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GraphCast TC Adapter
====================

Overview
--------

``graphcast_tc`` is an experimental foundation-weather adapter that keeps the shared storm trajectory interface while remaining lightweight enough for CI.

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
-----------

``graphcast_tc`` is an experimental foundation-weather adapter that keeps the shared storm trajectory interface while remaining lightweight enough for CI.

The PyHazards version is intentionally wrapper-style and should be treated as an adapter contract rather than a full reproduction of the original weather model.

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

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

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

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

**Paper:** `GraphCast: Learning skillful medium-range global weather forecasting <https://www.science.org/doi/10.1126/science.adi2336>`_ | **Repo:** `Repository <https://github.com/google-deepmind/graphcast>`__

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

Primary entrypoint: ``graphcast_tc``

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

- Track + Intensity

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

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(name="graphcast_tc", task="regression", input_dim=8, 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.