Cloud top height quality inspection method and device, computing equipment and storage medium

Abstract

The invention discloses a cloud top height quality inspection method and device, computing equipment and a storage medium, and belongs to the technical field of atmospheric detection and remote sensing, and the method comprises the steps: taking laser radar cloud profile data as a detection truth value, taking imaging type radiometer cloud product data as to-be-inspected data, and carrying out the quality inspection of the to-be-inspected data on the basis of the time-space matching of the two types of data; the method comprises the following steps: screening out unqualified data through a space-time uniformity test, and carrying out layer-by-layer and step-by-step analysis on qualified matching data to count a total sample size, correct detection, missing detection, false detection, a correct screening sample size and a proportion, a correct identification sample size of each cloud type including cirrus cloud and lower cloud, an error identification sample size and a corresponding cloud top height difference; therefore, complete and accurate cloud top height quality detection is realized.

Description

Technical Field

[0001] The present invention belongs to the field of atmospheric detection and remote sensing technology, and specifically relates to a cloud top height quality inspection method, device, computing equipment and storage medium. Background Art

[0002] As a basic parameter in the field of meteorology and climate, cloud top height has important scientific and application value in short-term weather forecasts, climate predictions, and cloud radiation forcing. Clouds are unevenly distributed in time and space, and the speed of change is affected by many factors, which puts high requirements on the time, spatial resolution, and coverage of instruments used to observe cloud top height. Spaceborne remote sensing technology provides a global perspective for cloud top height measurement. Among them, imaging radiometers that can be carried on geostationary satellites and polar-orbiting satellite platforms have become the most stable source of cloud observation data at present, relying on their extremely large observation range and high temporal resolution observation mode. Since imaging radiometers do not have the ability to directly detect cloud top height, they need to rely on cloud detection, cloud classification, and other previous steps to obtain prior values, and then invert cloud top height through radiation transfer models. However, imaging radiometers have poor observation capabilities for cirrus clouds, which are thin clouds composed of ice crystal particles that mainly exist at altitudes above 6 kilometers. This is because the large transmittance of cirrus clouds causes the observed radiation signal to be interfered by the radiation under the clouds. When there are only cirrus clouds in the actual scene, cirrus clouds cannot be accurately detected. When there are multiple cloud layers containing cirrus clouds in the actual scene, only low-level clouds are detected and classified. This leads to large errors in the cloud top height product data of cirrus clouds and related multi-layer clouds.

[0003] As one of the main observation targets of meteorological satellites in various countries, the current cloud top height product data all have operational verification methods. Taking my country's Fengyun-4 multi-channel scanning imaging radiometer (FY-4 / AGRI) as an example, the paper "Consistency Evaluation of the Accuracy of FY-4B and FY-4A Cloud Top Property Products" announced the accuracy evaluation results of the cloud top property products of Fengyun-4A (abbreviated as FY-4A) and B (abbreviated as FY-4B). The evaluation method is to use the cloud products of the US EOS / MODIS polar-orbiting meteorological satellite and Japan's second-generation geostationary meteorological satellite Himawari-8, which have partially overlapping fields of view and equivalent imaging performance, to verify the consistency of the accuracy of the cloud top products inverted by FY-4A and FY-4B. The conclusion is that the test results of the full sample show that the accuracy of the FY-4B cloud top parameter product is roughly equivalent to that of FY-4A, and has good consistency. Among them, the large difference in the multi-layer clouds of the B satellite is mainly due to the cloud classification misjudging cirrus clouds as multi-layer clouds. It can be seen that the misjudgment of the previous step caused by cirrus has a great impact on the cloud top height result. At the same time, although the Japanese and American imagers used in the test have certain differences in spectral channel design, calibration accuracy and radiation transmission mode used in the inversion algorithm, there is no obvious difference in the instrument and inversion mechanism. There are large differences in the detection and classification of cirrus clouds, which is a common problem of imaging radiometers, resulting in their inaccurate reference values ​​for cloud top height assessment.

[0004] In response to the above problems, a Chinese patent document with a publication number of CN116150151A discloses a cloud top height inspection method and system based on strict cloud phase matching, which includes obtaining a test source and a tested source to be matched in time and space and performing time and space matching; reading the time and space data of the test source and the tested source, searching for pixels with consistent cloud phases in the test source and the tested source, and calculating the distance between the pixels using the time and space data; calculating the time difference between the test source and the tested source using the time and space data, selecting pixels with consistent cloud phases within a preset time difference range and a preset distance range as matching data set output; using the matching data set to perform statistics on cloud top height products of the same phase, and generating statistical indicators for cloud top height inspection. This method strictly matches cloud phases, and its goal is to inspect cloud top heights on the premise that cloud classification is as consistent as possible, without evaluating the impact of passive remote sensing's failure to accurately detect and identify cirrus clouds on cirrus cloud heights and multi-layer cloud height inversion. Summary of the invention

[0005] In view of the above, in order to solve the technical problem of incomplete subsequent impact assessment caused by incorrect detection and classification of cirrus clouds in the existing cloud top height product quality inspection process, the present invention provides a cloud top height quality inspection method, device, computing equipment and storage medium, introduces a satellite-borne laser radar as a test truth value with higher sensitivity to cirrus clouds, and matches the observation data of the satellite-borne laser radar and imaging radiometer, and utilizes the laser radar profile observation results with higher sensitivity to cirrus clouds, and performs a step-by-step, layer-by-layer analysis of the errors existing in the cloud top height inversion based on the imaging radiometer observation data, including cloud detection and cloud classification, to achieve complete and accurate cloud top height quality inspection.

[0006] To achieve the above-mentioned purpose of the invention, an embodiment provides a cloud top height quality inspection method, comprising the following steps:

[0007] Obtain lidar cloud profile data as the true value of detection, imaging radiometer cloud product data to be inspected, and auxiliary data;

[0008] Based on the spatiotemporal data in the auxiliary data, the imaging radiometer cloud product data is temporally and spatially matched with the lidar cloud profile data and the matching data is determined;

[0009] Match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud products according to the matching of the spatiotemporal information in the matching data;

[0010] Perform a spatiotemporal homogeneity test on the matching data to filter out data that do not meet the spatiotemporal homogeneity requirements, obtain qualified matching data, and calculate the total sample size;

[0011] Compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out; among the correctly detected samples, compare the qualified imaging radiometer cloud classification results with the lidar cloud classification results, and count the number of correctly classified and incorrectly classified samples of each cloud type from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition;

[0012] Compare the imaging radiometer cloud product data and the lidar cloud profile data, and calculate the cloud top height difference of each single-layer cloud type that is correctly classified and incorrectly classified, the cloud top height difference and layer average height difference of correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and layer average height difference of incorrectly classified multi-layer clouds;

[0013] Output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of correct and incorrect classification for each cloud type, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection result.

[0014] Preferably, performing spatiotemporal matching of the imaging radiometer cloud product data with respect to the lidar cloud profile data based on the spatiotemporal auxiliary data and determining the matching data comprises:

[0015] Determine the time threshold and space threshold. The time and space auxiliary data include the measurement time and longitude and latitude. The imaging radiometer cloud product data include the imaging radiometer cloud product samples measured at different times and longitudes and latitudes.

[0016] Based on the measurement time and longitude and latitude corresponding to each profile in the lidar cloud profile data, the imaging radiometer cloud product samples with the closest spatial distance to each profile within each time threshold and spatial threshold are screened out to form matching data.

[0017] Preferably, performing a spatiotemporal uniformity test on the matching data to filter out data that does not meet the spatiotemporal uniformity requirements and obtain qualified matching data includes:

[0018] The matching data are tested for the spatiotemporal uniformity of the lidar profile, the internal uniformity of the radiometer pixels, and the peripheral uniformity of the radiometer pixels. The qualified matching data are obtained by combining the sample data that pass all three tests.

[0019] Preferably, the lidar profile spatiotemporal uniformity test refers to testing the uniformity between multiple basic profiles contained in each lidar cloud profile, calculating the average deviation of the cloud top heights and the difference in the number of layers of all basic profiles, when the number of layers changes or the average deviation of the cloud top height is greater than or equal to a given cloud top height deviation threshold, the spatiotemporal uniformity of all lidar cloud profile samples is considered unqualified and screened out, and when the number of layers is consistent and the average deviation of the cloud top height is less than a given cloud top height deviation threshold, all lidar cloud profile samples are considered qualified;

[0020] The radiometer pixel internal uniformity test refers to testing the uniformity between multiple pixels contained in each radiometer cloud product pixel, calculating the standard deviation of infrared band brightness temperature between all pixels, and when the brightness temperature standard deviation is greater than or equal to a given brightness temperature standard deviation threshold, the temporal and spatial uniformity of all radiometer cloud product samples are considered unqualified and are screened out; when the brightness temperature standard deviation is less than the given brightness temperature standard deviation threshold, all radiometer cloud product samples are considered qualified;

[0021] The radiometer pixel peripheral uniformity test refers to expanding the search time and space range based on the time threshold and the space threshold, selecting all cloud product samples whose latitude and longitude differences with each profile are within the search time and space range from the matching data, and calculating the average deviation between the cloud characteristics of all cloud product samples and the average cloud characteristics, wherein the average cloud characteristic is the average value of the cloud characteristics of all cloud product samples. When the average deviation is greater than or equal to the given cloud characteristic deviation threshold, the time and space uniformity of all cloud product samples is considered unqualified and are screened out. When the average deviation is less than the given cloud characteristic deviation threshold, all cloud product samples are considered qualified, wherein the cloud characteristic is a matrix composed of marking cloud as 1 and marking no cloud as 0.

[0022] Preferably, the cloud top height deviation threshold between lidar profiles is 0-1 km, the brightness temperature standard deviation threshold of the infrared band between radiometer pixels is 0-3 K, and the cloud feature deviation threshold between radiometer pixels is 0-0.25.

[0023] Preferably, the number of samples correctly classified and misclassified for each cloud type is counted from both single-layer clouds and multi-layer clouds in combination with cloud top height assessment as a judgment condition, including:

[0024] If the single-layer clouds identified by the lidar and the imaging radiometer are of the same type, they are recorded as correctly classified single-layer clouds, where single-layer cloud types include water clouds, ice clouds, and cirrus clouds;

[0025] If both the lidar and the imaging radiometer identified single-layer clouds but the types were inconsistent, and the cloud top height deviation was <3 km, it was recorded as misclassified single-layer clouds, corresponding to the situation in which the phase state judgment was wrong in the actual measurement but the layer identification was consistent;

[0026] If both the lidar and the imaging radiometer identified multi-layer clouds and the cloud top height deviation was <3 km, it was recorded as correctly classified multi-layer clouds;

[0027] If the lidar and imaging radiometer do not agree on the layer recognition, or both recognize multi-layer clouds but the cloud top height deviation is ≥3km, it is recorded as misclassified multi-layer clouds, which corresponds to the situation in which the imaging radiometer fails to recognize cirrus clouds or misidentifies high-altitude aerosols as cirrus clouds in actual measurements;

[0028] Count the number of correctly classified and incorrectly classified samples for each cloud type.

[0029] Preferably, the cloud top height difference refers to the difference between the cloud top height of the imaging radiometer and the cloud top height of the top cloud of the lidar cloud profile.

[0030] The layer top height difference refers to the difference between the cloud top height of the imaging radiometer and the layer top height of the closest layer measured by the lidar cloud profile;

[0031] The layer average height difference refers to the difference between the cloud top height of the imaging radiometer and the average height of multiple layers in the lidar cloud profile.

[0032] To achieve the above-mentioned purpose of the invention, the embodiment further provides a cloud top height quality inspection device, comprising:

[0033] A data acquisition module, which is used to acquire the lidar cloud profile data as the detection true value, the imaging radiometer cloud product data to be inspected, and the auxiliary data;

[0034] A time-space matching module, which is used to perform time-space matching of the imaging radiometer cloud product data relative to the lidar cloud profile data based on the time-space data in the auxiliary data and determine the matching data;

[0035] The cloud detection and classification matching module is used to match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud product according to the matching of the spatiotemporal information in the matching data;

[0036] A uniformity test module is used to perform a temporal and spatial uniformity test on the matching data to filter out data that does not meet the temporal and spatial uniformity requirements, obtain qualified matching data, and calculate the total sample size;

[0037] The cloud detection and classification result layer-by-layer comparison module is used to compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and to count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out. Among the correctly detected samples, the qualified imaging radiometer cloud classification results and the lidar cloud classification results are compared, and the number of correctly classified and incorrectly classified samples of each cloud type is counted from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition;

[0038] A difference calculation module is used to compare the matching imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height difference of each single-layer cloud type that is correctly classified and incorrectly classified, the cloud top height difference and layer average height difference of correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and layer average height difference of incorrectly classified multi-layer clouds;

[0039] The data output module is used to output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of each cloud type that is correctly and incorrectly classified, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection results.

[0040] To achieve the above-mentioned purpose of the invention, an embodiment further provides a computing device, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the above-mentioned cloud top height quality inspection method.

[0041] In order to achieve the above-mentioned purpose of the invention, an embodiment further provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the above-mentioned cloud top height quality inspection method is implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The cloud top height quality inspection method provided in the embodiment of the present invention gradually inspects the entire process link that affects the cloud top height of the radiation imaging meter, including cloud detection and cloud classification, and compares the cloud top height inverted by the radiation imaging meter with the layer actually detected by the laser radar to the greatest extent. In particular, it relies on the high-precision recognition of cirrus clouds by the satellite-borne laser radar, and evaluates the missed detection, false detection, and misclassified samples caused by cirrus clouds and other layers, making the cloud top height inspection results more complete and detailed. Compared with the detection results under cloud detection and cloud type consistency only, the number of effective cloud top height detection samples has increased by 127%, and the inversion accuracy of the cloud top height, especially the cloud top height of multi-layer clouds, has been more accurately judged under different pre-determination conditions, providing more comprehensive data support for subsequent product optimization. This method has broad application prospects in the fields of meteorological observation, climate research, and remote sensing data quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0045] Figure 1 is a flow chart of a cloud top height quality inspection method provided by an embodiment;

[0046] Figure 2 It is a specific flow chart of the uniformity test step in the cloud top height quality inspection method provided by an embodiment;

[0047] Figure 3 This is a specific flow chart of layer-by-layer comparison of cloud detection and classification results in a cloud top height quality inspection method provided by an embodiment;

[0048] Figure 4is a schematic diagram of a satellite-borne laser radar observation orbit (shown by a gray line) and an imaging radiometer observation coverage (shown in a red circle) provided by an embodiment;

[0049] Figure 5 It is a schematic diagram of a cloud profile cross section of a satellite-borne laser radar provided in an embodiment, and a comparison result thereof with a cloud top height (indicated by a triangle) of an imaging radiometer;

[0050] Figure 6 is a cloud top height inspection result diagram provided by an embodiment;

[0051] Figure 7 It is a schematic diagram of the structure of a cloud top height quality inspection device provided in one embodiment. DETAILED DESCRIPTION

[0052] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0053] like Figure 1 As shown, the cloud top height quality inspection method provided in the embodiment includes the following steps:

[0054] S1, data acquisition: obtain the lidar cloud profile data and its cloud determination results as the detection true value, the imaging radiometer cloud product data to be quality inspected, and the auxiliary data.

[0055] In the embodiment, obtaining the lidar cloud profile data is to obtain detailed information about the vertical distribution of the cloud layer through the lidar technology, specifically including the results of layer identification, layer classification and phase classification. Compared with other data, the lidar cloud profile data is clearer in terms of cloud layer information and the corresponding cloud top height, cloud bottom height and cloud thickness when used as the detection true value. The lidar classifies clouds through the scattering ability of cloud particles to lasers, polarization sensitivity, etc., and can also more accurately obtain the cloud phase, that is, according to the physical properties of the cloud (such as temperature, water phase state, etc.), the clouds are divided into liquid phase water clouds and ice phase ice clouds. Based on these lidar cloud profile data, cloud judgment results such as whether there are clouds, cloud type and cloud top height can be determined.

[0056] Imaging radiometer cloud product data refers to various characteristic information about clouds obtained by satellite imaging radiometers, including cloud detection, cloud classification and cloud top height data. The cloud top height data is a priori value obtained through previous steps such as cloud detection and cloud classification, and is obtained by inverting the radiation transfer model. Compared with the cloud top height data obtained by lidar, the cloud top height is not very accurate and is prone to missed detection of cirrus clouds.

[0057] Auxiliary data mainly include the measurement time information and measurement space information of lidar and satellite imaging radiometer, where the measurement space information is generally longitude and latitude information, and also includes the basic profile and basic pixel information of Level 1 (raw data after calibration).

[0058] From the sample perspective, imaging radiometer cloud product data include imaging radiometer cloud product samples measured at different times and longitudes and latitudes. Each imaging radiometer cloud product sample contains cloud detection, cloud classification and cloud top height data in a fixed size range (e.g. 4km×4km) corresponding to each acquisition time point.

[0059] S2, time-space matching: Based on the time-space data in the auxiliary data, the imaging radiometer cloud product data is time-space matched relative to the lidar cloud profile data and the matching data is determined.

[0060] The necessary step to conduct cloud top height quality inspection on the inspection data based on the detection true value is time-space matching. Specifically, the time-space auxiliary data is used to perform time-space matching on the imaging radiometer cloud product data relative to the lidar cloud profile data. When matching time and space, the time threshold and space threshold are first determined. The time threshold can be half of the time resolution of the geostationary satellite, such as 7.5 minutes. The space threshold takes into account the reduced resolution of the edge pixels of the geostationary satellite, which is usually about 1.5 times the center resolution (for example, if the center resolution is 4km×4km, the edge resolution may be reduced to 6km×6km). The threshold range should be appropriately expanded, for example 5km.

[0061] Then, based on the measurement time and longitude and latitude corresponding to each profile in the lidar cloud profile data, the imaging radiometer cloud product samples with the closest spatial distance to each profile within each time threshold and spatial threshold are screened out to form matching data. In the matching data, each profile corresponds to an imaging radiometer cloud product sample, where the spatial distance The Haversine formula is used for calculation:

[0062] (1)

[0063] in is the lidar longitude and latitude, are the longitude and latitude of the imaging radiometer cloud product sample, for and difference, for and difference, Represents the radius of the earth, which is 6371km.

[0064] S3, cloud detection and classification matching: match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud products according to the matching of the spatiotemporal information in the matching data.

[0065] Cloud samples with clouds are classified, and the cloud classification results obtained by imaging radiometer include water clouds, ice clouds, cirrus clouds, and multi-layer clouds, among which ice clouds are thick ice clouds without cirrus clouds. The layer height of the lidar is added for judgment. On the basis of direct comparison with the imaging radiometer cloud classification products, the comparison of other layers of lidar profile recognition is added to supplement the cases of missed detection and false detection of a single layer.

[0066] There are imaging radiometer cloud product samples with qualified matches for each profile in the matching data, and there are cloud detection and cloud classification results in the lidar cloud profile data, and there are cloud detection and cloud classification results in the imaging radiometer cloud products. In this way, the matching of cloud detection results and cloud classification results can be achieved based on the matching conditions in the matching data.

[0067] S4, homogeneity test: Perform a spatiotemporal homogeneity test on the matching data to filter out data that do not meet the spatiotemporal homogeneity requirements, obtain qualified matching data, and calculate the total sample size.

[0068] In actual detection, clouds may be unevenly distributed and the edges of clouds may change rapidly. In this case, since the imaging radiometer samples at a time interval of one point during detection, the uneven clouds and rapid edge changes at the sampling time point of the imaging radiometer often cause changes in the observed target. Some pixels contain both cloud pixels and cloud-free pixels, or single-layer cloud pixels and multi-layer cloud pixels. The same is true for lidar, which leads to inconsistency between the observed target of the lidar cloud profile and the observed target of the imaging radiometer cloud product. This is considered to be unqualified in terms of spatiotemporal uniformity. Therefore, the data in this case needs to be filtered out. This can be done based on the deviation between the lidar basic profiles, the internal (inter-pixel) deviation and external (peripheral) deviation or standard deviation of the imaging radiometer pixels, such as Figure 2 As shown, the specific process is:

[0069] The matching data is tested for the spatiotemporal uniformity of the lidar profile, that is, the uniformity of the multiple basic profiles contained in each lidar cloud profile is tested (for example, in a cloud profile with a spatial resolution of 5 km, the basic profile resolution is 333 meters, that is, it contains 15 basic profiles), and the average deviation of the cloud top height of all basic profiles and the difference in the number of layers are calculated. When the number of layers changes or the average deviation of the cloud top height is greater than or equal to the given cloud top height deviation threshold, the spatiotemporal uniformity of all lidar cloud profile samples is considered unqualified and they are screened out. When the number of layers is consistent and the average deviation of the cloud top height is less than the given cloud top height deviation threshold, all lidar cloud profile samples are considered qualified, and the average deviation threshold of the cloud top height can be set to 1 km.

[0070] The matching data is tested for uniformity within the radiometer pixels, that is, the uniformity between multiple pixels contained in each radiometer cloud product pixel is tested (for example, in a pixel with a spatial resolution of 4km×4km, each pixel has a resolution of 1km×1km, that is, it contains 16 pixels), and the standard deviation of brightness temperature in the infrared band (such as 10-12μm) between all pixels is calculated. When the brightness temperature standard deviation is greater than or equal to the given brightness temperature standard deviation threshold, the spatiotemporal uniformity of all radiometer cloud product samples is considered unqualified and they are screened out. When the brightness temperature standard deviation is less than the given brightness temperature standard deviation threshold, all radiometer cloud product samples are considered qualified. The brightness temperature standard deviation threshold can be set to 3K.

[0071] The imaging radiometer data with qualified uniformity within the pixel is tested for uniformity around the radiometer pixel, that is, the search time and space range is expanded based on the time threshold and space threshold. Generally, 2 times of the time threshold and 2 times of the space threshold are selected, that is, the time and space range determined by the time threshold and space threshold are expanded outward by 2 times as the search time and space range. All cloud product samples whose latitude and longitude differences with each profile are within the search time and space range are selected from the matching data, and the average deviation of the cloud characteristics of all cloud product samples from the average cloud characteristics is calculated. , where the average cloud feature is the average of the cloud features of all cloud product samples; when the average deviation When the average deviation is greater than or equal to the given cloud feature deviation threshold, the spatiotemporal uniformity of all cloud product samples is considered unqualified and is screened out. When it is less than a given cloud feature deviation threshold, all cloud product samples are considered qualified, qualified matching data is obtained, and the total sample size is counted;

[0072] (2)

[0073] Where N represents the number of samples within the search space-time range, represents the cloud property characteristics of the i-th closest sample; Represents the average cloud feature of all cloud product samples in the range, where the cloud feature is a matrix consisting of 1 for cloud and 0 for no cloud. The cloud feature deviation threshold can be set to 0.25.

[0074] Finally, all qualified samples of the three tests are combined to obtain qualified matching data.

[0075] S5, compare cloud detection and classification results layer by layer: compare the qualified matching imaging radiometer cloud detection results with the lidar cloud detection results, and count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out; among the correctly detected samples, compare the qualified matching imaging radiometer cloud classification results with the lidar cloud classification results, and count the number of correctly classified and incorrectly classified samples of each cloud type from the two aspects of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition.

[0076] In the embodiment, after counting the samples with and without clouds and the number of samples in the cloud detection results of the imaging radiometer, the number and proportion of correct detection (True mask, TM), the number and proportion of missed detection (False negative, FN), the number and proportion of false detection (False positive, FP), and the number and proportion of correct screening (True screen, TS) in the cloud detection results of the imaging radiometer are counted. Correct detection means that the laser radar detects clouds and the radiometer also detects clouds; missed detection means that the laser radar detects clouds and the radiometer does not detect clouds; false detection means that the laser radar does not detect clouds and the radiometer detects clouds; correct screening means that the laser radar does not detect clouds and the radiometer does not detect clouds.

[0077] In the embodiment, on the basis of comparing the cloud detection results, the cloud classification results of the correctly detected samples are also compared, specifically, the qualified matching imaging radiometer cloud classification results are compared with the lidar cloud classification results, and the number of samples of correct classification (True classification, TC) and false classification (False classification, FC) of each cloud type is counted from the two aspects of single-layer cloud and multi-layer cloud and combined with the cloud top height assessment as the judgment condition, wherein the correct classification means that the cloud type identified by the lidar is the same as the cloud type identified by the radiometer, and the false classification means that the radiometer fails to identify the cloud type confirmed by the lidar, specifically including:

[0078] By comparing the number of layers in the misclassified samples, the number of layers that can be identified by the imaging radiometer is divided into single layer and multi-layer, and the lidar can identify any number of layers until the signal energy is completely attenuated. If the imaging radiometer and the lidar are both single-layer clouds or multi-layer clouds (the number of lidar layers ≥ 2), and the cloud top height difference is <3km, then even if the cloud phase properties are misclassified, it is still judged as correct identification in terms of layers. If the cloud top height difference is >3km, it is considered that the imaging radiometer has missed or misdetected the high-altitude cloud layer and it is judged as a misclassification. If the number of layers is inconsistent, it is judged as a misclassification. The number of samples correctly classified and misclassified for each cloud type is counted.

[0079] More specifically, if Figure 3 As shown in the figure, the number of samples correctly classified and misclassified for each cloud type is counted from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition, including:

[0080] If the single-layer clouds identified by the lidar and the imaging radiometer are of the same type, they are recorded as correctly classified single-layer clouds, where single-layer cloud types include water clouds, ice clouds, and cirrus clouds;

[0081] If both the lidar and the imaging radiometer identified single-layer clouds but the types were inconsistent, and the cloud top height deviation was <3 km, it was recorded as misclassified single-layer clouds, corresponding to the situation in which the phase state judgment was wrong in the actual measurement but the layer identification was consistent;

[0082] If both the lidar and the imaging radiometer identified multi-layer clouds and the cloud top height deviation was <3 km, it was recorded as correctly classified multi-layer clouds;

[0083] If the lidar and imaging radiometer do not agree on the layer recognition, or both recognize multi-layer clouds but the cloud top height deviation is ≥3km, it is recorded as misclassified multi-layer clouds, which corresponds to the situation in which the imaging radiometer fails to recognize cirrus clouds or misidentifies high-altitude aerosols as cirrus clouds in actual measurements;

[0084] Count the number of correctly classified and incorrectly classified samples for each cloud type.

[0085] S6, calculate cloud top height differences: compare the matched imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height differences of each single-layer cloud type that are correctly classified and incorrectly classified, the cloud top height differences and layer-average height differences of correctly classified multi-layer clouds, and the cloud top height differences, layer top height differences and layer-average height differences of incorrectly classified multi-layer clouds.

[0086] In the embodiment, for each correctly classified sample and incorrectly classified sample of each cloud type, the cloud top height data included in the cloud product sample and the cloud top height data included in the corresponding radar cloud profile, the cloud top difference (CTD) between the two cloud top heights is calculated, which means calculating the difference between the cloud top height of the imaging radiometer and the cloud top height of the top cloud of the lidar cloud profile, and the calculation formula is:

[0087] (3)

[0088] where x represents each cloud type and the correct / false recognition samples for each type, is the number of samples for each cloud type, represents the cloud top height measured by the radiometer for the xth cloud type, Represents the cloud top height measured by the lidar for the xth cloud type.

[0089] For multi-layer clouds, due to the mixing effect of radiation signals, the limitation of instrument resolution and the limitation of inversion algorithm, the cloud top height inverted by the radiometric imaging meter may be inaccurate and closer to a certain height between two layers of clouds. At the same time, the prior value of the radiometric imaging meter will cause the same phenomenon to occur in pixels that are misjudged as multi-layer clouds. In addition, imaging radiometers have certain limitations in identifying cirrus clouds. Generally, optically thin cirrus clouds cannot be identified. Therefore, since multi-layer clouds contain cirrus clouds, imaging radiometers will largely identify multi-layer clouds as water clouds or thick ice clouds.

[0090] The cloud profile data of the LiDAR has complete cloud layer information, that is, the cloud top height and cloud base height of each cloud layer. Based on this feature of the cloud profile data of the LiDAR, the cloud top height difference and the layer average height difference are calculated for correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and layer average height difference are calculated for incorrectly classified multi-layer clouds. The difference is that the cloud top height difference is compared with the top cloud height in the LiDAR cloud profile, the layer top height difference is compared with the height of the layer closest to the imaging radiometer in the LiDAR cloud profile, and the layer average height difference is compared with the average value of the heights of multiple layers in the LiDAR cloud profile.

[0091] The layer top height difference refers to the difference between the cloud top height of the imaging radiometer and the layer top height of the closest layer measured by the lidar cloud profile. That is, find the layer closest to the radiometer in the cloud profile data and use its layer top height as , is substituted into formula (3) to calculate the height difference of the top layer of multi-layer clouds .

[0092] The layer-average height difference refers to the difference between the cloud top height of the imaging radiometer and the average height of multiple layers in the lidar cloud profile. That is, the cloud average height is calculated based on the cloud top height of multiple layers in the cloud profile data, and the cloud average height is used as , is substituted into formula (3) to calculate the average height difference of multiple layers of clouds .

[0093] S7, output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of each cloud type that is correctly and incorrectly classified, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection result.

[0094] After steps S4 to S7, the total sample size for quality inspection of imaging radiometer cloud product data, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, as well as the correct and incorrect classification sample size of each cloud type and the corresponding cloud top height difference, layer top height difference, and layer average height difference are obtained, and a complete cloud top height quality inspection result is obtained.

[0095] The embodiment also provides an experimental example based on the cloud top height quality inspection method, such as Figure 4 and Figure 5 As shown in the figure, the cloud profiles of the Atmosphere-1 Laser Radar (DQ-1 / ACDL), the cloud products of the Fengyun-4 Multi-channel Scanning Imaging Radiometer (FY-4 / AGRI) and auxiliary data from January 1, 2023 to January 16, 2023 are obtained. The spatial resolution of the DQ-1 / ACDL cloud profiles used is 5km along the track, the spatial resolution of the FY-4 / AGRI is 4km, and the time resolution is 15 minutes. The time threshold is set to 7.5 minutes, the spatial threshold is 5km, and the expanded search time and space range is set to a time gap of 15 minutes and a spatial gap of 10km. The above data are processed through steps S2 and S3. On the basis of spatiotemporal matching, the spatiotemporal homogeneity test is used to eliminate the inconsistency between the DQ-1 / ACDL cloud profile and the actual observation target of the FY-4 / AGRI cloud product caused by the uneven distribution of clouds and the rapid changes of the cloud edge. Among the 564,789 pairs of matching data obtained, the qualified rate of lidar is 93.1%, the qualified rate of radiation imager is 79.4%, and the total qualified rate is 75.9%.

[0096] For qualified matching data, through steps S4 and S5, the number of qualified samples obtained is 428,878, and the number of samples with correct detection (TM+TS) among the qualified samples is 348,920, accounting for 89.0%. The total number of samples (FP) where the radiometer detected clouds but the lidar did not detect clouds is 14,604, accounting for 3.4%. The total number of samples (FN) where the radiometer did not detect clouds but the lidar detected clouds is 32,731, accounting for 7.6%. In FN, the total number of samples where the lidar detected cirrus clouds is 29,177, accounting for 89.1%, which is the main source of radiometer cloud detection errors, as shown in Table 1:

[0097] Table 1 Sample size of test results

[0098]

[0099] At the same time, the value in brackets represents the test results of all samples that have not been tested for spatiotemporal uniformity, and the correct detection ratio is 83.5%. It can be seen that the spatiotemporal uniformity test improves the correct detection ratio when removing samples with low reliability.

[0100] In the above correctly detected samples, cloud classification is also performed in step S5. The specific classification results are shown in Table 2:

[0101] Table 2 Cloud classification results

[0102]

[0103] It can be seen that, except for the case where a single layer of ice cloud was misidentified as water cloud, the radiometer's misclassification of cloud layers mainly occurred in multi-layer cloud scenes, which was basically caused by the radiometer's missed detection of upper cirrus clouds. The comparison of multi-layer cloud classification results without the introduction of lidar layer height is shown in brackets, which misses the cases where upper cirrus clouds were missed in some multi-layer cloud scenes (i.e., the actual number of cloud layers ≥ 3).

[0104] Based on the cloud classification, the height difference between single-layer cloud and multi-layer cloud is calculated in step S6, and the result of the comparison between the cloud profile of the satellite-borne lidar and the cloud top height of the imaging radiometer is calculated, such as Figure 6 As shown in Table 3 and Table 4, compared with the detection results only under cloud detection and consistent cloud types, the number of effective cloud top height detection samples increased by 127%. The specific calculation results are shown in Table 3 and Table 4:

[0105] Table 3 Calculation results of cloud top height difference

[0106]

[0107] It can be seen that under the premise that the radiometer correctly identifies cirrus clouds, the error in the cloud top height of cirrus clouds is not large, and the main error comes from misclassification. However, the cirrus cloud height inverted by the radiometer slightly exceeds the average cloud top height of the lidar (which is basically lower than the cloud top height measured by the lidar compared to other cloud types), indicating that there may be over-correction of cirrus clouds.

[0108] Table 4 Calculation results of multi-layer cloud top difference and layer average difference

[0109]

[0110] The calculation results of the height difference of multi-layer clouds show that there will be a huge difference in the cloud top height of the multi-layer clouds that are misclassified directly. This is usually because the lidar detects extremely high cirrus clouds (above 10km). Under the influence of this layer of cirrus clouds, the radiometer overestimates the height of the misclassified cloud layer compared to the corresponding layer of the lidar (usually the second layer). If the average height of similar layers is compared, cirrus clouds are underestimated, while other cloud types are overestimated, which is in line with the expected results based on the optical properties of clouds.

[0111] Based on the same inventive concept, Figure 7 As shown, the embodiment also provides a cloud top height quality inspection device, including: a data acquisition module 71, a time-space matching module 72, a cloud detection and classification matching module 73, a uniformity testing module 74, a cloud detection and classification result layer-by-layer comparison module 75, a difference calculation module 76, and a data output module 77.

[0112] Among them, the data acquisition module 71 is used to obtain the lidar cloud profile data as the detection true value, the imaging radiometer cloud product data to be inspected, and the auxiliary data; the time-space matching module 72 is used to perform time-space matching of the imaging radiometer cloud product data relative to the lidar cloud profile data based on the time-space data in the auxiliary data and determine the matching data; the cloud detection and classification matching module 73 is used to match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud product according to the matching of time-space information in the matching data; the uniformity test module 74 is used to perform time-space uniformity test on the matching data to filter out data with unqualified time-space uniformity, obtain qualified matching data and count the total sample size; the cloud detection and classification result layer-by-layer comparison module 75 is used to compare the qualified matching imaging radiometer cloud detection results with the lidar cloud detection results, and count the correct detection, missed detection, false detection and so on. and the number and proportion of samples that are correctly screened out. Among the correctly detected samples, the qualified matching imaging radiometer cloud classification results are compared with the lidar cloud classification results, and the number of correctly classified and incorrectly classified samples of each cloud type is counted from the two aspects of single-layer cloud and multi-layer cloud and combined with the cloud top height assessment as the judgment condition; the difference calculation module 76 is used to compare the matching imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height difference of each correctly classified and incorrectly classified single-layer cloud type, the cloud top height difference and the average layer height difference of the correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and average layer height difference of the incorrectly classified multi-layer clouds; the data output module 77 is used to output the total sample size, the number and proportion of correctly detected, missed, falsely detected, and correctly screened out samples, the number of correctly and incorrectly classified samples of each cloud type, and the corresponding cloud top height difference, layer top height difference and average layer height difference as the complete cloud top height quality inspection result.

[0113] It should be noted that the cloud top height quality inspection device provided in the above embodiment should be illustrated by the division of the above functional modules when performing cloud top height detection, and the above functional distribution can be completed by different functional modules as needed, that is, the internal structure of the terminal or server is divided into different functional modules to complete all or part of the functions described above. In addition, the cloud top height quality inspection device provided in the above embodiment and the cloud top height quality inspection method embodiment belong to the same concept, and the specific implementation process is detailed in the cloud top height quality inspection method embodiment, which will not be repeated here.

[0114] The embodiment further provides a computing device, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, the computing device is used to implement the cloud top height quality inspection method, which specifically includes the following steps:

[0115] S1, data acquisition: acquiring lidar cloud profile data and cloud determination results as detection true values, imaging radiometer cloud product data to be inspected, and auxiliary data;

[0116] S2, time-space matching: Based on the time-space data in the auxiliary data, the imaging radiometer cloud product data is time-space matched with the lidar cloud profile data and the matching data is determined;

[0117] S3, cloud detection and classification matching: match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud products according to the matching of the spatiotemporal information in the matching data;

[0118] S4, homogeneity test: perform a spatiotemporal homogeneity test on the matching data to filter out data that do not meet the spatiotemporal homogeneity requirements, obtain qualified matching data, and calculate the total sample size;

[0119] S5, layer-by-layer comparison of cloud detection and classification results: Compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out; among the correctly detected samples, compare the qualified imaging radiometer cloud classification results with the lidar cloud classification results, and count the number of correctly classified and incorrectly classified samples of each cloud type from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition;

[0120] S6, calculate cloud top height differences: compare the imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height differences of each type of single-layer cloud that are correctly classified and incorrectly classified, the cloud top height differences and layer-average height differences of correctly classified multi-layer clouds, and the cloud top height differences, layer top height differences and layer-average height differences of incorrectly classified multi-layer clouds;

[0121] S7, data output: output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of each cloud type that is correctly and incorrectly classified, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection results.

[0122] The computing device provided in the embodiment, in addition to the processor and memory, also includes hardware required for other services such as internal bus, network interface, memory, etc. at the hardware level. The memory is a non-volatile memory, and the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the cloud top height quality inspection method described in the above steps S1-S7. Of course, in addition to the software implementation, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0123] Based on the same inventive concept, an embodiment further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the above-mentioned cloud top height quality inspection method is implemented, which specifically includes the following steps:

[0124] S1, data acquisition: acquiring lidar cloud profile data and cloud determination results as detection true values, imaging radiometer cloud product data to be inspected, and auxiliary data;

[0125] S2, time-space matching: Based on the time-space data in the auxiliary data, the imaging radiometer cloud product data is time-space matched with the lidar cloud profile data and the matching data is determined;

[0126] S3, cloud detection and classification matching: match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud products according to the matching of the spatiotemporal information in the matching data;

[0127] S4, homogeneity test: perform a spatiotemporal homogeneity test on the matching data to filter out data that do not meet the spatiotemporal homogeneity requirements, obtain qualified matching data, and calculate the total sample size;

[0128] S5, layer-by-layer comparison of cloud detection and classification results: Compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out; among the correctly detected samples, compare the qualified imaging radiometer cloud classification results with the lidar cloud classification results, and count the number of correctly classified and incorrectly classified samples of each cloud type from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition;

[0129] S6, calculate cloud top height differences: compare the imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height differences of each type of single-layer cloud that are correctly classified and incorrectly classified, the cloud top height differences and layer-average height differences of correctly classified multi-layer clouds, and the cloud top height differences, layer top height differences and layer-average height differences of incorrectly classified multi-layer clouds;

[0130] S7, data output: output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of each cloud type that is correctly and incorrectly classified, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection results.

[0131] In the embodiment, computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data.

[0132] The specific implementation methods described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud top height quality inspection method, characterized in that: The following steps are involved: Obtain lidar cloud profile data as the detection truth value, imaging radiometer cloud product data to be inspected, and auxiliary data; Based on the spatiotemporal data in the auxiliary data, the imaging radiometer cloud product data is temporally and spatially matched with the lidar cloud profile data and the matching data is determined; The cloud detection and cloud classification results in the lidar cloud profile data are matched with the cloud detection and cloud classification results in the imaging radiometer cloud products according to the matching of the spatiotemporal information in the matching data; Perform a spatiotemporal homogeneity test on the matching data to filter out data that do not meet the spatiotemporal homogeneity requirements, obtain qualified matching data, and calculate the total sample size; Compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out; among the correctly detected samples, compare the qualified imaging radiometer cloud classification results with the lidar cloud classification results, and count the number of correctly classified and incorrectly classified samples of each cloud type from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition; Compare the imaging radiometer cloud product data and the lidar cloud profile data, and calculate the cloud top height difference of each single-layer cloud type that is correctly classified and incorrectly classified, the cloud top height difference and layer average height difference of correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and layer average height difference of incorrectly classified multi-layer clouds; Output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of correct and incorrect classification for each cloud type, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection result.

2. The cloud top height quality inspection method according to claim 1, characterized in that: Based on the spatiotemporal auxiliary data, the imaging radiometer cloud product data is temporally and spatially matched with the lidar cloud profile data and the matching data is determined, including: Determine the time threshold and space threshold. The time and space auxiliary data include the measurement time and longitude and latitude. The imaging radiometer cloud product data include the imaging radiometer cloud product samples measured at different times and longitudes and latitudes. Based on the measurement time and longitude and latitude corresponding to each profile in the lidar cloud profile data, the imaging radiometer cloud product samples with the closest spatial distance to each profile within each time threshold and spatial threshold are screened out to form matching data.

3. The cloud top height quality inspection method according to claim 2, characterized in that: Perform a temporal and spatial homogeneity test on the matching data to filter out data that does not meet the temporal and spatial homogeneity requirements and obtain qualified matching data, including: The matching data are tested for the spatiotemporal uniformity of the lidar profile, the internal uniformity of the radiometer pixels, and the peripheral uniformity of the radiometer pixels. The qualified matching data are obtained by combining the sample data that pass all three tests.

4. The cloud top height quality inspection method according to claim 3 is characterized in that: The lidar profile spatiotemporal uniformity test refers to testing the uniformity of multiple basic profiles contained in each lidar cloud profile, calculating the average deviation of the cloud top height and the difference in the number of layers of all basic profiles. When the number of layers changes or the average deviation of the cloud top height is greater than or equal to a given cloud top height deviation threshold, the spatiotemporal uniformity of all lidar cloud profile samples is considered unqualified and screened out. When the number of layers is consistent and the average deviation of the cloud top height is less than the given cloud top height deviation threshold, all lidar cloud profile samples are considered qualified. The radiometer pixel internal uniformity test refers to testing the uniformity between multiple pixels contained in each radiometer cloud product pixel, calculating the standard deviation of infrared band brightness temperature between all pixels, and when the brightness temperature standard deviation is greater than or equal to a given brightness temperature standard deviation threshold, the temporal and spatial uniformity of all radiometer cloud product samples are considered unqualified and are screened out; when the brightness temperature standard deviation is less than the given brightness temperature standard deviation threshold, all radiometer cloud product samples are considered qualified; The radiometer pixel peripheral uniformity test refers to expanding the search time and space range based on the time threshold and the space threshold, selecting all cloud product samples whose latitude and longitude differences with each profile are within the search time and space range from the matching data, and calculating the average deviation between the cloud characteristics of all cloud product samples and the average cloud characteristics, wherein the average cloud characteristic is the average value of the cloud characteristics of all cloud product samples. When the average deviation is greater than or equal to the given cloud characteristic deviation threshold, the time and space uniformity of all cloud product samples is considered unqualified and are screened out. When the average deviation is less than the given cloud characteristic deviation threshold, all cloud product samples are considered qualified, wherein the cloud characteristic is a matrix composed of marking cloud as 1 and marking no cloud as 0.

5. The cloud top height quality inspection method according to claim 4, characterized in that: The cloud top height deviation threshold between lidar profiles is 0-1 km, the brightness temperature standard deviation threshold of the infrared band between radiometer pixels is 0-3 K, and the cloud feature deviation threshold between radiometer pixels is 0-0.

25.

6. The cloud top height quality inspection method according to claim 1, characterized in that: The number of samples correctly classified and misclassified for each cloud type is counted from both single-layer and multi-layer clouds, combined with cloud top height assessment as the judgment criterion, including: If the single-layer clouds identified by the lidar and the imaging radiometer are of the same type, they are recorded as correctly classified single-layer clouds, where single-layer cloud types include water clouds, ice clouds, and cirrus clouds; If both the lidar and the imaging radiometer identified single-layer clouds but the types were inconsistent, and the cloud top height deviation was <3 km, it was recorded as misclassified single-layer clouds, corresponding to the situation in which the phase state was wrongly determined in the actual measurement but the layer identification was consistent; If both the lidar and the imaging radiometer identified multi-layer clouds and the cloud top height deviation was <3 km, it was recorded as correctly classified multi-layer clouds; If the lidar and imaging radiometer do not agree on the layer recognition, or both recognize multi-layer clouds but the cloud top height deviation is ≥3km, it is recorded as misclassified multi-layer clouds, which corresponds to the situation in which the imaging radiometer fails to recognize cirrus clouds or misidentifies high-altitude aerosols as cirrus clouds in actual measurements; Count the number of correctly classified and incorrectly classified samples for each cloud type.

7. The cloud top height quality inspection method according to claim 1, characterized in that: The cloud top height difference refers to the difference between the cloud top height of the imaging radiometer and the cloud top height of the top cloud of the lidar cloud profile. The layer top height difference refers to the difference between the cloud top height of the imaging radiometer and the layer top height of the closest layer measured by the lidar cloud profile; The layer average height difference refers to the difference between the cloud top height of the imaging radiometer and the average height of multiple layers in the lidar cloud profile.

8. A cloud top height quality inspection device, characterized in that: include: A data acquisition module, which is used to acquire the lidar cloud profile data as the detection true value, the imaging radiometer cloud product data to be inspected, and the auxiliary data; A time-space matching module, which is used to perform time-space matching of the imaging radiometer cloud product data relative to the lidar cloud profile data based on the time-space data in the auxiliary data and determine the matching data; The cloud detection and classification matching module is used to match the cloud detection and cloud classification results in the lidar cloud profile data with the cloud detection and cloud classification results in the imaging radiometer cloud product according to the matching of the spatiotemporal information in the matching data; A uniformity test module is used to perform a temporal and spatial uniformity test on the matching data to filter out data that does not meet the temporal and spatial uniformity requirements, obtain qualified matching data, and calculate the total sample size; The cloud detection and classification result layer-by-layer comparison module is used to compare the qualified imaging radiometer cloud detection results with the lidar cloud detection results, and to count the number and proportion of samples that are correctly detected, missed, falsely detected, and correctly screened out. Among the correctly detected samples, the qualified imaging radiometer cloud classification results and the lidar cloud classification results are compared, and the number of correctly classified and incorrectly classified samples of each cloud type is counted from the perspectives of single-layer clouds and multi-layer clouds, combined with cloud top height assessment as the judgment condition; A difference calculation module is used to compare the matching imaging radiometer cloud product data with the lidar cloud profile data, calculate the cloud top height difference of each single-layer cloud type that is correctly classified and incorrectly classified, the cloud top height difference and layer average height difference of correctly classified multi-layer clouds, and the cloud top height difference, layer top height difference and layer average height difference of incorrectly classified multi-layer clouds; The data output module is used to output the total sample size, the sample size and proportion of correct detection, missed detection, false detection, and correct screening, the sample size of each cloud type that is correctly and incorrectly classified, and the corresponding cloud top height difference, layer top height difference, and layer average height difference as the complete cloud top height quality inspection results.

9. A computing device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the one or more processors execute the executable code, they are used to implement the cloud top height quality inspection method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the cloud top height quality inspection method described in any one of claims 1-7 is implemented.

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