Dense high ice cloud detection method and system based on water vapor and ice crystal absorption characteristics

Abstract

The present invention proposes a dense high ice cloud detection method and system based on water vapor and ice crystal absorption characteristics, the method comprising: obtaining water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data according to remote sensing image data of a target area from a meteorological satellite; calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data; calculating a cloud vapor index according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data; calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index to identify whether there is a dense high ice cloud in the target area. The present invention uses water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data to realize the detection and enhancement of dense high ice clouds. Compared with the prior art, it reduces misjudgment and interference, improves the recognition accuracy of dense high ice clouds, and thus makes weather forecasts more accurate.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and in particular to a method and system for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics. Background Art

[0002] As the main means of space-based detection, meteorological satellites are playing an increasingly important role in meteorological observation, disaster prevention and mitigation, and weather forecasting, especially in the field of weather forecasting. Meteorological satellites can be used to detect the origin, development direction and movement direction of typhoons, as well as the source of sandstorms, the evolution direction of plateau vortices, and the change direction of the Antarctic ozone hole. Meteorological satellite data can more effectively reveal the occurrence, development and extinction process of weather systems, and provide an objective basis for weather forecasting, especially short-term and nowcasting. While supporting modern weather forecasting, meteorological satellites are also promoting the development of Earth system science.

[0003] Geostationary meteorological satellites fly around the equator at the same speed as the Earth's rotation. Using the spectral information of different channels of meteorological satellites, high-frequency tracking and monitoring of relatively large-scale environmental and disaster events can be completed. Geostationary meteorological satellites can continuously observe the same area at a very high observation frequency, and are particularly suitable for monitoring rapidly changing small and medium-scale weather systems.

[0004] In high-impact weather such as heavy precipitation in summer, convective precipitation is often completed in the strong upward movement of the atmosphere. As the atmosphere rises rapidly in altitude, the temperature drops sharply, a large number of ice crystals appear, and the convective cloud tops show the characteristics of being dense and having many ice crystals. Dense high ice clouds often correspond to heavy precipitation weather in the cloud-covered area. Therefore, timely and accurate detection of dense high ice clouds is of great significance to weather forecasting. Summary of the invention

[0005] The present invention provides a dense high ice cloud detection method and system based on water vapor and ice crystal absorption characteristics, the main purpose of which is to provide a dense high ice cloud identification method to effectively improve the accuracy of weather forecasts.

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics, comprising:

[0007] According to the remote sensing image data of the meteorological satellite for the target area, the water vapor absorption spectrum detection data, the ice crystal absorption spectrum detection data and the atmospheric window area spectrum detection data are obtained;

[0008] Calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data;

[0009] Calculating a cloud vapor index based on the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data;

[0010] Calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index;

[0011] Based on the dense high ice cloud index, the location of the dense high ice cloud in the target area is determined.

[0012] Furthermore, the water vapor absorption spectrum detection data is remote sensing detection data of a 1.3um water vapor absorption spectrum, the ice crystal absorption spectrum detection data is remote sensing detection data of a 1.6um ice crystal absorption spectrum, and the atmospheric window spectrum detection data is remote sensing detection data of a 0.6um atmospheric window spectrum. The steps of obtaining water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data, and atmospheric window spectrum detection data based on remote sensing image data of a target area from a meteorological satellite include:

[0013] Collecting remote sensing image data of the target area by the meteorological satellite;

[0014] Performing radiation correction and geographic correction on the remote sensing image data to obtain corrected remote sensing image data;

[0015] For the corrected remote sensing image data, the remote sensing detection data of the 1.3um water vapor absorption spectrum, the remote sensing detection data of the 1.6um ice crystal absorption spectrum and the remote sensing detection data of the 0.6um atmospheric window spectrum are extracted.

[0016] Furthermore, the ice vapor index is calculated based on the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data, and the calculation formula is as follows:

[0017]

[0018] Among them, I 1316 represents the ice vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 16 Represents the ice crystal absorption spectrum detection data.

[0019] Furthermore, the cloud vapor index is calculated according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data, and the calculation formula is as follows:

[0020]

[0021] Among them, I 1306 represents the cloud vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 06 Represents the atmospheric window spectral detection data.

[0022] Furthermore, the dense high ice cloud index is calculated according to the ice vapor index and the cloud vapor index, and the calculation formula is as follows:

[0023] I′ ConvCloud =I 1306 *I 1316 ;

[0024] Among them, I′ ConvCloud represents the dense high ice cloud index, I 1316 represents the ice vapor index, I 1306 represents the cloud vapor index.

[0025] Further, the step of determining the location of dense high ice clouds in the target area based on the dense high ice cloud index comprises:

[0026] Compare the dense high ice cloud index of each detection data in the remote sensing image data of the target area with 0.9 and 0.6;

[0027] If the dense high ice cloud index of any detection data is greater than 0.9, the position corresponding to the any detection data is determined to be a dense high ice cloud, and the position of the dense high ice cloud in the target area is determined;

[0028] If the dense high ice cloud index of any detection data is between 0.6 and 0.9, the position corresponding to any detection data is determined to be a suspected dense high ice cloud.

[0029] Further, after identifying whether there are dense high ice clouds in the target area according to the dense high ice cloud index, the method further includes:

[0030] The dense high ice cloud index of the remote sensing image data of the target area is normalized, and the calculation formula is:

[0031] Among them, I min =0.6, I max =1.1,I ConvCloud is the normalized dense high ice cloud index, I′ ConvCloud is the dense high ice cloud index;

[0032] The normalized dense high ice cloud index is used to visualize the remote sensing image data.

[0033] In a second aspect, an embodiment of the present invention provides a dense high ice cloud detection system based on water vapor and ice crystal absorption characteristics, comprising:

[0034] An acquisition module is used to obtain water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data based on remote sensing image data of the target area from meteorological satellites;

[0035] An ice vapor module, used for calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data;

[0036] A cloud vapor module, used for calculating a cloud vapor index according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data;

[0037] A high ice cloud module, used for calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index;

[0038] An identification module is used to determine the location of dense high ice clouds in the target area based on the dense high ice cloud index.

[0039] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for detecting dense high ice clouds based on the absorption characteristics of water vapor and ice crystals when executing the computer program.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting dense high ice clouds based on the absorption characteristics of water vapor and ice crystals.

[0041] The embodiment of the present invention proposes a dense high ice cloud detection method and system based on water vapor and ice crystal absorption characteristics, which generates a dense high ice cloud detection index by using water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data, and determines whether there is a dense high ice cloud in the target area and the location of the dense high ice cloud according to the dense high ice cloud detection index of each detection data in the remote sensing image data, thereby realizing the detection and enhancement of dense high ice clouds. Compared with the prior art, it reduces misjudgment and interference, improves the recognition accuracy of dense high ice clouds, and thus makes weather forecasts more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics is provided for an embodiment of the present invention;

[0043] Figure 2 for Figure 1 Schematic diagram of the process of step S10;

[0044] Figure 3This is a schematic diagram of remote sensing detection data of 1.3um water vapor absorption spectrum collected by FY-4B / GHI in an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of remote sensing detection data of 1.6um ice crystal absorption spectrum collected by FY-4B / GHI in an embodiment of the present invention;

[0046] Figure 5 It is a schematic diagram of remote sensing detection data of the 0.6um atmospheric window spectral band collected by FY-4B / GHI in an embodiment of the present invention;

[0047] Figure 6 A schematic diagram of an ice vapor index provided by an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of a cloud vapor index provided by an embodiment of the present invention;

[0049] Figure 8 A schematic diagram of a dense high ice cloud index provided by an embodiment of the present invention;

[0050] Fig. 9 A schematic diagram of a dense high ice cloud index provided by an embodiment of the present invention;

[0051] Fig.10 A schematic diagram of a pseudo-color image of a dense high ice cloud index provided by an embodiment of the present invention;

[0052] Fig.11 A schematic diagram of a dense high ice cloud monitoring cloud map provided by an embodiment of the present invention;

[0053] Fig.12 A schematic diagram of the structure of a dense high ice cloud detection system based on water vapor and ice crystal absorption characteristics provided by an embodiment of the present invention;

[0054] Fig.13 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0057] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0058] In the embodiments of the present application, at least one refers to one or more; multiple refers to two or more. In the description of the present application, words such as "first", "second", "third", etc. are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0059] References to "one embodiment" or "some embodiments" etc. described in this specification mean that one or more embodiments of the present application include a particular feature, structure or characteristic described in conjunction with the embodiment. Thus, in this specification, the terms "include", "comprises", "has" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0060] In the field of meteorology, mesoscale moving clouds are the main cause of heavy rain. Mesoscale moving clouds are characterized by rapid development and small scale. Conventional observations are not enough to capture the details of changes in their life history and their structure. Therefore, satellite remote sensing has become a very important technical means in monitoring, near-term / short-term forecasting and other businesses.

[0061] The infrared spectrum threshold method is widely used to identify rainstorm clouds, and the brightness temperature of the thermal infrared spectrum near 11um is generally used as the physical quantity for investigation. Clouds with a cloud top brightness temperature ≤-32℃ are generally considered to be convective clouds, and when the cloud top brightness temperature is ≤-52℃, it is considered that the clouds have passed through the tropopause, the convection is very vigorous, and severe convective weather phenomena will also occur. The scale range, duration and shape of mesoscale clouds are the basis for the classification of mesoscale clouds.

[0062] In the prior art, convective clouds appear as thick white blocks in true color images, and true color remote sensing images are generally used to identify the distribution of various types of clouds in the image through manual experience. However, infrared cloud images use the brightness temperature method to distinguish convective cloud clusters. However, tall thin cirrus clouds are also a type of cloud with a lower temperature, so there are more misjudgments and interferences, resulting in low recognition accuracy of dense high ice clouds; and it is difficult to implement an automated algorithm for convective cloud identification in true color remote sensing images, and it is common for convective clouds to interfere with stratus clouds, high cirrus clouds, etc.

[0063] In response to the above problems, Figure 1 A flow chart of a dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics is provided for an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0064] S10: According to the remote sensing image data of the meteorological satellite for the target area, the water vapor absorption spectrum detection data, the ice crystal absorption spectrum detection data and the atmospheric window area spectrum detection data are obtained;

[0065] Fengyun-4B (FY-4B for short) is the second satellite in the Fengyun-4 geostationary meteorological satellite series. Fengyun-4B is equipped with a rapid imager, which provides regional high-resolution day and night continuous rapid monitoring with an interval better than 1 minute for short-term nowcasting needs; a multi-channel scanning imaging radiometer, which provides high-precision multi-spectral monitoring services for weather forecast and warning; an interferometric atmospheric vertical sounder, which provides three-dimensional atmospheric change information for numerical weather forecasting; and a space environment instrument package, which can carry out comprehensive detection of the solar and space environment.

[0066] Taking the FY-4B satellite as an example, the FY-4B satellite is equipped with a rapid imager, which is the world's first quantitative remote sensing instrument for high-frequency imaging day and night in geostationary orbit, and can provide 1-minute interval multi-spectral continuous observations in an area of ​​2000×2000km. Table 1 is a schematic diagram of the wavelength range of the main channels of FY-4B / GHI. As shown in Table 1, the spectral detection range is between 445-12500nm, the maximum spatial resolution is 250m, the temporal resolution is 1min, the coverage area is 2000km×2000km, and there are 7 visible and infrared channels.

[0067] Table 1

[0068]

[0069]

[0070] In the embodiment of the present invention, the FY-4B meteorological satellite and the rapid imager carried thereon are used as examples for explanation. The target area is an area requiring weather forecast, such as a city, and remote sensing image data of the target area in the FY-4B is collected.

[0071] As an implementation method, Figure 2 for Figure 1 The flow chart of step S10 is as follows: Figure 2 As shown, in step S10, the water vapor absorption spectrum detection data is the remote sensing detection data of the 1.3um water vapor absorption spectrum, the ice crystal absorption spectrum detection data is the remote sensing detection data of the 1.6um ice crystal absorption spectrum, and the atmospheric window spectrum detection data is the remote sensing detection data of the 0.6um atmospheric window spectrum. The steps of obtaining the water vapor absorption spectrum detection data, the ice crystal absorption spectrum detection data and the atmospheric window spectrum detection data according to the remote sensing image data of the target area by the meteorological satellite include:

[0072] S11: Collecting remote sensing image data of the target area from the meteorological satellite;

[0073] Specifically, FY-4B / GHI data are collected, and remote sensing image data of multiple times a day are obtained for the same target area, and the remote sensing image data are sorted according to the imaging time. The remote sensing image data are visible and near-infrared remote sensing images, which include spectral band data, gain and offset of different bands, solar altitude angle of the image, and image geographic information, wherein the image geographic information includes projection information and geometric information.

[0074] S12: performing radiation correction and geographic correction on the remote sensing image data to obtain corrected remote sensing image data;

[0075] Radiometric correction is performed on each remote sensing image data so that the pixel grayscale value of the remote sensing image data is converted into the apparent reflectance of the upper boundary of the atmosphere of the observed target with physical dimension. Specifically, according to the different band gain, bias and solar altitude angle of each visible light remote sensing image data center, each band in each remote sensing image data is converted from the pixel grayscale value of the remote sensing image data into the apparent reflectance value, see formula (1) and (2).

[0076] L = gain × DN + bias, (1)

[0077] Where L is the irradiance, DN is the pixel grayscale value of the remote sensing image data, gain and bias are the gain and bias values ​​corresponding to the sensor respectively.

[0078]

[0079] Where ref is the apparent reflectivity of the top of the atmosphere (i.e., the remote sensing image data after radiation correction), d is the distance between the sun and the earth, with the unit of 1AU, i.e., 1 astronomical unit; E0 is the solar irradiance, and each band corresponds to a different solar irradiance; θ is the solar zenith angle.

[0080] Then the radiometrically corrected remote sensing image data is geo-corrected to obtain the corrected remote sensing image data. Through geo-correction, the geographic coordinates of each image can be unified, and the geographic location and spatial resolution of all images can be kept consistent.

[0081] S13: For the corrected remote sensing image data, extract the remote sensing detection data of the 1.3um water vapor absorption spectrum, the remote sensing detection data of the 1.6um ice crystal absorption spectrum and the remote sensing detection data of the 0.6um atmospheric window spectrum.

[0082] In the embodiment of the present invention, the remote sensing detection data of the 1.3um water vapor absorption spectrum (abbreviated as ref 13 ) as the water vapor absorption spectrum detection data, and the remote sensing detection data of the 1.6um ice crystal absorption spectrum (abbreviated as ref 16 ) as the ice crystal absorption spectrum detection data, and the remote sensing detection data of the 0.6um atmospheric window spectrum (abbreviated as ref 06 ) as the atmospheric window spectral detection data.

[0083] For example, Figure 3 This is a schematic diagram of remote sensing detection data of the 1.3um water vapor absorption spectrum collected by FY-4B / GHI in an embodiment of the present invention. Figure 4 This is a schematic diagram of remote sensing detection data of the 1.6um ice crystal absorption spectrum collected by FY-4B / GHI in an embodiment of the present invention. Figure 5 Schematic diagram of remote sensing detection data of the 0.6um atmospheric window spectral band collected by FY-4B / GHI in an embodiment of the present invention, as shown in FIG. Figures 3 to 5 As shown in the figure, different colors are used to represent different numerical values. The comparison relationship can be seen from the comparison axis on the right side of the figure.

[0084] S20: calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data;

[0085] As an implementation manner, the ice vapor index is calculated according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data, as shown in formula (3):

[0086]

[0087] Among them, I 1316 represents the ice vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 16 Represents the ice crystal absorption spectrum detection data.

[0088] Figure 6 A schematic diagram of an ice vapor index provided by an embodiment of the present invention, such as Figure 6As shown in the figure, different colors are used to represent the sizes of different ice vapor indexes, and their comparison relationship can be found in the comparison axis on the right side of the figure.

[0089] S30: Calculating a cloud vapor index according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data;

[0090] As an implementation manner, the cloud vapor index is calculated according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data, as shown in formula (4):

[0091]

[0092] Among them, I 1306 represents the cloud vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 06 Represents the atmospheric window spectral detection data.

[0093] Figure 7 A schematic diagram of a cloud vapor index provided by an embodiment of the present invention, such as Figure 7 As shown in the figure, different colors are used to represent the sizes of different cloud vapor indices. The comparison relationship can be found in the comparison axis on the right side of the figure.

[0094] S40: Calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index;

[0095] As an implementation manner, the dense high ice cloud index is generated according to the ice vapor index and the cloud vapor index, as shown in formula (5):

[0096] I′ ConvCloud =I 1306 *I 1316 ; (5)

[0097] Among them, I′ ConvCloud represents the dense high ice cloud index, I 1316 represents the ice vapor index, I 1306 represents the cloud vapor index.

[0098] Figure 8 A schematic diagram of a dense high ice cloud index provided by an embodiment of the present invention is shown in FIG. Figure 8 As shown in the figure, different colors are used to represent the sizes of different dense high ice cloud indices. The comparison relationship can be seen from the comparison axis on the right side of the figure.

[0099] S50: Determine the location of dense high ice clouds in the target area based on the dense high ice cloud index.

[0100] In the embodiment of the present invention, the dense high ice cloud index of each remote sensing detection data in the target area is compared with 0.9. If it is greater than 0.9, the position corresponding to the remote sensing detection data is determined to be a dense high ice cloud; if the dense high ice cloud index is not greater than 0.9 but greater than 0.6, the position corresponding to the remote sensing detection data is determined to be a suspected dense high ice cloud.

[0101] Fig. 9 A schematic diagram of a dense high ice cloud index provided by an embodiment of the present invention is shown in FIG. Fig. 9 As shown, different dense high ice cloud indices correspond to different colors, and their corresponding relationship can be seen in the comparison axis on the right side of the figure.

[0102] In some embodiments, the step of determining the location of dense high ice clouds in the target area based on the dense high ice cloud index comprises:

[0103] Comparing the dense high ice cloud index of each detection data in the remote sensing image data of the target area with a preset threshold;

[0104] The detection data greater than the preset threshold is determined to be dense high ice cloud, and the position of the dense high ice cloud in the target area is determined.

[0105] In order to further visualize the dense high ice cloud in the embodiment of the present invention, the following steps are continued:

[0106] Normalized dense high ice cloud index I min and I max is the cutoff value, I min is the minimum value of the dense high ice cloud index in all remote sensing detection data in the target area, I max is the maximum value of the dense high ice cloud index in all remote sensing detection data in the target area, and the compression coefficient K is set, as shown in formula (6):

[0107]

[0108] Among them, I min and I max Can be adjusted based on experience, I ConvCloud is the normalized dense high ice cloud index, I′ ConvCloud It is a dense high ice cloud index.

[0109] For example, I min =0.6, I max =1.1.

[0110] After the normalization of the dense high ice cloud index, if the normalized dense high ice cloud index of a certain remote sensing detection data in the target area is equal to 0, then the location corresponding to the remote sensing detection data is a non-dense high ice cloud; if the normalized dense high ice cloud index of a certain remote sensing detection data in the target area is equal to 1, then the location corresponding to the remote sensing detection data is a dense high ice cloud area; if the normalized dense high ice cloud index of a certain remote sensing detection data in the target area is between 0 and 1, then the location corresponding to the remote sensing detection data is a suspected dense high ice cloud area, and the closer the normalized dense high ice cloud index is to 1, the more likely the location corresponding to the remote sensing detection data is a dense high ice cloud area.

[0111] Fig.10 A schematic diagram of a pseudo-color image of a dense high ice cloud index provided by an embodiment of the present invention is shown in FIG. Fig.10 As shown in the figure, the dense high ice cloud index is processed in pseudo color, and the index is mapped into a pseudo color space image using the "HOT" in the color lookup table as the pseudo color scheme. It is recommended to use the cv2.COLORMAP.HOT color lookup table for the ice cloud index pseudo color map, and other color schemes can also be used, as shown in formula (7):

[0112] RGB ConvCloud =CMAP(HOT, I ConvCloud ). (7)

[0113] Among them, RGB ConvCloud is a pseudo-color image of dense high ice cloud index, HOT is a pseudo-color scheme, I ConvCloud It is a dense high ice cloud index.

[0114] Fig.11 A schematic diagram of a dense high ice cloud monitoring cloud map provided by an embodiment of the present invention, such as Fig.11 As shown in the figure, the dense high ice cloud monitoring cloud image is based on the true color cloud image (RGB TrueColor ) and the dense high ice cloud index pseudo-color map are fused into a dense high ice cloud monitoring cloud map. The compressed normalized dense high ice cloud index is used as the weight to synthesize the RGB and ice cloud index pseudo-color maps, as shown in formula (8):

[0115] The weight of the ice cloud index in the fusion graph is calculated as follows:

[0116] W ConvCloud =K*I ConvCloud ;

[0117] W ConvCloud is the weight of the ice cloud index image in the fusion image, I ConvCloud It is the dense high ice cloud index, the compression coefficient K, generally K=0.9.

[0118] img Combine =RGBTruColor *W ConvCloud +RGB ConvCloud *(1-W ConvCloud ); (8)

[0119] Among them, img Combine This is a dense high ice cloud monitoring cloud image, RGB TruColor This is a true color cloud image of dense high ice clouds.

[0120] The present invention provides a method for detecting dense high ice clouds and visualizing the same based on the absorption characteristics of water vapor and ice crystals, which specifically solves the technical problem of distinguishing dense high ice clouds from clear skies and medium and low clouds, as well as a corresponding enhanced display solution.

[0121] By solving the above two problems, it is possible to achieve the high-efficiency screening and enhanced display of dense high ice clouds based on the geostationary satellite rapid imager, which has a strong indication of the heavy precipitation area. The results can be used for real-time weather monitoring and short-term weather forecasting and warning, giving full play to the disaster prevention and mitigation role of meteorological satellites.

[0122] Fig.12 A structural diagram of a dense high ice cloud detection system based on water vapor and ice crystal absorption characteristics provided by an embodiment of the present invention is shown in FIG. Fig.12 As shown, the system includes:

[0123] The acquisition module 1210 is used to obtain water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data according to the remote sensing image data of the target area by the meteorological satellite;

[0124] The ice vapor module 1220 is used to calculate the ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data;

[0125] The cloud vapor module 1230 is used to calculate the cloud vapor index according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data;

[0126] The high ice cloud module 1240 is used to generate a dense high ice cloud index according to the ice vapor index and the cloud vapor index;

[0127] The identification module 1250 is used to identify whether there are dense high ice clouds in the target area according to the dense high ice cloud index.

[0128] This embodiment is a system embodiment corresponding to the above method, and its specific implementation process is the same as that of the above method embodiment. For details, please refer to the above method embodiment, and this system embodiment will not elaborate on it.

[0129] Each module in the above-mentioned dense high ice cloud detection system based on water vapor and ice crystal absorption characteristics can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0130] Fig.13 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention, the computer device may be a server, and its internal structure diagram may be as follows Fig.13 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a computer storage medium and an internal memory. The computer storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the computer storage medium. The database of the computer device is used to store data generated or acquired during the execution of a dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics, such as remote sensing image data and water vapor absorption spectrum detection data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics is implemented.

[0131] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a method for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics in the above embodiment are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the embodiment of a method for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics are implemented.

[0132] In one embodiment, a computer storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of a dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics in the above embodiment are implemented. Alternatively, when the computer program is executed by a processor, the functions of each module / unit in the above embodiment of a dense high ice cloud detection system based on water vapor and ice crystal absorption characteristics are implemented.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0134] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0135] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics, characterized in that: include: According to the remote sensing image data of the meteorological satellite for the target area, the water vapor absorption spectrum detection data, the ice crystal absorption spectrum detection data and the atmospheric window area spectrum detection data are obtained; Calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data; Calculating a cloud vapor index based on the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data; Calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index; Determining the location of dense high ice clouds in the target area based on the dense high ice cloud index; Wherein, the ice vapor index is calculated according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data, and the calculation formula is as follows: Among them, I 1316 represents the ice vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 16 Represents the ice crystal absorption spectrum detection data; The cloud vapor index is calculated according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data, and the calculation formula is as follows: Among them, I 1306 represents the cloud vapor index, ref 13 represents the water vapor absorption spectrum detection data, ref 06 Represents the atmospheric window spectral band detection data; The dense high ice cloud index is calculated according to the ice vapor index and the cloud vapor index, and the calculation formula is as follows: I′ ConvCloud =I 1306 *I 1316 ; Among them, I′ ConvCloud represents the dense high ice cloud index, I 1316 represents the ice vapor index, I 1306 represents the cloud vapor index.

2. The dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics according to claim 1 is characterized in that: The water vapor absorption spectrum detection data is remote sensing detection data of a 1.3um water vapor absorption spectrum, the ice crystal absorption spectrum detection data is remote sensing detection data of a 1.6um ice crystal absorption spectrum, and the atmospheric window spectrum detection data is remote sensing detection data of a 0.6um atmospheric window spectrum. The steps of obtaining water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data, and atmospheric window spectrum detection data based on remote sensing image data of a target area from a meteorological satellite include: Collecting remote sensing image data of the target area by the meteorological satellite; Performing radiation correction and geographic correction on the remote sensing image data to obtain corrected remote sensing image data; For the corrected remote sensing image data, the remote sensing detection data of the 1.3um water vapor absorption spectrum, the remote sensing detection data of the 1.6um ice crystal absorption spectrum and the remote sensing detection data of the 0.6um atmospheric window spectrum are extracted.

3. The dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics according to claim 1 or 2 is characterized in that: The step of determining the location of dense high ice clouds in the target area based on the dense high ice cloud index comprises: Compare the dense high ice cloud index of each detection data in the remote sensing image data of the target area with 0.9 and 0.6; If the dense high ice cloud index of any detection data is greater than 0.9, the position corresponding to the any detection data is determined to be a dense high ice cloud, and the position of the dense high ice cloud in the target area is determined; If the dense high ice cloud index of any detection data is between 0.6 and 0.9, the position corresponding to any detection data is determined to be a suspected dense high ice cloud.

4. The dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics according to claim 1 is characterized in that: After determining the location of the dense high ice cloud in the target area based on the dense high ice cloud index, the method further includes: The dense high ice cloud index of the remote sensing image data of the target area is normalized, and the calculation formula is: Among them, I min =0.6, I max =1.1,I ConvCloud is the normalized dense high ice cloud index, I′ ConvCloud is the dense high ice cloud index; The normalized dense high ice cloud index is used to visualize the remote sensing image data.

5. A detection system for the dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics according to claim 1, characterized in that: include: An acquisition module is used to obtain water vapor absorption spectrum detection data, ice crystal absorption spectrum detection data and atmospheric window spectrum detection data based on remote sensing image data of the target area from meteorological satellites; An ice vapor module, used for calculating an ice vapor index according to the water vapor absorption spectrum detection data and the ice crystal absorption spectrum detection data; A cloud vapor module, used for calculating a cloud vapor index according to the water vapor absorption spectrum detection data and the atmospheric window spectrum detection data; A high ice cloud module, used for calculating a dense high ice cloud index according to the ice vapor index and the cloud vapor index; An identification module is used to determine the location of dense high ice clouds in the target area based on the dense high ice cloud index.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the dense high ice cloud detection method based on water vapor and ice crystal absorption characteristics as claimed in any one of claims 1 to 4 are implemented.

7. A computer storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting dense high ice clouds based on water vapor and ice crystal absorption characteristics as claimed in any one of claims 1 to 4 are implemented.

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