Abstract: Provided is a generation method for a learning model that can efficiently estimate altitude-by-altitude water vapor densities or precipitable water amounts. According to the present invention, a generation method for a learning model involves: acquiring training data that includes first measurement data measured by a microwave radiometer and altitude-by-altitude water vapor densities or precipitable water amounts obtained from second measurement data measured by a radiosonde: and, on the basis of the acquired training data, generating a learning model that, when first measurement data has been inputted, outputs altitude-by-altitude water vapor densities or precipitable water amounts.

Abstract: This water vapor observation device comprises an antenna, an RF amplifier, and processing circuitry. The antenna receives radio waves radiated from an atmosphere including water vapor. The RF amplifier amplifies the received radio waves and generates an observation signal. The processing circuitry is programmed to at least: use the observation signal to select a plurality of observation frequencies excluding an accuracy degraded frequency; and calculate a water vapor index using the spectral intensities of the plurality of observation frequencies. This configuration makes it possible to accurately observe a water vapor amount.

Abstract: A learning system of a precipitable water vapor estimation model includes a radio wave intensity acquisition part, a precipitable water vapor acquisition part, and a learning part. The radio wave intensity acquisition part acquires radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer. The precipitable water vapor acquisition part acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver. Based on the radio wave intensities of the plurality of frequencies and the precipitable water vapor at a plurality of time points in a particular period, the learning part subjects an estimation model to machine learning such that an input data based on the radio wave intensities of the plurality of frequencies is taken as an input to output the precipitable water vapor.

Abstract: This water vapor observation device comprises an antenna, an RF amplifier, and processing circuitry. The antenna receives radio waves radiated from an atmosphere including water vapor. The RF amplifier amplifies the received radio waves and generates an observation signal. The processing circuitry is programmed to at least: use the observation signal to select a plurality of observation frequencies excluding an accuracy degraded frequency; and calculate a water vapor index using the spectral intensities of the plurality of observation frequencies. This configuration makes it possible to accurately observe a water vapor amount.