A cloud top height verification method and system based on strict cloud phase state matching

Abstract

The application relates to a cloud top height inspection method and system based on strict cloud phase state matching, and relates to the technical field of atmospheric detection and remote sensing. The method comprises the following steps: acquiring an inspection source and a detected source to be matched in time and space, and performing time and space matching; reading time and space data of the inspection source and the detected source, searching for cloud phase state consistent pixels in the inspection source and the detected source, and calculating distances between the pixels by using the time and space data; calculating a time difference between the inspection source and the detected source by using the time and space data, selecting the cloud phase state consistent pixels within a preset time difference range and a preset distance range as matching data set output; and using the matching data set to statistically process cloud top height products of the same phase state, and generating statistical indexes of cloud top height inspection. Compared with general quality inspection, the strict cloud phase state improves the quality inspection precision, reduces the matching time consumption, and can evaluate the precision of the cloud top height for different cloud phase states.

Description

TECHNICAL FIELD

[0001] The application relates to the field of atmospheric sounding and remote sensing, in particular to a cloud top height inspection method, in particular to a cloud top height inspection method and system based on strict cloud phase state matching. BACKGROUND

[0002] The precision of satellite inversion products is affected by many factors, satellite product authenticity inspection is the key to product precision improvement and algorithm optimization, and is also the only way for remote sensing application. Among them, cloud top height is a main cloud physical parameter, which is not only an important parameter of the earth system model, but also is widely used in climate change research and space weather protection. Meteorological satellite remote sensing is the main way to obtain accurate global cloud top height information. The method for authenticity inspection of satellite inversion cloud top height products mainly matches the cloud top height products obtained by satellites, ground-based and airborne inversions in time and space, and uses the matching data to statistically analyze the deviation, root mean square error and correlation coefficient.

[0003] However, the method for authenticity inspection of satellite inversion cloud top height products only matches the data of the inspected source (cloud top height product needing to determine precision) and the inspection source (cloud top height obtained by satellite, ground-based and airborne inversions) in a certain time range and spatial range to obtain statistical results and individual case analysis results, and the results are inconsistent. Research shows that in addition to time and space matching, strict cloud phase state matching is also needed, that is, the cloud top height product and the same satellite product used for inspection are ice cloud, water cloud or mixed cloud, and only then the statistics is entered. When the cloud phase state is inconsistent, the sample is not entered into the statistics, so that the statistical results are consistent with the individual case analysis results. SUMMARY

[0004] At present, the quality inspection of most cloud top heights only performs time and space matching of cloud top height, and does not perform strict cloud phase state matching, so the statistical results are inconsistent with the individual case analysis results. The purpose of the present application is to provide a cloud top height inspection method and system based on strict cloud phase state matching, which is of great significance for accurate inspection of cloud top height by seeking a cloud top height inspection result consistent with individual case analysis.

[0005] To achieve the above object, the application provides the following technical scheme:

[0006] In a first aspect, the application provides a cloud top height inspection method based on strict cloud phase state matching, comprising the following steps:

[0007] Obtaining the inspection source and the inspected source to be matched in time and space and performing time and space matching;

[0008] Read the spatiotemporal data of the test source and the test source after spatiotemporal matching. The spatiotemporal data includes the latitude and longitude, time, cloud top height and cloud phase dataset of the test source and the test source.

[0009] Search for pixels in the test source and the tested source that have the same cloud phase, and use the spatiotemporal data to calculate the distance between the pixels in the test source and the tested source.

[0010] The time difference between the test source and the tested source is calculated using the time in the spatiotemporal data, and pixels with consistent cloud phases within a preset time difference range and a preset distance range are selected as the matching dataset output.

[0011] The matching dataset is used to statistically analyze cloud top height products of the same phase to generate statistical indicators for cloud top height verification.

[0012] As a further aspect of the present invention, the source to be tested is a cloud top height product whose accuracy needs to be determined. The source of verification is the cloud top height obtained by satellite, ground-based, or airborne inversion. The accuracy of the source to be tested is verified using the source of verification.

[0013] As a further aspect of the present invention, searching for pixels whose cloud phase states are consistent between the inspection source and the inspected source also includes:

[0014] Search for pixels with consistent cloud phases to output data, generating the latitude and longitude, time, cloud top height, and cloud phase of the tested source, as well as the latitude and longitude, time, cloud top height, and cloud phase dataset of the tested source.

[0015] As a further aspect of the present invention, the pixel is the smallest unit by which the sensor scans and samples ground scenery.

[0016] As a further aspect of the present invention, the statistical indicators for cloud top height verification include the average deviation, root mean square error, and correlation coefficient calculated using the matching dataset. The statistical indicators for cloud top height verification are used to evaluate the accuracy of satellite remote sensing products.

[0017] The average deviation is:

[0018]

[0019] In the formula, Indicates the average deviation; N represents the number of matched samples; x i Indicates the data to be tested; x oi Indicates the source data for testing;

[0020] The root mean square error is:

[0021]

[0022] RMSE = sqrt(1 / N * Σ(xi - x)2) where RMSE represents root mean square error; N represents number of matched samples; x i represents data to be tested; x oi represents source data for testing;

[0023] Corr = 1 / N * Σ(xi - x)(x - x) where Corr represents correlation coefficient; N represents number of matched samples; x

[0024]

[0025] Bias = 1 / N * Σ(xi - x) where Bias represents bias; N represents number of matched samples; x i represents data to be tested; x oi represents source data for testing; represents mean of data samples to be tested; represents mean of source data for testing.

[0026] As a further scheme of the present application, the statistical indicators of the cloud top height testing further include bias, skewness, kurtosis and median of the testing calculated by using the matched data set, and the statistical indicators of the cloud top height testing are used for evaluating precision of satellite remote sensing products;

[0027] wherein the bias is Bias = 1 / N * Σ(xi - x) where Bias represents bias; N represents number of matched samples; x

[0028] Bias = 1 / N * Σ(xi - x) where Bias represents bias; N represents number of matched samples; x i -x oi

[0029] Bias = 1 / N * Σ(xi - x) where Bias represents bias; N represents number of matched samples; x i represents data to be tested; x oi represents source data for testing, bias is calculated for each matched sample pixel, and the bias value obtained can be used for drawing bias spatial distribution map;

[0030] Skewness = 1 / N * Σ(xi - x)2 where Skewness represents skewness; N represents number of matched samples; s represents standard deviation; x

[0031]

[0032] Skewness = 1 / N * Σ(xi - x)2 where Skewness represents skewness; N represents number of matched samples; s represents standard deviation; x i represents data to be tested; represents mean of data samples to be tested;

[0033] Kurtosis = 1 / N * Σ(xi - x)4 where Kurtosis represents kurtosis; N represents number of matched samples; s represents standard deviation; x

[0034]

[0035] Kurtosis = 1 / N * Σ(xi - x)4 where Kurtosis represents kurtosis; N represents number of matched samples; s represents standard deviation; x i represents data to be tested; represents mean of data samples to be tested;

[0036] The median is calculated by arranging the variable values ​​in the sample in ascending order to form a sequence. The variable value in the middle position of the sequence is called the median. When the number of variable values ​​N is odd, the variable value in the middle position is the median. When N is even, the median is the average of the two variable values ​​in the middle position.

[0037] As a further aspect of the present invention, the skewness is a statistical measure describing the distribution pattern of data. A skewness of 0 indicates that the data distribution pattern is skewed to the same degree as the normal distribution; a skewness greater than 0 indicates that the data distribution pattern is positively skewed or right-skewed compared to the normal distribution; a skewness less than 0 indicates that the data distribution pattern is negatively skewed or left-skewed compared to the normal distribution; the larger the absolute value of the skewness, the greater the degree of skewness of the distribution pattern.

[0038] As a further aspect of the present invention, the kurtosis is a statistical measure describing the steepness of the distribution of all values ​​in the population. A kurtosis of 0 indicates that the population data distribution is as steep as the normal distribution; a kurtosis greater than 0 indicates that the population data distribution is steeper than the normal distribution, and has a leptokurtic peak; a kurtosis less than 0 indicates that the population data distribution is flatter than the normal distribution, and has a flat-topped peak. The larger the absolute value of the kurtosis, the greater the difference between the steepness of its distribution and the normal distribution.

[0039] As a further aspect of the present invention, the accuracy of the source under test is tested using the test source to obtain the test result, which is then presented in the form of a spatial distribution map, a statistical histogram, a time series line graph, a statistical report, or a quality inspection report.

[0040] Secondly, the present invention also provides a cloud top height verification system based on strict cloud phase matching, used to perform the cloud top height verification method based on strict cloud phase matching as described above. The cloud top height verification system based on strict cloud phase matching includes:

[0041] The inspection acquisition module is used to acquire the inspection source and the inspected source to be matched in spatiotemporal and perform spatiotemporal matching.

[0042] The spatiotemporal data reading module is used to read the spatiotemporal data of the test source and the test source after spatiotemporal matching. The spatiotemporal data includes the latitude and longitude, time, cloud top height and cloud phase dataset of the test source and the test source.

[0043] The distance calculation module is used to search for pixels with the same cloud phase in the test source and the tested source, and to calculate the distance between the pixels in the test source and the tested source using the spatiotemporal data.

[0044] The time difference calculation module is used to calculate the time difference between the test source and the test source using the time in the spatiotemporal data.

[0045] The data set output statistics module is configured to select pixels with consistent cloud phase within a preset time difference range and a preset distance range as a matching data set output, and use the matching data set to statistically analyze cloud top height products of the same phase to generate statistical indexes of cloud top height verification.

[0046] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a computer program running on the processor, and the processor implements the steps of the cloud top height verification method based on strict cloud phase matching when executing the program.

[0047] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the program implements the steps of the cloud top height verification method based on strict cloud phase matching when executed by a processor.

[0048] The technical solution provided by the present application can include the following beneficial effects:

[0049] The cloud top height verification method and system based on strict cloud phase matching provided by the present application read the prepared source data to be verified and the verification source data, read the longitude, latitude, time, cloud top height and cloud phase data. The distance between the pixels of the source data to be verified and the verification source data is calculated using the longitude and latitude of the two, the time difference between the two is calculated using the time of the source data to be verified and the verification source data, and the pixels with consistent cloud phase within a certain time difference range and a certain distance range of the source data to be verified and the verification source data are selected as the matching data set output. The statistical indexes of cloud top height verification such as bias, root mean square error and correlation coefficient are calculated using the matching data set. The present application fully considers the technical features of the current cloud top height product based on the spaceborne multi-channel radiometer, is based on extrapolation theory, aims to develop an accurate and effective multi-layer cloud top height quality inspection algorithm to provide more accurate multi-layer cloud top height quality inspection results, and can be used to improve the fine support level of domestic Fengyun meteorological satellites. Compared with general quality inspection, strict cloud phase improves the quality inspection accuracy, reduces the matching time consumption, and can evaluate the accuracy of cloud top height for different cloud phases.

[0050] These aspects or other aspects of the present application will be more apparent in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application. In the drawings:

[0052] Figure 1 A flow chart of a cloud top height verification method based on strict cloud phase state matching provided for an embodiment of the present application is provided.

[0053] Figure 2 A flow chart of FY-4 cloud top height product quality verification in a cloud top height verification method based on strict cloud phase state matching provided for an embodiment of the present application is provided.

[0054] Figure 3 A structural block diagram of a cloud top height verification system based on strict cloud phase state matching provided for an embodiment of the present application is provided.

[0055] Figure 4 A hardware architecture diagram of a computer device in some embodiments of the present application is provided.

[0056] The purposes, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0057] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the following described embodiments or technical features can be combined in any manner to form new embodiments without conflict.

[0058] It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0060] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. In addition, it should be noted that only parts related to the present application are shown in the drawings, not all structures.

[0061] In the related art, since the quality verification of most cloud top heights at present only performs spatiotemporal matching of cloud top height, strict cloud phase matching is not performed, and the statistical results and the results of individual case analysis are inconsistent.

[0062] The application aims to provide a cloud top height verification method and system based on strict cloud phase state matching, which fully considers the technical features of the cloud top height product based on the satellite-borne multi-channel radiation imager, is based on extrapolation theory, aims to develop accurate and effective multi-layer cloud top height quality inspection algorithm, and provides more accurate multi-layer cloud top height quality inspection result, which can be used to improve the fine support level of domestic Fengyun meteorological satellite.

[0063] In some embodiments of the application, referring to FIGS. Figure 1 and Figure 2 The application provides a cloud top height verification method based on strict cloud phase state matching, which comprises the following steps S10-S50:

[0064] In step S10, a verification source and a verification target to be matched in time and space are obtained and matched in time and space.

[0065] In step S20, time and space data of the verification source and the verification target matched in time and space are read, and the time and space data include latitude, longitude, time, cloud top height and cloud phase state data set of the verification source and the verification target.

[0066] In step S30, cloud phase state consistent pixels in the verification source and the verification target are searched, and the distance between the pixels of the verification source and the verification target is calculated by using the time and space data.

[0067] In step S40, the time difference between the verification source and the verification target is calculated by using the time in the time and space data, and the cloud phase state consistent pixels within the preset time difference range and the preset distance range are selected as the matching data set output.

[0068] In step S50, the cloud top height products of the same phase state are counted by using the matching data set, and the statistical index of the cloud top height verification is generated.

[0069] The verification target is a cloud top height product to be determined in accuracy, the verification source is a cloud top height obtained by satellite, ground or airborne inversion, and the accuracy of the verification target is verified by using the verification source.

[0070] The cloud top height verification method based on strict cloud phase state matching of the application selects the cloud phase state consistent pixels as the matching data set during verification, performs time and space matching of the verification target (the verification target refers to the cloud top height product to be determined in accuracy) and the verification source during cloud top height verification, searches the cloud phase state consistent pixels for data output, generates the latitude, longitude, time, cloud top height and cloud phase state data set of the verification target and the latitude, longitude, time, cloud top height and cloud phase state data set of the verification source, wherein the pixel is the smallest unit of the ground scene scanned and sampled by the sensor, and finally the cloud top height products of the same phase state are counted to generate the statistical index of the cloud top height verification.

[0071] Therefore, the flow of the cloud top height verification in the embodiment of the present application is as follows: first, the prepared to-be-verified source data and the verification source data (cloud top height obtained by satellite, ground-based, airborne, etc. inversion) are read, and the latitude, longitude, time, cloud top height and cloud phase data set are read. The distance between the to-be-verified data and the verification source data is calculated by using the latitude and longitude of the two, the time difference between the to-be-verified data and the verification source data is calculated by using the time of the two, and the pixels with consistent cloud phase in a certain time difference range and a certain distance range of the to-be-verified source data and the verification source data are selected as the matching data set output. The statistical indicators such as bias, root mean square error and correlation coefficient are calculated by using the matching data set.

[0072] In the embodiment, the statistical indicators of the cloud top height verification include the average bias, the root mean square error and the correlation coefficient calculated by using the matching data set, and the statistical indicators of the cloud top height verification are used for evaluating the precision of satellite remote sensing products.

[0073] wherein, the average bias is:

[0074]

[0075] In the formula, Bias represents the average bias; N represents the number of matching samples; x represents the to-be-verified data; and x represents the verification source data. i oi

[0076] The root mean square error is:

[0077]

[0078] In the formula, RMSE represents the root mean square error; N represents the number of matching samples; x represents the to-be-verified data; and x represents the verification source data. i oi

[0079] The correlation coefficient is:

[0080]

[0081] In the formula, Corr represents the correlation coefficient; N represents the number of matching samples; x represents the to-be-verified data; and x represents the verification source data. i oi

[0082] ​​​​​​​​​In the embodiment, the statistical indexes of the cloud top height verification also include a verification bias, a skewness, a kurtosis and a median calculated by using the matched data set, and the statistical indexes of the cloud top height verification are used for evaluating the precision of satellite remote sensing products.

[0083] The verification bias is:

[0084] Bias = x i -x oi

[0085] In the formula, Bias represents the verification bias; x i represents the data to be verified; x oi represents the data to be verified; the bias value obtained by calculating the bias of each matched sample pixel can be used to draw a bias spatial distribution map.

[0086] The skewness is a statistical quantity describing the shape of data distribution, and the skewness of 0 indicates that the skewness of the data distribution is the same as that of the normal distribution; the skewness greater than 0 indicates that the data distribution is positively skewed or right-skewed compared with the normal distribution, the skewness less than 0 indicates that the data distribution is negatively skewed or left-skewed compared with the normal distribution, and the greater the absolute value of the skewness, the greater the skewness of the distribution.

[0087] The skewness is:

[0088]

[0089] In the formula, Skewness represents the skewness; N represents the number of matched samples; s represents the standard deviation; x i represents the data to be verified; represents the mean of the data to be verified;

[0090] The kurtosis is a statistical quantity describing the steepness of the distribution shape of all values in a population, and the kurtosis of 0 indicates that the data distribution of the population is the same as the steepness of the normal distribution; the kurtosis greater than 0 indicates that the data distribution of the population is steep compared with the normal distribution, and is a sharp peak; the kurtosis less than 0 indicates that the data distribution of the population is flat compared with the normal distribution, and is a flat peak, and the greater the absolute value of the kurtosis, the greater the difference between the steepness of the distribution and the normal distribution.

[0091] The kurtosis is:

[0092]

[0093] In the formula, Kurtosis represents the kurtosis; N represents the number of matched samples; s represents the standard deviation; x i represents the data to be verified; represents the mean of the data to be verified;

[0094] The median is that the variable values in the sample are arranged in order of size to form a sequence, and the variable value at the middle position of the variable sequence becomes the median value; when the number of variable values N is odd, the variable value at the middle position is the median; when N is even, the median is the average of the two variable values at the middle position.

[0095] The power resources in multiple regions are configured and optimized by task division according to geographical positions and supply and demand of power spot data in the multiple regions, resource coordination in a self-covered region, and power resource configuration in adjacent edge regions.

[0096] In the embodiment, the accuracy of the test source in testing the tested source is utilized to obtain a test result, and the test result is displayed in various forms such as a spatial distribution diagram, a statistical histogram, a time sequence line chart, a statistical report or a quality test report.

[0097] The cloud top height test method based on strict cloud phase state matching provided by the embodiment of the application fully considers the technical features of the cloud top height product based on the spaceborne multi-channel radiation imager, is based on extrapolation theory, aims to develop an accurate and effective multi-layer cloud top height quality inspection algorithm to provide more accurate multi-layer cloud top height quality inspection results, and can be used to improve the fine support level of domestic Fengyun meteorological satellites. Compared with general quality inspection, the strict cloud phase state improves the quality inspection accuracy, reduces the matching time consumption, and can perform accuracy evaluation on the cloud top height for different cloud phase states.

[0098] It should be understood that although the above steps are described in a certain order, the steps are not necessarily executed in the above order. Unless explicitly stated herein, the execution of the steps has no strict order limitation, and the steps can be executed in other orders. Moreover, part of the steps of the embodiment can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0099] Referring to Figure 3 Some embodiments of the application also provide a cloud top height test system based on strict cloud phase state matching, which comprises:

[0100] The test acquisition module 100 is configured to acquire historical transaction data of the power market, process the historical transaction data to obtain risk data features and risk quantitative analysis results of each historical transaction data.

[0101] The spatio-temporal data reading module 200 is configured to read spatio-temporal data of the test source and the tested source after spatio-temporal matching, wherein the spatio-temporal data comprises longitude, latitude, time, cloud top height and cloud phase data set of the test source and the tested source.

[0102] The distance calculation module 300 is configured to search for cloud phase consistent pixels in the test source and the tested source, and calculate the distance between the pixels of the test source and the tested source by using the spatio-temporal data.

[0103] The time difference calculation module 400 is configured to calculate the time difference between the test source and the tested source by using the time in the spatio-temporal data.

[0104] The data set output statistics module 500 is configured to select the cloud phase consistent pixels within a preset time difference range and a preset distance range as a matching data set output, and use the matching data set to perform statistics on the cloud top height product of the same phase to generate a statistical index of cloud top height test.

[0105] The cloud top height test system based on strict cloud phase matching provided by the application reads the prepared test source data and test source data, and reads longitude, latitude, time, cloud top height and cloud phase data. The longitude and latitude of the test data and the test source data are used to calculate the distance between the pixels of the two, the time of the test data and the test source data is used to calculate the time difference between the two, and the cloud phase consistent pixels of the test source data and the test source data within a certain time difference range and a certain distance range are selected as a matching data set output. The matching data set is used to calculate the statistical index of cloud top height test such as bias, root mean square error and correlation coefficient.

[0106] It should be noted that the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the application, and are not for limiting purposes. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0107] For example, as shown in Figure 2 In the application, the cloud top property product is taken as an example of FY-4 geostationary satellite / imager. In the balance of earth's atmospheric energy budget, cloud has a particularly significant role in affecting climate change, and is an important factor. FY-4 cloud top properties include cloud top height, cloud top pressure and cloud top temperature. Cloud top height is the distance of cloud top from the ground, and is one of the most basic cloud parameters, which is important in many fields such as aviation meteorological support and numerical weather prediction. Cloud top height helps to understand the development degree and evolution trend of cloud system.

[0108] Among them, the cloud top temperature is one of the most basic parameters of the cloud. The cloud top temperature is defined as the effective radiation temperature of the cloud top when a certain area on the earth's surface is covered by the cloud, which represents the temperature of the top of the highest cloud. The cloud top pressure is one of the most basic parameters of the cloud, which is a good indicator of the dynamics of the cloud layer.

[0109] The significance of studying the cloud top pressure is not only because it represents the dynamics characteristics, but also because it is often closely related to the thermodynamic factors. The cloud top pressure is defined as the pressure value of the cloud top, which can be used to represent the height of the cloud top development, with the unit of hPa. The smaller the cloud top pressure value, the greater the height of the cloud top development, and vice versa.

[0110] The FY-4 product quality inspection system mainly uses the inspection source cloud top property product to inspect the FY-4 satellite cloud top property product. The inspection steps mainly include data decoding, data matching (mainly including time matching, spatial matching and normalization processing), quality inspection index calculation, quality inspection scheme selection and quality inspection result output of the data to be inspected (FY-4 cloud top property product) and the inspection source data. The quality inspection indexes are bias, mean bias, root mean square error, correlation coefficient, kurtosis, skewness, median, etc. The overall scheme of the quality inspection of the FY-4 cloud top property product is described in detail.

[0111] (1) Description of the FY-4 cloud top property product to be inspected

[0112] FY-4B contains three products of cloud top height, cloud top pressure and cloud top temperature. The specific information is shown in the following table, and the time resolution of the product is 15 min, day and hour, and the spatial resolution of the product is 4KM 15 min, day and hour.

[0113] (2) Description of the inspection source product

[0114] The inspection sources of the FY-4 satellite cloud top property product include Aqua / Terra MODIS, CALIPSO, ground-based cloud radar, global sounding data and large-scale experimental cloud top property products.

[0115] Among them, the MODIS sensor carried by the Aqua and Terra satellites has 36 channels, becoming the first high spatial resolution detector with a carbon dioxide slice band. Aqua / Terra MODIS can make comprehensive and consistent synchronous observations of clouds and their related properties in the atmosphere. Moreover, Aqua / Terra MODIS has high-resolution multispectral data. The cloud top property element data in the MOD06 / MYD06 product of Aqua / Terra MODIS is used in this project to inspect the quality of the FY-4 cloud top property product.

[0116] The sounding data of the observation station is mainly obtained by means of the sounding balloon which carries various radio detectors to the air, so as to determine the meteorological elements such as temperature, humidity and air pressure at each height in the upper air, and meanwhile, the radar is used to locate the freely flying balloon to obtain the longitude, latitude and height data of the balloon. The sounding balloon has a weight of 300-1500 g (a smaller one will self-burst after rising to a certain height), and is filled with a proper amount of hydrogen or helium, and can rise to 30-40 km away from the ground. The general detection balloon used by the upper air meteorological station has a rising speed of 6-8 m / s, and is self-broken after rising to about 30 km in the upper air. The sounding observation time is generally 00 and 12 of the world time.

[0117] (3) FY-4 cloud top property product inspection process is:

[0118] Firstly, the prepared data to be inspected and the inspection source data are read, decoded, preprocessed and the like. Since the observation time and the spatial resolution of the FY-4 cloud top property product and the inspection source data are different, then according to the characteristics of the data itself, a suitable method is selected to perform time matching and spatial matching processing on the data to be inspected and the inspection source data. After the data matching, the cloud top property product data of the FY-4 satellite is inspected. The inspection work firstly selects the inspection indexes (bias, average bias, root mean square error, correlation coefficient, kurtosis, skewness, median and the like) suitable for the characteristics of the cloud top property product itself, and according to the needs of the user, the FY-4 cloud top property product data of different regions, user-defined regions and different time periods are calculated respectively. Finally, the results of each inspection index are output in the form of quality inspection report, statistical report, histogram and broken line analysis graph and the like.

[0119] (4) Data matching method

[0120] The FY-4 cloud top property product to be inspected and the cloud top property product of the inspection source (Aqua / Terra MODIS, CALIPSO, ground-based cloud radar, global sounding data and large-scale experiment) may have differences in time resolution, spatial resolution and product category. Before the quantitative quality inspection, the FY-4 and the inspection source product need to be matched. This process specifically includes time matching processing, spatial matching processing and normalization processing and the like.

[0121] I. Time matching method

[0122] Among them, the time matching of the FY-4 cloud top property product and the Aqua / Terra MODIS cloud top property product.

[0123] Time matching threshold: ±5min

[0124] Time matching method:

[0125] Terra and Aqua two satellites at the same time with Aqua / Terra MODIS sensor, can be 1-2 times a day to obtain the same data, FY-4 satellite product time resolution is 15min, Aqua / Terra MODIS cloud top property product product release cycle is 5min, at the same time considering the cloud changes fast, so, FY-4 cloud top property product time as the benchmark, looking for ±5min (not including end point) time range within the nearest Aqua / Terra MODIS cloud top property product, directly for time matching.

[0126] In which, FY-4 cloud top property product and CALIPSO cloud top property product time matching.

[0127] Time matching threshold: ±10min

[0128] Time matching method:

[0129] CALIPSO is a stationary satellite, and the cloud top property product release cycle: 10min, for trajectory product. FY-4 cloud top property product is every 15min, at the same time considering the cloud changes fast characteristics. So, in this test method to FY-4 satellite cloud top property product time as the benchmark, looking for ±10min (not including end point value) range within the nearest CALIPSO satellite cloud top property product for time matching.

[0130] II, spatial matching method

[0131] In which, FY-4 cloud top property product and Aqua / Terra MODIS cloud top property product spatial matching

[0132] Spatial matching threshold: 1km

[0133] Spatial matching method:

[0134] FY-4 satellite cloud top property product spatial resolution is 1km, while the resolution of Aqua / Terra MODIS cloud top property product is 1km. Spatial matching method according to FY-4 satellite each cloud top property product pixel, looking for 1km range (i.e. FY-4 cloud top product a pixel size) within the nearest Aqua / Terra MODIS cloud top property product pixel, for spatial matching processing.

[0135] In which, FY-4 cloud top property product and CALIPSO cloud top property product spatial matching

[0136] Spatial matching threshold: 1km

[0137] Spatial matching method:

[0138] The spatial resolution of the FY-4 satellite cloud topographic product is 1 km, and the resolution of the CALIPSO cloud topographic product is also 1 km. The spatial matching method finds the nearest CALIPSO cloud topographic product pixel within a 1 km range (i.e., the size of one pixel of the FY-4 cloud topographic product) for each cloud topographic product pixel of the FY-4 satellite, and performs spatial matching processing.

[0139] In this embodiment, the quality inspection indicators include: deviation, average deviation, root mean square error, correlation coefficient, kurtosis, skewness, and median. When evaluating the quality of a product, it is necessary to use some quantitative indicators to assess the quality status of the satellite remote sensing product. In this invention, multiple indicators such as deviation, average deviation, root mean square error, correlation coefficient, kurtosis, skewness, and median are selected to evaluate the accuracy of the satellite remote sensing product.

[0140] (5) Quality inspection results

[0141] The quality inspection results of FY-4 cloud-top properties will be presented in various formats, including spatial distribution maps, statistical histograms, time-series line graphs, scatter plots of deviation, emissivity, and optical thickness, statistical reports, and quality inspection reports. Furthermore, this invention allows users to independently select different regions (the required latitude and longitude range) and key areas for displaying the quality inspection results of cloud-top properties.

[0142] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0143] This embodiment also provides a computer device, such as... Figure 4 As shown, the computer device includes multiple computer devices 1000. In this embodiment, the components of the cloud top height verification system based on strict cloud phase matching can be distributed among different computer devices 1000. Each computer device 1000 can be a smartphone, tablet, laptop, desktop computer, rack server, blade server, tower server, or cabinet server (including independent servers or server clusters composed of multiple servers), etc., that executes a program. The computer device 1000 in this embodiment includes, but is not limited to, a memory 1001 and a processor 1002 that can communicate with each other via a system bus. It should be noted that... Figure 4Only the computer device 1000 with the component storage 1001 and the processor 1002 is shown, but it should be understood that all the shown components are not required for implementation, and more or fewer components can be alternatively implemented.

[0144] In the embodiment, the storage 1001 (i.e., the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 1001 can be an internal storage unit of the computer device 1000, such as a hard disk or a memory of the computer device 1000. In other embodiments, the storage 1001 can also be an external storage device of the computer device 1000, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 1000. Of course, the storage 1001 can include both the internal storage unit and the external storage device of the computer device 1000. In the embodiment, the storage 1001 is generally used to store an operating system and various application software installed on the computer device, such as the cloud top height verification system based on strict cloud phase state matching of the embodiments. In addition, the storage 1001 can also be used to temporarily store various data that have been output or will be output.

[0145] The processor 1002 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 1002 is generally used to control the overall operation of the computer device 1000. In the embodiment, the processor 1002 is used to run the program code or process data stored in the storage 1001. The processors 1002 of the plurality of computer devices 1000 of the computer device of the embodiment collectively implement the cloud top height verification method based on strict cloud phase state matching of the embodiments when the computer programs are executed, and the method includes:

[0146] Obtaining a verification source and a verification target to be spatio-temporally matched and performing spatio-temporal matching;

[0147] Reading spatio-temporal data of the verification source and the verification target after spatio-temporal matching, the spatio-temporal data including latitude and longitude, time, cloud top height, and cloud phase state data sets of the verification source and the verification target;

[0148] searching for the same phase state of the cloud in the test source and the tested source, and calculating the distance between the pixels of the test source and the tested source by using the space-time data;

[0149] calculating the time difference between the test source and the tested source by using the time in the space-time data, and selecting the same phase state of the cloud as the matching data set output within the preset time difference range and the preset distance range;

[0150] statistically analyzing the cloud top height product of the same phase state by using the matching data set, and generating the statistical index of the cloud top height test.

[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus a general hardware platform, and of course can also be realized by hardware. Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiment methods.

[0152] The embodiments of the present application also provide a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application market, etc., which stores a computer program. When the program is executed by a processor, the corresponding function is realized. The computer-readable storage medium of the present embodiment stores the cloud top height test system based on strict cloud phase state matching of the embodiments, and when the program is executed by a processor, the cloud top height test method based on strict cloud phase state matching of the embodiments is realized. The method comprises:

[0153] obtaining the test source and the tested source to be matched in space-time and performing space-time matching;

[0154] reading the space-time data of the test source and the tested source after space-time matching, wherein the space-time data comprises the latitude, longitude, time, cloud top height and cloud phase state data set of the test source and the tested source;

[0155] searching for the same phase state of the cloud in the test source and the tested source, and calculating the distance between the pixels of the test source and the tested source by using the space-time data;

[0156] calculating the time difference between the test source and the tested source by using the time in the space-time data, and selecting the same phase state of the cloud as the matching data set output within the preset time difference range and the preset distance range;

[0157] The cloud top height products of the same phase are statistically processed by using the matching data set to generate statistical indexes of cloud top height verification.

[0158] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment.

[0159] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include an installation medium, such as a CD-ROM, floppy disks, or tape system, a computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc., a non-volatile memory such as flash, magnetic media (e.g., a hard disk or optical storage), registers, or other similar types of storage, etc. The storage medium can further include other types of storage as well, or combinations thereof. Additionally, the storage medium can be located in a first computer system in which the program is executed, or it can be located in a second different computer system which connects to the first computer system over a network such as the Internet. The second computer system can provide program instructions to the first computer for execution. The term "storage medium" can include two or more storage mediums which can reside in different locations, e.g., in different computer systems that are connected over a network. The storage medium can store program instructions (e.g., as an installed program) which can be executed by one or more processors.

[0160] Of course, the storage medium provided by the embodiment of the present application includes computer executable instructions, and the computer executable instructions are not limited to the cloud top height verification operation based on strict cloud phase matching as described above, but can also perform the related operations in the cloud top height verification method based on strict cloud phase matching provided by any embodiment of the present application.

[0161] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A cloud top height verification method based on strict cloud phase state matching, characterized in that, The method comprises the following steps: obtaining a test source and a testee to be matched in time and space and performing time and space matching; reading time and space data of the test source and the testee after time and space matching, wherein the time and space data comprises longitude and latitude, time, cloud top height and cloud phase data set of the test source and the testee; searching for pixels with consistent cloud phase in the test source and the testee, and calculating distance between the pixels of the test source and the testee by using the time and space data; calculating time difference between the test source and the testee by using time in the time and space data, and selecting pixels with consistent cloud phase 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 indexes of cloud top height test.

2. The strict cloud phase state match based cloud top height verification method according to claim 1, wherein, The testee is a cloud top height product to be determined in accuracy, and the test source is cloud top height obtained by satellite, ground or airborne inversion, which is used to test accuracy of the testee.

3. The strict cloud phase state match based cloud top height verification method of claim 2, wherein, The searching for pixels with consistent cloud phase in the test source and the testee further comprises: searching for pixels with consistent cloud phase to perform data output, and generating longitude and latitude, time, cloud top height and cloud phase data set of the test source and the testee.

4. The strict cloud phase state match based cloud top height verification method of claim 3, wherein, The pixel is the smallest unit of scanning and sampling of a ground scene by a sensor.

5. The strict cloud phase state match based cloud top height verification method of claim 3, wherein, The statistical indexes of the cloud top height test comprise average deviation, root mean square error and correlation coefficient calculated by using the matching data set, and the statistical indexes of the cloud top height test are used to evaluate accuracy of satellite remote sensing products; The average deviation is: wherein represents the average deviation; N represents the number of matching samples; x i represents the data to be tested; x oi represents the source data for testing; The root mean square error is: where: RMSE represents the root mean square error; N represents the number of matching samples; x i represents the data to be tested; x oi represents the source data for testing; The correlation coefficient is: wherein Corr represents a correlation coefficient; N represents a number of matching samples; x i represents the test data; x oi represents the test data; x represents the test data sample mean; and represents the test data sample mean.

6. The strict cloud phase state match based cloud top height verification method of claim 5, wherein, The statistical indexes of the cloud top height test further comprise test deviation, skewness, kurtosis and median calculated by using the matching data set, and the statistical indexes of the cloud top height test are used to evaluate accuracy of satellite remote sensing products; The test deviation is: Bias = x i - x oi In the formula, Bias represents the test bias; x i represents the data to be tested; x oi represents the test source data, and the bias value obtained by performing bias calculation on the matched sample pixels one by one can be used to draw a bias spatial distribution map; The skewness is: where Skewness represents skewness; N represents the number of matching samples; s represents standard deviation; x i represents data to be tested; represents the mean of the data sample to be tested; The kurtosis is: where Kurtosis represents kurtosis; N represents the number of matching samples; s represents standard deviation; x i represents data to be tested; represents the mean of the data sample to be tested; The median is arranging variable values in a sample in order of size to form a sequence, and a variable value in the middle position of the sequence becomes the median; when the number N of variable values is odd, the variable value in the middle position is the median; when N is even, the median is an average of two variable values in the middle position.

7. The strict cloud phase state match based cloud top height verification method of claim 6, wherein, The skewness is a statistical quantity describing data distribution, and a skewness of 0 indicates that the data distribution is the same as the skewness of a normal distribution; a skewness greater than 0 indicates that the data distribution is positively skewed or right skewed compared with the normal distribution, and a skewness less than 0 indicates that the data distribution is negatively skewed or left skewed compared with the normal distribution, and the greater the absolute value of the skewness, the greater the skewness of the distribution.

8. The strict cloud phase state match based cloud top height verification method of claim 6, wherein, The kurtosis is a statistical quantity describing the steepness of the distribution of all values in the population. A kurtosis of 0 indicates that the data distribution of the population is the same as the steepness of the normal distribution. A kurtosis greater than 0 indicates that the data distribution of the population is steeper than the normal distribution, which is a sharp peak. A kurtosis less than 0 indicates that the data distribution of the population is flatter than the normal distribution, which is a flat peak. The greater the absolute value of the kurtosis, the greater the difference between the steepness of the distribution and the normal distribution.

9. The strict cloud phase state match based cloud top height verification method of claim 2, wherein, The accuracy of the test source is tested by the test source, and a test result is obtained. The test result is displayed in the form of a spatial distribution map, a statistical histogram, a time series line chart, a statistical report or a quality inspection report.

10. A cloud top height verification system based on strict cloud phase matching, characterized by, The cloud top height test system based on strict cloud phase state matching is used to perform the cloud top height test method based on strict cloud phase state matching. The test acquisition module is configured to acquire the test source and the testee and perform time-space matching. The time-space data reading module is configured to read time-space data of the test source and the testee after time-space matching. The time-space data includes longitude, latitude, time, cloud top height and cloud phase state data sets of the test source and the testee. The distance calculation module is configured to search for pixels with consistent cloud phase states in the test source and the testee, and calculate distances between pixels of the test source and the testee by using the time-space data. The time difference calculation module is configured to calculate a time difference between the test source and the testee by using time in the time-space data. The data set output statistical module is configured to select pixels with consistent cloud phase states within a preset time difference range and a preset distance range as a matching data set output, and perform statistics on cloud top height products of the same phase state by using the matching data set, to generate statistical indexes of cloud top height test.

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