Method and system for distinguishing cloud layer by independently applying infrared radiometer

Abstract

The invention discloses a method and a system for distinguishing a cloud layer by independently applying an infrared radiometer. The method comprises the following steps: S1, carrying out standardization processing on infrared brightness temperature observation data; s2, based on the infrared brightness temperature observation data after standardization processing, independently discriminating a cloud layer through a brightness temperature algorithm and a fluctuation algorithm; and S3, integrating judgment results of the brightness temperature algorithm and the fluctuation algorithm, and outputting a final cloud layer judgment result. According to the method, systematic interference of dust accumulation on observation data is effectively reduced through an innovative standardized processing technology, and the data quality is remarkably improved; in addition, the technical problem that the prior art cannot work independently in areas lacking matched observation facilities is solved, and an automatic cloud monitoring solution which is low in cost and high in reliability is provided for meteorological observation.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud layer identification, and in particular to a method and system for independently using an infrared radiometer to identify clouds. Background Art

[0002] Ground-based infrared radiometers measure infrared radiation emitted downward from the atmosphere. These instruments are compact and portable, easy to operate, provide stable observations, and are inexpensive. They have been deployed at many observation sites. Infrared radiometers use a reflective lens to vertically receive the total infrared radiation emitted downward from the atmosphere. A transparent protective cover surrounds the lens to reduce contamination from rain, snow, dust, and other weather conditions. However, in open field observation environments, dust accumulation is inevitable. This dust accumulation contaminates the observed data, causing an overall increase in infrared brightness temperature. Before using the observed data, necessary quality control is required to minimize the impact of contamination noise on the algorithm.

[0003] Infrared brightness temperature, as measured by infrared radiometers, can be used to detect cloud cover. However, existing ground-based infrared radiometer cloud detection methods require additional observational data and cannot function independently. Furthermore, for observation stations in remote areas such as the Qinghai-Tibet Plateau, these auxiliary observational data are unavailable.

[0004] The discrimination algorithms in the existing technology all require the use of observation data from other instruments as a judgment premise or to obtain a basis for judgment; in addition, the algorithms all directly use the observed brightness temperature for judgment, without considering the noise impact of dust accumulation in the lens during actual observation, and without quality control of the data. The algorithms in the existing technology rely on historical sounding data to establish theoretical clear-sky brightness temperature. In areas where sounding observations are lacking (such as remote plateau areas), the corresponding relationship between ground observation data and clear-sky brightness temperature will be inaccurate. In addition, the algorithms in the existing technology do not correct the brightness temperature drift caused by contamination of the infrared radiometer lens (such as dust and frost adhesion), and are prone to systematic errors due to environmental interference during actual deployment.

[0005] For infrared radiometers that observe independently in remote observation stations such as the Qinghai-Tibet Plateau, the existing identification methods have their defects. Therefore, it is necessary to independently develop a cloud observation algorithm for infrared radiometers. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for independently using an infrared radiometer to identify cloud layers, which solves the technical problem that the existing technology cannot work independently in areas lacking supporting observation facilities, and provides a low-cost, highly reliable automated cloud monitoring solution for meteorological observation.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for independently using an infrared radiometer to identify cloud layers comprises the following steps:

[0009] S1. Standardize infrared brightness temperature observation data;

[0010] S2, based on the standardized infrared brightness temperature observation data, independently identify the cloud layer using the brightness temperature algorithm and the fluctuation algorithm;

[0011] S3, integrates the judgment results of brightness temperature algorithm and fluctuation algorithm, and outputs the final cloud layer judgment result;

[0012] In S2, the cloud layer is independently identified by the brightness temperature algorithm, specifically including:

[0013] For infrared brightness temperature observation data at a single moment, extract the fixed period data of the adjacent days before and after according to different seasons; sort the fixed period data by size, and calculate the average value of the low value series at the front of the sorted data as the infrared brightness temperature value corresponding to the daily cycle at that moment; repeat the above steps to calculate the infrared brightness temperature values corresponding to the daily cycle at multiple moments to obtain the single-day clear sky infrared brightness temperature daily cycle; extract the maximum value in the single-day clear sky infrared brightness temperature daily cycle as the brightness temperature reference threshold T ref ; Compare the observation data: When the infrared brightness temperature observation data is greater than the preset brightness temperature threshold T, it is determined that there are clouds in the sky at the corresponding moment, otherwise, there are no clouds; the setting range of the brightness temperature threshold T is T ref <T<2T ref .

[0014] Preferably, the observation data in S1 only includes the observation data collected by the infrared radiometer, and there is no need to set up external auxiliary equipment or collect other historical observation data.

[0015] Preferably, in S1, the standardization processing of infrared brightness temperature observation data specifically includes:

[0016] The infrared brightness temperature observation data are divided into daily units, the minimum value of the infrared brightness temperature observation data for a single day is extracted, and the minimum value of the infrared brightness temperature observation data for a single day is subtracted from all the infrared brightness temperature observation data for a single day to obtain the standardized infrared brightness temperature observation data.

[0017] Preferably, in S2, independently identifying the cloud layer using the wave algorithm specifically includes:

[0018] Calculate the standard deviation of the observation time within a fixed time range. When the standard deviation of the observation time is greater than the preset standard deviation threshold, it is determined that there are clouds in the sky at the corresponding time; otherwise, there are no clouds. The standard deviation calculation formula is as follows:

[0019]

[0020] Where σ represents the standard deviation, N represents the total number of observed infrared brightness temperature data, and xi represents the i-th observed infrared brightness temperature, and μ represents the average value of the infrared brightness temperature observation data.

[0021] Preferably, in S3, the judgment result of the integrated brightness temperature algorithm and the fluctuation algorithm specifically includes:

[0022] When both the brightness temperature algorithm and the fluctuation algorithm determine that there is no cloud, the final result is cloudless; when either algorithm determines that there is cloud, the final result is cloud.

[0023] A system for independently using an infrared radiometer to identify cloud layers, applying any of the above methods for independently using an infrared radiometer to identify cloud layers, comprising:

[0024] Data preprocessing module, used to standardize infrared brightness temperature observation data;

[0025] Independent discrimination module, used to independently discriminate cloud layer using brightness temperature algorithm and fluctuation algorithm based on standardized infrared brightness temperature observation data;

[0026] The comprehensive judgment module is used to integrate the judgment results of the brightness temperature algorithm and the fluctuation algorithm and output the final cloud judgment result.

[0027] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for independently using an infrared radiometer to identify cloud layers as described above is implemented.

[0028] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] (1) The independent cloud discrimination method for ground-based infrared radiometers provided by the present invention effectively reduces the systematic interference of dust accumulation on observation data through innovative standardization processing technology, significantly improving data quality;

[0030] (2) This method is completely based on the infrared radiometer's own observation data and does not rely on any external auxiliary equipment or other historical observation data. It is particularly suitable for deployment and application in remote observation sites such as the Qinghai-Tibet Plateau. It solves the technical problem that existing technologies cannot work independently in areas lacking supporting observation facilities, and provides a low-cost, highly reliable automated cloud monitoring solution for meteorological observation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A schematic flow chart of a method for independently using an infrared radiometer to identify cloud layers provided by the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] like Figure 1 As shown, the present invention provides a method for independently using an infrared radiometer to identify cloud layers, comprising the following steps:

[0036] S1. Standardize infrared brightness temperature observation data;

[0037] S2, based on the standardized infrared brightness temperature observation data, independently identify the cloud layer using the brightness temperature algorithm and the fluctuation algorithm;

[0038] S3: Combine the judgment results of brightness temperature algorithm and fluctuation algorithm to output the final cloud judgment result.

[0039] Preferably, in S1, the standardization processing of infrared brightness temperature observation data specifically includes:

[0040] The infrared brightness temperature observation data was divided into daily units, and the minimum value of the daily infrared brightness temperature observation data was extracted. This minimum value was subtracted from all the infrared brightness temperature observation data for that day to obtain the standardized infrared brightness temperature observation data. After this standardization process, the influence of dust accumulation in the observation data on the cloud discrimination algorithm was significantly reduced. The cloud discrimination algorithm uses this standardized data for cloud layer judgment.

[0041] Furthermore, the cloud discrimination algorithm developed in this paper is composed of two independent algorithms. These two algorithms respectively use infrared brightness temperature values and their fluctuations to determine cloud cover (referred to as the brightness temperature algorithm and the fluctuation algorithm, respectively). The combination of these two methods ultimately produces the optimal judgment result. The following describes each method in detail.

[0042] 1) Brightness temperature algorithm

[0043] On clear days, the infrared brightness temperature observed by the infrared radiometer is mainly affected by the diurnal and seasonal cycles of temperature. In a shorter time frame (such as a few days), the influence of seasonal temperature changes can be ignored, and the infrared brightness temperature shows a relatively stable diurnal variation. On cloudy days, the observed infrared brightness temperature includes the additional contribution of clouds, superimposing the changes in the cloud layer on the diurnal cycle. Based on the known diurnal cycle of infrared brightness temperature under clear sky conditions, the presence of clouds can be detected by comparing the observed values with the corresponding diurnal cycle. In S2, the independent identification of clouds by the brightness temperature algorithm specifically includes:

[0044] For infrared brightness temperature observation data at a single moment, extract the fixed period data of the adjacent days before and after according to different seasons; sort the fixed period data by size, and calculate the average value of the low value series at the front of the sorted data as the infrared brightness temperature value corresponding to the daily cycle at that moment; repeat the above steps to calculate the infrared brightness temperature values corresponding to the daily cycle at multiple moments to obtain the single-day clear sky infrared brightness temperature daily cycle; extract the maximum value in the single-day clear sky infrared brightness temperature daily cycle as the brightness temperature reference threshold T ref ; Compare the observation data: When the infrared brightness temperature observation data is greater than the preset brightness temperature threshold T, it is determined that there are clouds in the sky at the corresponding moment, otherwise, there are no clouds; the setting range of the brightness temperature threshold T is T ref <T<2T ref Among them, the formula for calculating the average value of the low-value series is as follows:

[0045]

[0046] Among them, Y represents the average value of low-value infrared brightness temperature observation data, M represents the total number of low-value infrared brightness temperature data, and x i Represents the i-th infrared brightness temperature in the low-value series.

[0047] 2) Fluctuation Algorithm

[0048] In cloudy skies, the infrared brightness temperature observed at the zenith primarily comes from clouds, making it highly sensitive to cloud variations. It changes rapidly with changes in the cloud layer, such as cloud base height and microphysical variations within the clouds. Based on this characteristic, we developed a fluctuation algorithm. This method uses the dispersion (standard deviation) of infrared brightness temperature to distinguish between cloudy and clear skies. The standard deviation at the corresponding moment is calculated by sliding the image. When the standard deviation exceeds a certain threshold, the sky is considered to be cloudy.

[0049] Furthermore, in S2, the cloud layer is independently identified by the wave algorithm, specifically including:

[0050] Calculate the standard deviation of the observation time within a fixed time range. When the standard deviation of the observation time is greater than the preset standard deviation threshold, it is determined that there are clouds in the sky at the corresponding time; otherwise, there are no clouds. The standard deviation formula is as follows:

[0051]

[0052] Where σ represents the standard deviation, N represents the total number of observed infrared brightness temperature data, and x i represents the i-th observed infrared brightness temperature, and μ represents the average value of the infrared brightness temperature observation data.

[0053] Furthermore, in S3, the judgment results of the integrated brightness temperature algorithm and the fluctuation algorithm specifically include:

[0054] When both the brightness temperature algorithm and the fluctuation algorithm determine that there is no cloud, the final result is cloudless; when either algorithm determines that there is cloud, the final result is cloud.

[0055] In a specific embodiment, a ground-based infrared radiometer field observation was conducted in Lhasa, Tibet. The infrared radiometer observation frequency band was 9.4-11.8 μm. Based on the observed infrared brightness temperature data, the cloud layer discrimination algorithm was used to identify the cloud layer. The specific steps are as follows:

[0056] (1) Observation data quality control - dust removal and standardization. Specific methods: First, divide the infrared brightness temperature observation data into daily units; then, for the data of a single day, extract the minimum infrared brightness temperature of that day; finally, subtract the minimum brightness temperature value of that day from the brightness temperature data of that day.

[0057] (2) Brightness temperature algorithm. Specific method: First, for infrared brightness temperature at a single moment, extract the fixed period data of the adjacent days before and after it according to different seasons. Sort the data by size, and calculate the average value of the low-value sequence at the front of the sorted data (the low-value sequence of the first 10% of the sorted data) as the infrared brightness temperature value corresponding to the daily cycle at that moment. The average value of the low-value sequence is calculated as follows:

[0058]

[0059] Among them, Y represents the average value of infrared brightness temperature observation low value data, M represents the total number of top 10% low value data, and x i Represents the i-th infrared brightness temperature in the low-value series.

[0060] Repeat the above steps to calculate the daily cycle of clear-sky infrared brightness temperature. Then, extract the maximum value of the daily cycle of clear-sky infrared brightness temperature as the reference threshold for comparison. Finally, compare the observed data. If the observed brightness temperature is greater than 150% of the reference threshold, the sky is considered to be cloud-free at that time. Otherwise, there are no clouds.

[0061] (3) Fluctuation algorithm. Specific method: First, calculate the standard deviation of the observation time within a fixed time range by sliding. The formula for solving the standard deviation is as follows:

[0062]

[0063] Where σ represents the standard deviation, N represents the total number of observed infrared brightness temperature data, and x i represents the observed ith infrared brightness temperature, μ represents the average value of the infrared brightness temperature observation data Finally, the standard deviation is compared. When the standard deviation at the observation time is greater than a preset standard deviation threshold, for example, the standard deviation threshold is 0.3, when the standard deviation at the observation time is greater than 0.3, it is determined that there are clouds in the sky at the corresponding time; otherwise, there are no clouds.

[0064] (4) Comprehensive judgment: If both the brightness temperature algorithm and the fluctuation algorithm determine that there is no cloud at the corresponding moment, the corresponding moment is finally judged to be cloudless; otherwise, it is judged to be cloudy.

[0065] Since there are no other direct cloud observation devices near the infrared radiometer, the only option is to compare the cloud layer identification results with the relative humidity results from adjacent sounding data. The results show that the cloud layer identification results obtained by the present invention are more than 70% consistent with the cloud layer identification results from the sounding relative humidity, indicating that the identification results are reliable.

[0066] A system for independently using an infrared radiometer to identify cloud layers, applying any of the above methods for independently using an infrared radiometer to identify cloud layers, comprising:

[0067] Data preprocessing module, used to standardize infrared brightness temperature observation data;

[0068] Independent discrimination module, used to independently discriminate cloud layer using brightness temperature algorithm and fluctuation algorithm based on standardized infrared brightness temperature observation data;

[0069] The comprehensive judgment module is used to integrate the judgment results of the brightness temperature algorithm and the fluctuation algorithm and output the final cloud judgment result.

[0070] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for independently using an infrared radiometer to identify cloud layers as described above is implemented.

[0071] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0072] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for identifying cloud cover using an independent infrared radiometer, characterized in that: The following steps are involved: S1. Standardize infrared brightness temperature observation data; S2, based on the standardized infrared brightness temperature observation data, independently identify the cloud layer using the brightness temperature algorithm and the fluctuation algorithm; S3, integrates the judgment results of brightness temperature algorithm and fluctuation algorithm, and outputs the final cloud layer judgment result; In S2, independently identifying the cloud layer using the brightness temperature algorithm specifically includes: For infrared brightness temperature observation data at a single moment, extract the fixed period data of the adjacent days before and after according to different seasons; sort the fixed period data by size, and calculate the average value of the low value series at the front of the sorted data as the infrared brightness temperature value corresponding to the daily cycle at that moment; repeat the above steps to calculate the infrared brightness temperature values corresponding to the daily cycle at multiple moments to obtain the single-day clear sky infrared brightness temperature daily cycle; extract the maximum value in the single-day clear sky infrared brightness temperature daily cycle as the brightness temperature reference threshold T ref ; Compare the observation data: When the infrared brightness temperature observation data is greater than the preset brightness temperature threshold T, it is determined that there are clouds in the sky at the corresponding moment, otherwise, there are no clouds; the setting range of the brightness temperature threshold T is T ref <T<2T ref .

2. The method for identifying cloud layer by independently using an infrared radiometer according to claim 1, characterized in that: The observation data in S1 only include the observation data collected by the infrared radiometer, and there is no need to set up external auxiliary equipment or collect other historical observation data.

3. The method for identifying cloud layer by independently using an infrared radiometer according to claim 1, characterized in that: In S1, the standardization processing of infrared brightness temperature observation data specifically includes: The infrared brightness temperature observation data are divided into daily units, the minimum value of the infrared brightness temperature observation data for a single day is extracted, and the minimum value of the infrared brightness temperature observation data for a single day is subtracted from all the infrared brightness temperature observation data for a single day to obtain the standardized infrared brightness temperature observation data.

4. The method for identifying cloud layer by independently using an infrared radiometer according to claim 1, characterized in that: In S2, independently identifying the cloud layer using the wave algorithm specifically includes: Calculate the standard deviation of the observation time within a fixed time range. When the standard deviation of the observation time is greater than the preset standard deviation threshold, it is determined that there are clouds in the sky at the corresponding time; otherwise, there are no clouds. The standard deviation calculation formula is as follows: Where σ represents the standard deviation, N represents the total number of observed infrared brightness temperature data, and x i represents the i-th observed infrared brightness temperature, and μ represents the average value of the infrared brightness temperature observation data.

5. The method for identifying cloud layer by independently using an infrared radiometer according to claim 1, characterized in that: In S3, the judgment results of the integrated brightness temperature algorithm and the fluctuation algorithm specifically include: When both the brightness temperature algorithm and the fluctuation algorithm determine that there is no cloud, the final result is cloudless; when either algorithm determines that there is cloud, the final result is cloud.

6. A system for independently using an infrared radiometer to identify cloud layers, using the method for independently using an infrared radiometer to identify cloud layers according to any one of claims 1 to 5, characterized in that: include: Data preprocessing module, used to standardize infrared brightness temperature observation data; Independent discrimination module, used to independently discriminate cloud layer using brightness temperature algorithm and fluctuation algorithm based on standardized infrared brightness temperature observation data; The comprehensive judgment module is used to integrate the judgment results of the brightness temperature algorithm and the fluctuation algorithm and output the final cloud judgment result.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for independently using an infrared radiometer to identify cloud layers according to any one of claims 1 to 5 is implemented.

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