Pith. sign in

REVIEW 1 cited by

Predicting basin and landfalling hurricane numbers from sea surface temperature

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv physics/0701170 v2 pith:B2QWHIVK submitted 2007-01-15 physics.ao-ph

classification physics.ao-ph
keywords basinnumberhurricanepredictingpredictionhurricaneslandfallingnumbers
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We are building a hurricane number prediction scheme based on first predicting main development region sea surface temperature (SST), then predicting the number of hurricanes in the Atlantic basin given the SST prediction, and finally predicting the number of US landfalling hurricanes based on the prediction of the number of basin hurricanes. We have described a number of SST prediction methods in previous work. We now investigate the empirical relationship between SST and basin hurricane numbers, and put this together with the SST predictions to make predictions of both basin and landfalling hurricane numbers.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A guided diffusion model pre-trained on climate simulations reconstructs multi-layer global ocean temperature from sparse observations, reporting low MSE on CMIP6 and EN4 data.

Pith tools