In situ measurement station for monitoring wind and water properties in extreme hydrodynamic conditions

Patent metadata

Field	Value
Publication number	US-12228697-B2
Application number	US-202318153232-A
Country	US
Kind code	B2
Filing date	Jan 11, 2023
Priority date	Nov 13, 2020
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

The present disclosure describes various embodiments of systems, apparatuses, and methods for large-scale processing of weather-related data. For one such system, the system comprises a database of weather-related data providing from at least one weather monitoring station and at least one processor for coordinating a data processing job for processing a set of input weather-related data from the database. Accordingly, the input data comprises sensor data from the at least one weather monitoring station positioned on an open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event, wherein the at least one processor is configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event. Other systems, apparatuses, and methods are also provided.

First claim

Therefore, at least the following is claimed:

1. A system for large-scale processing of weather-related data comprising:
a database of weather-related data providing from at least one weather monitoring station anchored to an open shoreline within a littoral zone of a coastal area;
at least one processor for coordinating a data processing job for processing a set of input weather-related data at the coastal area from the database;
wherein the input weather-related data comprises sensor data from the at least one weather monitoring station positioned on the open shoreline during a hydrodynamic event comprising storm surge and wave forces at the coastal area, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event;
wherein the at least one processor is configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event comprising the storm surge and wave forces at the coastal area; and
wherein an individual weather monitoring station comprises:
an upper mast section having at least one elongated post equipped with an upper instrumentation package, wherein the upper instrumentation package at the upper mast section includes an orientation sensor to detect an orientation of the upper mast section relative to the earth surface's coordinate system, communications circuitry that is configured to establish a communications network with a network base station; at least a wind velocity sensor, and a control unit that is configured to at least receive sensor data and transmit communication data via the communications circuitry;
at least one lower mast section including at least one elongated post equipped with a lower instrumentation package that is coupled to the upper mast section, wherein the lower instrumentation package includes at least a water depth sensor and a wave direction sensor; and
a helical anchoring system that is coupled to the lower mast section, wherein the helical anchoring system includes at least one helical subsurface anchor for inserting into a ground surface within the littoral zone of the coastal area.

2. The system of claim 1 , wherein the at least one processor is further configured to identify and predict patterns of extreme hydrodynamic events based on an assimilation of meteorological observations derived from the weather-related data that include surface wind observations at the open shoreline.

3. The system of claim 2 , wherein the sensor data comprises surface wind velocity measurements obtained from the at least one weather monitoring station at the open shoreline.

4. The system of claim 3 , wherein the sensor data comprises water level measurements obtained from the at least one weather monitoring station at the open shoreline.

5. The system of claim 4 , wherein the sensor data comprises wind pressure sensor data and air temperature sensor data from the open shoreline.

6. The system of claim 5 , wherein the sensor data further comprises water temperature data and salinity sensor data from the open shoreline.

7. The system of claim 6 , wherein the sensor data further comprises erosion sensor data from the open shoreline.

8. The system of claim 7 , wherein the sensor data is augmented with video data obtained from the at least one weather monitoring station at the open shoreline.

9. The system of claim 1 , wherein the at least one processor is configured to utilize machine learning to extract wave field information from the sensor data supplied by the at least one weather monitoring station.

10. The system of claim 1 , wherein the at least one processor is configured to compute parameters of a storm structure based on the sensor data supplied by the at least one weather monitoring station.

11. The system of claim 1 , wherein the at least one processor is configured to predict behavior of a storm based on the sensor data supplied by the at least one weather monitoring station.

12. The system of claim 1 , wherein the at least one processor is configured to train the improved weather prediction model using the sensor data obtained from the at least one weather monitoring station at the open shoreline during an actual landfall hurricane event.

13. A method for large-scale processing of weather-related data comprising:
accessing, by a computer-based system, a database of weather-related data providing from at least one weather monitoring station anchored to an open shoreline within a littoral zone of a coastal area; and
assimilating a set of input weather-related data from the database and generating, using machine learning, an improved weather prediction model for a hydrodynamic event comprising a storm surge and wave forces at the coastal area,
wherein the input weather-related data comprises sensor data from the at least one weather monitoring station positioned on the open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event comprising the storm surge and wave forces at the coastal area, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event; and
wherein an individual weather monitoring station comprises:
an upper mast section having at least one elongated post equipped with an instrumentation package, wherein the instrumentation package at the upper mast section includes an orientation sensor to detect an orientation of the upper mast section relative to the earth surface's coordinate system, communications circuitry that is configured to establish a communications network with a network base station; at least a wind velocity sensor, and a control unit that is configured to at least receive sensor data and transmit communication data via the communications circuitry;
at least one lower mast section including at least one elongated post equipped with a lower instrumentation package that is coupled to the upper mast section, wherein the lower instrumentation package includes at least a water depth sensor and a wave direction sensor;
a helical anchoring system that is coupled to the lower mast section, wherein the helical anchoring system includes at least one helical subsurface anchor for inserting into a ground surface within the littoral zone of the coastal area.

14. The method of claim 13 , further comprising predicting, by the computer-based system using machine learning, patterns of extreme hydrodynamic events based on an assimilation of meteorological observations derived from
the weather-related data that include surface wind observations at the open shoreline.

15. The method of claim 13 , wherein the sensor data comprises surface wind velocity measurements, water level measurements, wind pressure sensor data, air temperature sensor data, water temperature data, salinity sensor data, and erosion sensor data, each obtained from the at least one weather monitoring station on the open shoreline.

16. The method of claim 13 , wherein the sensor data is augmented with video data obtained from the at least one weather monitoring station at the open shoreline.

17. The method of claim 13 , further comprising:
extracting, by the computer-based system using machine learning, wave field information from the sensor data supplied by the at least one weather monitoring station;
computing, by the computer-based system using machine learning, parameters of a storm structure based on the sensor data supplied by the at least one weather monitoring station; and
predicting, by the computer

Assignees

• Univ Florida
• Univ Clarkson
• Woods Hole Oceanographic Inst

Inventors

• Masters Forrest J
• Fernandez-Caban Pedro L
• Phillips Brian M
• Ferraro Christopher C
• Raubenheimer Britt
• Lu Wei-Ting

Classifications

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G01W1/10

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G06N20/00

  Machine learning · CPC title

• H04Q9/00 Primary

  Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom · CPC title