Patent Description:
It has been known to use a GNSS receiver, a microwave radiometer, etc. for the observation of a precipitable water vapor, that is, the observation of water vapor.

Water vapor observation based on a GNSS receiver utilizes multi-frequency radio waves emitted from satellites. 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, making it possible to observe the water vapor amount. Water vapor observation using a global navigation satellite system (GNSS) can provide stable measurement without calibration. As GNSS involves satellites distributed all over the sky, it is possible to obtain an average value of water vapor over a wide range of the sky, but water vapor in a local range cannot be observed. Patent Document <NUM> describes such a water vapor observation based on GNSS.

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. With the directivity of an antenna or a horn of a receiver, it is possible to measure water vapor in a local range of the sky compared to water vapor observation based on GNSS. However, regular calibration with liquid nitrogen is required to prevent equipment drift and to measure the correct brightness temperature. Nonetheless, liquid nitrogen is difficult to transport and handle. Patent Document <NUM> describes such a microwave radiometer.

<CIT> discloses a GPS survey with correction of errors arising from variations in atmospheric water content, comprising a GPS network of <NUM> satellites and one earth-based GPS receiver. Precipitable water vapor is obtained from the zenith wet delay. Calibration is ensured by obtaining an independent water vapor measurement using e.g. a water vapor radiometer.

"<NPL>) discloses a comparison of different measurement techniques for evaluating integrated precipitate water vapor including estimates obtained from a network of ground-based GPS receivers, radiosonde observations, and estimates from a ground-based water vapor radiometer.

The disclosure provides techniques to make it possible to observe a precipitable water vapor in a local range without calibration using liquid nitrogen.

The above problem is solved by the claimed subject-matter which defines the present invention.

An embodiment of the disclosure will be described below with reference to the drawings.

<FIG> is a view showing a configuration of a learning system <NUM> of a precipitable water vapor estimation model and a precipitable water vapor estimation system <NUM> (also referred to as an estimation system <NUM>) according to this embodiment.

As shown in <FIG>, in this embodiment, the learning system <NUM> of the precipitable water vapor estimation model and the precipitable water vapor estimation system <NUM> are built on a same computer system, but they may be operated independently. That is, it is possible that only the learning system <NUM> is installed, or only the precipitable water vapor estimation system <NUM> is installed.

The learning system <NUM> shown in <FIG> includes a radio wave intensity acquisition part <NUM>, a precipitable water vapor acquisition part <NUM>, and a learning part <NUM>.

The radio wave intensity acquisition part <NUM> shown in <FIG> acquires radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer <NUM>. In this embodiment, radio wave intensities of N (N=<NUM>) different frequencies of <NUM> or more and within <NUM> are acquired. The radio wave intensity acquisition part <NUM> acquires radio wave intensities [p(f1), p(f2),. , p(f29), p(f30)] of <NUM> different frequencies (f1, f2,. , f29, f30). Herein, the radio wave intensity is indicated as p(f), where f indicates the frequency. The radio wave intensities of a plurality of frequencies acquired by the radio wave intensity acquisition part <NUM> are stored to a storage part <NUM> as time-series data D2 of radio wave intensities.

As shown in <FIG>, the peak of intensity of radio waves radiated from water vapor and cloud water in the sky is at <NUM>. In <FIG>, the received intensity p(f) of the microwave radiometer <NUM> is shown, where f indicates the frequency. For example, a radio wave of <NUM> contains a precipitable water vapor, that is, a water vapor component, and a cloud water component. To remove the cloud water amount contained in the radio wave of <NUM>, the cloud water component is calculated based on radio wave intensities of frequencies other than <NUM>. Thus, radio wave intensities of a plurality of mutually different frequencies are required. Although <NUM> has been shown as an example, since the water vapor component and the cloud water component are also contained in frequencies other than <NUM>, the combination of frequencies is not limited to a combination of <NUM> and frequencies other than <NUM>. In this embodiment, "N=<NUM>" has been set, but the number of N may be changed as appropriate. The frequency range may include <NUM> or <NUM> ± <NUM>. Although N=<NUM> in this embodiment, it is not limited thereto. N may be a natural number of <NUM> or more to improve the accuracy of specifying the water vapor component and the cloud water component.

In addition, in this embodiment, a black body is periodically passed through a receiving range of an antenna of the microwave radiometer <NUM> by an actuator, and the radio wave from the black body whose intensity is known and the radio wave from the sky are received. The received intensity p(f) of the microwave radiometer <NUM> is a radio wave intensity ps(f) from the sky minus a radio wave intensity pb(f) from the black body. Of course, the microwave radiometer <NUM> is not limited thereto, and a mirror may be periodically moved to receive radio waves from the black body.

The precipitable water vapor acquisition part <NUM> shown in <FIG> acquires a precipitable water vapor calculated based on an atmospheric delay (strictly speaking, a tropospheric delay) of a GNSS signal received by a GNSS receiver <NUM>. It is known that a precipitable water vapor (precipitable water vapor; PWV) according to GNSS may be calculated based on a GNSS signal, a coordinate value such as an altitude, an atmospheric temperature, and an atmospheric pressure. The precipitable water vapor acquisition part <NUM> acquires a GNSS precipitable water vapor using a GNSS signal and altitude information obtained from the GNSS receiver <NUM>, and an atmospheric temperature and an atmospheric pressure obtained from a weather sensor <NUM>. The GNSS precipitable water vapor acquired by the precipitable water vapor acquisition part <NUM> is stored to the storage part <NUM> as time-series data D1 of precipitable water vapor of GNSS.

The learning part <NUM> shown in <FIG> subjects an estimation model 43a to machine learning based on the time-series data D1 of precipitable water vapor and the time-series data D2 of radio wave intensity. Specifically, 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 <NUM> subjects the estimation model 43a 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. A teacher data set used by the learning part <NUM> is data in which a precipitable water vapor at a time point t is associated with an input data based on radio wave intensities [p(f1), p(f2),. , p(f29), p(f30)] of a plurality of frequencies at the same time point t. As long as the input data is a data based on radio wave intensities of a plurality of frequencies, the input data may be the radio wave intensities themselves of the plurality of frequencies, or may be a data obtained by reducing the dimension of the radio wave intensities of the plurality of frequencies. If the estimation model 43a is a supervised machine learning model, various models such as linear regression, regression trees, random forests, support vector machines, neural networks, ensemble, etc. may be used. In this embodiment, although will be described in detail later, a polynomial regression using terms of second order or higher, which is a multiple regression with multiple types of variables, is adopted, but the embodiment is not limited thereto.

As shown in <FIG>, the learning system <NUM> may include a dimension reduction part <NUM> which performs a dimension reduction process on radio wave intensities of a plurality of frequencies and calculates a dimensionally reduced input data representing the radio wave intensities of the plurality of frequencies. By reducing the dimension, it is possible to reduce the number of dimensions while reproducing the original features that are present in the radio wave intensities of the plurality of frequencies, making it possible to reduce the calculation cost and avoid the curse of dimensionality (overlearning). The dimension reduction method of this embodiment is principal component analysis (PCA), but the dimension reduction method is not limited thereto, and other algorithms such as factor analysis, multiple factor analysis, Autoencoder, independent component analysis, non-negative matrix factorization, etc. may also be used.

In this embodiment, using principal component analysis, the dimension reduction part <NUM> selects a first principal component, a second principal component, and a third principal component as the input data. Of course, the embodiment is not limited thereto, and various modifications are possible. For example, the input data may be the first principal component only of the principal component analysis, or may be the first principal component and the second principal component. That is, a particular number (an arbitrary natural number of <NUM> or more) of principal components from the first order onward are selected as the input data. The particular number may be appropriately set according to the required accuracy. The reason why the first principal component is always included is that the reproducibility of the original feature of the first principal component is the highest.

A standardization processing part <NUM> shown in <FIG> performs a standardization process on radio wave intensities [p(f1), p(f2),. , p(f29), p(f30)] of a plurality of frequencies at a plurality of time points before the dimension reduction process according to principal component analysis is performed. The standardization processing part <NUM> performs a standardization process on the time-series data D2 of radio wave intensity stored in the storage part <NUM>, and stores time-series data D3 of standardized radio wave intensity to the storage part <NUM>. The standardization process is a process for performing centering to set the mean to <NUM> and performing scaling to set the standard deviation to <NUM>. In the standardization process, by calculating a mean and a standard deviation for respective radio wave intensities at a plurality of time points and dividing, by the standard deviation, a value obtained by subtracting the mean from the original data, each original radio wave intensity is converted into a standardized radio wave intensity. The calculated mean and standard deviation are stored to the storage part <NUM> as standardization parameters for use in a standardization process of the precipitable water vapor estimation system <NUM> to be described later (see <FIG>).

The learning system <NUM> of this embodiment includes the dimension reduction part <NUM> and the standardization processing part <NUM>, but these parts may also be omitted.

Taking a first principal component PC1, a second principal component PC2, and a third principal component PC3 as an input data, the learning part <NUM> shown in <FIG> constructs an estimation model 43a for calculating a precipitable water vapor (PWV). The estimation model 43a is a conversion formula using multiple regression and is expressed by Formula (<NUM>) below. By performing fitting using the least squares method, the following unknown coefficients S<NUM> to S<NUM> are calculated to construct the estimation model 43a. [Math <NUM>] <MAT>.

The precipitable water vapor estimation system <NUM> shown in <FIG> includes the radio wave intensity acquisition part <NUM> and an estimation part <NUM>. Using the estimation model 43a constructed by the learning part <NUM>, the estimation part <NUM> receives an input data based on radio wave intensities of a plurality of frequencies acquired by the radio wave intensity acquisition part <NUM> and outputs a corresponding precipitable water vapor. Although the radio wave intensities [p(f1), p(f2),. , p(f29), p(f30)] of a plurality of frequencies at an estimation time point may be inputted to the estimation part <NUM>, to improve accuracy, a standardization processing part <NUM> and a dimension reduction part <NUM> may be provided.

Using predetermined parameters, the standardization processing part <NUM> shown in <FIG> performs a standardization process on the radio wave intensities of the plurality of frequencies before a dimension reduction process is performed by the dimension reduction part <NUM>. The standardization parameters are parameters (mean, standard deviation) calculated by the standardization processing part <NUM> of the learning system <NUM>. The standardization processing part <NUM> does not calculate the parameters (mean, standard deviation), but the rest of the process is the same as that of the standardization processing part <NUM> of the learning system <NUM>.

The dimension reduction part <NUM> shown in <FIG> performs a dimension reduction process on the radio wave intensities of the plurality of frequencies and calculates a dimensionally reduced input data representing the radio wave intensities of the plurality of frequencies. The dimension reduction part <NUM> uses the same parameters as the parameters calculated by the dimension reduction part <NUM> of the learning system <NUM>.

A learning method of the precipitable water vapor estimation model will be described with reference to <FIG>. As shown in <FIG>, in step ST100, the radio wave intensity acquisition part <NUM> acquires radio wave intensities of a plurality of frequencies among radio waves received by the microwave radiometer. In step ST101, the precipitable water vapor acquisition part <NUM> acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by the GNSS receiver. The order of steps ST100 and ST101 is not particularly specified.

In next step ST102, the standardization processing part <NUM> performs a standardization process on the radio wave intensities of the plurality of frequencies at a plurality of time points. In next step ST103, the dimension reduction part <NUM> performs a dimension reduction process on the radio wave intensities of the plurality of frequencies according to principal component analysis, and calculates a dimensionally reduced input data representing the radio wave intensities of the plurality of frequencies. In next step ST104, 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 <NUM> subjects an estimation model to machine learning such that the input data based on the radio wave intensities of the plurality of frequencies are taken as an input to output the precipitable water vapor.

A precipitable water vapor estimation method will be described with reference to <FIG>. As shown in <FIG>, in step ST201, the radio wave intensity acquisition part <NUM> acquires radio wave intensities of a plurality of frequencies among radio waves received by the microwave radiometer. In next step ST202, the standardization processing part <NUM> performs a standardization process on the radio wave intensities of the plurality of frequencies. In next step ST203, the dimension reduction part <NUM> performs a dimension reduction process on the radio wave intensities of the plurality of frequencies according to principal component analysis, and calculates a dimensionally reduced input data representing the radio wave intensities of the plurality of frequencies. In next step ST204, using the estimation model 43a which has been subjected to machine learning such that the input data based on the radio wave intensities of the plurality of frequencies is taken as an input to output the precipitable water vapor, the estimation part <NUM> outputs the precipitable water vapor corresponding to the input data based on the acquired radio wave intensities of the plurality of frequencies.

<FIG> is a diagram showing comparison between a precipitable water vapor in a period estimated by an estimation model constructed by the learning system <NUM> and the precipitable water vapor estimation system <NUM> and a precipitable water vapor in the same period based on Sonde data. The Sonde data are data published by the Japan Meteorological Agency and are actual meteorological observation values measured by flying real balloons equipped with sensors to the sky. As shown in <FIG>, the root mean square error (RMSE) is <NUM>, indicating that a certain degree of accuracy is obtained.

In addition, in the present method, since radio wave intensities of a plurality of frequencies are acquired, even if noise is contained in the radio wave intensities of some frequencies due to adoption of a general-purpose amplifier with a high noise temperature, as a plurality of frequencies are used, the influence of noise can be suppressed. Thus, for example, compared to the case of estimating a precipitable water vapor according to a particular arithmetic expression using two specific frequencies, the present method is considered to be robust against noise. Conversely, even if some noise is contained, since it can be covered with the plurality of frequencies, the equipment used does not necessarily need to have high performance, and it is possible to reduce the cost of the system.

As described above, the learning system <NUM> of the precipitable water vapor estimation model of this embodiment includes the radio wave intensity acquisition part <NUM>, the precipitable water vapor acquisition part <NUM>, and the learning part <NUM>. The radio wave intensity acquisition part <NUM> acquires radio wave intensities of a plurality of frequencies among radio waves received by the microwave radiometer <NUM>. The precipitable water vapor acquisition part <NUM> acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by the GNSS receiver <NUM>. 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 <NUM> subjects the estimation model 43a 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.

The learning method of the precipitable water vapor estimation model of this embodiment includes steps below. Radio wave intensities of a plurality of frequencies are acquired among radio waves received by the microwave radiometer <NUM>. A precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by the GNSS receiver <NUM> is acquired. 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 estimation model 43a is subjected 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.

The precipitable water vapor estimation system of this embodiment includes the radio wave intensity acquisition part <NUM> and the estimation part <NUM>. The radio wave intensity acquisition part <NUM> acquires radio wave intensities of a plurality of frequencies among radio waves received by the microwave radiometer <NUM>. Using the estimation model 43a which has been subjected to machine learning such that an input data based on radio wave intensities of the plurality of frequencies is taken as an input to output a precipitable water vapor, the estimation part <NUM> outputs the precipitable water vapor corresponding to an input data based on the acquired radio wave intensities of the plurality of frequencies.

The precipitable water vapor estimation method of this embodiment includes steps below. Radio wave intensities of a plurality of frequencies are acquired among radio waves received by the microwave radiometer <NUM>. Using the estimation model 43a which has been subjected to machine learning such that an input data based on radio wave intensities of the plurality of frequencies is taken as an input to output a precipitable water vapor, the precipitable water vapor corresponding to an input data based on the acquired radio wave intensities of the plurality of frequencies is outputted.

According to the learning method, the estimation method, and the system described above, since machine learning is performed using an input data based on radio wave intensities of a plurality of frequencies, machine learning can clarify the correlation between the radio wave intensities and the precipitable water vapor, which could not be clarified with a single frequency because the radio wave intensity contains both water vapor content and cloud water, and it becomes possible to estimate the water vapor content (precipitable water vapor). Further, since the radio wave intensities and the GNSS-based precipitable water vapor at a plurality of time points in a particular period are used, microwave radiometer-based local water vapor data with non-matching absolute values can be converted into reliable local water vapor data with matching absolute values. Highly reliable data can be acquired even without calibrating the microwave radiometer with liquid nitrogen.

As described in this embodiment, the dimension reduction parts <NUM> and <NUM> may be included to perform a dimension reduction process on the radio wave intensities of the plurality of frequencies and calculate a dimensionally reduced input data representing the radio wave intensities of the plurality of frequencies. By performing dimension reduction in this manner, since frequencies with good sensitivity processed by the portion of the receiver having good performance are selected from among the plurality of frequencies, estimation may be performed even with a general-purpose inexpensive amplifier. That is, without dimension reduction, frequency bands with poor sensitivity processed by the portion of the receiver having poor performance are directly used for estimation, and the data in the frequency bands with poor sensitivity adversely affect the estimation accuracy. With dimension reduction, it is possible to omit the trouble of manually removing frequencies with poor sensitivity from the plurality of frequencies, and thus it is possible to avoid deterioration of the estimation accuracy.

As described in this embodiment, the dimension reduction parts <NUM> and <NUM> may perform dimension reduction according to principal component analysis and select a particular number of principal components from the first order onward as the input data. Thus, principal component analysis may be used for dimension reduction.

As in the learning system <NUM> of this embodiment, the standardization processing part <NUM> may be included to perform a standardization process on the radio wave intensities of the plurality of frequencies at a plurality of time points before the dimension reduction process is performed by the dimension reduction part <NUM>. As in the precipitable water vapor estimation system <NUM> of this embodiment, the standardization processing part <NUM> may be included to perform a standardization process on the radio wave intensities of the plurality of frequencies using a predetermined standardization parameter before the dimension reduction process is performed by the dimension reduction part <NUM>. Thus, it is possible to appropriately reduce the dimension and improve the estimation accuracy.

As described in this embodiment, the radio wave intensity acquisition part <NUM> may acquire radio wave intensities of N different frequencies, where n is a natural number of <NUM> or more, and the dimension reduction parts <NUM> and <NUM> may dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N. In this manner, by performing dimension reduction, it is possible to reduce the number of dimensions while reproducing the original features that are present in the radio wave intensities of the N frequencies, making it possible to reduce the calculation cost and avoid the curse of dimensionality (overlearning).

Claim 1:
A learning system (<NUM>) of a precipitable water vapor estimation model, comprising:
a radio wave intensity acquisition part (<NUM>) configured to acquire radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer (<NUM>);
a precipitable water vapor acquisition part (<NUM>) configured to acquire a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver (<NUM>); and
a learning part (<NUM>) configured to subject an estimation model (43a) 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, 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.