_{SLP}^{212}$ for ${\mathsf{\Gamma}}_{SLP}$. x-axis is the index value and y-axis is the amplitude. The vectors are plotted as 1-D time series.The D matrix is formed by ${\widehat{\eta}}_{3.4}(t+3$ months).

**Figure 3.**Reshaped column vectors (in clockwise order starting from upper left), ${\mathbf{v}}_{SLP}^{0}$, ${\mathbf{v}}_{SLP}^{1}$, ${\mathbf{v}}_{SLP}^{2}$ and ${\mathbf{v}}_{SLP}^{212}$ for ${\mathsf{\Gamma}}_{SLP}$. The vectors were reshaped to 2-D matrices with same shape as the original SLP map ($25\times 72$). The W matrix is formed by ${\widehat{\eta}}_{3.4}(t+3$ months).

**Figure 4.**Monthly NINO3.4 predictions (January 2012 to December 2017) using SVD based dimensionality reduction compared with the original NINO3.4 index (black curve). x-axis is months starting from January, 2012.

**Figure 5.**Monthly NINO1.2 predictions (January 2012 to December 2017) using SVD based dimensionality reduction compared with the original NINO1.2 index (black curve). x-axis is months starting from January, 2012.

**Figure 6.**Annual Atlantic ACE predictions (2012 to 2018) using SVD based dimensionality reductions compared with the Poisson regression based seasonal predictions done by Colorado State University in August each year (dashed blue curve) and the original ACE curve (black curve).

**Figure 7.**Autocorrelation function plots for residuals from ACE prediction models. Dashed blue lines show intervals for 95% confidence.

Model | Role in the Model | Spatial Resolution | Original Temporal Resolution | Temporal Resolution Used in the Model | Data Source |
---|---|---|---|---|---|

NINO3.4 (${\eta}_{34}\left(t\right)$) | Predictand | NA | Monthly | Bimonthly | NOAA |

NINO1 + 2 (${\eta}_{1\phantom{\rule{3.33333pt}{0ex}}+\phantom{\rule{3.33333pt}{0ex}}2}\left(t\right)$) | Predictand | NA | Monthly | Bimonthly | NOAA |

ACE ($\varphi \left(t\right)$) | Predictand | NA | Annual | Bimonthly | HURDAT2 |

Sea Level Pressure ($SLP(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Sea Surface Temperature ($SST(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Zonal Wind Speed at 850 mb ($UWN{D}_{850mb}(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Meridional Wind Speed at 850 mb ($VWN{D}_{850mb}(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Zonal Wind Speed at 200 mb ($UWN{D}_{200mb}(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Meridional Wind Speed at 200 mb ($VWN{D}_{200mb}(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

Relative Humidity at 700 mb ($R{H}_{700mb}(t,x,y)$) | Predictor | ${5}^{\circ}\times {5}^{\circ}$ | 4 times Daily | Bimonthly | NCEP-NCAR |

**Table 2.**ACE pediction efficiency metrics for different prediction windows compared with CSU seasonal predictions made in August.

Model | MAE | RMSE | R2 | MSSS |
---|---|---|---|---|

CSU August Prediction | 51.71 | 62.26 | 0.0719 | −9.48% |

Fused CNN with ${t}_{p}=3$ months | 41.44 | 46.05 | 0.4571 | 40.09% |

Fused CNN with ${t}_{p}=6$ months | 61.15 | 83.31 | 0.0800 | −96.04% |

Fused CNN with ${t}_{p}=9$ months | 39.11 | 56.80 | 0.3079 | 8.87% |

Fused CNN with ${t}_{p}=12$ months | 37.19 | 48.45 | 0.5779 | 33.70% |

Fused CNN with ${t}_{p}=18$ months | 61.19 | 81.00 | 0.0656 | −85.35% |

**Table 3.**Prediction reliability metrics over the Test set (2012–2018) for different prediction windows with significance level of 0.05.

Model | Ljung Box Q Score | p-Value | Null ${\mathit{H}}_{0}$ |
---|---|---|---|

CSU August Prediction | 5.45 | 0.49 | Accepted |

Fused CNN with ${t}_{p}=3$ months | 10.57 | 0.1 | Accepted |

Fused CNN with ${t}_{p}=6$ months | 4.82 | 0.57 | Accepted |

Fused CNN with ${t}_{p}=9$ months | 6.66 | 0.35 | Accepted |

Fused CNN with ${t}_{p}=12$ months | 7.02 | 0.32 | Accepted |

Fused CNN with ${t}_{p}=18$ months | 3.86 | 0.7 | Accepted |

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**MDPI and ACS Style**

Asthana, T.; Krim, H.; Sun, X.; Roheda, S.; Xie, L.
Atlantic Hurricane Activity Prediction: A Machine Learning Approach. *Atmosphere* **2021**, *12*, 455.
https://doi.org/10.3390/atmos12040455

**AMA Style**

Asthana T, Krim H, Sun X, Roheda S, Xie L.
Atlantic Hurricane Activity Prediction: A Machine Learning Approach. *Atmosphere*. 2021; 12(4):455.
https://doi.org/10.3390/atmos12040455

**Chicago/Turabian Style**

Asthana, Tanmay, Hamid Krim, Xia Sun, Siddharth Roheda, and Lian Xie.
2021. "Atlantic Hurricane Activity Prediction: A Machine Learning Approach" *Atmosphere* 12, no. 4: 455.
https://doi.org/10.3390/atmos12040455