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FourCastNet TC Adapter
======================

Overview
--------

``fourcastnet_tc`` completes the first wave of experimental foundation-weather storm adapters in the staged roadmap.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Tropical Cyclone

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Experimental Adapter

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Track + Intensity

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Tropical Cyclone Benchmark </benchmarks/tropical_cyclone_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``fourcastnet_tc`` completes the first wave of experimental foundation-weather storm adapters in the staged roadmap.

The PyHazards version is intentionally lightweight and uses the same trajectory output contract as the other storm baselines.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Tropical Cyclone Benchmark </benchmarks/tropical_cyclone_benchmark>`

**Mapped benchmark ecosystems:** :doc:`IBTrACS </benchmarks/ibtracs>`

External References
-------------------

**Paper:** `FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators <https://arxiv.org/abs/2202.11214>`_ | **Repo:** `Repository <https://github.com/NVlabs/FourCastNet>`__

Registry Name
-------------

Primary entrypoint: ``fourcastnet_tc``

Supported Tasks
---------------

- Track + Intensity

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(name="fourcastnet_tc", task="regression", input_dim=8, history=6, horizon=5)
   preds = model(torch.randn(2, 6, 8))
   print(preds.shape)

Notes
-----

- Experimental adapter: intended for shared-evaluator prototyping rather than exact weather-model parity.