Microwave Radiometer Calibration via Reference Signal

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Solution Overview

Problem

Conventional methods for observing precipitable water vapor, such as using GNSS and microwave radiometers, face challenges in accurately measuring local water vapor without requiring calibration with liquid nitrogen, which is difficult to handle and transport.

Innovation Solution

A learning system that combines data from multiple frequency radio waves received by a microwave radiometer and GNSS signals, employing machine learning to estimate precipitable water vapor using a polynomial regression model, and dimension reduction techniques to reduce noise and improve accuracy, eliminating the need for liquid nitrogen calibration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a microwave radiometer is used to measure water vapor in a local range, then measurement precision is improved, but device complexity increases due to the need for liquid nitrogen calibration

Engineering Contradiction:
Improvewater vapor measurement precisionVSAvoidcalibration system complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a microwave transmitter as an intermediary device that emits reference microwave signals. This transmitter serves as a mediator between the microwave radiometer and the atmosphere, providing a stable reference signal that eliminates the need for liquid nitrogen calibration. The reference signal from the transmitter allows the radiometer to measure water vapor content by comparing atmospheric microwave signals against this known reference, thereby simplifying the calibration system while maintaining measurement precision.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements self-service calibration by using the microwave transmitter to continuously provide reference signals. The microwave radiometer automatically compares received signals against these reference signals, enabling self-calibration without external intervention or liquid nitrogen. This self-service mechanism eliminates the need for manual calibration procedures and complex calibration equipment, directly resolving the contradiction between measurement precision and device complexity.

Inventive Principle:
Principle #25Self-service

2Reliability

If liquid nitrogen calibration is implemented, then reliability of measurement is improved, but ease of operation deteriorates due to handling difficulties

Engineering Contradiction:
Improvemeasurement reliabilityVSAvoidcalibration operation ease
Core Design Contradiction:
ReliabilityVSEase of operation

Solution Approach 1:

The patent replaces the mechanical/physical calibration system (liquid nitrogen) with an electronic signal-based system (microwave transmitter). Instead of using physical liquid nitrogen to cool and calibrate the radiometer, the system uses electronically generated microwave reference signals. This substitution eliminates the need for handling, storing, and transporting liquid nitrogen, dramatically improving ease of operation while maintaining measurement reliability through the stable reference signals.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Area of stationary object

If GNSS is used to observe water vapor over a wide range, then area of observation is improved, but measurement precision deteriorates due to inability to observe local water vapor

Engineering Contradiction:
Improveobservation areaVSAvoidlocal water vapor measurement precision
Core Design Contradiction:
Area of stationary objectVSMeasurement precision

Solution Approach 1:

The patent merges the advantages of both GNSS and microwave radiometer systems. It combines the wide-area observation capability of GNSS with the local measurement precision of microwave radiometers by using the microwave transmitter-radiometer system to provide localized reference measurements. This merged approach allows the system to maintain wide observation coverage while achieving high local measurement precision, resolving the contradiction between observation area and measurement precision.

Inventive Principle:
Principle #5Merging (Combining)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach allows for reliable estimation of precipitable water vapor in a local range with high accuracy, as demonstrated by a root mean square error of 1.8 mm, and reduces system costs by making the equipment less dependent on high-performance components.

Implementation Method 1

Water vapor observation based on a microwave radiometer exploits the radiation of radio waves from water vapor in the atmosphere and measures radio waves from water vapor and cloud

Methodology Applied
Scientific EffectRadio wave radiation from water vapor: Thermal Radiation

Implementation Method 2

If radio waves of two or more different frequencies emitted from four or more satellites can be received, an amount of delay in the radio waves can be detected. The amount of delay in radio waves corresponds to the water vapor amount

Methodology Applied
Scientific EffectRadio wave delay:

Data Source

PatentEP4184222B1Precipitable water estimation model learning system, precipitable water estimation system, method, and program
Publication Date: 2025.03.26 FURUNO ELECTRIC CO LTD
  • EP4184222B1 patent drawingFigure 1
  • EP4184222B1 patent drawingFigure 2
  • EP4184222B1 patent drawingFigure 3

AI summary

Provided is a technology capable of observing water vapor in a local range without requiring calibration using liquid nitrogen. A precipitable water estimation model learning system (4) comprises: a radio wave intensity acquisition part (40) for acquiring a radio wave intensity for a plurality of wave frequencies among radio waves received by a microwave radiometer (3); a precipitable water acquisition part (41) for acquiring precipitable water calculated on the basis of an atmospheric delay of a GNSS signal received by a GNSS receiver (2); and a learning part (43) for causing an estimation model (43a) to machine learn such that precipitable water is outputted on the basis of the radio wave intensity for the plurality of wave frequencies and the precipitable water at a plurality of times in a predetermined period, and using, as input, input data based on the radio wave intensity for the plurality of wave frequencies.