System and Method for Determining Tropical Cyclone Intensity via the Moored …
US20250306243A1
Abstract
A method of forecasting a maximum wind intensity associated with tropical cyclones, the method includes identifying a three-dimensional (3-D) field of ocean temperature and velocity, identifying a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation, determining a set of heat fluxes associated with ocean heat, and generating a 2-D map of the maximum potential intensity (MPI) based on (i) the set of heat fluxes and (ii) the first and second sets of data. The method may include training a machine learning model based on the first and second sets of data or the 2-D map of the MPI, and performing, based on the trained machine learning model and the 2-D map of the MPI, a mitigating activity corresponding to anticipated effects associated with the determined upper bound for tropical cyclone wind speed at a geographical location.
Description (excerpt)
CROSS-REFERENCE This Application is a nonprovisional application of and claims the benefit of priority under 35 U.S.C. § 119 based on U.S. Provisional Patent Application No. 63/570,326 filed on Mar. 27, 2024. The Provisional Application and all references cited herein are hereby incorporated by reference into the present disclosure in their entirety. FEDERALLY-SPONSORED RESEARCH AND DEVELOPMENT The United States Government has ownership rights in this invention. Licensing inquiries may be directed to Office of Technology Transfer, US Naval Research Laboratory, Code 1004, Washington, DC 20375, USA; +1.202.767.7230; nrltechtran@us.navy.mil, referencing Navy Case #211918. TECHNICAL FIELD The present disclosure is related to forecasting a maximum wind intensity, and more specifically to, but not limited to a system and method of determining tropical cyclone intensity via the Moored Maximum Potential Intensity (MMPI) framework and the implementation of that framework via an artificial intelligence machine learning model. BACKGROUND The traditional tropical cyclone (TC) potential intensity (PI) concept was pioneered in 1986 by K. Emanuel. Emanuel's PI is based on the Carnot heat engine model, in which the ocean acts as a “hot plate” for atmospheric convection. Emanuel's PI (EMPI) is traditionally calculated with coefficients of exchange for momentum and enthalpy, sea surface temperature (SST), outflow temperature, and moist static energy (MSE) disequilibrium. The latter is often parameterized in terms of latent and sensible heat flux (LH, SH) or convective available potential energy (CAPE). While it is known that TCs are driven by the ocean, the ocean is only explicitly involved in calculating EMPI via SST. EMPI includes no subsurface information, such as ocean heat content (OHC) or dynamic controls of the ocean heat fluxes. SUMMARY This summary is intended to introduce, in simplified form, a selection of concepts that are further described in the Detailed Description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. Instead, it is merely presented as a brief overview of the subject matter described and claimed herein. The present disclosure provides for a method of forecasting a maximum wind intensity associated with tropical cyclones. The method may include identifying, by a processing device, a first set of data comprising a three-dimensional (3-D) field of ocean temperature and velocity, wherein at least a portion of the first set of data is identified in situ and via one or more remote sensors. The method may include identifying, by the processing device, a second set of data comprising a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation wherein at least a portion of the second set of data is identified in situ and via one or more remote sensors. The method may include determining, by the processing device, based on one or more dynamic ocean processes, a set of heat fluxes associated with ocean heat, and generating, by the processing device, a 2-D map of the maximum potential intensity (MPI) based on (i) the set of heat fluxes and (ii) the first and second sets of data. The method may include training, by the processing device, a machine learning model based on the first set of data, the second set of data, or the 2-D map of the MPI, and performing, based on the trained machine learning model and the 2-D map of the MPI, a mitigating activity corresponding to anticipated effects associated with the determined upper bound for tropical cyclone wind speed at a geographical location. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates an example diagram illustrating a moored maximum potential intensity average and Emanuel's maximum potential intensity average, in accordance with one or more aspects described herein. FIG. 2 illustrates an example diagram illustrating trends of moored maximum potential intensity average, Emanuel's maximum potential intensity, and regression, in accordance with one or more aspects described herein. FIG. 3 illustrates an example diagram illustrating ocean heat content trends, in accordance with one or more asp
Filing details
- Inventors
- Robert K. Forney
- Assignee
- The Government Of The United States Of America, As Represented By The Secretary …
- Filed
- Mar 26, 2025
- Granted
- Application pending
Bibliographic data and excerpted text sourced from Google Patents (public record) as part of IP TechMatch's current-filings monitor. This filing is not part of the 2019 historical archive. For the authoritative full text, drawings, and legal status, see the source links above or consult USPTO records directly.