Learning system of precipitable water vapor estimation model, precipitable water vapor estimation system, method, and computer-readable recording medium

A learning system of a precipitable water vapor estimation model, comprising:

• a microwave radio meter configured to receive radio waves, wherein the microwave radio meter is not calibrated with liquid nitrogen;
• a GNSS receiver configured to receive a GNSS signal;
• processing circuitry configured to:
• acquire radio wave intensities of a plurality of frequencies among the radio waves received from the microwave radiometer at each of a plurality of time points in a training period;
• acquire a precipitable water vapor calculated based on an atmospheric delay of the GNSS signal received from the GNSS receiver at each of the plurality of time points in the training period;
• perform principal component analysis on the radio wave intensities of the plurality of frequencies at each of the plurality of time points in the training period so as to select a particular number of principal components corresponding to the radio wave intensities of the plurality of frequencies at each of the plurality of time points;
• and
• perform machine learning to train an estimation model using, as training data, for each of the plurality of time points in the training period, a corresponding set including the principal components corresponding to the radio wave intensities of the plurality of frequencies and the precipitable water vapor,
• wherein the estimation model is configured to receive principal components corresponding to radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point as input data and to output an estimated precipitable water vapor.