Tropical Cyclone EVOlution Model

TC Evolution is an experimental tropical cyclone current-intensity estimation model developed in 2025 and currently described as Beta 1.6. It is designed to estimate a storm's present maximum sustained wind (V_max) from a short sequence of storm-centred environmental and satellite inputs rather than produce a long-range forecast.

The system combines gridded environmental data, storm-centred infrared and water-vapour satellite imagery, sea-surface temperature, land masking, and recent storm-history scalars into a single neural architecture. In its Beta 1.6 form, TC Evolution uses a multimodal sequence model with separate encoders for full-storm satellite structure, zoomed inner-core satellite structure, and environmental fields, followed by a temporal Transformer encoder.

History

TC Evolution began as a 2025 prototype focused on tropical cyclone current-intensity analysis from storm-centred gridded data. The earliest versions were built to answer a practical question: whether a neural model could infer present intensity from recent storm evolution, rather than rely on a single image or on long-range forecast logic.

The project later evolved into a two-stage training workflow:

Stage A

The first major training stage used a satellite-only pretraining approach based on long-term storm-centred IR and WV imagery from 1998 to 2024. This stage was intended to teach the model broad tropical-cyclone structural recognition before environmental data were added.

Stage B

The second major stage fine-tuned the model on a multimodal dataset spanning 2015 to 2024. This stage added environmental wind and height fields, sea-surface temperature, land masking, and scalar storm-history features. The resulting architecture became the basis of the operational Beta line.

Beta 1.6

Beta 1.6 refers to the operational inference implementation using the Stage B multimodal architecture. In this version, the model is driven by:

• storm-centred GFS environmental fields,
• storm-centred GOES infrared and water-vapour imagery,
• OISST sea-surface temperature,
• land masking,
• and ATCF storm-history data for recent motion and prior intensity.

Operation

TC Evolution is designed for current intensity analysis. It does not operate as a traditional long-range forecasting model. Instead, it takes a short sequence of recent storm-centred frames, processes their structure and environment, and predicts the storm's present intensity.

In operational use, the system:

• reads storm history from ATCF best-track or operational b-deck style data,
• gathers recent environmental fields from GFS,
• gathers recent satellite imagery from GOES,
• gathers sea-surface temperature data,
• builds storm-centred grids for several recent synoptic times,
• normalises the data using checkpoint statistics,
• and estimates the current V_max in knots.

Principles

TC Evolution was built around several design principles.

Evolution over snapshot analysis

The model is intended to represent the idea that tropical cyclone intensity is not purely a visual snapshot problem. Instead, intensity depends on how the storm has been evolving over recent hours. For that reason, the model uses:

• previous intensity,
• recent intensity tendency,
• storm motion,
• and a sequence of recent frames rather than a single time step.

Inner-core structure matters

The system uses both a full storm-centred satellite crop and a zoomed inner-core crop. This was intended to let the model separate broad-scale storm organisation from features such as eye definition, eyewall structure, and core symmetry.

Environment matters

The model does not treat the cyclone as an isolated image. Environmental wind fields, low- and upper-level flow, shear-related diagnostics, SST, and land interaction are included because storm intensity is strongly linked to environmental context.

Operational practicality

The inference system was designed around data sources that can be retrieved operationally, especially for storms not yet present in finalized archival best-track files.

Architecture

The Beta 1.6 implementation uses a neural network named TropicalCurrentIntensityNet.

Input format

The model ingests a storm-centred sequence with shape:

[channels, time, height, width]

In Beta 1.6, the input uses:

• 16 channels
• 41 × 41 spatial grids
• a short sequence of recent 6-hourly frames

Channel structure

The 16 channels are arranged as follows:

Channel group	Channels	Description
Environmental fields	10	u10, v10, u850, v850, u200, v200, gh850, rh850, shear200_850, shear850_sfc
Satellite full field	2	infrared (IR), water vapour (WV)
Satellite zoom field	2	zoomed infrared, zoomed water vapour
Surface context	2	sea-surface temperature (SST), land mask

Scalar features

In addition to the gridded input, TC Evolution uses a separate scalar feature vector. In Beta 1.6, this includes:

• latitude,
• longitude encoded as sine and cosine,
• month encoded as sine and cosine,
• basin one-hot encoding,
• zonal and meridional storm motion,
• V_max at t−6 h,
• V_max at t−12 h,
• 6-hour intensity change,
• and availability flags for recent intensities.

Branch encoders

The model uses three separate 2D residual backbones:

Full satellite encoder

The full_sat_enc branch processes the two-channel full storm satellite input.

Zoom satellite encoder

The zoom_sat_enc branch processes the two-channel inner-core zoom satellite input.

Environmental encoder

The env_enc branch processes the remaining twelve channels:

• ten environmental fields,
• SST,
• and land mask.

Each branch is built from:

• convolution layers,
• GroupNorm,
• GELU activation,
• residual 2D blocks,
• progressive downsampling,
• and adaptive average pooling.

Temporal encoder

For each time step, the outputs of the three branches are concatenated and projected into a shared hidden representation. These per-frame embeddings are then fed into a temporal Transformer encoder with:

• a learned class token,
• learned temporal positional embeddings,
• hidden dimension 384,
• 8 attention heads,
• and 4 Transformer layers.

The temporal encoder is intended to summarize short-term storm evolution rather than a single static frame.

Scalar fusion and output

The scalar feature vector is processed by a small scalar network and fused with the Transformer summary. The fused representation is then used to produce the final intensity estimate.

The Beta 1.6 operational implementation predicts intensity through a delta formulation:

<math>\hat{V}_{t} = V_{t-6} + \Delta V</math>

This means the model predicts the present intensity as the previous 6-hour intensity plus a learned adjustment.

Data

Stage A training data

The satellite-only pretraining stage used storm-centred data from 1998–2024, consisting of:

• infrared imagery,
• water-vapour imagery,
• zoomed infrared imagery,
• and zoomed water-vapour imagery.

This stage was intended to provide long-horizon structural pretraining for tropical cyclone appearance.

Stage B training data

The multimodal fine-tuning stage used storm-centred data from 2015–2024. These data included:

• GFS-derived environmental fields,
• satellite IR and WV imagery,
• zoomed satellite imagery,
• SST,
• land mask,
• and best-track intensity labels.

Label source

Training targets were based on synoptic tropical cyclone intensity labels. For operational 2025-style testing and inference, ATCF best-track or b-deck style records were used when final archival products were not the intended operational source.

Spatial setup

The storm-centred grid configuration used in Beta 1.6 is:

Parameter	Value
Output resolution	0.25°
Outer box size	10.0°
Inner zoom size	4.0°
Final grid size	41 × 41

Training

Stage A results

The satellite-only stage was treated mainly as a representation-learning stage rather than the final operational model. Development logs reported the following Stage A test metrics:

Metric	Value
Score	8.620
RMSE	5.73 kt
RMSE 96+	9.91 kt
RMSE 137+	13.91 kt

Stage B results

The multimodal fine-tuning stage improved performance and became the basis of the operational model. Development logs reported the following final Stage B test metrics:

Metric	Value
Score	7.413
RMSE	5.47 kt
RMSE 96+	7.24 kt
RMSE 137+	12.54 kt
RMSE 96–112	6.86 kt
RMSE 113–136	7.39 kt
RMSE 137+ bin	12.54 kt

These results were interpreted as an improvement over the satellite-only stage, especially in the stronger-storm regime.

Performance

Informal 2025 operational spot checks

During 2025-style operational testing, several storm cases were manually compared against best-track values and the Advanced Dvorak Technique (ADT).

Storm ID	Time (UTC)	Best-track Vmax	TC Evolution	Absolute error
AL132025	2025-10-28 12:00	155 kt	151.5 kt	3.5 kt
AL082025	2025-09-27 00:00	120 kt	100.0 kt	20.0 kt
AL022025	2025-06-29 18:00	40 kt	37.1 kt	2.9 kt

In this small informal sample:

• the model performed very well on one extreme-intensity case,
• underestimated one major hurricane case,
• and performed very well on one weak-storm case.

ADT comparison

The same spot-check sample was also compared against ADT values provided during testing. In that limited comparison:

• TC Evolution outperformed ADT on one extreme-intensity case,
• ADT outperformed TC Evolution on one mid-major hurricane case,
• and both were effectively correct on one weak-storm case.

This comparison was informal and not presented as a formal full-season skill study.

Operational implementation

The Beta 1.6 inference code includes:

• safe PyTorch checkpoint loading,
• ATCF storm-history retrieval,
• GFS file selection and download,
• GOES file selection and processing,
• OISST retrieval and interpolation,
• storm-centred frame construction,
• and final model inference from a saved multimodal checkpoint.

The operational implementation normalises gridded and scalar features using statistics stored in the model checkpoint.

Strengths

Observed and intended strengths of TC Evolution include:

• explicit modelling of storm evolution rather than single-frame regression,
• separate treatment of full-storm and inner-core satellite structure,
• integration of environmental context,
• direct use of previous intensity history,
• and demonstrated ability to represent very intense hurricanes in at least some operational spot checks.

Limitations

Known limitations of Beta 1.6 include:

• case-dependent errors in some major-hurricane situations,
• limited formal operational verification relative to established methods,
• dependence on upstream file availability and storm-centred data extraction quality,
• potential data-distribution differences between training imagery and operational imagery,
• and the fact that the system remains a beta research model rather than an official operational standard.

Naming

The name TC Evolution reflects the central idea of the model: tropical cyclone intensity is treated as an evolving state shaped by recent storm history, internal structure, and environmental conditions.

Intended use

TC Evolution is intended for:

• experimental tropical cyclone current-intensity analysis,
• case-study review,
• research prototyping,
• and internal benchmarking against satellite-based techniques.

It is not intended to replace official agency products without broader validation and full operational testing.