.. This file is generated by scripts/render_model_docs.py. Do not edit by hand. Tropical Cyclone MLP ==================== Overview -------- ``tropicalcyclone_mlp`` complements ``hurricast`` with a lighter-weight hurricane baseline that uses the same storm-history input contract. 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 Implemented .. 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 ` .. container:: catalog-stat-note Primary benchmark-family link used for compatible evaluation coverage. Description ----------- ``tropicalcyclone_mlp`` complements ``hurricast`` with a lighter-weight hurricane baseline that uses the same storm-history input contract. The adapter is useful for practical low-cost intensity and trajectory experiments in basin-filtered settings. Benchmark Compatibility ----------------------- **Primary benchmark family:** :doc:`Tropical Cyclone Benchmark ` **Mapped benchmark ecosystems:** :doc:`TCBench Alpha ` External References ------------------- **Paper:** `Deep Learning Experiments for Tropical Cyclone Intensity Forecasts `_ | **Repo:** `Repository `__ Registry Name ------------- Primary entrypoint: ``tropicalcyclone_mlp`` Supported Tasks --------------- - Track + Intensity Programmatic Use ---------------- .. code-block:: python import torch from pyhazards.models import build_model model = build_model(name="tropicalcyclone_mlp", task="regression", input_dim=8, history=6) preds = model(torch.randn(2, 6, 8)) print(preds.shape) Notes ----- - Outputs are lead-time sequences of latitude, longitude, and intensity targets.