Weather observation device, weather observation system, estimation method, and computer program

Abstract

[PROBLEM] Provided are a weather observation device, a weather observation system, an estimation method, and a computer program. [SOLUTION] A weather observation device comprises: an acquisition unit for acquiring observation data based on received radio waves of a microwave radiometer and an environmental temperature of an environment in which the microwave radiometer is installed; a conversion unit for converting the observation data into first luminance temperature data having luminance temperature as a physical dimension; an estimation unit for estimating second luminance temperature data which serves as a base when calculating the luminance temperature data in the sky above the microwave radiometer from the environmental temperature; and a calculation unit for calculating the luminance temperature data in the sky above the microwave radiometer from the first luminance temperature data and the second luminance temperature data.

Description

Weather observation device, weather observation system, estimation method, and computer program

[0001] The present invention relates to a meteorological observation device, a meteorological observation system, an estimation method, and a computer program.

[0002] Weather forecasts are based on altitude profiles of atmospheric water vapor, humidity, temperature, air pressure, and cloud liquid water. These parameters are observed using microwave radiometers, which measure brightness temperatures by receiving weak radio waves at different frequencies emitted from the target.

[0003] A microwave radiometer includes an antenna for receiving microwaves and a receiving circuit for amplifying a signal corresponding to the received microwaves, and outputs a brightness temperature based on the signal strength obtained from the receiving circuit. To ensure the accuracy of the output brightness temperature, calibration has traditionally been performed using the signal strengths of radio waves received from a room-temperature blackbody and a blackbody at liquid nitrogen temperature. However, calibration using liquid nitrogen is difficult to perform on an aircraft, ship, or mountain. Patent Document 1 proposes a microwave radiometer that uses a heater to perform calibration using a blackbody at a known higher temperature and a room-temperature blackbody, eliminating the need for liquid nitrogen calibration.

[0004] Japanese Patent Application Publication No. 03-104877

[0005] Eliminating the need for liquid nitrogen and periodic heater calibrations would make it easier to use on ships or aircraft.

[0006] The present disclosure aims to provide a weather observation device, a weather observation system, an estimation method, and a computer program that can eliminate the need for periodic calibration.

[0007] A meteorological observation device according to one aspect of the present disclosure includes an acquisition unit that acquires observation data based on radio waves received by a microwave radiometer and an environmental temperature of an environment in which the microwave radiometer is installed; a conversion unit that converts the observation data into first brightness temperature data in which brightness temperature is a physical dimension; an estimation unit that estimates, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer; and a calculation unit that calculates brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

[0008] In one aspect of the present disclosure, a meteorological observation device converts observation data obtained from a microwave radiometer into first brightness temperature data, and calculates brightness temperature data for the sky by adding second brightness temperature data estimated from the environmental temperature of the installation environment of the microwave radiometer to the first brightness temperature data. Rather than directly estimating the absolute value of the brightness temperature when the sky is observed at the observation point, it is possible to indirectly estimate the brightness temperature data for the sky above the observation point based on the base brightness temperature of the observation point, which may vary depending on the observation point and the season. This makes it possible to eliminate the need for periodic calibration using liquid nitrogen.

[0009] In the meteorological observation device according to one aspect of the present disclosure, the conversion unit converts a difference in reception intensity of radio waves received from two different measurement targets into a difference in brightness temperature data.

[0010] In one aspect of the present disclosure, first brightness temperature data is used, which is obtained by converting the difference in reception intensity between two different measurement targets, not limited to a room-temperature blackbody, into a brightness temperature. The reception intensity when observing a blackbody at a temperature that can serve as a reference, such as 100°C or 50°C, can be used, without being limited to a room-temperature blackbody. Using the difference makes it possible to calculate brightness temperature data with high accuracy in response to changes in the base brightness temperature data that can vary depending on the observation location or the season. The measurement target is not limited to a blackbody, and may be a radio wave absorber for which the relationship between temperature and reception intensity of radiated radio waves is known in advance.

[0011] In a meteorological observation device according to one aspect of the present disclosure, the conversion unit converts the difference between the reception strength of radio waves received from a radio wave absorber at a reference temperature and the reception strength of radio waves received from the sky into a difference in brightness temperature data.

[0012] In one aspect of the present disclosure, first brightness temperature data is used, which is obtained by converting the difference between the received intensity from a room-temperature blackbody at an observation point and the received intensity from the sky into brightness temperature data, allowing the brightness temperature data to be calculated with high accuracy in response to changes in the base brightness temperature data that may vary depending on the observation point and the season.

[0013] In the meteorological observation device according to one aspect of the present disclosure, the conversion unit obtains the first brightness temperature data using a learning model that, when a difference in reception strength of radio waves received by the microwave radiometer is input, outputs a corresponding difference in brightness temperature.

[0014] According to one aspect of the present disclosure, it is possible to accurately calculate the difference in brightness temperature data from the difference in reception intensity under various conditions using a learning model that has learned the relationship between the difference in reception intensity obtained by a microwave radiometer and the difference in brightness temperature data. By using a model that learns relative values ​​rather than absolute values ​​that may be affected by base brightness temperature data that may change depending on the observation location and season, it is possible to accurately calculate brightness temperature data in response to changes in the base brightness temperature data.

[0015] In the meteorological observation device according to one aspect of the present disclosure, the estimation unit estimates a radiance temperature of the radio wave absorber at a reference temperature at the observation point as the second radiance temperature data.

[0016] In one aspect of the present disclosure, the base second brightness temperature data is estimated using a relational expression learned according to the environmental temperature of the environment in which the microwave radiometer is installed, thereby enabling brightness temperature data of the sky to be calculated with high accuracy according to the observation point and the season.

[0017] In the meteorological observation device according to one aspect of the present disclosure, the estimation unit estimates second brightness temperature data from the environmental temperature using a relational expression that indicates a correspondence relationship between the environmental temperature and the brightness temperature of a radio wave absorber at a reference temperature, the relational expression being obtained by regression analysis based on measurement data of the environmental temperature and the brightness temperature of the radio wave absorber.

[0018] In one aspect of the present disclosure, the second brightness temperature data is estimated using a relational expression with the actually measured brightness temperature of a radio wave absorber in the environment, thereby making it possible to accurately calculate the brightness temperature of the sky corresponding to the observation point and season without requiring special processing for calibration, such as adjusting the temperature to room temperature.

[0019] In the meteorological observation device according to one aspect of the present disclosure, the calculation unit calculates brightness temperature data for the sky by adding the first brightness temperature data to the second brightness temperature data.

[0020] In one aspect of the present disclosure, a meteorological observation device estimates first brightness temperature data as a relative value rather than estimating the absolute value of the brightness temperature data, and then adds base second brightness temperature data to the relative value to calculate brightness temperature data for the sky. While the difference in brightness temperature data does not change significantly depending on the observation location or season, the brightness temperature data of the reference radio wave absorber may change depending on the observation location. By adding the first brightness temperature data and the second brightness temperature data, it is possible to accommodate differences between observation locations and seasons.

[0021] A meteorological observation device according to one aspect of the present disclosure includes a meteorological data estimation unit that estimates at least one of precipitable water vapor, cloud water content, water vapor content, and temperature distribution at an observation point based on the brightness temperature data of the sky calculated by the calculation unit.

[0022] In one aspect of the present disclosure, it is possible to estimate the altitude distribution of precipitable water, cloud water content, water vapor content, or temperature at an observation point with sufficient accuracy, even without the need for regular calibration using liquid nitrogen.

[0023] A meteorological observation device according to one aspect of the present disclosure uses a relational equation calibrated from the reception strength of radio waves received from a radio wave absorber cooled with liquid nitrogen and the reception strength of radio waves received from a radio wave absorber at a reference temperature to create first learning data including a reception strength difference and a brightness temperature difference, and uses the created first learning data to train the learning model.

[0024] In one aspect of the present disclosure, the learning model is trained to output a brightness temperature difference relative to a reception intensity difference so that the relationship between the reception intensity difference and the brightness temperature difference satisfies a relational expression calibrated using liquid nitrogen. The calibration object is not limited to a blackbody, and may be a microwave absorber having a known brightness temperature depending on temperature, such as a housing of a microwave radiometer or a part of a moving object.

[0025] In a meteorological observation device according to one aspect of the present disclosure, the learning model is trained using the first learning data before the device is shipped.

[0026] In one aspect of the present disclosure, before shipping the meteorological observation device itself (the device itself), a learning model is used that outputs brightness temperature data that satisfies a relational equation calibrated with liquid nitrogen. Before shipping and immediately after shipping, there is not enough observation data collected to satisfy the learning conditions of the learning model, but conversion using the calibrated relational equation is highly accurate. This makes it possible to calculate brightness temperature data for the sky with high accuracy.

[0027] A meteorological observation device according to one aspect of the present disclosure creates second learning data including the difference between the reception strength of radio waves received from the sky at an observation point and the reception strength of radio waves received from a radio wave absorber at a reference temperature at the observation point, and a brightness temperature difference derived from analysis data at the observation point, and updates the learning model using the second learning data.

[0028] In one aspect of the present disclosure, after shipping of the meteorological observation device itself (the device itself), the learning model is updated to accommodate changes over time in the microwave radiometer and to reproduce brightness temperature data calculated by simulation from reliable analytical data. The learning model trained to satisfy the relational expression calibrated with liquid nitrogen is updated to a model that reproduces the analytical data. This makes it possible to calculate brightness temperature data in response to changes over time in the microwave radiometer, even without requiring periodic calibration using liquid nitrogen.

[0029] In a meteorological observation device according to one aspect of the present disclosure, the learning model is updated with the second learning data after the device is shipped.

[0030] In one aspect of the present disclosure, after shipment, when it becomes possible to collect enough observation data to satisfy the learning conditions of the learning model, the learning model is updated to reproduce proven analytical data. After shipment of the meteorological observation device, it becomes possible to calculate brightness temperature data that corresponds to the observation location and season and is in line with changes over time in the microwave radiometer.

[0031] A meteorological observation system according to one aspect of the present disclosure includes a microwave radiometer that receives radio waves at an observation point, and a meteorological observation device that derives meteorological data based on data from the microwave radiometer, wherein the meteorological observation device includes an acquisition unit that acquires observation data based on the radio waves received by the microwave radiometer and an environmental temperature of an installation environment of the microwave radiometer, a conversion unit that converts the observation data into first brightness temperature data having brightness temperature as a physical dimension, an estimation unit that estimates, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer, and a calculation unit that calculates brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

[0032] An estimation method according to one aspect of the present disclosure includes a computer acquiring observation data based on radio waves received by a microwave radiometer and an environmental temperature of an environment in which the microwave radiometer is installed, converting the observation data into first brightness temperature data in which brightness temperature is a physical dimension, estimating, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data for the sky above the microwave radiometer, calculating brightness temperature data for the sky above the microwave radiometer from the first brightness temperature data and the second brightness temperature data, and estimating at least one of precipitable water vapor, cloud water content, water vapor content, and temperature vertical distribution at an observation point using the calculated brightness temperature data for the sky.

[0033] A computer program according to one aspect of the present disclosure causes a computer to acquire observation data based on radio waves received by a microwave radiometer and an environmental temperature of an environment in which the microwave radiometer is installed, convert the observation data into first brightness temperature data in which brightness temperature is a physical dimension, estimate second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer from the environmental temperature, and calculate brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

[0034] 1 is a block diagram showing the configuration of a meteorological observation device of a first embodiment; FIG. 2 is an explanatory diagram of the functions of a processing unit; FIG. 3 is a schematic diagram of a learning model; FIG. 4 is a flowchart showing an example of a processing procedure of a learning model generation method; FIG. 5 is a flowchart showing an example of a processing procedure of a meteorological observation device in operation; FIG. 6 is a flowchart showing an example of a re-learning procedure of a learning model in operation; FIG. 7 is an explanatory diagram of the transition of learning of a learning model in a meteorological observation device; FIG. 8 is a flowchart showing an example of a processing procedure during operation of a meteorological observation device in a second embodiment; FIG. 9 is a flowchart showing an example of a processing procedure during operation of a meteorological observation device in the second embodiment; FIG. 10 is a diagram showing an example of a display of meteorological data on a monitoring device; FIG. 11 is a flowchart showing an example of a processing procedure during operation of a meteorological observation device in the second embodiment;

[0035] The present disclosure will be specifically described with reference to the drawings showing embodiments thereof.

[0036] [First Embodiment] Fig. 1 is a block diagram showing the configuration of a meteorological observation device 1 according to a first embodiment. The meteorological observation device 1 may be installed at a fixed observation point, such as on the ground or a mountain, or may be installed on a mobile object that moves around the globe, such as a ship or an aircraft. The meteorological observation device 1 acquires signals or digital data output from a microwave radiometer 2, and estimates and outputs meteorological data such as precipitable water. A monitor device 3 is connected to the meteorological observation device 1. The meteorological observation device 1, the microwave radiometer 2, and the monitor device 3 may be configured as an integrated device.

[0037] The microwave radiometer 2 disclosed herein is a device that receives weak radio waves radiated from an observation target by frequency and outputs the reception intensity of the received radio waves as observation data. The microwave radiometer 2 receives radio waves from substances contained in the air above an observation point and is used to estimate meteorological data. The microwave radiometer 2 includes an antenna that receives microwaves and a receiving circuit that amplifies the signal received by the antenna, and outputs the signal intensity obtained from the receiving circuit.

[0038] The monitor device 3 is a user interface. The monitor device 3 receives an image signal and outputs a screen showing observation data from the meteorological observation device 1 based on the received image signal. The monitor device 3 uses a liquid crystal display, an organic EL (Electro Luminescence) display, or the like. The monitor device 3 may be provided with a touch panel with a built-in display or physical buttons to accept switching of observation data and display range.

[0039] The received intensity P for each frequency obtained when observing the sky using the microwave radiometer 2 sky is the brightness temperature when the received intensity is the radiation amount from a black body, that is, the brightness temperature (T sky ) can be expressed by converting it into a linear equation. The brightness temperature (a quantity of radio waves observed) when observing the sky varies depending on the materials present in the sky above the observation point, making it possible to estimate the amount of water vapor and cloud liquid water. The relationship between the received intensity obtained from the microwave radiometer 2 and the brightness temperature can be expressed by a linear equation. This linear equation can be specified using the received intensity when black bodies of different known temperatures are directly observed (at close range). To improve the accuracy of the calibration, the temperature of liquid nitrogen is used as one of the different known temperatures. The received intensity when observing a black body (a 77 K black body) immersed in liquid nitrogen at normal pressure (1 atmosphere) and the received intensity when observing a black body of a known temperature (e.g., a 300 K black body) are measured in advance, and the linear equation is determined from the known brightness temperatures of the black body at these temperatures. After calibration, when the received intensity is obtained by observing the sky, the determined linear equation can be converted to a brightness temperature. However, the determined linear equation changes to a non-negligible extent due to changes in the observation point, seasonal changes, and aging of the microwave radiometer 2. In other words, the reception strength corresponding to a room-temperature blackbody (300 K), which serves as an absolute index, may change depending on the observation location, season, and secular changes. Calibration using liquid nitrogen every time the observation location or season changes should be avoided as much as possible due to the scarcity of liquid nitrogen and the handling issues.

[0040] While the reception intensity corresponding to the radiance temperature of a room-temperature blackbody, which serves as an absolute index, varies with aging of the microwave radiometer 2, the observation location, and the season, the "difference (relative value)" between the reception intensity and the radiance temperature is little affected by the observation location or season. For this reason, the meteorological observation device 1 in the first embodiment performs conversion to radiance temperature dimensional data using a learning model M1 that learns the relationship between the reception intensity difference and the radiance temperature dimensional data difference, thereby avoiding the need for periodic calibration using liquid nitrogen.

[0041] The configuration and operation for realizing such a weather observation device 1 will be described.

[0042] The weather observation device 1 includes a processing unit 10, a storage unit 11, an input / output unit 12, and a communication unit 13. The processing unit 10 includes one or more arithmetic processing devices such as a central processing unit (CPU), a micro-processing unit (MPU), a graphics processing unit (GPU), etc. The processing unit 10 also includes a temporary storage medium such as a static random access memory (SRAM) or a dynamic random access memory (DRAM). The processing unit 10 reads an information processing program P1 stored in the storage unit 11 into the temporary storage medium and executes it, thereby causing a general-purpose computer to perform various processes described below.

[0043] The memory unit 11 is a relatively large-capacity non-volatile memory area such as a solid state drive (SSD) or a hard disk drive. The memory unit 11 stores a program (program product) required for the processing unit 10 to execute processing, and reference setting data. The program product includes an information processing program P1, screen setting data to be displayed on the monitor device 3, and graph description data. The memory unit 11 stores a learning model M1. The memory unit 11 stores observation data obtained from the microwave radiometer 2.

[0044] One of the information processing program (computer product) P1 and the learning model M1 stored in the memory unit 11 may be the information processing program P9 or the learning model M9 stored in a computer-readable non-transitory storage medium 9, which the processing unit 10 reads and stores in the memory unit 11. One of the information processing program P1 and the learning model M1 may be downloaded by the processing unit 10 from a server device or another download server via the communication unit 13 and stored in the memory unit 11.

[0045] The input / output unit 12 is an interface with other devices. The input / output unit 12 includes, for example, ports compatible with serial communication standards such as RS485 and USB (Universal Serial Bus) and a connection terminal for a monitor device such as HDMI (High-Definition Multimedia Interface) (registered trademark). The microwave radiometer 2 and the monitor device 3 are connected to the input / output unit 12. The processing unit 10 acquires data output from the microwave radiometer 2 from the input / output unit 12. The processing unit 10 can output a screen showing text or graphs indicating numerical values ​​representing calculation results from the input / output unit 12 to the monitor device 3. The processing unit 10 can accept operation signals from an operation interface provided in the monitor device 3 or the meteorological observation device 1 itself, and can accept an instruction to start measurement or switch the output content to the monitor device 3. The processing unit 10 may output the calculation results to an external device from the input / output unit 12.

[0046] The communication unit 13 is a communication module that realizes communication. The communication unit 13 is, for example, a network card. The communication unit 13 may be a communication module for satellite communication. The communication unit 13 may be a communication module that communicates with another server device via a land-based device using an AIS (Automatic Identification System) dedicated frequency. The processing unit 10 can obtain sea condition data for a specified date and time within a specified range from an external service that provides data obtained from satellites via the communication unit 13.

[0047] The operation of the weather observation device 1 configured as above will be described in further detail below. Fig. 2 is an explanatory diagram of the functions of the processing unit 10. The processing unit 10 functions as an acquisition unit 101, a preprocessing unit 102, a conversion unit 103, an estimation unit 104, a calculation unit 105, a first learning unit 106, and a second learning unit 107.

[0048] The processing unit 10 functions as the acquisition unit 101 and acquires the observation data output from the microwave radiometer 2. The observation data output from the microwave radiometer 2 is, for example, data on reception strength at different frequencies (frequency distribution of reception strength). If the processing unit 10 is set to observe at a resolution of 1 GHz, it can acquire a group of numerical data on reception strength for approximately 90 points. The observation resolution of the microwave radiometer 2 is variable, and the setting may be changed according to an instruction from the meteorological observation device 1. If the processing unit 10 is set to observe at a resolution of 2 GHz, it can acquire a group of numerical data on reception strength for approximately 50 points (p n (f n ), n=1-50). The processing unit 10, as the acquiring unit 101, may acquire a group of numerical data of reception intensity by narrowing down to a specific wavelength band emitted from an object to be observed, such as water vapor or oxygen. For example, the processing unit 10 may acquire 40 points of numerical data (p n (f n ), n = 1-40) and obtain 40 points of numerical data (p n (f n ), n = 1-40).

[0049] The processing unit 10, as the acquisition unit 101, handles the value obtained by subtracting the value corresponding to the reception intensity of the radio waves emitted from a room-temperature blackbody at the observation point from the reception intensity value output from the microwave radiometer 2 as observation data. This observation data (reception intensity difference) is hereinafter referred to as P sky-bb The processing unit 10 calculates the observation data P by subtracting the value of the reception intensity obtained when the microwave radiometer 2 actually observes a black body at the environmental temperature at the observation point from the value of the reception intensity output from the microwave radiometer 2 that observed the sky. sky-bb The acquisition unit 101 may use, as a reference, a value corresponding to the received intensity of radio waves emitted from a black body at room temperature (300 [K]), for example, a black body at 100° C. heated by a heater.

[0050] The processing unit 10 functions as the acquisition unit 101 to acquire the environmental temperature of the installation environment of the microwave radiometer 2. The processing unit 10 acquires temperature data from a temperature sensor installed together with the microwave radiometer 2 through the function of the acquisition unit 101. The temperature sensor uses a thermocouple, a resistance thermometer, or the like. The temperature sensor may be a radiation temperature sensor targeted at the microwave radiometer 2. The temperature sensor may measure the temperature inside the housing of the microwave radiometer 2, may measure the temperature on the surface of the housing, or may be provided near the antenna to measure the temperature around the antenna. The temperature sensor may be provided to measure the air temperature of a ship or facility on which the microwave radiometer 2 is installed.

[0051] The processing unit 10 functions as a preprocessing unit 102 that performs dimension reduction and standardization (normalization) processing on the reception intensity values ​​obtained from the microwave radiometer 2. The processing unit 10 may also perform noise removal processing as the preprocessing unit 102. Note that the function of the processing unit 10 as the preprocessing unit 102 can be omitted.

[0052] The processing unit 10 functions as a conversion unit 103 that converts the observation data acquired from the microwave radiometer 2 by the acquisition unit 101 into first brightness temperature data in which brightness temperature is the physical dimension. The first brightness temperature data (brightness temperature difference) is hereinafter referred to as T diff As described above, the observation data is a value P obtained by subtracting a value corresponding to the received intensity of radio waves emitted from a room-temperature blackbody at the observation point from the received intensity when observing an observation target, for example, in the sky. sky-bb The observation data may be a value obtained by subtracting a value corresponding to the received radio wave intensity from a black body at a reference temperature, which is not limited to room temperature, from the received radio wave intensity when observing the observation target. The object from which the subtraction is made is not limited to a black body, but may also be a radio wave absorber whose temperature and brightness temperature correspond to each other.

[0053] The conversion unit 103 converts the observation data P sky-bb The first brightness temperature data T diff The learning model M1 is used to convert the observed data P sky-bb When the first brightness temperature data T diff It is trained to output

[0054] The processing unit 10 functions as an estimation unit 104 that estimates a base value (second brightness temperature data) when estimating the brightness temperature of the sky above the microwave radiometer 2 from the environmental temperature acquired by the acquisition unit 101. The estimation unit 104 estimates, from the environmental temperature, second brightness temperature data corresponding to the amount of radiation from a room-temperature blackbody in the environment for each frequency. Hereinafter, this second brightness temperature data will be referred to as T bb When the acquisition unit 101 uses observation data from which a value corresponding to the received intensity of radio waves radiated from a black body at a reference temperature has been subtracted, the estimation unit 104 may estimate a radiance temperature corresponding to the amount of radiation from a black body at a reference temperature in the environment from the environmental temperature. The estimation unit 104 obtains fitting data (relational equations) by regression analysis or the like from experimental data obtained by measuring the environmental temperature obtained by the temperature sensor of the acquisition unit 101 and the radiance temperature of a black body (or a black body at a reference temperature, or another radio wave absorber at a reference temperature) at that environmental temperature, and calculates second radiance temperature data T bb Make sure you can estimate the following.

[0055] The processing unit 10 calculates the first brightness temperature data T diff and the second brightness temperature data T bb The brightness temperature data of the sky above the observation target is hereinafter referred to as T sky The processing unit 10, as the calculation unit 105, calculates the second brightness temperature data T bb , the first brightness temperature data T corresponding to the difference between the brightness temperature of the room temperature black body and the brightness temperature of the sky above the target. diff and calculate the brightness temperature data T sky That is, the processing unit 10, as the calculation unit 105, calculates brightness temperature data T sky To, T sky =T bb +T diff It is calculated as follows.

[0056] The processing unit 10 functions as a first learning unit 106 that generates a learning model M1 during the testing stage (before shipment, before observation starts) of the microwave radiometer 2. The processing unit 10 performs calibration using liquid nitrogen (77 K) before shipment using the first learning unit 106, and stores the relational equation (linear equation) obtained by the calibration in the storage unit 11. Specifically, the processing unit 10 uses the microwave radiometer 2 to receive radio waves emitted from a blackbody at a relatively high temperature, such as room temperature of 300 K, and calculates the observed data (frequency distribution of reception intensity) P hot and the observation data (frequency distribution of reception intensity) P obtained by receiving radio waves emitted from a cooled black body at 77K. cold The processing unit 10 calculates the frequency distribution of the observation data from the difference in reception intensity (P hot -P cold ) and the brightness temperature T of a high-temperature blackbody such as 300K. hot and a brightness temperature T of 77K cold The processing unit 10 uses this linear equation to calculate the various observation data P sky-bb and the corresponding brightness temperature difference T diff At this time, the processing unit 10 generates a set of observation data P obtained by observing a blackbody of a known temperature other than liquid nitrogen (77 K) with the microwave radiometer 2 as first learning data. sky-bb and the corresponding first brightness temperature data T diff The processing unit 10 may use the first learning data to store the first learning data. The processing unit 10 uses the created first learning data to learn the learning model M1 by the first learning unit 106 (see FIGS. 4 and 7). The learning process of the learning model M1 by the first learning unit 106 may be executed by another learning device instead of the processing unit 10. The learning model M1 that has learned the calibrated linear equation relationship may be implemented in the weather observation device 1 after being executed by another learning device.

[0057] The processing unit 10 functions as a second learning unit 107 that advances learning of the learning model M1 during the operation stage of the microwave radiometer 2 (after shipment and after the start of observation). The processing unit 10, through the second learning unit 107, learns the learning model M1 using analytical data on meteorology such as ERA5 (The fifth generation ECMWF atmospheric reanalysis) obtained via an external service or server device. The analytical data is, for example, high-precision data such as temperature, pressure, humidity, wind, and air pressure at a predetermined location (a grid point on the Earth). The analytical data is not limited to ERA5, and other versions of ERA analytical data may be used, or analytical data obtained from other meteorological information centers may be used. The analytical data does not have to be ERA5; it may be sonde data observed by a radiosonde, or analytical data of past observation data obtained from a meteorological satellite. The processing unit 10, through the function of the second learning unit 107, learns the brightness temperature difference T at the observation point obtained by executing a simulation using the analytical data at a predetermined location. diff The data is generated as training data. The simulation is performed using a forward model, for example, the AM atmospheric model at the Harvard-Smithsonian Center for Astrophysics. Other simulation models may also be used for the simulation. The processing unit 10 generates training data by using the second learning unit 107 to generate the actual observation data P obtained after the start of observation. sky-bb and the brightness temperature difference T obtained by simulation for that observation point. diff The second learning unit 107 performs learning using the second learning data. The details of the function (learning) of the second learning unit 107 will be described later.

[0058] 3 is a schematic diagram of the learning model M1. The learning model M1 is generated by the functions of the first learning unit 106 and the second learning unit 107 based on the observation data P output from the microwave radiometer 2. sky-bb When the first brightness temperature data (brightness temperature difference data) T diff The learning model M1 is trained to output the observed data P sky-bb and an input layer M11 into which the first brightness temperature data Tdiff and an intermediate layer M13 that learns parameters including at least one of weights and biases.

[0059] Observation data P input to the input layer M11 sky-bb is a group of numerical values ​​obtained by subtracting a value corresponding to the reception strength of radio waves radiated from a room-temperature blackbody at each frequency from each of the above-mentioned data on reception strength at each frequency (frequency distribution of reception strength). sky-bb For example, the set of approximately 50 subtracted numerical data (p n (f n )-p bb (f n ), n = 1-50).

[0060] The output layer M12 outputs the first brightness temperature data. The first brightness temperature data is the brightness temperature difference T diff This is the frequency distribution of

[0061] The learning model M1 is trained so that the observation data actually obtained from the microwave radiometer 2 can reproduce brightness temperature data that is reliable as meteorological data. The learning model M1 is trained by the function of the first learning unit 106 so as to reproduce the relationship between the received intensity and brightness temperature data that satisfies the linear relationship obtained by calibration using liquid nitrogen. The learning model M1 is trained so that it can reproduce analysis data after operation.

[0062] The learning model M1 is not limited to one using a neural network, but may be multiple regression, linear regression, a regression tree, a random forest, a support vector machine, or the like.

[0063] 4 is a flowchart showing an example of a processing procedure for generating the learning model M1. The processing unit 10 of the meteorological observation device 1 executes the following processing using the first learning unit 106 before shipping the meteorological observation device 1 itself.

[0064] The processing unit 10 extracts the observation data P from the first learning data created by calibration using liquid nitrogen. sky-bb and the corresponding first brightness temperature data (difference) T diffA pair of is selected (step S601).

[0065] The processing unit 10 selects the set of observation data P sky-bb The processing unit 10 inputs the first brightness temperature data T diff and the first brightness temperature data T diff The processing unit 10 calculates the difference between the two or the evaluation function (step S603). The processing unit 10 updates the parameters of the learning model M1 based on the calculation result of step S603 (step S604), and the process proceeds to step S605.

[0066] The processing unit 10 determines whether the learning condition is satisfied (step S605). The learning condition is the first brightness temperature data T diff and the first brightness temperature data T diff The learning condition may be, for example, that the difference between the number of times is equal to or less than a predetermined value, or that a high evaluation is calculated using the evaluation function.

[0067] If the processing unit 10 determines that the learning conditions are not satisfied (S605: NO), the processing returns to step S601. If the processing unit 10 determines that the learning conditions are satisfied (S605: YES), the processing unit 10 stores the data of the learning model M1, including the updated parameters and the configuration description data of the neural network, in the storage unit 11 (step S606), and ends the processing.

[0068] The learning model M1 is trained by the processing procedure shown in FIG. 4 before shipping and is stored in the storage unit 11. Thereafter, by inputting the observation data obtained from the microwave radiometer 2 into the learning model M1, the first brightness temperature data T that reproduces the calibrated relational equation using liquid nitrogen can be obtained. diff Then, the meteorological observation device 1 stores a copy of the learning model M1 after learning according to the processing procedure shown in FIG. 4 in the storage unit 11, and performs re-learning of the learning model M1 (described later in FIG. 6, etc.).

[0069] 5 is a flowchart showing an example of the processing procedure of the operating meteorological observation device 1. After the start of operation, the processing unit 10 of the meteorological observation device 1 starts up and executes the following observation process when an operation to start observation is received via the operation interface of the monitor device 3.

[0070] The processing unit 10, as the acquisition unit 101, acquires observation data (raw data of reception intensity by frequency) from the microwave radiometer 2 (step S101). The processing unit 10, as the pre-processing unit 102, executes pre-processing such as dimension reduction processing and / or standardization processing (step S102). The processing unit 10, as the acquisition unit 101, executes processing to subtract a value corresponding to the reception intensity of radio waves radiated from a black body at the observation point from the observation data, and obtains the observation data P sky-bb (a group of reception intensities subtracted by frequency) is acquired (step S103). The order of steps S102 and S103 may be reversed depending on the content of pre-processing.

[0071] The processing unit 10 functions as the acquisition unit 101 and acquires the environmental temperature of the microwave radiometer 2 (step S104).

[0072] The processing unit 10 converts the observation data P acquired in step S103 into sky-bb is input to the learning model M1 to obtain the first brightness temperature data T diff (brightness temperature data group by frequency) (step S105). The processing unit 10, using the function of the estimation unit 104, converts the second brightness temperature data T bb (brightness temperature data group by frequency) is estimated (step S106).

[0073] The processing unit 10 calculates the observed data after the processes in S102 and S103, the environmental temperature acquired in step S104, and the first brightness temperature data T diff and the second brightness temperature data T bb The data is stored in association with the date (date and time) of the observation and the location information indicating the observation location (step S107).

[0074] The processing unit 10 calculates the first brightness temperature data Tdiff and the second brightness temperature data T bb From the brightness temperature data T sky is calculated (step S108).

[0075] The processing unit 10 calculates the brightness temperature data T sky An atmospheric radiation spectrum is derived from the frequency spectrum (step S109), and an oxygen spectrum, a water vapor spectrum, and a cloud water spectrum are estimated from the spectral intensity and peak shape (step S110). The oxygen spectrum estimated in step S110 represents the amount of oxygen contained in the air from the observation point on the ground or at sea to the sky above. Similarly, the water vapor spectrum represents the amount of water vapor contained in the air from the observation point to the sky above, and corresponds to precipitable water vapor. Similarly, the cloud water spectrum represents the amount of cloud water contained in the air from the observation point to the sky above. The processing unit 10 calculates the amount of water vapor in the sky above the observation point based on the estimated spectra (step S111), and calculates the altitude distribution of water vapor density or precipitable water vapor at the position observed by the microwave radiometer 2 (step S112).

[0076] The processing unit 10 stores the data estimated or calculated in steps S110-S112 in the storage unit 11 (step S113), outputs a screen including a graph showing the results to the monitor device 3 (step S114), and ends the process.

[0077] As described above, the meteorological observation device 1 of the first embodiment enables estimation using the learning model M1 without requiring periodic calibration of the microwave radiometer 2 using a blackbody at liquid nitrogen temperature (77 K).

[0078] In parallel with the processing procedure shown in Fig. 5, the learning model M1 is then re-learned by the function of the second learning unit 107 so that reliable ERA and simulation results can be reproduced. Fig. 6 is a flowchart showing an example of a re-learning procedure for the learning model M1 during operation. After starting operation, the processing unit 10 of the meteorological observation device 1 starts up and starts the observation process of Fig. 4, and then, as the second learning unit 107, executes the following re-learning process in parallel with the procedure shown in Fig. 5.

[0079] The processing unit 10 acquires and stores the observation data P sky-bb In step S701, the processing unit 10 reads out the observation data P sky-bb The observation data P is selected as learning data from among the data stored in association with the date (date and time) of observation and position information. sky-bb Read out.

[0080] The processing unit 10 acquires the analysis data for the date corresponding to the observation data read in step S701 from a reliable information provider (external server) such as ERA5 (step S702). The processing unit 10 provides the acquired analysis data to a simulator and calculates the observation data P sky-bb The analyzed brightness temperature data for each frequency at the observation position is generated (step S703). The analyzed brightness temperature data generated in step S703 is data (brightness temperature difference) obtained by subtracting the brightness temperature of a room-temperature blackbody at the observation position on the corresponding date.

[0081] The processing unit 10 uses the observation data P acquired in step S701 sky-bb is input to the learning model M1 (step S704), and the first brightness temperature data T diff and the analytical brightness temperature at the observation position, or the evaluation function is calculated (step S705).

[0082] The processing unit 10 updates the parameters of the learning model M1 based on the calculation result of step S705 (step S706), and ends one round of re-learning processing.

[0083] The processing unit 10 processes the observation data P sky-bb The processing unit 10 executes the process shown in FIG. 6 every time it acquires the observation data P sky-bb The procedure shown in FIG. 6 may be repeated using the analytical brightness temperature data corresponding to

[0084] FIG. 7 is an explanatory diagram of the transition of learning of the learning model M1 in the meteorological observation device 1. FIG. 7 shows that the content of the learning data referenced by the function of the first learning unit 106 and the function of the second learning unit 107 for the learning model M1 is different. Before the start of operation, the first learning unit 106 learned the brightness of a blackbody (brightness temperature T hot The received intensity P when the black body ( hot and a blackbody at liquid nitrogen temperature (brightness temperature T cold The received intensity P when the black body ( cold and the brightness temperature difference, and the observation data (received intensity difference) P sky-bb and brightness temperature difference T diff is used as learning data. For example, the linear expression is T diff = coefficient k × P sky-bb +T s is.

[0085] After the start of operation, the second learning unit 107 learns the observation data P actually received by the microwave radiometer 2. sky-bb and the brightness temperature difference T diff The set of and is used in addition to the training data.

[0086] In this way, the meteorological observation device 1 of the first embodiment can seamlessly transition from conversion to brightness temperature data based on a linear equation defined by calibration using liquid nitrogen to conversion using the learning model M1 that has been trained to reproduce reliable analytical data. After operation begins, it becomes possible to use the learning model M1 to calculate brightness temperature data so as to reproduce highly accurate analytical data from observation data obtained by the microwave radiometer 2, without periodically performing calibration using a blackbody with the temperature of liquid nitrogen (77 K), which is difficult to handle.

[0087] In the first embodiment, the learning model M1 uses the "difference" between the reception intensity from a room-temperature blackbody and the reception intensity from the sky as an explanatory variable, and the "difference" between the radiance temperature of the room-temperature blackbody and the radiance temperature when the sky is observed as a target variable. This makes it possible to estimate the base radiance temperature of the observation point (second radiance temperature data T bb This technology makes it possible to obtain brightness temperature data accurately regardless of location or season, without the need for periodic calibration using liquid nitrogen, by responding to fluctuations in the Earth's surface temperature (equivalent to 1000 sq. m).

[0088] FIG. 8 shows an example of weather data displayed on the monitor device 3. Using brightness temperature data converted using the learning model M1, the monitor device 3 can display the brightness temperature spectrum of water vapor at a target observation point. In FIG. 8, the monitor device 3 displays the altitude distribution of water vapor density and precipitable water vapor calculated from the brightness temperature spectrum based on the processing procedure shown in FIG. 5. The screen shown in FIG. 8 also displays the time distribution of precipitable water vapor as a result of continuous observation by the meteorological observation device 1. In this way, the meteorological observation device 1 can estimate and display precipitable water vapor as weather data.

[0089] [Second embodiment] In the second embodiment, the difference between the result of conversion to brightness temperature data obtained by calibration using liquid nitrogen before shipping and the result of conversion to brightness temperature data using a learning model M1 that is updated based on sequential analysis data is output.

[0090] The hardware configuration of the meteorological observation device 1 in the second embodiment, other than the processing content described below, is the same as that of the meteorological observation device 1 in the first embodiment. Therefore, among the configurations of the meteorological observation device 1 in the second embodiment, the configurations common to the meteorological observation device 1 in the first embodiment are assigned the same reference numerals and detailed descriptions thereof will be omitted.

[0091] 9 and 10 are flowcharts showing an example of a processing procedure during operation of the meteorological observation device 1 in the second embodiment. Among the processing procedures shown in Fig. 9 and 10, steps common to the processing procedure shown in Fig. 5 of the first embodiment are assigned the same step numbers, and detailed descriptions thereof will be omitted.

[0092] After the processing of steps S101 to S108, the processing unit 10 calculates the observation data P acquired in step S103 using the relational expression calibrated using liquid nitrogen before shipping. sky-bb and the second brightness temperature data T estimated in step S106. bb In step S121, the processing unit 10 calculates brightness temperature data for the sky based on the calibration based on the observation data (reception intensity difference) P sky-bb and the first brightness temperature data (brightness temperature difference) is calculated by the linear coefficient (coefficient k in FIG. 7) sky-bb Multiplied by the second brightness temperature data T bb The brightness temperature data for the sky is calculated by adding

[0093] The processing unit 10 calculates and stores the error between the brightness temperature data of the sky calculated in step S121 and the brightness temperature data of the sky based on the learning model M1 calculated in step S108 (step S122).

[0094] The processing unit 20 executes the processes from step S109 to S114, outputs the stored error to the screen including the graph output in step S114 (step S123), and ends the process.

[0095] FIG. 11 shows an example of weather data displayed on the monitoring device 3 of the second embodiment. Similar to FIG. 8 , the display example shown in FIG. 11 includes the radiance temperature spectrum of water vapor at the target observation point. Compared to FIG. 8 , FIG. 11 displays the error in text between the radiance temperature data calculated based on the conversion using the learning model M1 and the radiance temperature data calculated using the relational equation (coefficient k) defined by liquid nitrogen calibration. In this way, it may be possible to present a situation in which a deviation occurs with respect to the relational equation defined by liquid nitrogen calibration due to the usage environment, season, or aging of the microwave radiometer 2. This demonstrates that reliable analytical data such as ERA5 can be reproduced without re-performing liquid nitrogen calibration.

[0096] Third Embodiment A meteorological observation device 1 according to a third embodiment further uses a meteorological model M2 that is trained to output estimated meteorological data such as precipitable water at an observation point when brightness temperature data in the sky is input.

[0097] The hardware configuration of the meteorological observation device 1 in the third embodiment, other than the processing content described below, is the same as that of the meteorological observation device 1 in the first embodiment. Therefore, among the configurations of the meteorological observation device 1 in the third embodiment, the configurations common to the meteorological observation device 1 in the first embodiment are assigned the same reference numerals and detailed description thereof will be omitted.

[0098] 12 is an explanatory diagram of the functions of the processing unit 10 of the meteorological observation device 1 of the third embodiment. In the third embodiment, the processing unit 10 functions as a meteorological data estimation unit 108 in addition to an acquisition unit 101, a preprocessing unit 102, a conversion unit 103, an estimation unit 104, a calculation unit 105, a first learning unit 106, and a second learning unit 107.

[0099] In the third embodiment, the processing unit 10 calculates the brightness temperature data T sky The weather data estimation unit 108 estimates estimated weather data using the weather model M2. sky is a set of numerical data indicating brightness temperatures by frequency. The estimated meteorological data is at least one of the following: altitude distribution of precipitable water, cloud water content, water vapor content (density), and altitude distribution of temperature.

[0100] 13 is a schematic diagram of the meteorological model M2. The meteorological model M2 calculates the brightness temperature data T sky The weather model M2 has an input layer M21 to which the brightness temperature data T sky is input, the system is trained to output estimated weather data including the amount of precipitable water at the observation point.

[0101] The output layer M22 outputs at least one of precipitable water vapor, cloud water content, water vapor density altitude distribution, and temperature altitude distribution as estimated weather data. In the example shown in FIG. 13 , the output layer M22 of the weather model M2 outputs altitude-specific values ​​of water vapor density. The altitude distribution of water vapor density indicates water vapor density at altitude. Precipitable water vapor is the amount of water vapor in the sky above the observation point, and is the amount when all of that water vapor condenses. Therefore, it can be calculated if the water vapor density value at altitude is obtained. Cloud water content is the amount of water that constitutes clouds integrated vertically. The output layer M22 of the weather model M2 may output both the water vapor density altitude distribution and the temperature responsivity distribution, or may also output other estimated weather data. The data output from the output layer M22 of the weather model M2 may be a collection of multiple learning models trained for each output data.

[0102] The intermediate layer M23 in the meteorological model M2 is the brightness temperature data T sky The meteorological model M2 is trained using second training data including the calculated brightness temperature data T sky The weather model M2 is trained so that the estimated weather data output when the input is a reference weather data that is reliable as weather data for that day. The weather model M2 is trained so that the brightness temperature data T above the observation point calculated by the calculation unit 105 from the observation data obtained from the microwave radiometer 2 at a known observation point on a certain date and time. sky When the above is input into the neural network, the parameters are updated and learned so that the difference between the output numerical data group and the reference weather data for that day is minimized.

[0103] The weather model M2 is not limited to one using a neural network, but may also be multiple regression, linear regression, a regression tree, a random forest, a support vector machine, or the like.

[0104] As described above, the correct reference meteorological data used as the second training data are precipitable water, cloud liquid water, water vapor density altitude distribution, and temperature altitude distribution. The reference meteorological data may be atmospheric data that is ERA5 analysis data at each predetermined location, or meteorological data at an observation point obtained by calculations such as simulation or regression analysis based on the atmospheric data. In the case of ERA analysis data, the predetermined location refers to a grid point on the Earth that is preset by ERA, and in the case of Sonde data, it refers to the observation position of a radiosonde. The reference meteorological data may also be data simulated as reference meteorological data at an observation point from Sonde data observed by a radiosonde. The reference meteorological data may also be data obtained from a satellite.

[0105] Fig. 14 is a flowchart showing an example of a processing procedure during operation of the weather observation device 1 in the third embodiment. Of the processing procedure shown in Fig. 14, steps common to the processing procedure shown in Fig. 5 of the first embodiment are assigned the same step numbers, and detailed explanations thereof will be omitted.

[0106] After the processing of steps S101 to S108, the processing unit 10 calculates the brightness temperature data T skyThe processing unit 10 acquires the estimated weather data output from the weather model M2 (step S132), stores the data in the storage unit 11 (step S133), and outputs a screen including a graph showing the results to the monitor device 3 (step S114).

[0107] As with the learning model M1, the weather model M2 may be re-learned using observation data obtained after operation begins, calculated brightness temperature data in the sky, and corresponding analysis data.

[0108] In the third embodiment, too, a seamless transition is made from conversion to brightness temperature data based on a linear equation defined by calibration using liquid nitrogen to conversion using a learning model M1 that has been trained to be able to reproduce reliable analytical data. In the third embodiment, the meteorological observation device 1 can directly estimate the atmospheric radiation spectrum and the amount of precipitable water vapor obtained from the calculated brightness temperature data in the sky using a meteorological model M2. In the third embodiment, the base brightness temperature of the observation point (second brightness temperature data T), which may vary depending on the observation point and season, is also used. bb This technology makes it possible to estimate meteorological data accurately in response to shaking of the Earth's atmosphere (equivalent to 1000m / s), regardless of location or season, without the need for periodic calibration using liquid nitrogen.

[0109] [Fourth Embodiment] The meteorological observation device 1 may be communicatively connected via a wireless or wired network to a microwave radiometer 2 mounted on a mobile body such as a ship or a microwave radiometer 2 fixedly installed at an observation point such as on a mountain, and may be installed at a location remote from the observation point. The monitor device 3 is used by a user who desires estimated brightness temperature data corresponding to the observation data, and may be installed at a location remote from both the meteorological observation device 1 and the microwave radiometer 2.

[0110] 15 is a schematic diagram of a weather observation system 100 according to the fourth embodiment. A microwave radiometer 2 is installed on a ship S. The microwave radiometer 2 is capable of transmitting and receiving data via wireless communication with a weather observation device 1 installed on land or another ship. A monitor device 3 is installed at a location remote from the weather observation device 1, and is capable of transmitting and receiving data to and from the weather observation device 1 via wireless communication or wired communication.

[0111] In this way, even if the devices are installed separately, the meteorological observation device 1 acquires observation data based on the radio waves received by the microwave radiometer 2 via communication, inputs the acquired observation data to the learning model M1, and derives estimated brightness temperature data corresponding to the observation data. The meteorological observation device 1 can create screen data including a brightness temperature spectrum graph as the estimated brightness temperature data and output it to the monitor device 3.

[0112] In the meteorological observation system 100, the microwave radiometer 2 mounted on the ship may have only the functions of the acquisition unit 101, preprocessing unit 102, and conversion unit 103 out of the functions of the processing unit 10 of the meteorological observation device 1 shown in Fig. 2 , and the conversion of observation data into brightness temperature data may be performed on board the ship. In this case, processes such as acquiring altitude distribution data of humidity, temperature, atmospheric pressure, wind, or cloud liquid water content as reference brightness temperature data and re-training the learning model M1 are performed by the meteorological observation device 1 installed on land, and only the results are provided again to the microwave radiometer 2.

[0113] The embodiments disclosed above are illustrative in all respects and are not restrictive. The scope of the present invention is defined by the claims, and includes all modifications within the meaning and scope of the claims.

[0114] Furthermore, independent claims and dependent claims described in the claims can be combined with each other in any and all combinations, regardless of the reference format. Furthermore, although the claims use a format in which a claim references two or more other claims (multiple claim format), this is not limited to this format. Multiple claims that reference at least one other multiple claim (multi-multi claim format) may also be used.

[0115] The following additional notes are provided regarding the above-described embodiment.

[0116] (Supplementary Note 1) A meteorological observation device comprising: an acquisition unit that acquires observation data based on radio waves received by a microwave radiometer and an environmental temperature of an environment in which the microwave radiometer is installed; a conversion unit that converts the observation data into first brightness temperature data in which brightness temperature is a physical dimension; an estimation unit that estimates, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer; and a calculation unit that calculates brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

[0117] (Supplementary Note 2) The meteorological observation device according to Supplementary Note 1, wherein the conversion unit converts a difference in reception intensity of radio waves received from two different measurement targets into a difference in brightness temperature data.

[0118] (Supplementary Note 3) The meteorological observation device according to Supplementary Note 1, wherein the conversion unit converts a difference between the reception strength of radio waves received from a radio wave absorber at a reference temperature and the reception strength of radio waves received from the sky into a difference in brightness temperature data.

[0119] (Supplementary Note 4) The meteorological observation device according to any one of Supplementary Notes 1 to 3, wherein the conversion unit obtains the first brightness temperature data using a learning model that outputs a corresponding difference in brightness temperature when a difference in reception strength of radio waves received by the microwave radiometer is input.

[0120] (Supplementary Note 5) The meteorological observation device according to any one of Supplementary Notes 1 to 4, wherein the estimation unit estimates a radiance temperature of a radio wave absorber at a reference temperature at an observation point as the second radiance temperature data.

[0121] (Supplementary Note 6) The meteorological observation device according to any one of Supplementary Notes 1 to 5, wherein the estimation unit estimates the second brightness temperature data from the environmental temperature using a relational expression that indicates a correspondence relationship between the environmental temperature and the brightness temperature of the radio wave absorber at a reference temperature, the relational expression being obtained by regression analysis based on measurement data of the environmental temperature and the brightness temperature of the radio wave absorber.

[0122] (Supplementary Note 7) The meteorological observation device according to any one of Supplementary Notes 1 to 6, wherein the calculation unit calculates the brightness temperature data of the sky by adding the first brightness temperature data to the second brightness temperature data.

[0123] (Supplementary Note 8) The meteorological observation device according to any one of Supplementary Notes 1 to 7, further comprising a meteorological data estimation unit that estimates at least one of precipitable water mass, cloud water content, water vapor content, and temperature altitude distribution at an observation point using the brightness temperature data of the sky calculated by the calculation unit.

[0124] (Supplementary Note 9) A meteorological observation device as described in Supplementary Note 4, which creates first learning data including a difference in receiving intensity and a difference in brightness temperature using a relational equation calibrated from the receiving intensity of radio waves received from a radio wave absorber cooled with liquid nitrogen and the receiving intensity of radio waves received from a radio wave absorber at a reference temperature, and learns the learning model using the created first learning data.

[0125] (Supplementary Note 10) The meteorological observation device according to Supplementary Note 9, wherein the learning model is learned using the first learning data before shipping of the device.

[0126] (Supplementary Note 11) A meteorological observation device according to any one of Supplementary Notes 4, 9, and 10, which creates second learning data including a difference between the reception strength of radio waves received from the sky at an observation point and the reception strength of radio waves received from a radio wave absorber at a reference temperature at the observation point, and a brightness temperature difference derived from analysis data at the observation point, and updates the learning model using the second learning data.

[0127] (Supplementary Note 12) The meteorological observation device according to any one of Supplementary Note 4 and Supplementary Note 9-10, wherein the learning model is updated with the second learning data after shipping of the device.

[0128] (Supplementary Note 13) A meteorological observation system including: a microwave radiometer that receives radio waves at an observation point; and a meteorological observation device that derives meteorological data based on data from the microwave radiometer, wherein the meteorological observation device comprises: an acquisition unit that acquires observation data based on the radio waves received by the microwave radiometer and an environmental temperature of an installation environment of the microwave radiometer; a conversion unit that converts the observation data into first brightness temperature data having brightness temperature as a physical dimension; an estimation unit that estimates, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the sky above the microwave radiometer; and a calculation unit that calculates brightness temperature data of the sky above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

[0129] (Supplementary Note 14) An estimation method in which a computer acquires observation data based on radio waves received by a microwave radiometer and the environmental temperature of an environment in which the microwave radiometer is installed, converts the observation data into first brightness temperature data in which brightness temperature is the physical dimension, estimates second brightness temperature data from the environmental temperature that serves as a basis for calculating brightness temperature data of the sky above the microwave radiometer, calculates brightness temperature data of the sky above the microwave radiometer from the first brightness temperature data and the second brightness temperature data, and estimates at least one of precipitable water vapor, cloud water content, water vapor content, and temperature altitude distribution at an observation point using the calculated brightness temperature data of the sky.

[0130] (Supplementary Note 15) A computer program that causes a computer to execute the following processes: acquire observation data based on radio waves received by a microwave radiometer and the environmental temperature of an environment in which the microwave radiometer is installed; convert the observation data into first brightness temperature data in which brightness temperature is a physical dimension; estimate, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer; and calculate brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data. term

[0131] Not necessarily all objects or advantages may be achieved in accordance with any particular embodiment described herein. Thus, for example, one skilled in the art will appreciate that a particular embodiment may be configured to operate to achieve or optimize one or more advantages as taught herein without necessarily achieving other objects or advantages as taught or suggested herein.

[0132] All processes described herein may be embodied and fully automated by software code modules executed by a computing system including one or more computers or processors. The code modules may be stored on any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods may be embodied in dedicated computer hardware.

[0133] Many other variations beyond those described herein will be apparent from this disclosure. For example, depending on the embodiment, certain operations, events, or functions of any of the algorithms described herein may be performed in a different sequence, added, merged, or omitted entirely (e.g., not all described acts or events are necessary to execute an algorithm). Furthermore, in certain embodiments, operations or events may be performed in parallel rather than sequentially, e.g., via multithreading, interrupt processing, or multiple processors or processor cores, or on other parallel architectures. Furthermore, different tasks or processes may be performed by different machines and / or computing systems that may function together.

[0134] The various illustrative logical blocks and modules described in connection with the embodiments disclosed herein may be implemented or executed by a machine such as a processor. The processor may be a microprocessor, but alternatively, the processor may be a controller, microcontroller, or state machine, or a combination thereof. The processor may include electrical circuitry configured to process computer-executable instructions. In another embodiment, the processor includes an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable device that performs logical operations without processing computer-executable instructions. A processor may also be implemented as a combination of computing devices, such as a combination of a digital signal processor (DSP) and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration. Although described herein primarily with reference to digital technology, a processor may also include primarily analog elements. For example, some or all of the signal processing algorithms described herein may be implemented by analog circuitry or mixed analog and digital circuitry. The computing environment can include any type of computer system, including, but not limited to, a microprocessor, mainframe computer, digital signal processor, portable computing device, device controller, or computer system based on a computational engine within an appliance.

[0135] Unless otherwise specified, conditional language such as "can," "could," "would," or "potential" is understood within the context in which it is generally used to convey that certain embodiments include certain features, elements, and / or steps, while other embodiments do not. Thus, such conditional language does not generally imply that features, elements, and / or steps are required in any manner in one or more embodiments, or that one or more embodiments necessarily include logic for determining whether those features, elements, and / or steps are included in or performed in any particular embodiment.

[0136] Disjunctive language such as "at least one of X, Y, Z," unless specifically stated otherwise, is understood in its general context to indicate that an item, term, etc. can be either X, Y, Z, or any combination thereof (e.g., X, Y, Z). Thus, such disjunctive language does not generally imply that a particular embodiment requires at least one of X, at least one of Y, or at least one of Z, respectively, to be present.

[0137] Any process descriptions, elements, or blocks in the flow diagrams described herein and / or illustrated in the accompanying drawings should be understood as potentially representing modules, segments, or portions of code, comprising one or more executable instructions for implementing a particular logical function or element in the process. Alternative embodiments are included within the scope of the embodiments described herein, in which elements or functions may be performed out of order, substantially simultaneously, or in reverse order from that shown or described, depending on the functionality involved, as will be understood by those skilled in the art.

[0138] Unless otherwise expressly stated, numeral terms such as "one" should generally be construed to include one or more described items. Thus, phrases such as "one device configured to" are intended to include one or more listed devices. Such one or more listed devices may also be collectively configured to perform the recited reference. For example, "a processor configured to perform the following A, B, and C" may include a first processor configured to perform A and a second processor configured to perform B and C. Additionally, even if a specific number of enumerations of the introduced embodiments are explicitly recited, those skilled in the art should construe such enumerations to typically mean at least the recited number (e.g., the mere enumeration of "two enumerations" without other modifiers typically means at least two enumerations, or two or more enumerations).

[0139] In general, it will be appreciated by those skilled in the art that the terms used herein generally intend "non-limiting" terms (e.g., the term "including" should be interpreted as "including but not limited to at least," the term "having" should be interpreted as "having at least," the term "including" should be interpreted as "including, but not limited to," etc.).

[0140] For purposes of description, the term "horizontal" as used herein is defined as a plane parallel to the plane or surface of the floor of the area in which the described system is used or the plane in which the described method is performed, regardless of its orientation. The term "floor" can be interchanged with the terms "ground" or "water surface." The term "vertical / plumb" refers to a direction perpendicular / vertical to a defined horizontal line. Terms such as "upper," "lower," "below," "top," "side," "higher," "lower," "above," "over," "below," etc. are defined relative to the horizontal plane.

[0141] As used herein, the terms "attach," "connect," "mate," and other related terms, unless otherwise noted, should be interpreted to include detachable, movable, fixed, adjustable, and / or removable connections or couplings. Connections / couplings include direct connections and / or connections with intermediate structures between the two components described.

[0142] Unless otherwise expressly stated, as used herein, numbers preceded by terms such as "approximately," "about," and "substantially" are inclusive of the recited number and also refer to an amount close to the recited amount that performs the desired function or achieves the desired result. For example, "approximately," "about," and "substantially" refer to values ​​less than 10% of the recited numerical value, unless otherwise expressly stated. As used herein, features of the disclosed embodiments preceded by terms such as "approximately," "about," and "substantially" refer to features that have some variability that also perform the desired function or achieve the desired result for that feature.

[0143] Many variations and modifications may be made to the above-described embodiments, and these elements should be understood to be among other acceptable examples. All such modifications and variations are intended to be included within the scope of this disclosure and are protected by the following claims.

[0144] REFERENCE SIGNS LIST 1 Meteorological observation device 10 Processing unit 101 Acquisition unit 102 Preprocessing unit 103 Conversion unit 104 Estimation unit 105 Calculation unit 106 First learning unit 107 Second learning unit 108 Meteorological data estimation unit 11 Storage unit P1 Information processing program M1 Learning model M2 Meteorological model 2 Microwave radiometer 3 Monitoring device

Claims

1. A meteorological observation device comprising: an acquisition unit that acquires observation data based on radio waves received by a microwave radiometer and the environmental temperature of the environment in which the microwave radiometer is installed; a conversion unit that converts the observation data into first brightness temperature data in which brightness temperature is the physical dimension; an estimation unit that estimates second brightness temperature data from the environmental temperature, which serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer; and a calculation unit that calculates brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

2. The meteorological observation device according to claim 1, wherein the conversion unit converts the difference in reception strength of radio waves received from two different measurement targets into the difference in brightness temperature data.

3. The meteorological observation device according to claim 1, wherein the conversion unit converts the difference between the reception strength of radio waves received from a radio wave absorber at a reference temperature and the reception strength of radio waves received from the sky into a difference in brightness temperature data.

4. A meteorological observation device as described in any one of claims 1 to 3, wherein the conversion unit obtains the first brightness temperature data using a learning model that outputs a corresponding difference in brightness temperature when a difference in reception strength of radio waves received by the microwave radiometer is input.

5. The meteorological observation device according to claim 1, wherein the estimation unit estimates the radiance temperature of the radio wave absorber at a reference temperature at the observation point as the second radiance temperature data.

6. The meteorological observation device according to claim 1, wherein the estimation unit estimates the second brightness temperature data from the environmental temperature using a relational expression that indicates the correspondence between the environmental temperature and the brightness temperature of the radio wave absorber at a reference temperature, the relational expression being obtained by regression analysis based on measurement data of the environmental temperature and the brightness temperature of the radio wave absorber.

7. The meteorological observation device according to claim 1, wherein the calculation unit calculates brightness temperature data for the sky by adding the first brightness temperature data to the second brightness temperature data.

8. The meteorological observation device according to claim 1, further comprising a meteorological data estimation unit that estimates at least one of the precipitable water mass, cloud water content, water vapor content, and temperature altitude distribution at the observation point based on the brightness temperature data of the sky calculated by the calculation unit.

9. The meteorological observation device according to claim 4, wherein first learning data including a difference in receiving strength and a difference in brightness temperature is created using a relational equation calibrated from the receiving strength of radio waves received from a radio wave absorber cooled with liquid nitrogen and the receiving strength of radio waves received from a radio wave absorber at a reference temperature, and the created first learning data is used to train the learning model.

10. The meteorological observation device according to claim 9, wherein the learning model is trained using the first learning data before shipping the device.

11. The meteorological observation device according to claim 4, which creates second learning data including the difference between the reception strength of radio waves received from the sky at an observation point and the reception strength of radio waves received from a radio wave absorber at a reference temperature at said observation point, and the brightness temperature difference derived from the analysis data at said observation point, and updates said learning model using said second learning data.

12. The meteorological observation device according to claim 11, wherein the learning model is updated with the second learning data after the device is shipped.

13. A meteorological observation system comprising: a microwave radiometer that receives radio waves at an observation point; and a meteorological observation device that derives meteorological data based on data from the microwave radiometer, wherein the meteorological observation device comprises: an acquisition unit that acquires observation data based on the radio waves received by the microwave radiometer and the environmental temperature of the installation environment of the microwave radiometer; a conversion unit that converts the observation data into first brightness temperature data having brightness temperature as the physical dimension; an estimation unit that estimates, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the sky above the microwave radiometer; and a calculation unit that calculates brightness temperature data of the sky above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

14. An estimation method in which a computer acquires observation data based on radio waves received by a microwave radiometer and the environmental temperature of the environment in which the microwave radiometer is installed, converts the observation data into first brightness temperature data in which brightness temperature is the physical dimension, estimates second brightness temperature data from the environmental temperature that serves as a basis for calculating brightness temperature data for the sky above the microwave radiometer, calculates brightness temperature data for the sky above the microwave radiometer from the first brightness temperature data and the second brightness temperature data, and estimates at least one of precipitable water vapor, cloud water content, water vapor content, and temperature altitude distribution at an observation point using the calculated brightness temperature data for the sky.

15. A computer program causing a computer to execute the following processes: acquire observation data based on radio waves received by a microwave radiometer and the environmental temperature of the environment in which the microwave radiometer is installed; convert the observation data into first brightness temperature data in which brightness temperature is the physical dimension; estimate, from the environmental temperature, second brightness temperature data that serves as a basis for calculating brightness temperature data of the atmosphere above the microwave radiometer; and calculate brightness temperature data of the atmosphere above the microwave radiometer from the first brightness temperature data and the second brightness temperature data.

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