Pole orbit satellite cloud phase product quality inspection method, device, equipment and medium

Abstract

The application discloses a polar orbit satellite cloud phase state product quality inspection method, device, equipment and medium. The method comprises the following steps: acquiring satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected is polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprises other similar meteorological satellite MODIS data and CALIPSO data. The satellite data to be inspected and the inspection source satellite data are subjected to data processing respectively to obtain target satellite data to be inspected and target inspection source data. According to the target satellite data to be inspected and the target inspection source data, a quality inspection index corresponding to the satellite data to be inspected is determined, and a quality inspection result corresponding to the satellite data to be inspected is determined according to the quality inspection index. The technical scheme of the embodiment of the application builds a business real-time inspection system of satellite cloud phase state products, improves the inspection efficiency and accuracy of data quality, and provides a correction basis for error adjustment of satellite cloud phase state products.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite data, and in particular to a polar-orbit satellite cloud phase product quality inspection method, device, equipment and medium. BACKGROUND

[0002] As a core data source of meteorological monitoring, satellite cloud phase products have been widely used in climate modeling, disaster warning and aviation safety fields. Domestic and foreign satellites such as Fengyun, MODIS, CALIPSO, etc. use infrared spectrum, laser radar and other technologies to retrieve the ice / water phase distribution in the cloud, forming a quantitative product covering the whole world.

[0003] However, the reliability of cloud phase products directly affects the accuracy of meteorological services. Ice cloud misjudgment may lead to failure of aviation icing risk warning, and water cloud recognition deviation will distort the calculation of solar radiation transmission.

[0004] Traditional manual sampling inspection only covers local areas and has poor timeliness (requires several weeks), which cannot meet the business quality monitoring needs of polar-orbit meteorological satellites such as Fengyun satellite 5-minute orbit segment products. Especially in rapidly evolving weather such as typhoons and strong convection, real-time quality defects of cloud phase products may cause systematic errors in the forecast chain, and an automatic and full-chain inspection mechanism is urgently needed. SUMMARY

[0005] The present application provides a polar-orbit satellite cloud phase product quality inspection method, device, equipment and medium to build a business real-time inspection system for satellite cloud phase products, improve the inspection efficiency and accuracy of data quality, and provide correction basis for error adjustment of satellite cloud phase products.

[0006] According to an aspect of the present application, a polar-orbit satellite cloud phase product quality inspection method is provided. The method comprises:

[0007] Obtaining satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected includes polar-orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, the inspection source satellite data includes other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data, and the satellite data to be inspected and the inspection source satellite data both include cloud phase products;

[0008] Respectively processing the satellite data to be inspected and the inspection source satellite data to obtain target satellite data to be inspected and target inspection source data, wherein the data processing includes data preprocessing and data matching processing;

[0009] According to the target to-be-inspected data and the target inspection source data, a quality inspection index corresponding to the to-be-inspected satellite data is determined, and a quality inspection result corresponding to the to-be-inspected satellite data is determined according to the quality inspection index, wherein the quality inspection index at least includes ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate, and overall cloud phase state hit rate.

[0010] According to another aspect of the present application, a polar orbit satellite cloud phase state product quality inspection device is provided. The device comprises:

[0011] A satellite data acquisition module is configured to acquire to-be-inspected satellite data and inspection source satellite data, wherein the to-be-inspected satellite data includes FY-3E, FY-3D and FY-3F satellite data of Fengyun polar orbit meteorological satellite, and the inspection source satellite data includes other internationally recognized high-quality meteorological satellite data MODIS data and CALIPSO data, and the to-be-inspected satellite data and the inspection source satellite data both include cloud phase state products.

[0012] A satellite data processing module is configured to perform data processing on the to-be-inspected satellite data and the inspection source satellite data respectively to obtain target to-be-inspected data and target inspection source data, wherein the data processing includes data preprocessing and data matching processing.

[0013] A data quality inspection module is configured to determine a quality inspection index corresponding to the to-be-inspected satellite data according to the target to-be-inspected data and the target inspection source data, and determine a quality inspection result corresponding to the to-be-inspected satellite data according to the quality inspection index, wherein the quality inspection index at least includes ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate, and overall cloud phase state hit rate.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected with the at least one processor in communication; wherein

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the polar orbit satellite cloud phase state product quality inspection method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the polar orbit satellite cloud phase state product quality inspection method according to any one of the embodiments of the present application when executed by the processor.

[0019] The technical scheme of the embodiment of the application is as follows: obtaining satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected includes FY-3E, FY-3D and FY-3F satellite data of Fengyun polar orbit meteorological satellites, the inspection source satellite data includes other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data, and the satellite data to be inspected and the inspection source satellite data both include cloud phase state products; performing data processing on the satellite data to be inspected and the inspection source satellite data respectively to obtain target satellite data to be inspected and target inspection source data, wherein the data processing includes data preprocessing and data matching processing; determining a quality inspection index corresponding to the satellite data to be inspected according to the target satellite data to be inspected and the target inspection source data, and determining a quality inspection result corresponding to the satellite data to be inspected according to the quality inspection index, thereby solving the error problem caused by real-time quality defects of cloud phase state products, achieving a business real-time inspection system for constructing satellite cloud phase state products, improving the inspection efficiency and accuracy of data quality, and providing a correction basis for error adjustment of satellite cloud phase state products.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0022] Figure 1 is a flow chart of a polar orbit satellite cloud phase state product quality inspection method provided according to the embodiment of the application;

[0023] Figure 2 is a structural diagram of a polar orbit satellite cloud phase state product quality inspection device provided according to the embodiment of the application;

[0024] Figure 3 is a structural diagram of an electronic device for implementing the polar orbit satellite cloud phase state product quality inspection method according to the embodiment of the application. DETAILED DESCRIPTION

[0025] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application in order to make the technical personnel in the technical field better understand the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the ordinary technical personnel in the technical field without creative labor should belong to the protection scope of the present application.

[0026] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include all the steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0027] Figure 1 The flowchart of the polar orbit satellite cloud phase product quality inspection method provided by the embodiments of the present application can be applied to the quality inspection of the cloud phase product of the FY-3 series satellite. The method can be executed by a polar orbit satellite cloud phase product quality inspection device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0028] S101, acquiring satellite data to be inspected and inspection source satellite data.

[0029] The satellite data to be inspected refers to the China Fengyun No. 3 series polar orbit meteorological satellite, which is used for global atmospheric and environmental monitoring. The satellite data to be inspected includes FY-3E, FY-3D and FY-3F satellite data of the Fengyun polar orbit meteorological satellite, and the inspection source satellite data includes other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data. The satellite data to be inspected and the inspection source satellite data both include cloud phase products.

[0030] ​It needs to be explained that in the balance of the earth's atmospheric energy, the cloud has a particularly significant role, and is an important factor affecting climate change. FY-3 includes cloud phase product. FY-3 product quality inspection system mainly uses inspection source (Aqua / Terra MODIS, CALIPSO) cloud phase product to inspect FY-3 satellite cloud phase product.

[0031] Specifically, the satellite data to be inspected and the inspection source satellite data are obtained through the satellite data network. The Fengyun polar orbit meteorological satellite cloud phase product of the satellite data to be inspected is the MERSI product, which includes cloud phase. MERSI is an imager on FY-3, and the imagers MERSI on each satellite of FY-3 have differences in type, but are all called MERSI. The specific information is shown in the following table, and the time resolution of the product is 5 minutes, day, ten, month, the spatial resolution of the product is 1KM segment product and 5KM day, ten, month, and the product coverage is 80° zenith angle range.

[0032] The inspection source of the cloud phase product of the satellite data to be inspected includes the cloud phase products of Aqua / Terra MODIS and CALIPSO. The inspection source of the FY-3 satellite cloud phase product includes the cloud phase products of Aqua / Terra MODIS and CALIPSO.

[0033] The MODIS sensor carried by Aqua and Terra satellites has 36 channels, becoming the first high spatial resolution detector with a carbon dioxide slice band. Aqua / Terra MODIS can comprehensively and consistently synchronously observe the cloud in the atmosphere and its related properties. Moreover, Aqua / Terra MODIS has high-resolution multi-spectral data. The cloud phase element data in the MOD06 / MYD06 product of Aqua / Terra MODIS is used in the project to inspect the quality of the FY-3 cloud phase product. Many experts and scholars use MODIS cloud phase product for related research, for example, Lin Lin et al. research the algorithm of MODIS cloud phase product, Zhao Shiwai and Zhao Zengliang et al. compare and analyze the cloud phase product based on MODIS cloud phase data and numerical mode, etc. Considering the suggestion of the relevant product responsible person and combining the MODIS cloud phase / type product as the cloud phase product quality inspection inspection source of the GOES-R geostationary satellite in the United States, the cloud phase / type product of Aqua / Terra MODIS is taken as the main inspection source of the FY-3 cloud phase product.

[0034] The CALIOP sensor onboard CALIPSO primarily provides data on clouds and aerosols, such as vertical profiles and feature layer distributions. Level 2 cloud products mainly provide the following parameters: layer integrated extinction backscattering, lidar depolarization ratio, cloud top and bottom heights, and cloud optical thickness. Its horizontal resolution is 333 m, 1 km, and 5 km. Quality verification of the FY-3 cloud phase products primarily utilizes the Feature Classification Flag values ​​from the CALIPSO L2 products CAL_LID_L2_01kmCLay_Exp-Prov-V4-30 and CAL_LID_L2_05kmCLay_Exp-Prov-V4-30. Many experts and scholars have used CALIPSO cloud phase products for related research. For example, Huo Juan et al. used CALIPSO cloud products to analyze and study the physical properties of clouds over land and sea. They combined CALIPSO cloud phase products with the US GOES-R geostationary satellite as the verification source of their cloud phase products. Therefore, CALIPSO cloud phase products were used as the secondary verification source of FY-3 cloud phase products.

[0035] S102. The satellite data to be tested and the source satellite data to be tested are processed separately to obtain the target data to be tested and the target source data to be tested.

[0036] Data processing includes data preprocessing and data matching. Data processing mainly includes data decoding of the satellite data to be tested (FY-3 cloud phase product) and data matching of the source satellite data (mainly including time matching, spatial matching and normalization processing).

[0037] Specifically, the data of the satellite to be tested and the data of the source satellite to be tested are processed separately to obtain the target data to be tested and the target source data to be tested.

[0038] For example, the step of processing the satellite data to be inspected and the source satellite data to obtain target data to be inspected and target source data includes:

[0039] The satellite data to be tested and the satellite data to be tested are respectively preprocessed to obtain the first data to be tested and the first data to be tested. The data preprocessing includes data decoding, format conversion and invalid value handling.

[0040] The first data to be tested and the first source data to be tested are respectively subjected to data matching processing to obtain the target data to be tested and the target source data to be tested. The data matching processing includes spatial matching processing, temporal matching processing and normalization processing.

[0041] Specifically, after obtaining the satellite data to be inspected and the satellite data of the inspection source, the satellite data to be inspected and the satellite data of the inspection source are decoded and preprocessed to obtain first satellite data to be inspected and first satellite data of the inspection source. Since the observation time and spatial resolution of the cloud phase product of the satellite data to be inspected and the satellite data of the inspection source are different, then according to the characteristics of the data, the first satellite data to be inspected and the first satellite data of the inspection source are processed by spatial matching, time matching and normalization to obtain target satellite data to be inspected and target satellite data of the inspection source. After data matching, the cloud phase product data of the target satellite data to be inspected and the target satellite data of the inspection source are inspected.

[0042] Exemplarily, the data matching processing of the first satellite data to be inspected and the first satellite data of the inspection source respectively to obtain target satellite data to be inspected and target satellite data of the inspection source comprises:

[0043] The time of the cloud phase product of the first satellite data to be inspected is taken as a reference time, the time of the cloud phase product of the first satellite data of the inspection source is matched with the reference time to obtain second satellite data of the inspection source, and the first satellite data to be inspected is determined as second satellite data to be inspected, wherein the time error of the second satellite data to be inspected and the second satellite data of the inspection source is within a second time threshold range;

[0044] Each pixel of the cloud phase product of the second satellite data to be inspected is taken as a reference pixel, the pixel of the cloud phase product of the first satellite data of the inspection source is matched with the reference pixel to obtain third satellite data of the inspection source, and the second satellite data to be inspected is determined as third satellite data to be inspected, wherein the spatial error of the third satellite data to be inspected and the third satellite data of the inspection source is within a third spatial threshold range;

[0045] The third satellite data of the inspection source and the cloud phase data of the third satellite data of the inspection source are uniformly converted into a standardized classification system to obtain target satellite data to be inspected and target satellite data of the inspection source.

[0046] For different satellite data of the inspection source, the time matching, spatial matching and uniform conversion are different.

[0047] In terms of time matching: in the case of the first test source data being Aqua / Terra MODIS cloud phase products, the second time threshold range is ±5 min. The Terra and Aqua satellites are both equipped with Aqua / Terra MODIS sensors, and can obtain data of the same place once or twice a day, the time resolution of the FY-3 satellite product is 5 min, the product release period of the Aqua / Terra MODIS cloud phase product is 5 min, and the cloud changes quickly, so the time of the FY-3 cloud phase product is taken as the reference, the nearest Aqua / Terra MODIS cloud phase product within the time range of ±5 min (not including the end points) is searched, and time matching is directly performed, so as to obtain the second test source data and the second data to be tested.

[0048] In the case of the first test source data being CALIPSO cloud phase products, the second time threshold range is ±5 min. CALIPSO is a polar orbit satellite, and the cloud phase product release period is 10 min, which is a track product. The FY-3 cloud phase product is once every 5 min, and the cloud changes quickly. Therefore, in this test method, the time of the FY-3 satellite cloud phase product is taken as the reference, the nearest CALIPSO satellite cloud phase product within the range of ±5 min (not including the end points) is searched, and time matching is performed, so as to obtain the second test source data and the second data to be tested.

[0049] In terms of spatial matching: in the case of the second test source data being Aqua / Terra MODIS cloud phase products, the third spatial threshold range is 0.5 km. The spatial resolution of the FY-3 satellite cloud phase product is 1 km, and the resolution of the Aqua / Terra MODIS cloud phase product is 1 km. The spatial matching method searches for the nearest Aqua / Terra MODIS cloud phase product pixel within a range of 0.5 km (i.e. the size of one pixel of the FY-3 cloud top product) according to each cloud phase product pixel of the FY-3 satellite, and performs spatial matching processing, so as to obtain the third test source data and the third data to be tested.

[0050] In the case of the second test source data being CALIPSO cloud phase products, the third spatial threshold range is 1 km. The spatial resolution of the FY-3 satellite cloud phase product is 1 km, and the resolution of the CALIPSO cloud phase product is 1 km. The spatial matching method searches for the nearest CALIPSO cloud phase product pixel within a range of 1 km (i.e. the size of one pixel of the FY-3 cloud top product) according to each cloud phase product pixel of the FY-3 satellite, and performs spatial matching processing, so as to obtain the third test source data and the third data to be tested.

[0051] In data normalization, MODIS is different from FY-3, and a conversion needs to be made first. The cloud phase Cloud_Phase_Infrared output of MODIS: 0 clear sky, 1 water cloud, 2 ice cloud, 3 mixed cloud, 6 is an uncertain phase. The output of Calipso is stored in bits, and in the 6-7 bits of FeatureClassificationFlag, 0 is uncertain, 1 is ice phase, 2 is water phase, and 3 is oriented ice crystals, so that the target to be tested data and the target test source data can be obtained.

[0052] S103, according to the target to be tested data and the target test source data, determining the quality inspection index corresponding to the satellite data to be tested, and determining the quality inspection result corresponding to the satellite data to be tested according to the quality inspection index.

[0053] Among them, the quality inspection index at least includes ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate and overall cloud phase hit rate.

[0054] Specifically, the quality inspection index corresponding to the satellite data to be tested is calculated according to the target to be tested data and the target test source data, and the quality inspection result is exhibited through the quality inspection index.

[0055] Exemplarily, the quality inspection index corresponding to the satellite data to be tested is determined according to the target to be tested data and the target test source data, including: counting the first sample number of ice phase pixels detected by the target to be tested data and the target test source data within a preset space-time; counting the second sample number of cloud phase detection of the target test source data as ice phase pixels, but the cloud phase detection of the target to be tested data as non-ice phase pixels within a preset space-time; counting the third sample number of cloud phase detection of the target to be tested data as ice phase pixels, but the cloud phase detection of the target test source data as non-ice phase pixels within a preset space-time; counting the fourth sample number of water phase pixels detected by the target to be tested data and the target test source data within a preset space-time; determining the quality inspection index corresponding to the satellite data to be tested according to the first sample number, the second sample number, the third sample number and the fourth sample number.

[0056] Among them, the cloud phase evaluation index describes that the cloud phase product of the test source satellite data is taken as a reference, and the cloud phase result of the test source satellite data is selected as ice phase, and the cloud phase result of the satellite data to be tested is non-ice phase type, which is defined as a missed judgment; the cloud phase result of the test source is non-ice phase type, and the cloud phase result of the satellite data to be tested is ice phase, which is defined as a false judgment.

[0057] Specifically, the detailed process of calculating the quality inspection index of the FY-3 satellite cloud phase product is as follows: first, the samples that are cloud pixels and are non-uncertain pixels in both the inspection source and the FY-3 identification result are selected, where the first sample number a represents the number of samples in the same spatial range in the matching time period that are detected as ice phase by both the FY-3 satellite data and the inspection source; the second sample number b represents the number of samples that are detected as ice phase by the inspection source but as non-ice phase by the FY-3, that is, the number of samples of the FY-3 cloud phase missed identification; the third sample number c represents the number of samples that are detected as non-ice phase by the inspection source but as ice phase by the FY-3, that is, the number of samples of the FY-3 cloud phase misidentification; and the fourth sample number d represents the number of samples that are detected as water phase by both the FY-3 satellite and the inspection source. Finally, the quality inspection index is calculated based on the first sample number a, the second sample number b, the third sample number c, and the fourth sample number d.

[0058] Exemplarily, the determining the quality inspection index corresponding to the satellite data to be inspected based on the first sample number, the second sample number, the third sample number, and the fourth sample number comprises: determining an ice phase accuracy rate corresponding to the satellite data to be inspected based on the first sample number and the second sample number; determining a water phase accuracy rate corresponding to the satellite data to be inspected based on the first sample number, the third sample number, and the fourth sample number; determining an ice phase false alarm rate corresponding to the satellite data to be inspected based on the first sample number and the third sample number; determining a water phase false alarm rate corresponding to the satellite data to be inspected based on the second sample number and the fourth sample number; and determining an overall cloud phase hit rate corresponding to the satellite data to be inspected based on the first sample number, the second sample number, the third sample number, and the fourth sample number.

[0059] It should be noted that the quality inspection index can include the ice phase accuracy rate, the water phase accuracy rate, the ice phase false alarm rate, the water phase false alarm rate, and the overall cloud phase hit rate.

[0060] Specifically, the ice phase accuracy rate is calculated based on the first sample number a and the second sample number b; the water phase accuracy rate is calculated based on the first sample number a, the third sample number c, and the fourth sample number d; the ice phase false alarm rate is calculated based on the first sample number a and the third sample number c; the water phase false alarm rate is calculated based on the second sample number b and the fourth sample number d; and the overall cloud phase hit rate is calculated based on the first sample number a, the second sample number b, the third sample number c, and the fourth sample number d.

[0061] Exemplarily, the calculation of each quality inspection index is realized in the following manner:

[0062] The ice phase accuracy rate POD_ice is calculated as follows:

[0063] The water phase accuracy rate POD_water is calculated as follows:

[0064] Accuracy of water phase POD_water:

[0065]

[0066] False alarm rate of ice phase FAR_ice:

[0067]

[0068] False alarm rate of water phase FAR_water:

[0069]

[0070] Overall cloud phase hit rate HR:

[0071]

[0072] KSS score:

[0073] KSS = 1 - (a + b) / (c + d) , ,

[0074] Wherein: a refers to the first sample number, b refers to the second sample number, c refers to the third sample number, and d refers to the fourth sample number.

[0075] It is worth noting that the technical scheme of the embodiment of the present application can customize different spatial windows for quality inspection of satellite product cloud phase product according to actual needs. For example, the customized spatial window can include a key area (such as China and the surrounding area) and a customized area (defined according to the latitude and longitude coordinate range).

[0076] For example, the display form of the quality inspection result includes a spatial distribution map, a deviation statistical chart, a histogram of identification indexes, a sequence line chart of different identification indexes changing with time, a statistical table and a quality inspection report. The deviation spatial distribution also needs to add a 5-minute segment drawing. When MODIS is used as the inspection source, the 1km and 5km MODIS products need to be inspected and calculated respectively.

[0077] (1) Spatial distribution map

[0078] The deviation of the satellite data to be inspected and the inspection source satellite data can be intuitively displayed through the spatial distribution map. The user can choose to view the spatial distribution of the cloud phase product deviation of the satellite data to be inspected, which can be displayed in the form of dynamic playing or static display. The user can also set the specific latitude and longitude range according to the actual needs to display the selected area in detail.

[0079] (2) Deviation histogram

[0080] The inspection results (bias) of the satellite data cloud phase state product to be inspected can be visually displayed by a statistical histogram. In addition, users can select the area of their interest according to actual needs to display the statistical histogram.

[0081] (3) Time series line chart

[0082] The errors of the satellite data cloud phase state product to be inspected can be visually displayed by a time series line chart. Users can visually view the real-time quality change of the product. In addition, users can check the inspection source data according to actual needs to superimpose the results of quality inspection using different inspection sources.

[0083] (4) Statistical table

[0084] The errors of the satellite data cloud phase state product to be inspected can be visually displayed by a statistical table.

[0085] (5) Quality inspection report

[0086] The quality inspection results of the satellite data cloud phase state product to be inspected are made in the form of a monthly report.

[0087] After the spatiotemporal matching of the data to be inspected and the inspection source data, the matching results are stored in hdf format by day.

[0088] When the cloud phase 5min segment product takes the Aqua MODIS cloud phase data as the test source, the storage result is discrete point data, and the data set includes: time (FY_Date), row (FY_Line), column (FY_Pixel), longitude (FY_Lon), latitude (FY_Lat), cloud phase (FY_CPH), cloud type (FY_CTY), cloud type and phase QA (FY_CPT_QA), cloud detection (FY_CLM), day and night identifier (FY_DNFlag, D / N), underlying surface classification (FY_Underlying_Surface), 8.5 and 11um channel brightness temperature difference (FY_BTD8.5-11), 11 and 12um channel brightness temperature difference (FY_BTD11-12), 7.2um channel brightness temperature (FY_BT7.2), 11um channel brightness temperature (FY_BT11), Aqua MODIS row (AQ_Line) column (AQ_Pixel), Aqua MODIS longitude (AQ_Lon), Aqua MODIS latitude (AQ_Lat), Aqua MODIS cloud phase (AQ_CPT), Aqua MODIS 1KM cloud phase (AQ_CPT_1KM), Aqua MODIS multi-layer cloud identifier (AQ_Multi_Flag); there are also related variables of Terra, and the test source (Aqua / Terra MODIS, CALIPSO, ground-based cloud radar, FY-3D and FY-4A) stores discrete point data, and finally all day results are output and sent to the archive.

[0089] The technical scheme of the embodiment of the application, by acquiring satellite data to be tested and test source satellite data, wherein the satellite data to be tested includes Fengyun polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the test source satellite data includes other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data, and the satellite data to be tested and the test source satellite data both include cloud phase products; the satellite data to be tested and the test source satellite data are respectively subjected to data processing to obtain target satellite data to be tested and target test source data, wherein the data processing includes data preprocessing and data matching processing; according to the target satellite data to be tested and the target test source data, a quality test index corresponding to the satellite data to be tested is determined, and a quality test result corresponding to the satellite data to be tested is determined according to the quality test index, so that the error problem caused by real-time quality defects of the cloud phase product is solved, a business real-time test system for constructing satellite cloud phase products is obtained, the test efficiency and accuracy of data quality are improved, and a correction basis is provided for error adjustment of the satellite cloud phase product.

[0090] Figure 2A structural schematic diagram of a polar orbit satellite cloud phase state product quality inspection device provided by an embodiment of the present application is shown in FIG. 1. Figure 2 As shown in the figure, the device comprises:

[0091] A satellite data acquisition module 301 is configured to acquire satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected comprises Fengyun polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprises other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data, and the satellite data to be inspected and the inspection source satellite data both comprise cloud phase state products.

[0092] A satellite data processing module 302 is configured to perform data processing on the satellite data to be inspected and the inspection source satellite data respectively to obtain target satellite data to be inspected and target inspection source data, wherein the data processing comprises data preprocessing and data matching processing.

[0093] A data quality inspection module 303 is configured to determine a quality inspection index corresponding to the satellite data to be inspected according to the target satellite data to be inspected and the target inspection source data, and determine a quality inspection result corresponding to the satellite data to be inspected according to the quality inspection index, wherein the quality inspection index at least comprises ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate and overall cloud phase state hit rate.

[0094] The technical scheme of the embodiment of the present application, by acquiring satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected comprises Fengyun polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprises other internationally recognized high-quality meteorological satellite MODIS data and CALIPSO data, and the satellite data to be inspected and the inspection source satellite data both comprise cloud phase state products, performing data processing on the satellite data to be inspected and the inspection source satellite data respectively to obtain target satellite data to be inspected and target inspection source data, wherein the data processing comprises data preprocessing and data matching processing, determining a quality inspection index corresponding to the satellite data to be inspected according to the target satellite data to be inspected and the target inspection source data, and determining a quality inspection result corresponding to the satellite data to be inspected according to the quality inspection index, solves the error problem caused by real-time quality defects of cloud phase state products, achieves a business real-time inspection system for constructing satellite cloud phase state products, improves the inspection efficiency and accuracy of data quality, and provides a correction basis for error adjustment of satellite cloud phase state products.

[0095] Optionally, the satellite data processing module 302 comprises:

[0096] a data processing unit, configured to perform data preprocessing on the satellite data to be verified and the satellite data of the verification source respectively to obtain first satellite data to be verified and first satellite data of the verification source, wherein the data preprocessing comprises data decoding, format conversion and invalid value processing;

[0097] a data matching unit, configured to perform data matching processing on the first satellite data to be verified and the first satellite data of the verification source respectively to obtain target satellite data to be verified and target satellite data of the verification source, wherein the data matching processing comprises spatial matching processing, time matching processing and normalization processing.

[0098] Optionally, the data matching unit is specifically configured to:

[0099] match the time of the cloud phase product of the first satellite data to be verified with the time of the cloud phase product of the first satellite data of the verification source to obtain second satellite data of the verification source, and determine the first satellite data to be verified as second satellite data to be verified, wherein the time error between the second satellite data to be verified and the second satellite data of the verification source is within a second time threshold range;

[0100] match each pixel of the cloud phase product of the second satellite data to be verified with the pixel of the cloud phase product of the first satellite data of the verification source to obtain third satellite data of the verification source, and determine the second satellite data to be verified as third satellite data to be verified, wherein the spatial error between the third satellite data to be verified and the third satellite data of the verification source is within a third spatial threshold range;

[0101] convert the third satellite data of the verification source and the cloud phase data of the third satellite data of the verification source into a standardized classification system to obtain the target satellite data to be verified and the target satellite data of the verification source.

[0102] Optionally, the data quality verification module 303 comprises:

[0103] a first sample number determination unit, configured to count a first sample number of ice phase pixels detected by the target satellite data to be verified and the target satellite data of the verification source within a preset time and space;

[0104] a second sample number determination unit, configured to count a second sample number of ice phase pixels detected by the target satellite data of the verification source but non-ice phase pixels detected by the target satellite data to be verified within the preset time and space;

[0105] a third sample number determination unit, configured to count a third sample number of ice phase pixels detected by the target satellite data to be verified but non-ice phase pixels detected by the target satellite data of the verification source within the preset time and space;

[0106] A fourth sample number determination unit is configured to count a fourth sample number of the target data to be inspected and the water phase pixel detected within a preset space-time.

[0107] A quality inspection index determination unit is configured to determine a quality inspection index corresponding to the satellite data to be inspected according to the first sample number, the second sample number, the third sample number and the fourth sample number.

[0108] Optionally, the quality inspection index determination unit is specifically configured to:

[0109] determine an ice phase accuracy rate corresponding to the satellite data to be inspected according to the first sample number and the second sample number;

[0110] determine a water phase accuracy rate corresponding to the satellite data to be inspected according to the first sample number, the third sample number and the fourth sample number;

[0111] determine an ice phase false alarm rate corresponding to the satellite data to be inspected according to the first sample number and the third sample number;

[0112] determine a water phase false alarm rate corresponding to the satellite data to be inspected according to the second sample number and the fourth sample number;

[0113] determine an overall cloud phase state hit rate corresponding to the satellite data to be inspected according to the first sample number, the second sample number, the third sample number and the fourth sample number.

[0114] Optionally, the data quality inspection module 303 is further configured to:

[0115] an ice phase accuracy rate POD_ice:

[0116]

[0117] a water phase accuracy rate POD_water:

[0118]

[0119] an ice phase false alarm rate FAR_ice:

[0120]

[0121] a water phase false alarm rate FAR_water:

[0122]

[0123] an overall cloud phase state hit rate HR:

[0124]

[0125] KSS score: KSS = ,

[0126] Wherein, a refers to the first sample number, b refers to the second sample number, c refers to the third sample number, and d refers to the fourth sample number.

[0127] Optionally, the display form of the quality inspection result comprises a spatial distribution map, a deviation statistical chart, a histogram of a discrimination index, a sequence line chart of different discrimination indexes changing over time, a statistical table and a quality inspection report.

[0128] The polar orbit satellite cloud phase product quality inspection device provided by the embodiment of the application can execute the polar orbit satellite cloud phase product quality inspection method provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0129] Embodiment four.

[0130] Figure 3 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the applications described and / or claimed in this document.

[0131] As shown in Figure 3 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0133] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the polar-orbiting satellite cloud phase product quality inspection method.

[0134] In some embodiments, the polar-orbiting satellite cloud phase product quality inspection method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the polar-orbiting satellite cloud phase product quality inspection method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the polar-orbiting satellite cloud phase product quality inspection method by any other appropriate means, such as by means of firmware.

[0135] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0136] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0137] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0139] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0140] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0141] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in series, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0142] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for quality checking of polar orbiting satellite cloud phase product, characterized in that, include: Acquire satellite data to be tested and source satellite data, wherein the satellite data to be tested includes polar-orbiting meteorological satellite data FY-3E, FY-3D and FY-3F, and the source satellite data includes other similar meteorological satellite data MODIS and CALIPSO data. Both the satellite data to be tested and the source satellite data include cloud phase products. The data to be tested and the data to be tested from the source satellite are processed separately to obtain the target data to be tested and the target data to be tested. The data processing includes data preprocessing and data matching processing. Based on the target data to be inspected and the target inspection source data, the quality inspection indicators corresponding to the satellite data to be inspected are determined, and the quality inspection results corresponding to the satellite data to be inspected are determined based on the quality inspection indicators. The quality inspection indicators include at least ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate, and overall cloud phase hit rate. The step of processing the satellite data to be tested and the source satellite data to obtain the target data to be tested and the target source data includes: The satellite data to be tested and the satellite data to be tested are respectively preprocessed to obtain the first data to be tested and the first data to be tested. The data preprocessing includes data decoding, format conversion and invalid value handling. The first data to be tested and the first source data to be tested are respectively subjected to data matching processing to obtain the target data to be tested and the target source data to be tested. The data matching processing includes spatial matching processing, temporal matching processing and normalization processing. The step of performing data matching processing on the first data to be tested and the first source data to be tested to obtain target data to be tested and target source data includes: The time of the cloud phase product of the first data to be tested is used as the reference time. The time of the cloud phase product of the first source data to be tested is matched with the reference time to obtain the second source data to be tested. The first data to be tested is determined as the second data to be tested. The time error between the second data to be tested and the second source data to be tested is within the second time threshold range. Each pixel of the cloud phase product of the second data to be tested is used as a reference pixel. The pixels of the cloud phase product of the first source data are matched with the reference pixels to obtain the third source data. The second data to be tested is determined as the third data to be tested. The spatial error between the third data to be tested and the third source data is within the range of the third spatial threshold. The third inspection source data and the cloud phase data of the third inspection source data are uniformly converted into a standardized classification system to obtain the target inspection data and the target inspection source data.

2. The method of claim 1, wherein, The step of determining the quality inspection indicators corresponding to the satellite data to be inspected based on the target data to be inspected and the target source data to be inspected includes: The number of target data to be tested and the number of first samples in which ice phase pixels were detected are counted within a preset time and space. The number of second samples in which the cloud phase detection of the target test source data is icy phase pixels, but the cloud phase detection of the target test data is non-icy phase pixels, is counted within a preset time and space. The number of third samples in which the cloud phase detection of the target data to be tested is icy phase pixels, but the cloud phase detection of the target test source data is non-icy phase pixels, is counted within a preset time and space. The number of the target data to be tested and the number of the fourth sample in which water phase pixels were detected are counted within the preset time and space. The quality inspection indicators corresponding to the satellite data to be inspected are determined based on the number of the first sample, the number of the second sample, the number of the third sample, and the number of the fourth sample.

3. The method according to claim 2, characterized in that, The step of determining the quality inspection indicators corresponding to the satellite data to be inspected based on the number of the first sample, the number of the second sample, the number of the third sample, and the number of the fourth sample includes: Based on the number of the first sample and the number of the second sample, determine the ice phase accuracy corresponding to the satellite data to be tested; The water phase accuracy corresponding to the satellite data to be tested is determined based on the number of the first sample, the number of the third sample, and the number of the fourth sample. The false alarm rate of the ice phase corresponding to the satellite data to be tested is determined based on the number of the first sample and the number of the third sample. The water phase false alarm rate corresponding to the satellite data to be tested is determined based on the number of the second sample and the number of the fourth sample. The overall cloud phase hit rate corresponding to the satellite data to be tested is determined based on the number of the first sample, the number of the second sample, the number of the third sample, and the number of the fourth sample.

4. The method according to claim 3, characterized in that, The step of determining the quality inspection indicators corresponding to the satellite data to be inspected based on the number of the first sample, the number of the second sample, the number of the third sample, and the number of the fourth sample includes: Ice phase accuracy POD_ice: ， Water phase accuracy POD_water: ， Ice phase false alarm rate (FAR_ice): ， False Alarm Rate (FAR_water): ， Overall cloud phase hit rate (HR): ， KSS rating: KSS= , , Where a refers to the number of the first sample, b refers to the number of the second sample, c refers to the number of the third sample, and d refers to the number of the fourth sample.

5. The method according to claim 1, characterized in that, The quality inspection results are presented in the following formats: spatial distribution map, deviation statistics chart, histogram of identification indicators, line graph of the change sequence of different identification indicators over time, statistical table, and quality inspection report.

6. A quality inspection device for polar-orbiting satellite cloud phase products, characterized in that, include: The satellite data acquisition module is used to acquire satellite data to be tested and source satellite data. The satellite data to be tested is cloud phase product data of polar-orbiting meteorological satellites, including FY-3E, FY-3D and FY-3F satellite data. The source satellite data includes MODIS data and CALIPSO data. Both the satellite data to be tested and the source satellite data include cloud phase products. The satellite data processing module is used to process the satellite data to be inspected and the source satellite data to be inspected to obtain target data to be inspected and target source data to be inspected. The data processing includes data preprocessing and data matching processing. The data quality inspection module is used to determine the quality inspection indicators corresponding to the satellite data to be inspected based on the target data to be inspected and the target inspection source data, and to determine the quality inspection results corresponding to the satellite data to be inspected based on the quality inspection indicators. The quality inspection indicators include at least ice phase accuracy, water phase accuracy, ice phase false alarm rate, water phase false alarm rate and overall cloud phase hit rate. The satellite data processing module includes: The data processing unit is used to preprocess the satellite data to be tested and the satellite data to be tested to obtain the first data to be tested and the first data to be tested. The data preprocessing includes data decoding, format conversion and invalid value processing. The data matching unit is used to perform data matching processing on the first data to be tested and the first test source data respectively to obtain the target data to be tested and the target test source data. The data matching processing includes spatial matching processing, temporal matching processing and normalization processing. The data matching unit is specifically used for: The time of the cloud phase product of the first data to be tested is used as the reference time. The time of the cloud phase product of the first source data to be tested is matched with the reference time to obtain the second source data to be tested. The first data to be tested is determined as the second data to be tested. The time error between the second data to be tested and the second source data to be tested is within the second time threshold range. Each pixel of the cloud phase product of the second data to be tested is used as a reference pixel. The pixels of the cloud phase product of the first source data are matched with the reference pixels to obtain the third source data. The second data to be tested is determined as the third data to be tested. The spatial error between the third data to be tested and the third source data is within the range of the third spatial threshold. The third inspection source data and the cloud phase data of the third inspection source data are uniformly converted into a standardized classification system to obtain the target inspection data and the target inspection source data.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the polar-orbiting satellite cloud phase product quality inspection method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the polar-orbiting satellite cloud phase product quality inspection method according to any one of claims 1-5.

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