Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2025
Data sources: ZENODO
ZENODO
Other literature type . 2025
Data sources: Datacite
ZENODO
Other literature type . 2025
Data sources: Datacite
versions View all 2 versions
addClaim

LTDW - Long-Term Deterministic Weather Theory

Theory Solution for Extreme Atmospheric Phenomena
Authors: A-Marl, JP;

LTDW - Long-Term Deterministic Weather Theory

Abstract

LTDW - Long-Term Deterministic Weather Theory: Solution for Extreme Atmospheric Phenomena UPDATED VERSION - v1.7 dated May-13, 2026 (supersedes Executive Summary Sponsorship heading)Why This Matters Now - As climate volatility intensifies and predictive models struggle to keep pace with planetary-scale disruptions, the need for deterministic weather forecasting has never been more urgent. Today’s probabilistic systems offer ranges and likelihoods, but they fall short of delivering the precision required to anticipate and mitigate extreme atmospheric phenomena. By having this theory now available (and before artificial general intelligence fully emerges) we immediately lay the groundwork by establishing the necessary metadata and capabilities in which advanced artificial intelligence systems can encode, validate, and optimize long-range deterministic forecasts. This is a task of generational significance, one we begin today with the hope of seeing it concluded within a few years once we can have Long-Term Deterministic Weather for Extreme Atmospheric Phenomena at 99.9% confidence (up to 1 year), as synthetic cognition evolves to meet the scale and complexity of Earth’s atmosphere. The objective is to encode an interoperable and essential set of conditions required to run deterministic weather forecasting over long time horizons. JP A-Marl LTDW Theory Solution is now complete and published Only the Executive Summary has been published. The full LTDW Theory will be avaiable for the next stage of human-AI evolution for its implementation at planetary scale. A Blockchain Financial Instrument for LTDW A blockchain‑based financial instrument offers a clean, auditable, and neutral pathway for LTDW's hardware and software implementation. Follow up on LTDW Interested parties seeking to lead in climate global resilience and innovation are invited to contact the author. All rights reserved Copyright © 2025/6 JP A-Marl DOI 10.5281/zenodo.17798798 May 2026, JP A-Marl jpamarl.phi@gmail.com

1-Year Forecast of Storms, Hurricanes, Typhoons Landfall and Extreme Atmospheric Phenomena Causing Loss of Life and Severe Damage

Keywords

LTDW, Long-Term Deterministic Weather, year-ahead hurricane forecast, deterministic landfall, catastrophe model, cat-bond, sovereign risk transfer, extreme weather prediction, climate risk engineering, JP A-Marl LTDW Weather

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green