Water vapor observation device, system, method, and computer readable storage medium

Abstract

The present application is a water vapor observation device, a water vapor observation system, a water vapor observation method, and a computer-readable storage medium. The water vapor observation device (10) includes an antenna (40), a radio frequency amplifier (30), a frequency selection unit (21), and a water vapor index calculation unit (22), wherein the antenna (40) receives radio waves radiated from an atmosphere containing water vapor, the radio frequency amplifier (30) amplifies the received radio waves to generate an observation signal, the frequency selection unit (21) selects a plurality of observation frequencies except for a precision degradation frequency based on the observation signal, and the water vapor index calculation unit (22) calculates a water vapor index using spectral intensities of the plurality of observation frequencies, thereby enabling observation of the amount of water vapor with good precision.

Description

TECHNICAL FIELD

[0001] The present application relates to a technology of observing water vapor. BACKGROUND

[0002] In the past, a water vapor observation device as shown in Patent Literature 1 has been designed.

[0003] PRIOR ART DOCUMENTS

[0004] PATENT LITERATURE

[0005] Patent Literature 1: Japanese Patent Laid-Open No. 2013-224884 SUMMARY

[0006] PROBLEMS TO BE SOLVED BY THE INVENTION

[0007] As a method of observing water vapor, in addition to the method using electric waves shown in Patent Literature 1, a method using electric waves generated by water vapor is also considered.

[0008] However, in the case of using electric waves generated from water vapor, the signal strength of the observation object is small, and it is difficult to observe the water vapor amount with high precision.

[0009] Thus, an object of the present application is to observe the water vapor amount with high precision.

[0010] TECHNICAL MEANS FOR SOLVING PROBLEMS

[0011] The water vapor observation device of the present application includes an antenna, a radio frequency (RF) amplifier, a frequency selection section, and a water vapor index calculation section. The antenna receives electric waves radiated from an atmosphere containing water vapor. The RF amplifier amplifies the received electric waves to generate an observation signal. The frequency selection section selects a plurality of observation frequencies except for a precision degradation frequency in accordance with the observation signal. The water vapor index calculation section calculates a water vapor index using spectral intensities of the plurality of observation frequencies.

[0012] In the structure, the spectral intensities of the plurality of observation frequencies required for the calculation of the water vapor index with high precision are obtained.

[0013] EFFECTS OF THE INVENTION

[0014] According to the present application, it is possible to observe the water vapor amount with high precision. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A functional block diagram for showing the structure of the water vapor observation device of the first embodiment of the present application.

[0016] Figure 2 A functional block diagram for showing the structure of the frequency selection section.

[0017] Figure 3 A diagram illustrating the concept of a calculated water vapor index.

[0018] Figure 4 A flowchart for calculating the water vapor index is provided.

[0019] Figure 5 A flowchart for selecting the accuracy degradation frequency.

[0020] Figure 6 This is a flowchart illustrating the main processes in an outlier detection method.

[0021] Figure 7 A spectral characteristic diagram illustrating the detection concept of the first outlier detection method.

[0022] Figure 8 This is a flowchart illustrating a second method for detecting outliers.

[0023] Figure 9 In the diagram, the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0024] Figure 10 This is a flowchart illustrating a third method for detecting outliers.

[0025] Figure 11 A spectral characteristic diagram illustrating the detection concept of the third outlier detection method.

[0026] Figure 12 This is a flowchart illustrating the fourth method for detecting outliers.

[0027] Figure 13 A spectral characteristic diagram illustrating the detection concept of the fourth outlier detection method.

[0028] Figure 14 This is a flowchart illustrating the fifth method for detecting outliers.

[0029] Figure 15 (A) Figure 15 (B) is a spectral characteristic diagram illustrating the detection concept of the fifth outlier detection method.

[0030] Figure 16 This is a flowchart illustrating the method for switching observation frequencies.

[0031] Figure 17 A spectral characteristic diagram illustrating the concept of the observation frequency switching method.

[0032] Figure 18 This is a flowchart of an interpolation method for representing the spectral intensity of an observed frequency.

[0033] Figure 19A spectrum characteristic diagram for explaining the concept of the interpolation method of the spectrum intensity of the observation frequency.

[0034] Figure 20 (A) of FIG. 1 is a functional block diagram showing the structure of the water vapor observation device in the water vapor observation system according to the second embodiment of the present application, Figure 20 (B) of FIG. 1 is a functional block diagram showing the structure of the observation frequency selection device in the water vapor observation system according to the second embodiment of the present application.

[0035] Figure 21 A graph showing an example of the Noise Factor (NF) characteristic of the RF amplifier.

[0036] Figure 22 A flowchart of the precision degradation frequency processing.

[0037] Figure 23 A flowchart of the water vapor observation processing.

[0038] Figure 24 A functional block diagram showing the structure of the water vapor observation system according to the third embodiment of the present application.

[0039] Figure 25 A functional block diagram showing the structure of the water vapor observation system according to the fourth embodiment of the present application.

[0040] [Explanation of symbols]

[0041] 1B, 1C: Water vapor observation system

[0042] 10, 10A, 10B, 10C: Water vapor observation device

[0043] 20, 20A, 20B, 20C: Operation section

[0044] 21: Frequency selection section

[0045] 22: Water vapor index calculation section

[0046] 23: Intermediate signal processing section

[0047] 24: Spectrum generation section

[0048] 30: RF amplifier

[0049] 40: Antenna

[0050] 50: Input section

[0051] 60: Observation frequency selection device

[0052] 61: NF characteristic measurement section

[0053] 62: Frequency selection section

[0054] 70: Receiving and Dispatch Department

[0055] 80, 80C: Information processing devices

[0056] 81, 81C: CPU

[0057] 82: Storage Department

[0058] 83: Receiving and Dispatch Department

[0059] 84: Display Section

[0060] 85: Operations Department

[0061] 90: Data communication network

[0062] 211: Outlier Detection Department

[0063] 212: Precision Degradation Frequency Selection Unit

[0064] 213: Observation Frequency Selection Department

[0065] 221: Outlier Detection Department

[0066] 810: Water Vapor Index Calculation Section

[0067] 811: Frequency Selection Unit

[0068] 812: Water Vapor Observation Department Detailed Implementation

[0069] (First Implementation)

[0070] Referring to the drawings, the water vapor observation apparatus, water vapor observation method, and water vapor observation procedure of the first embodiment of the present invention will be described. Figure 1 This is a functional block diagram illustrating the structure of the water vapor observation device according to the first embodiment of the present invention. Figure 2 This is a functional block diagram illustrating the structure of the frequency selection unit.

[0071] (Water vapor observation device 10)

[0072] like Figure 1 As shown, the water vapor observation device 10 includes a computing unit 20, an RF amplifier 30, and an antenna 40. The antenna 40 is connected to the RF amplifier 30, and the RF amplifier 30 is connected to the computing unit 20.

[0073] Antenna 40 has a specified sensitivity to high-frequency signals (RF signals) in the gigahertz band (GHz band). Antenna 40 receives high-frequency signals radiated from the atmosphere and outputs them to RF amplifier 30. The high-frequency signals radiated from the atmosphere include radio waves radiated from water vapor. The spectrum of water vapor has characteristics corresponding to the state of water vapor, such as the amount of water vapor.

[0074] RF amplifier 30 is a so-called low-noise amplifier (LNA) that has specified amplification characteristics for high-frequency signals in the gigahertz band. RF amplifier 30 is implemented by specified electronic circuitry. RF amplifier 30 amplifies the high-frequency signal input from antenna 40 and outputs it as an observation signal in the RF band.

[0075] The arithmetic unit 20 is implemented using prescribed electronic circuits, a computer or other arithmetic processing devices, integrated circuits (ICs), etc. The arithmetic unit 20 includes a frequency selection unit 21, a water vapor index calculation unit 22, and an intermediate signal processing unit 23.

[0076] The intermediate signal processing unit 23 includes a down-converter and an intermediate frequency (IF) amplifier. RF band observation signals are input to the intermediate signal processing unit 23.

[0077] The intermediate signal processing unit 23 downconverts the RF band observation signal to the IF band and amplifies it, then outputs the processed observation signal. Furthermore, the intermediate signal processing unit 23 may also include a filtering circuit to filter the downconverted observation signal. In this case, for example, the intermediate signal processing unit 23 performs filtering to suppress frequency components outside the frequency band required for water vapor observation.

[0078] like Figure 2 As shown, the frequency selection unit 21 includes an outlier detection unit 211, an accuracy degradation frequency selection unit 212, and an observation frequency selection unit 213.

[0079] The observation signal from the intermediate signal processing unit 23 is input to the outlier detection unit 211. The outlier detection unit 211 detects outliers in the spectrum of the observation signal. Furthermore, examples of specific methods for detecting outliers will be described later. Here, in this invention, outliers in the spectrum of the observation signal are caused by the structure and specifications of the device, the external radio wave environment, i.e., the observation environment, and are not caused by water vapor from the observed object. For example, when an inexpensive RF amplifier 30 is used to reduce the cost of the device, there may be cases where the amplification rate of the RF amplifier 30 varies due to temperature, individual differences, etc., or the amplification rate is different at each frequency. In such cases, outliers will occur. In addition, the frequency of the water vapor spectrum may sometimes overlap with the frequency band of specific communication systems such as 5G. Therefore, when the communication signal of the communication system is included in the observation signal, outliers will occur.

[0080] The accuracy degradation frequency selection unit 212 selects the frequency of the abnormal value as the accuracy degradation frequency.

[0081] The observation frequency selection unit 213 selects multiple observation frequencies other than the accuracy degradation frequency based on the accuracy degradation frequency. Furthermore, examples of specific methods for selecting multiple observation frequencies will be described later. The frequency selection unit 21 outputs the multiple observation frequencies to the water vapor index calculation unit 22.

[0082] The observed signal is input to the water vapor index calculation unit 22. The water vapor index calculation unit 22 detects the spectral intensity of multiple observation frequencies selected by the frequency selection unit 21 from the observed signal. The water vapor index calculation unit 22 uses the spectral intensity of the multiple observation frequencies to calculate a water vapor index that is highly correlated with the amount of water vapor.

[0083] (Example of calculating water vapor index)

[0084] Figure 3 This is a diagram illustrating a concept of calculating a water vapor index. The observation frequency selection unit 213 selects observation frequencies f1 and f2 as multiple observation frequencies. For example, observation frequency f1 is typically selected near the peak frequency of the water vapor spectrum (e.g., near approximately 22 GHz). Alternatively, for example, observation frequency f2 is selected as a frequency higher than observation frequency f1.

[0085] Furthermore, the selection of observation frequencies f1 and f2 is not limited to these. Observation frequency f1 can also be a higher frequency than observation frequency f2. In addition, it can be appropriately selected based on the calculation method of the water vapor index.

[0086] The water vapor index calculation unit 22 detects the spectral intensity Df1 at the observation frequency f1 and the spectral intensity Df2 at the observation frequency f2. The water vapor index calculation unit 22 uses the spectral intensity Df1 and the spectral intensity Df2 to calculate the water vapor index.

[0087] For example, it is known that when using the spectral intensity Df1 of the observation frequency f1 and the spectral intensity Df2 of the observation frequency f2, as described above, a water vapor index highly correlated with water vapor quantity can be obtained through the applicant's past inventions (for example, see U.S. Patent Application Publication No. 2014 / 0035779). The water vapor index calculation unit 22 uses the above information and the spectral intensity Df1 and spectral intensity Df2 to calculate the water vapor index.

[0088] (The effect of the structure of the water vapor observation device 10)

[0089] With the aforementioned structure, the water vapor observation device 10 selects multiple observation frequencies by detecting and excluding frequencies that degrade in accuracy. Therefore, the water vapor observation device 10 can detect the spectral intensities of multiple observation frequencies that accurately reflect the amount of water vapor. Consequently, the water vapor observation device 10 can calculate water vapor parameters with high accuracy by using these spectral intensities. Furthermore, by using the accurately calculated water vapor parameters, the water vapor observation device 10 can, for example, accurately observe the amount of water vapor.

[0090] (Water vapor observation method of the first embodiment)

[0091] The description illustrates various processing configurations for water vapor observation performed by functional units with different processing characteristics. However, the processing in the computation unit 20 can also be programmed and stored in a storage medium or the like, and the water vapor observation can be achieved by executing the program using a computation processing device. In this case, the computation processing device, for example, according to... Figure 4 , Figure 5 The process can be performed using the flowchart shown. Figure 4 A flowchart for calculating the water vapor index is provided. Figure 5 A flowchart for selecting the accuracy degradation frequency.

[0092] The processing unit uses the observed signal to select the accuracy degradation frequency (S11). More specifically, the processing unit generates the spectrum of the observed signal (S21). The processing unit detects outliers in the spectral intensity from the spectrum (S22). The processing unit selects the frequency of the outlier as the accuracy degradation frequency (S23).

[0093] The processing unit selects multiple observation frequencies that are different from the accuracy degradation frequency (S12). The processing unit detects the spectral intensity of the multiple observation frequencies from the observation signal (S13). The processing unit uses the spectral intensity of the multiple observation frequencies to calculate the water vapor index (S14).

[0094] (Examples of specific methods for detecting outliers)

[0095] In the water vapor observation device 10 of the present invention, several methods as outlier detection methods can be used. Therefore, the outlier detection methods will be described in sequence below. Furthermore, these outlier detection methods can be performed individually or by using the results obtained from a combination of multiple methods to detect outliers.

[0096] Figure 6 This is a flowchart illustrating the main processes in an outlier detection method. Hereinafter, the description will focus on the outlier detection unit 211; however, in the case where the outlier detection process is programmed, the processing is performed by the computational processing unit.

[0097] The outlier detection unit 211 sets the normal range (normal range by frequency) of the spectral intensity of each frequency based on the waveform of the observed signal's spectrum (S221). The outlier detection unit 211 uses the normal range by frequency and each spectral intensity for each frequency to determine whether it is within or outside the normal range.

[0098] If the spectral intensity is outside the normal range (S222: Yes), the outlier detection unit 211 determines that the spectral intensity of the frequency is an outlier (S223). Subsequently, the accuracy degradation frequency selection unit 212 selects the frequency detected in the form of the outlier as the accuracy degradation frequency.

[0099] If the spectral intensity is within the normal range (S222: No), the anomaly detection unit 221 determines that the spectral intensity of the frequency is normal (S224).

[0100] (First detection method)

[0101] Figure 7 A spectral characteristic diagram illustrating the detection concept of the first outlier detection method. Figure 7 In the diagram, the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0102] In the first detection method, the outlier detection unit 211 uses a spectral setting region ZRn to detect outliers. The spectral setting region ZRn is obtained by setting a range of acceptable normal spectral intensity within the frequency band of the observed signal. The spectral setting region ZRn can be set, for example, by referring to past measurement results or simulations obtained under noise conditions without water vapor observation.

[0103] The anomaly detection unit 211 determines whether a frequency is abnormal or normal based on whether the spectral intensity of each frequency is within the spectral setting region ZRn. More specifically, the anomaly detection unit 211 obtains the normal range of the spectral intensity of the frequency of the target frequency from the spectral setting region ZRn. If the spectral intensity of the observed signal is outside the normal range, the anomaly detection unit 211 detects that the spectral intensity is an anomaly.

[0104] For example, if Figure 7 For example, the outlier detection unit 211 obtains the normal range ZNna of the observation frequency fa from the spectrum setting region ZRn. The outlier detection unit 211 detects the spectral intensity Dfa of the observation frequency fa in the observation signal. If the outlier detection unit 211 detects that the spectral intensity Dfa is outside the normal range ZNna, it detects that the spectral intensity Dfa is an outlier. Therefore, the accuracy degradation frequency selection unit 212 detects that the observation frequency fa is an accuracy degradation frequency.

[0105] (Second detection method)

[0106] Figure 8 This is a flowchart illustrating a second method for detecting outliers. Figure 9 A spectral characteristic diagram illustrating the detection concept of the second outlier detection method. Figure 9 In the diagram, the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0107] In the second detection method, the outlier detection unit 211 uses the approximate characteristics of spectral intensity to detect outliers. These approximate characteristics are set by the coefficients of an approximate function calculated using the spectral intensities at multiple frequencies arranged on the frequency axis. The approximate function can be a linear function, a quadratic function, etc., and by setting it to a linear function, the computational load is reduced.

[0108] The outlier detection unit 211 moves multiple frequency groups from which approximate characteristics are calculated along the frequency axis and calculates the approximate characteristics for each frequency group (S231). The outlier detection unit 211 calculates the change in the approximate characteristics along the frequency axis (S232).

[0109] The outlier detection unit 211 has a preset threshold for the amount of change. If the amount of change is above the threshold, the outlier detection unit 211 considers it to be outside the normal range (S233: Yes) and makes an anomaly determination (S234). If the amount of change is below the threshold, the outlier detection unit 211 considers it to be within the normal range (S233: No) and makes a normal determination (S235).

[0110] For example, if Figure 9For example, the outlier detection unit 211 approximates the spectral intensities of frequencies fd3, fd2, and fd1 arranged on the frequency axis using a linear function, and uses its coefficients as approximation characteristic Kd321. The outlier detection unit 211 approximates the spectral intensities of frequencies fd2, fd1, and fa arranged on the frequency axis using a linear function, and uses its coefficients as approximation characteristic Kd21a. The outlier detection unit 211 approximates the spectral intensities of frequencies fd1, fa, and fe1 arranged on the frequency axis using a linear function, and uses its coefficients as approximation characteristic Kdae. The outlier detection unit 211 approximates the spectral intensities of frequencies fa, fe1, and fe2 arranged on the frequency axis using a linear function, and uses its coefficients as approximation characteristic Kae12. The outlier detection unit 211 approximates the spectral intensities of frequencies fe1, fe2, and fe3 arranged on the frequency axis using a linear function, and uses its coefficients as approximation characteristic Ke123.

[0111] Next, the outlier detection unit 211 calculates the changes in adjacent approximate characteristics on the frequency axis. For example, the outlier detection unit 211 calculates the changes in approximate characteristics Kd321 and Kd21a, and the changes in approximate characteristics Kd21a and Kdae. The outlier detection unit 211 calculates the changes in approximate characteristics Kdae and Kae12, and the changes in approximate characteristics Kae12 and Ke123.

[0112] Here, as Figure 9 As shown, when an outlier (spectral intensity Dfa) is included, the approximate characteristic including the spectral intensity Dfa changes significantly compared to the approximate characteristic before and after it. Therefore, a threshold is set for the amount of change, and the outlier detection unit 211 can detect the outlier as long as the detected value is above the threshold.

[0113] For example, in Figure 9 In this case, the approximate characteristics of the spectral intensity Dfa, which is an outlier, change significantly compared to the approximate characteristics without the spectral intensity Dfa. Therefore, the outlier detection unit 211 detects this situation and can determine and detect that the spectral intensity Dfa is an outlier. Consequently, the accuracy degradation frequency selection unit 212 detects the observation frequency fa, which has the outlier, as the accuracy degradation frequency.

[0114] (Third detection method)

[0115] Figure 10 This is a flowchart illustrating a third method for detecting outliers. Figure 11 A spectral characteristic diagram illustrating the detection concept of the third outlier detection method. Figure 11 In the diagram, the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0116] In the third detection method, similar to the second detection method, the outlier detection unit 211 uses an approximate characteristic of spectral intensity to detect outliers. However, in the third detection method, the outlier detection unit 211 calculates the approximate characteristic based on the spectral intensity of the entire frequency band of the observed frequency. In this case, the approximate function representing the approximate characteristic is set by a predefined function, such as one whose waveform is similar to a water vapor spectrum. This function can be appropriately set, for example, based on past observations of water vapor.

[0117] The outlier detection unit 211 calculates an approximate characteristic based on the spectral intensity of the entire frequency band of the observed frequency (S241). The outlier detection unit 211 sets the spectral intensity (inferred spectral intensity) of each frequency of the approximate characteristic within a normal range that incorporates the observation error (S242).

[0118] If the spectral intensity is outside the normal range (S243: Yes), the outlier detection unit 211 makes an anomaly determination (S244). If the spectral intensity is within the normal range (S243: No), the outlier detection unit 211 makes a normal determination (S245).

[0119] For example, if Figure 11 In the example, the outlier detection unit 211 calculates the approximate characteristic REF based on the spectral intensity of the overall frequency band of the observed frequency.

[0120] Next, the outlier detection unit 211 sets an upper limit threshold THmax and a lower limit threshold THmin for the approximate characteristic REF. The outlier detection unit 211 sets the intensity range between the upper limit threshold THmax and the lower limit threshold THmin as the normal range ZNn. The outlier detection unit 211 compares the spectral intensity with the normal range ZNn for each observation frequency.

[0121] Here, as Figure 11 As shown, at the observation frequency fa, the spectral intensity Dfa is outside the normal range ZNn. Therefore, the outlier detection unit 211 detects this situation and can determine and detect that the spectral intensity Dfa is an outlier. Consequently, the accuracy degradation frequency selection unit 212 detects the observation frequency fa with the outlier as the accuracy degradation frequency.

[0122] For example, the detection of such outliers can also be done as follows: Figure 11 As shown, the determination is performed only within a specified frequency range (FROB) containing the peak frequency of the water vapor spectrum or within the frequency range of a specific interference wave. This reduces the computational load without compromising the accuracy of water vapor observation. Furthermore, this selection of the frequency range (FROB) for determination can also be applied to both the first and second detection methods.

[0123] (Fourth detection method)

[0124] Figure 12 This is a flowchart illustrating the fourth method for detecting outliers. Figure 13 A spectral characteristic diagram illustrating the detection concept of the fourth outlier detection method. Figure 13 In the diagram, the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0125] In the fourth detection method, the outlier detection unit 211 uses the difference in spectral intensity of adjacent frequencies to detect outliers.

[0126] The outlier detection unit 211 calculates the change in spectral intensity between adjacent frequencies (S251).

[0127] If the change in spectral intensity is outside the normal range (S252: Yes), the outlier detection unit 211 makes an anomaly determination (S253). If the change in spectral intensity is within the normal range (S252: No), the outlier detection unit 211 makes a normal determination (S254).

[0128] For example, if Figure 13 For example, the outlier detection unit 211 calculates the change ΔDab between the spectral intensity Dfa of the observed frequency fa and the spectral intensity Dfb of the observed frequency fb. The outlier detection unit 211 calculates the change ΔDbc between the spectral intensity Dfb of the observed frequency fb and the spectral intensity Dfc of the observed frequency fc.

[0129] The outlier detection unit 211 has a preset threshold for the amount of change. The threshold is set based on the amount of change when there are no outliers, and can be appropriately set based on past observation results, etc.

[0130] The outlier detection unit 211 detects that the change ΔDab is greater than a threshold, thus detecting an outlier. Conversely, the outlier detection unit 211 detects that the change ΔDbc is less than a threshold, thus detecting no outlier. Based on these results, the outlier detection unit 211 determines that the spectral intensity Dfa of the observation frequency fa, which is contained only in the change ΔDab, is an outlier, while the spectral intensity Dfb of the observation frequency fb, which is contained in both changes, is not an outlier. Therefore, the accuracy degradation frequency selection unit 212 detects the observation frequency fa, which has the outlier, as an accuracy degradation frequency.

[0131] For example, the detection of such outliers can also be done as follows: Figure 13 As shown, observations are only performed within a specified frequency range (FROB) containing the peak frequency of the water vapor spectrum or within the frequency range of specific interfering waves. This reduces computational load without compromising the accuracy of water vapor observations.

[0132] (Fifth detection method)

[0133] Figure 14This is a flowchart illustrating the fifth method for detecting outliers. Figure 15 (A) Figure 15 (B) is a spectral characteristic diagram illustrating the detection concept of the fifth outlier detection method. Figure 15 (A) Figure 15 (B) shows the spectral characteristics at different observation times. Figure 15 (A) Figure 15 In (B), the horizontal axis represents the frequency of the observed signal, and the vertical axis represents the spectral intensity.

[0134] In the fifth detection method, the outlier detection unit 211 uses the time characteristics (time change) of the spectral intensity to detect outliers.

[0135] The outlier detection unit 211 calculates the spectral intensity of each observed frequency at time t1 (S261). The outlier detection unit 211 calculates the spectral intensity of each observed frequency at time t2 (a time different from time t1) (S262).

[0136] The outlier detection unit 211 calculates the temporal characteristics of the spectrum, specifically the temporal change of the spectral intensity at each observation frequency (S263).

[0137] If the change in spectral intensity is outside the normal range (S264: Yes), the outlier detection unit 211 makes an anomaly determination (S265). If the change in spectral intensity is within the normal range (S264: No), the outlier detection unit 211 makes a normal determination (S266).

[0138] For example, if Figure 15 For example, the outlier detection unit 211 calculates the time change of the spectral intensity Dfd(t1) of the observed frequency fd at time t1 and the spectral intensity Dfd(t2) of the observed frequency fd at time t2.

[0139] The outlier detection unit 211 has a preset threshold for the amount of change over time. The threshold is set based on the amount of change when there are no outliers, and can be appropriately set based on past observation results, etc.

[0140] The outlier detection unit 211 detects that the amount of time change at the observation frequency fd is greater than a threshold, thus detecting an outlier. Therefore, the accuracy degradation frequency selection unit 212 selects the observation frequency fd containing the outlier as the accuracy degradation frequency.

[0141] (Method for switching observation frequencies)

[0142] Figure 16 This is a flowchart illustrating the method for switching observation frequencies. Figure 17A spectral characteristic diagram is provided to illustrate the concept of the observation frequency switching method. Furthermore, the following explanation focuses on the observation frequency selection unit 213; however, when the observation frequency switching process is programmed, the processing is performed by the arithmetic processing unit.

[0143] When the observation frequency previously used for water vapor observation, in other words, the water vapor index calculation unit 22, is detected as an accuracy-deteriorating frequency (S31: Yes), the observation frequency selection unit 213 selects an adjacent frequency (a frequency in the spectrum) as a new observation frequency (S32). At this time, if the adjacent frequency is also determined to be an accuracy-deteriorating frequency, the observation frequency selection unit 213 selects the next adjacent frequency as a new observation frequency.

[0144] For example, if Figure 17 For example, if the spectral intensity Dfa of the observed frequency fa is an outlier, the accuracy degradation frequency selection unit 212 selects the observed frequency fa as the accuracy degradation frequency. The observed frequency selection unit 213 selects the frequency fb adjacent to the observed frequency fa as the new observed frequency. When performing such processing, errors in the water vapor index may occur due to the switching of the observed frequency. However, the magnitude of these errors is sufficiently small compared to the errors generated when using outliers. Therefore, the water vapor observation device 10 can calculate the water vapor index while suppressing accuracy degradation.

[0145] Furthermore, while the description illustrates selecting a frequency on the higher side compared to the accuracy degradation frequency as the new observation frequency, a frequency on the lower side can also be selected as the new observation frequency. Additionally, the switching frequency may not be adjacent to the accuracy degradation frequency, or it may be a nearby frequency.

[0146] (Interpolation method for spectral intensity of observed frequencies)

[0147] Figure 18 This is a flowchart of an interpolation method for representing the spectral intensity of an observed frequency. Figure 19 A spectral characteristic diagram is provided to illustrate the concept of the interpolation method for the spectral intensity of the observed frequency. Furthermore, the following explanation will focus on the observed frequency selection unit 213 and the water vapor index calculation unit 22. However, when the switching process of the observed frequency is programmed, the processing is performed by the arithmetic processing device.

[0148] When the observation frequency previously used for water vapor observation, in other words, the water vapor index calculation unit 22, is detected as an accuracy-deteriorating frequency (S41: Yes), the observation frequency selection unit 213 selects the frequencies (a frequency in the spectrum) adjacent to each other on the frequency axis as the interpolation frequency (S42). At this time, if the adjacent frequency is also determined to be an accuracy-deteriorating frequency, the observation frequency selection unit 213 selects the frequency that is even more adjacent as the interpolation frequency.

[0149] The water vapor index calculation unit 22 detects the spectral intensity of the observation frequency used for interpolation, and calculates the spectral intensity of the original observation frequency (the observation frequency that is determined to be the frequency of accuracy degradation) based on the spectral intensity of the interpolation frequency (S43).

[0150] For example, if Figure 19 In the example where the spectral intensity Dfa of the observed frequency fa is an outlier, the accuracy degradation frequency selection unit 212 selects the observed frequency fa as the accuracy degradation frequency. The observed frequency selection unit 213 selects the frequencies fb1 and fb2 adjacent to the observed frequency fa as the interpolation frequencies.

[0151] The water vapor index calculation unit 22 detects the spectral intensity Dfb1 of the interpolation frequency fb1 and the spectral intensity Dfb2 of the interpolation frequency fb2. Based on the spectral intensities Dfb1 and Dfb2, the water vapor index calculation unit 22 calculates the interpolated spectral intensity Dfac of the observed frequency fa. Specifically, for example, the water vapor index calculation unit 22 calculates the interpolated spectral intensity Dfac by using a weighted average value corresponding to the frequency spacing (sampling frequency, etc.).

[0152] By performing this process, the water vapor observation device 10 can calculate water vapor parameters with high accuracy without changing the apparent observation frequency.

[0153] (Second Implementation)

[0154] The methods described above demonstrate the selection of accuracy-degraded frequencies after actually generating the spectrum of the observed frequencies. However, by using the following structure and method, accuracy-degraded frequencies can be selected even without generating the spectrum of the observed frequencies.

[0155] (Structure of a water vapor observation system)

[0156] Figure 20 (A) is a functional block diagram showing the structure of the water vapor observation device in the water vapor observation system of the second embodiment of the present invention. Figure 20 (B) is a functional block diagram showing the structure of the observation frequency selection device of the water vapor observation system according to the second embodiment of the present invention.

[0157] The water vapor observation system includes Figure 20 The water vapor observation device 10A shown in (A) and Figure 20 It is constructed by the observation frequency selection device shown in (B).

[0158] like Figure 20As shown in (A), the water vapor observation device 10A of the second embodiment includes an arithmetic unit 20A, an RF amplifier 30, an antenna 40, and an input unit 50. The arithmetic unit 20A includes a water vapor index calculation unit 22 and an intermediate signal processing unit 23. The RF amplifier 30, antenna 40, water vapor index calculation unit 22, and intermediate signal processing unit 23 are the same as those in the water vapor observation device 10 of the first embodiment, so the description of the same parts is omitted.

[0159] The input unit 50 receives the observation frequency selected by the observation frequency selection device 60, as described later. The input unit 50 outputs the input observation frequency to the water vapor index calculation unit 22. The water vapor index calculation unit 22 uses the observation frequency to calculate the water vapor index.

[0160] like Figure 20 As shown in (B), the observation frequency selection device 60 includes an NF characteristic measurement unit 61 and a frequency selection unit 62.

[0161] The NF characteristic measurement unit 61 is configured, for example, by an NF meter. The NF characteristic measurement unit 61 measures the NF characteristics of the RF amplifier 30. The NF characteristic measurement unit 61 outputs the measurement results of the NF characteristics to the frequency selection unit 62.

[0162] The frequency selection unit 62 uses the measurement results of the NF characteristics to select multiple observation frequencies, excluding the frequency with degraded accuracy.

[0163] Figure 21 This is a graph illustrating an example of the NF characteristics of an RF amplifier. Generally speaking, as... Figure 21 As shown, LNA and other RF amplifiers have a high NF band BFex and a low NF band BFad.

[0164] The electromagnetic waves generated by water vapor have weak signal strength, but the influence of NF is significant, greatly affecting the calculation error of water vapor indicators.

[0165] The frequency selection unit 62 selects the frequency band BFex, which has a high NF (Frequency Indicator), as the frequency band where accuracy degrades. The frequency selection unit 62 selects multiple observation frequencies, excluding the frequency band where accuracy degrades. More preferably, the frequency selection unit 62 selects multiple observation frequencies within the frequency band BFad, which has a low NF.

[0166] By performing this configuration and processing, multiple observation frequencies, selected excluding frequency bands where accuracy deteriorates, are input to the water vapor index calculation unit 22. Therefore, the water vapor index calculation unit 22 can calculate the water vapor index with high accuracy. Furthermore, by selecting multiple observation frequencies within the frequency band BFad (where NF is low), i.e., the frequency band with high spectral intensity, the water vapor index calculation unit 22 can calculate the water vapor index with even higher accuracy.

[0167] (Second Embodiment: Water Vapor Observation Method)

[0168] The description illustrates various processing methods for water vapor observation, each executed through functional units with different processing capabilities. However, the processing in the water vapor observation system can also be programmed and stored in a storage medium or the like, and the water vapor observation can be achieved by executing the program using a processing unit. In this case, the processing unit, for example, according to... Figure 22 , Figure 23 The process can be performed using the flowchart shown. Figure 22 This is a flowchart for processing frequency degradation to improve accuracy. Figure 23 The flowchart for water vapor observation processing.

[0169] like Figure 22 As shown, the processing unit measures the NF characteristics of the LNA (RF amplifier) ​​(S51). The processing unit selects the frequency band with high NF as the frequency band of accuracy degradation (S52).

[0170] like Figure 23 As shown, the processing unit detects a high-precision frequency band near the peak frequency of the spectral intensity of the water vapor index (S61). The peak frequency of the spectral intensity of the water vapor index can be detected based on previously acquired water vapor spectra. Furthermore, the high-precision frequency band is a frequency band that is obtained based on NF characteristics and is different from the frequency band of the precision degradation frequency.

[0171] The processing unit selects the observation frequency near the peak frequency within the high-precision frequency band (S62).

[0172] The processing unit selects at least one additional observation frequency, distinct from the observed frequencies near the peak frequency. The processing unit then detects the spectral intensity of these multiple observation frequencies (S63).

[0173] The processing unit calculates the water vapor index (S64) based on the spectral intensity of these multiple observation frequencies.

[0174] Through this processing, the computing device can calculate the water vapor index with high accuracy. Furthermore, the computing device can calculate the water vapor index with high accuracy simply by selecting the observation frequency excluding the frequency band where accuracy deteriorates, and even when selecting the observation frequency at a frequency far from the peak frequency, the water vapor index can be calculated with the specified accuracy.

[0175] However, by selecting the observation frequency within a high-precision frequency band, the computing device can calculate the water vapor index with better accuracy, and by selecting the observation frequency near the peak frequency, it can calculate the water vapor index with even better accuracy.

[0176] (Third Implementation)

[0177] In the first embodiment, a form of water vapor observation, including the calculation of water vapor indices, is shown using a single water vapor observation device. However, a data communication network can be used to construct a water vapor observation system identical to the water vapor observation device of the first embodiment.

[0178] Figure 24 This is a functional block diagram illustrating the structure of the water vapor observation system according to the third embodiment.

[0179] like Figure 24 As shown, the water vapor observation system 1B includes a water vapor observation device 10B and an information processing device 80. The water vapor observation device 10B and the information processing device 80 are connected via a data communication network 90. ​​The information processing device 80 can be implemented, for example, by a personal computer. The data communication network 90 can be implemented via the Internet, a local area network (LAN), or the like.

[0180] The water vapor observation device 10B includes a computing unit 20B, an RF amplifier 30, an antenna 40, and a transceiver unit 70. The computing unit 20B includes a water vapor index calculation unit 22 and an intermediate signal processing unit 23. The transceiver unit 70 is connected to the water vapor index calculation unit 22. The transceiver unit 70 has an interface function for the water vapor observation device 10B to the data communication network 90.

[0181] The information processing device 80 includes a central processing unit (CPU) 81, a storage unit 82, a transceiver unit 83, a display unit 84, and an operation unit 85. The CPU 81, storage unit 82, transceiver unit 83, display unit 84, and operation unit 85 are connected by a data bus.

[0182] The CPU 81 performs various processes by executing programs stored in the storage unit 82. For example, the CPU 81 functions as a frequency selection unit 811 by executing a frequency selection program stored in the storage unit 82. The frequency selection unit 811 performs the same processes as the frequency selection unit 21. Additionally, the CPU 81 functions as a water vapor observation unit 812 by executing a water vapor observation program stored in the storage unit. The water vapor observation unit 812 calculates water vapor parameters and observes the amount of water vapor, etc.

[0183] The storage unit 82 stores various programs executed by the CPU 81, and also stores water vapor indicators from the water vapor observation device 10B. Furthermore, the storage unit 82 may also be located externally, not within the information processing device 80, and connected to the data communication network 90 (e.g., a server).

[0184] The transceiver unit 83 has an interface function for the information processing device 80 of the data communication network 90. ​​The display unit 84 is implemented by an LCD or the like, and can display water vapor observation results, water vapor indicators, the spectrum of the observed signal, etc. The operation unit 85 is implemented by a keyboard, mouse, touch panel, etc., and accepts operation inputs related to water vapor observation.

[0185] Even with this structure, the water vapor observation system 1B can achieve the same effect as the water vapor observation device 10.

[0186] (Fourth Implementation)

[0187] The difference between the fourth embodiment and the third embodiment of the water vapor observation system is that the water vapor index is calculated at the information processing device side. The other structures of the fourth embodiment are the same as those of the third embodiment, therefore descriptions of the same parts are omitted.

[0188] Figure 25 This is a functional block diagram illustrating the structure of the water vapor observation system according to the fourth embodiment.

[0189] like Figure 25 As shown, the water vapor observation system 1C includes a water vapor observation device 10C and an information processing device 80C. The water vapor observation device 10C and the information processing device 80C are connected via a data communication network 90.

[0190] The water vapor observation device 10C includes a processing unit 20C. The processing unit 20C includes a spectrum generation unit 24 and an intermediate signal processing unit 23. The spectrum generation unit 24 generates the spectrum of the observed signal. The spectrum generation unit 24 transmits the generated spectrum to the information processing device 80C via a transceiver unit 70 and a data communication network 90.

[0191] The information processing device 80C includes a CPU 81C. In addition to the processing performed by the CPU 81 in the third embodiment, the CPU 81C executes a water vapor index calculation program stored in the storage unit, thereby functioning as a water vapor index calculation unit 810. The water vapor index calculation unit 810 is the same as the water vapor index calculation unit 22, calculating the water vapor index based on the spectral intensity of multiple observed frequencies.

[0192] Even with this structure, the water vapor observation system 1C can achieve the same effect as the water vapor observation device 10.

[0193] Furthermore, the arithmetic unit of the water vapor observation device can be constructed solely by the intermediate signal processing unit 23, and the information processing device can have the function of the spectrum generation unit 24.

[0194] Furthermore, the structures and processes described in each embodiment can be appropriately combined to achieve corresponding effects.

Claims

1. A water vapor observation device, comprising: Antenna, used to receive radio waves radiated from the atmosphere containing water vapor; A radio frequency amplifier amplifies the received radio waves to generate an observation signal; The frequency selection unit selects multiple observation frequencies, excluding those with degraded accuracy, based on the observed signal. as well as The water vapor index calculation unit calculates the amount of water vapor using the spectral intensities of the multiple observed frequencies. The frequency selection unit includes: The outlier detection unit sets a normal range for the spectral intensity for each frequency of the observed signal, and detects outliers based on the spectral intensity at each frequency outside the normal range. The accuracy degradation frequency selection unit selects the frequency of the outlier as the accuracy degradation frequency; and The observation frequency selection unit selects one of the plurality of observation frequencies, excluding the accuracy-degrading frequencies. The outlier detection unit sets the normal range by setting an upper and lower threshold for the inferred spectral intensity of each frequency of the approximate characteristic calculated based on past observations, and detects outliers within a specified frequency range that includes the peak frequency of the spectral intensity. The approximate characteristic is set by the coefficients of an approximate function calculated using the spectral intensities at multiple frequencies arranged on the frequency axis.

2. The water vapor observation device according to claim 1, wherein, The outlier detection unit uses the approximate characteristic calculated based on the spectral intensities of three adjacent frequencies on the frequency axis to detect the outlier.

3. The water vapor observation device according to claim 1 or 2, wherein, The outlier detection unit sets a normal range for each frequency based on the approximate characteristics. The abnormal value is detected based on the spectral intensity at frequencies outside the normal range by frequency.

4. The water vapor observation device according to claim 1 or 2, wherein, The outlier detection unit detects outliers based on the difference in spectral intensity between adjacent frequencies arranged on the frequency axis of the spectrum.

5. The water vapor observation device according to claim 1 or 2, wherein, The outlier detection unit detects outliers based on the temporal changes in the spectrum.

6. The water vapor observation device according to claim 1 or 2, wherein, The observation frequency selection unit selects at least one of the plurality of observation frequencies from the spectrum of the observed signal that is adjacent to the accuracy degradation frequency.

7. The water vapor observation device according to claim 1 or 2, wherein, The observation frequency selection unit selects the two frequencies on the frequency axis that sandwich the accuracy-degraded frequency as interpolation frequencies. The water vapor index calculation unit calculates the spectral intensity of the observed frequency, which is the same as the accuracy degradation frequency, based on the spectral intensity of the interpolation frequency.

8. A water vapor observation system, comprising: Water vapor observation device according to any one of claims 1 to 7; as well as The noise figure characteristic measurement unit measures the noise figure characteristics of the radio frequency amplifier. The frequency selection unit uses the measurement results of the noise figure characteristics to select the plurality of observation frequencies.

9. A method for observing water vapor, comprising: Receives radio waves radiated from the water vapor-containing atmosphere. The received radio waves are amplified to generate an observation signal. Based on the observed signal, select multiple observation frequencies, excluding the accuracy degradation frequencies, from the observed signal. The amount of water vapor is calculated using the spectral intensities of the multiple observation frequencies. The step of selecting the plurality of observation frequencies, excluding the accuracy degradation frequency, based on the observed signal includes: The normal range of spectral intensity is set for each frequency of the observed signal, and outliers are detected based on the spectral intensity at each frequency outside the normal range. The frequency of the outliers is selected as the accuracy degradation frequency; and Select the plurality of observation frequencies excluding the aforementioned accuracy degradation frequency. The steps of setting the normal range of the spectral intensity for each frequency of the observed signal, and detecting the outlier based on the spectral intensity at each frequency outside the normal range, include: The normal range is set by setting upper and lower threshold values ​​for the inferred spectral intensity of each frequency of the approximate characteristic calculated based on past observations, and outliers within a specified frequency range including the peak frequency of the spectral intensity are detected. The approximate characteristic is set by the coefficients of an approximate function calculated using the spectral intensities at multiple frequencies arranged on a frequency axis.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a computer, cause the computer to: Amplify the radio waves radiated from the water vapor-containing atmosphere. The observation signal is generated based on the amplified radio waves. Based on the observed signal, select multiple observation frequencies, excluding the accuracy degradation frequencies, from the observed signal. The water vapor index is calculated using the spectral intensities of the multiple observation frequencies. The computer-readable storage medium further enables the computer to: A normal range for the spectral intensity is set for each frequency of the observed signal, and outliers are detected based on the spectral intensity at each frequency outside the normal range. The frequency of the outliers is selected as the accuracy degradation frequency. Select the plurality of observation frequencies excluding the aforementioned accuracy degradation frequency. The normal range is set by setting upper and lower threshold values ​​for the inferred spectral intensity of each frequency of the approximate characteristic calculated based on past observations, and outliers within a specified frequency range including the peak frequency of the spectral intensity are detected. The approximate characteristic is set by the coefficients of an approximate function calculated using the spectral intensities at multiple frequencies arranged on a frequency axis.

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