Understanding Tropical Cyclone Track and Intensity Prediction: A Deep Dive into TCN Dataset and Models

Definition of the Problem

The prediction of tropical cyclone (TC) track and intensity presents a complex spatiotemporal challenge. To address this, we developed the TropiCycloneNet (TCN) dataset, which is tailored to encapsulate temporal and spatial elements pertinent to hurricane forecasting.

The TCN dataset is categorized into three core components:

1. Inherent Attribute Data: This includes essential geographical and meteorological attributes like longitude, latitude, atmospheric pressure, and wind speed, denoted as (Data_{1d}).

2. Meteorological Grid Data: Encompassing detailed variables derived from meteorological grids, such as geopotential height, which are referred to as (Data_{3d}).

3. Environmental Data (Env-Data): This includes temporal factors like movement velocity, historical directional changes (over the past 24 hours), and subtropical high regions.

For any given TC, we collect observational data (x_t) at discrete time points, represented as (X = {x_1, x_2, …, x_n}). Each observation can be unwrapped into specific components at time (t):

[
xt = {x{1d}^{t}, x{3d}^{t}, x{env}^{t}}
]

The goal of our TCN model (denoted as (TCNM)) is to predict future outcomes denoted as (\hat{Y} = {\hat{y}{n+1}, \hat{y}{n+2}, …, \hat{y}{n+m}}), aiming to closely approximate the true TC trajectory and intensity—referred to as (Y = {y{n+1}, y{n+2}, …, y_{n+m}}).

In our analysis, we utilize (n = 8) for historical data input and (m = 4) for forecasting, which translates to the model receiving 42 hours of TC data to predict the next 24 hours.

TropiCycloneNet Dataset

The TCN dataset ((TCN_{D})) consists of historical data for TCs developed in six major ocean regions during recent decades. This dataset includes 3,630 TCs classified across six intensity categories, predominantly sourced from the Southern Hemisphere. TCN’s multivariate and multimodal structure allows for a diverse data analysis.

Overview of Data Categories

• Inherent Attribute Data

• Meteorological Grid Data

• Environmental Data

The contributions of each of these data categories toward enhancing the accuracy of deep learning predictions are detailed in a comparative table. One of the significant strengths of (TCN_{D}) is its diversity and comprehensiveness in comparison to existing datasets like the CMA-BST, TCTSCI, and IBTrACS, as detailed in an accompanying comparison table.

This dataset stands as an open resource, with periodic updates ensuring that it remains relevant and reliable. It is accessible via platforms such as GitHub and Zenodo for broader community use.

Types of Data in TCN

Inherent Attributes Data

This foundational data directly reflects a TC’s status, including coordinates, atmospheric pressure, and wind measurements. Normalization processes have been applied to enhance the model’s learning capacity, ensuring consistency across datasets.

Meteorological Grid Data

Utilizing resources like the ERA5 dataset, we extract features that represent the TC’s environments, including grid data for geopotential heights, surface temperatures, and various wind components, crafted through careful spatial and temporal resolution choices.

Environmental Data

Recognizing that environmental conditions play a critical role in TC development, we incorporate data on seasonal factors, direction, velocity, and regional geographical contexts. One notable method used is one-hot encoding for location data to prevent misleading numerical relationships.

TropiCycloneNet Model

The TCN model employs a Generative Adversarial Network (GAN) structure, built around several components:

1. 3D-Data Encoder and 1D-Data Encoder: These modules extract spatiotemporal features from the diverse datasets.

2. Environment-Time Network: Acknowledges the changing environmental role over time and incorporates this understanding into predictive modeling.

3. Multiple Generators: Allow for generating varied prediction outputs, improving robustness against discrepancies in the historical data.

The model’s architecture is illustrated in a diagram detailing data flow and module interdependencies.

Experimental Setup and Metrics

To evaluate the efficacy of the TCN model, comprehensive experiments were conducted, using a substantial dataset spanning from 1950 to 2021, divided into training (80%) and testing (20%) sets. We measure performance through absolute errors in predicting TC tracks and intensity metrics such as atmospheric pressure and wind speeds.

Performance Evaluation

The TCN’s performance is assessed across six major sea areas, demonstrating impressive predictive capabilities compared to models trained on more localized data. Our model significantly outperforms conventional methods by leveraging the extensive TCN dataset.

Improvements and Observations

TCN showcased notable advantages in scenarios involving TCs from diverse sea areas, emphasizing the rich dataset’s role in model training. The model exhibited a consistent improvement in prediction accuracy for varying intensities and behaviors of TCs over different seas.

Comparison with State-of-the-Art Methods

The TCN model was benchmarked against nine established deep learning methods, which include both classic and modified architectures aimed specifically at TC forecasting. Across multiple tests, TCN consistently outperformed these models, highlighting its ability to leverage heterogeneous meteorological data for improved predictive accuracy.

Deep Learning Context

The comparisons illustrate that while traditional models struggle with multidimensional data, TCN thrives due to its sophisticated input strategies and diverse data architecture. The model’s architectural innovations—such as the Generator Chooser Network—are pivotal in this performance leap.

Qualitative Analysis of Predictions

For a tangible understanding of the TCN model’s efficacy, visual analyses of predictions against ground truth were conducted. These visualizations reveal how TCN contextualizes information through environmental factors to make predictions, providing insights into its operational capabilities under varying storm conditions.

Focus on Rapid Intensifying and Recurving Cases

Special attention has been paid to particularly chaotic TC scenarios, such as rapid intensifications and recurving behaviors. Here TCN exhibits significant promise, maintaining a degree of prediction reliability even when severe meteorological anomalies arise.

In summary, the TCN dataset and model represent a significant stride in the deep learning framework for TC forecasting. With a focus on integrating multivariate meteorological data, TCN continues to enhance our understanding and prediction accuracy in this critical area of meteorology, paving the way for future advancements.