A method for evaluating and visualizing the spatiotemporal coverage of atmospheric occultation detection

Abstract

The present invention relates to the field of occultation detection technology, and discloses a temporal and spatial coverage evaluation and visualization method of atmospheric occultation detection, which obtains occultation event distribution latitude and longitude data, including overall regional data and local regional data; creates a geospatial point object, establishes a target coordinate system; spatially intersects to form an overall regional geospatial object and a local regional geospatial object within a certain area; performs local temporal distribution of occultation events and spatial distribution of occultation events on the overall regional geospatial object and the local regional geospatial object, respectively, and forms local temporal distribution evaluation results and spatial distribution evaluation results; the obtained local temporal distribution evaluation results and spatial distribution evaluation results are combined to form a final integrated evaluation result. The temporal and spatial coverage evaluation of the present invention is accurate and effective, and can dynamically display local regional evaluation results according to specific needs.

Description

Technical Field

[0001] The present invention relates to the technical field of occultation detection, and in particular to a method for evaluating the spatiotemporal coverage of atmospheric occultation detection. Background Art

[0002] High-quality atmospheric and ocean observation data are crucial in the study of global change and extreme weather. As climate change intensifies, the frequency of extreme weather events is increasing, leading to an increasing demand for these data.

[0003] Occultation detection technology, with its unique advantages, such as the ability to provide high vertical resolution atmospheric profiles, has become a key technical means to meet this demand. In digital weather forecasting, these detection data can improve the accuracy of forecasts, especially in terms of temporal and spatial coverage. Therefore, it is particularly necessary to build a large-scale occultation detection network.

[0004] However, the current polar-orbiting LEO (Low Earth Orbit) satellites are mainly concentrated in mid- and high-latitude regions, resulting in a gap in observation data in mid- and low-latitude regions. Some mid- and low-latitude regions, such as key areas such as the Qinghai-Tibet Plateau, are an indispensable part of the formation of the global climate. In order to fill this gap, it is necessary to deploy satellite constellations with low-inclination orbits in the corresponding mid- and low-latitude regions. The selection of the satellite's inclination is a complex decision-making process, which directly affects the coverage and efficiency of occultation detection. The existing detection spatiotemporal coverage evaluation method fails to fully consider the coverage of key areas under the premise of large-scale constellation deployment plans, and the evaluation results are not intuitive enough. The effectiveness and applicability of the evaluation results also need to be strengthened. Summary of the invention

[0005] The present invention aims to provide a method for evaluating the spatiotemporal coverage of atmospheric occultation detection, so as to achieve an overall and local spatiotemporal coverage evaluation under the premise of considering large-scale occultation deployment, so as to achieve the accuracy and effectiveness of the evaluation.

[0006] A method for evaluating the temporal and spatial coverage of atmospheric occultation detection comprises the following steps:

[0007] Step 1: obtaining the longitude and latitude data of the occultation event distribution, wherein the longitude and latitude data of the occultation event distribution includes the overall area data and the local area data required for attention;

[0008] Step 2: Create a geospatial point object based on the longitude and latitude coordinates of the occultation event distribution data and establish a target coordinate system;

[0009] Step 3: spatial intersection: intersect the geospatial point objects corresponding to the overall area data and the local area data with the geospatial point objects of the preset grid division in the target coordinate system, respectively, to form the overall area geospatial object and the local area geospatial object of a certain area range;

[0010] Step 4: Perform local time distribution and spatial distribution of occultation events on the overall regional geographic space object and the local regional geographic space object, respectively, to form local time distribution evaluation results and spatial distribution evaluation results;

[0011] Step five: Combine the local time distribution assessment results and the spatial distribution assessment results to form the final integrated assessment results.

[0012] Further, the spatial distribution of the occultation event includes a preliminary spatial distribution assessment and a precise spatial distribution assessment;

[0013] The precise spatial distribution assessment calculates and evaluates the spatial equal-area grid distribution, the spatial longitude and latitude grid distribution, the spatial heterogeneity and the spatial distribution density respectively to obtain a precise spatial distribution assessment.

[0014] A preliminary spatial distribution assessment is formed by detecting the coverage of the latitude band corresponding to the latitude local time distribution map of the descending node local time occultation detection; a precise spatial distribution assessment is formed by calculating and evaluating the spatial equal-area grid distribution, spatial longitude and latitude grid distribution, spatial heterogeneity and spatial distribution density respectively to obtain a precise spatial distribution assessment.

[0015] Furthermore, the overall regional geographic spatial object only needs a preliminary spatial distribution assessment to obtain a spatial distribution assessment result; the local regional geographic spatial object needs to continue to conduct precise spatial distribution assessment on the basis of the preliminary spatial distribution assessment to obtain a spatial distribution assessment result.

[0016] Furthermore, in the spatial equal-area grid distribution, the 200 km*200 km equal-area grid is used as the division grid in step three for spatial intersection.

[0017] Furthermore, in the spatial longitude and latitude grid distribution, the 2 degree*2 degree equal longitude and latitude grids are used as the division grids in step three for spatial intersection.

[0018] Furthermore, the spatial heterogeneity of the occultation event coverage is evaluated, and the heterogeneity standard deviation is calculated:

[0019]

[0020] Where n is the number of grids, x i is the number of occultation detection events in each grid, is the average number of occultation detection times under all grids; S is the heterogeneity standard deviation, which is used to directly reflect the spatial heterogeneity of occultation event coverage. The larger the heterogeneity standard deviation, the more obvious the heterogeneity.

[0021] Furthermore, the spatial density of the occultation event coverage is evaluated, and the spatial coverage density is calculated:

[0022]

[0023] The present invention can take into account both the overall area and the local area, and under the premise of limited calculation amount, enable both the most concerned local area and the overall area to obtain accurate and effective atmospheric occultation detection spatiotemporal coverage assessment, and compared with the conventional assessment method, the assessment of the present invention is more accurate and the assessment result is more intuitive. By setting equal longitude and latitude grids and equal area grids, the calculation and visual errors caused by the division can be overcome, making the assessment result closer to the actual situation.

[0024] The present invention also provides a visualization method for evaluating the spatiotemporal coverage of atmospheric occultation detection, which uses the integrated evaluation results obtained by the aforementioned spatiotemporal coverage evaluation method for atmospheric occultation detection for visualization. The visualization method includes the following contents: partitioning display according to a preset display strategy; wherein, on the display interface, at least two areas are included, namely, a data area and an image area. The data area is used to display data information, and the image area is used to display image information. For local areas, a dynamic pop-up window will be used in the corresponding image area for local display.

[0025] Furthermore, the display strategy includes that if the local area that needs attention is dynamic and continuous, a pop-up window used to display the image information of the local area will form a dynamic interface that moves along its trajectory. When the window is not popped up, its dynamic interface is reduced to a dot and moves on the track line of the overall area corresponding to the image area. When the window needs to pop up for display, the window pops up for display at the current dot position. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of Embodiment 1 of the present invention.

[0027] Figure 2 This is the frequency distribution diagram of the local time of occultation detection when the local time of the descending node is 6:00.

[0028] Figure 3 This is the dimensional local time distribution diagram of the occultation detection coverage results when the descending node is 6:00 local time.

[0029] Figure 4 This is the frequency distribution diagram of the local time of occultation detection when the local time of the descending node is 11:30.

[0030] Figure 5 This is the dimensional local time distribution diagram of the occultation detection coverage results when the descending node is 11:30 local time.

[0031] Figure 6 This is the frequency distribution diagram of the local time of occultation detection when the local time of the descending node is 15:30.

[0032] Figure 7 This is the dimensional local time distribution diagram of the occultation detection coverage results when the descending node local time is 15:30.

[0033] Figure 8 Schematic diagram of the division of equal longitude and latitude grids (2 degrees * 2 degrees).

[0034] Fig. 9 This is a visualization diagram of the spatial occultation detection coverage of equal longitude and latitude grids (2 degrees * 2 degrees).

[0035] Fig.10 This is a schematic diagram of the generated 200 km*200 km grid.

[0036] Fig.11 It is a spatial occultation detection coverage map of equal-area grid. DETAILED DESCRIPTION

[0037] The following is further described in detail through specific implementation methods:

[0038] The spatial and temporal coverage assessment and visualization method of atmospheric occultation detection in this scheme is used to describe the distribution of occultation events within a given range. Analyze the local time distribution of occultation events. Analyze the spatial distribution of occultation events, including the number, longitude and latitude grids and equal-area gridding results, spatial heterogeneity, spatial distribution density, etc., and establish an integrated occultation detection coverage assessment method and visualization method. This scheme can use existing GNSS data to complete the accurate assessment of the spatial and temporal coverage of occultation detection.

[0039] Embodiment 1

[0040] The embodiment is basically as shown in the attached Figure 1 As shown: The method for evaluating the spatiotemporal coverage of atmospheric occultation detection in this embodiment includes the following contents:

[0041] The first step is to obtain the longitude and latitude data of occultation event distribution from existing channels. This longitude and latitude data of occultation event distribution includes the overall area data and local area data that need to be paid attention to.

[0042] For example, when the overall area to be concerned is China and the local area is Tibet Autonomous Region of my country, the longitude and latitude data of the occultation event distribution for multiple days / single day / 3 hours / 6 hours in China and the Qinghai-Tibet Plateau region within China are obtained through spatial links. These data can be obtained from COSMIC, METOP, Spire or Tianmu, which have existing observations.

[0043] The second step is to create a geospatial point object based on the longitude and latitude coordinate points in the shapefile format and establish a target coordinate system. In this embodiment, the created geospatial point object includes an overall region point object and a local region point object. All overall region point objects form an overall region object set, and all local region point objects form a local region object set.

[0044] Generally speaking, atmospheric occultation observation events utilize the refraction of GNSS navigation signals in the atmosphere. By calculating the time delay generated by the GNSS signal, the physical state of the atmosphere at the intersection of the signal refraction is reversed based on the spatial positions of the GNSS satellite and the observation satellite. Therefore, the longitude and latitude of the intersection point are generally used as the spatial position of the occultation observation event.

[0045] By using Python's geopandas geospatial object processing package, or geospatial object processing software such as qgis and ArcGIS, geospatial point objects are created according to the latitude and longitude coordinates of the occultation observation events for subsequent spatial intersection processing.

[0046] In this embodiment, a geospatial point object is created based on the longitude and latitude coordinate points of the acquired GNSS data, and a target coordinate system is established. Among them, the geospatial point object created by the longitude and latitude coordinate points corresponding to the Chinese region is a general region point object, and the set formed by all the general region point objects is a general region object set. The geospatial point object created by the longitude and latitude coordinate points corresponding to the Qinghai-Tibet Plateau region is a local region point object, and all the local region point objects together form a local region object set.

[0047] The third step is spatial intersection. The overall area point objects corresponding to the overall area and the geographic spatial point objects of the preset grid division on the overall area are intersected in the target coordinate system. The local area point objects corresponding to the local area and the geographic spatial point objects of the preset grid division on the local area are intersected in the target coordinate system, and the intersection relationship is determined to form a certain area range geographic spatial object in the shapefile format after grid division.

[0048] Determine the intersection relationship between the positions of two geospatial objects in the same target coordinate system.

[0049] Performs a spatial join on two geospatial datasets (GeoDataFrame) and counts the number of points within each region.

[0050] 1. Import the geopandas library:

[0051] Import the geopandas library and give it a short alias of gpd. geopandas is a Python library for working with geospatial data. It extends the Pandas library to allow geospatial operations on DataFrames.

[0052] 2. Define the spatial connection function:

[0053] A function named spatial_join is defined, which accepts two parameters: ro_gdf and region_gdf. The comment explains the meaning of these two parameters:

[0054] ro_gdf: A GeoDataFrame object containing the longitude and latitude scatter information of occultation detections.

[0055] region_gdf: A GeoDataFrame object representing the region range.

[0056] The function uses the gpd.sjoin() method to spatially join the two GeoDataFrames. It finds the points that are within the region defined by region_gdf. The joined data is stored in the joined variable.

[0057] 3. Calculate the number of points in each region:

[0058] First, we select data from the joined DataFrame (joined) by index (index_right), and then use the value_counts() method to count the number of points in each index value (i.e., within each region). The result is stored in the counts variable.

[0059] 4. Add the counts to the region-wide GeoDataFrame:

[0060] Add the calculated number of points to the original region_gdf as a new column point_count. This way, each region will have a number of points associated with it.

[0061] Performs a spatial join on two geospatial datasets and counts the number of points within each region, then adds those counts to the original region GeoDataFrame.

[0062] Spatial intersection allows the following functions to be implemented in the evaluation of occultation detection coverage:

[0063] 1. Use the occultation observation event geospatial point object and the geospatial surface object of the given area to perform spatial intersection to determine whether the occultation observation event is within the given geospatial range;

[0064] 2. Use the occultation event geospatial point object and the latitude and longitude grid or equal area grid geospatial area object to perform spatial intersection to calculate the number of occultation events under each grid and perform subsequent analysis and visualization.

[0065] Technically, spatial intersection can be achieved through Python's geopandas geospatial object processing package, or geospatial processing software such as qgis and ArcGIS.

[0066] In the fourth step, the local time distribution and spatial distribution of occultation events are respectively performed on the overall regional geographic space objects and the local regional geographic space objects formed in the third step, and the local time distribution evaluation results and spatial distribution evaluation results are respectively formed.

[0067] The local time of an occultation event refers to the local time at the longitude and latitude coordinates of the corresponding tangent point when the occultation observation occurs. Because the state of the atmosphere has strong daily variation characteristics, such as the temperature difference between day and night, the atmospheric circulation between day and night, etc., the occultation detection results at the same location but different local times have great differences in their role and impact on the weather forecast system. Therefore, accurately describing the local time distribution of occultation detection is an important part of occultation coverage assessment.

[0068] The local time of an occultation event is the time observed at a specific location when the occultation occurs. It depends on the local difference from Greenwich Mean Time or UTC. For example, if the local time is one hour ahead of Greenwich Mean Time during daylight saving time, then the local time of the occultation event will be one hour ahead of Greenwich Mean Time.

[0069] Generally, atmospheric occultation products will provide the standard time of observation (UTC / GMT). When calculating the local time based on the longitude of the occultation observation location, the calculation method is as follows:

[0070] UTC / GMT-Lon diff / 15

[0071] Among them, Lon diff It is the longitude difference of the occultation detection observation position from the standard time (0 degrees).

[0072] The longitude difference of the occultation detection observation location from the standard time (0 degrees) is actually the difference in longitude between the two locations. This difference is obtained through observation data and calculations, and can be used to determine the longitude offset of the observation location relative to the standard time.

[0073] Standard time, including UTC (Coordinated Universal Time) and GMT (Greenwich Mean Time), is a reference system used to unify and standardize time worldwide.

[0074] After obtaining the local time, the local time of the occultation event is visualized, mainly through Figure 2 The frequency distribution diagram shown and Figure 3 The latitude local time distribution diagram shown is developed. However, these two diagrams are more conducive to viewing the overall area, but not conducive to viewing the local area.

[0075] Descending node local time refers to the time when the satellite passes through the equatorial plane when flying from the northern hemisphere to the southern hemisphere.

[0076] In order to observe the atmospheric state at different times of the day and to achieve better temporal and spatial coverage of occultation detection, it is generally necessary to monitor over a continuous period of time. Because the temperature and humidity vary greatly between day and night, in order to make the detection results more accurate, it is generally monitored 24 hours a day. Especially at key points in each time period, the meteorological changes can vary greatly, such as Figure 2 , Figure 3 6:00 and Figure 4 Figure 5 11:30 and Figure 6 Figure 7 The weather changes corresponding to 15:30 are huge.

[0077] pass Figure 2 , Figure 4 , Figure 6 The corresponding local time frequency distribution diagram of occultation detection at the descending node local time can intuitively show the main distribution time periods of occultation detection events at the descending node local time, and can determine whether the corresponding spatial and temporal coverage distribution of atmospheric occultation detection meets the needs according to specific needs.

[0078] pass Figure 3 , Figure 5 and Figure 7The distribution map of the latitude local time of occultation detection at the descending node local time can reveal the relationship between latitude and occultation detection: the distribution map can clearly show the occultation detection situation of the descending node local time in different latitudes. This helps to understand the distribution and change law of occultation phenomena in different latitudes, and further reveals the relationship between latitude and occultation detection. Through the distribution map, it can be intuitively seen which latitudes have better occultation detection conditions. This provides an important reference for the regional selection of occultation detection, which helps to optimize the observation layout and improve the observation efficiency. The distribution map can provide important data support for related scientific research and engineering applications. For example, in the research of meteorology, climate, space environment and other fields, the map can be used to analyze the impact of occultation events on the earth's environment and human activities. At the same time, the distribution map can also provide a reference for the optimization design and improvement of occultation detection equipment.

[0079] In this embodiment, the local time frequency distribution diagram is detected by the occultation of the descending node local time in the overall area to see whether the corresponding demand period is covered, so as to form a local time distribution evaluation result.

[0080] Similarly, through the local time distribution map of the latitude of the descending node local time occultation detection, we can see whether the corresponding latitude band is covered within the overall area to form a preliminary spatial distribution assessment. For the overall area, only the preliminary spatial distribution assessment can be used. At this time, the preliminary spatial distribution assessment is the spatial distribution assessment result, which together with the local time distribution assessment result constitutes an integrated assessment result, which is used to evaluate and reflect the temporal and spatial coverage of atmospheric occultation detection. However, the accuracy and effectiveness of this assessment are not significantly better than the current general method. The general overall area is generally divided into a single country area, a single continent area of ​​the five continents, or a global area.

[0081] In this embodiment, for one or several local areas of greatest concern in the overall area, it is necessary to continue to accurately evaluate the spatial distribution of occultation events.

[0082] The precise evaluation of the spatial distribution of occultation events is to calculate and evaluate the spatial equal-area grid distribution, the spatial longitude and latitude grid distribution, the spatial heterogeneity, and the spatial distribution density, respectively, to obtain a precise spatial distribution evaluation. The preliminary spatial distribution evaluation and the precise spatial distribution evaluation together form the spatial distribution evaluation result. In this way, the final evaluation result can be made more accurate and effective. In addition, this embodiment treats the overall area and the local area differently, and can greatly improve the accuracy and effectiveness of the evaluation for the area of ​​interest under the premise of limited calculation amount.

[0083] Specifically, accurate assessment of the spatial distribution of occultation events, including:

[0084] (I) Spatial equal longitude and latitude grids and occultation event coverage assessment: divide the equal longitude and latitude grid space, spatially intersect the equal longitude and latitude grids with the occultation event geographic space point objects, and obtain the equal longitude and latitude grid space occultation detection coverage map, which can intuitively observe the occultation event coverage under the equal longitude and latitude grid space division.

[0085] Since the spatial distribution of occultation detection events has a certain degree of randomness, that is, the coverage and spatial location of daily occultation events in a region may vary. In order to better describe the coverage of occultation detection events, we can establish a spatial equal latitude and longitude grid and use the grid to spatially intersect with the occultation event geospatial point object to obtain the number of occultation detection events in each grid, so as to conduct quantitative occultation detection coverage evaluation and visualization.

[0086] The equal longitude and latitude grid means that all grid objects have the same longitude and latitude. The equal longitude and latitude grid refers to a grid formed by the longitude and latitude lines interweaving on the earth at fixed intervals. It is the earliest and most widely used geospatial coordinate grid in the field of geology.

[0087] In this embodiment, the generation of the medium latitude and longitude grid can be implemented using the geopandas geospatial extension package of Python to create a geographic grid and add the polygons of each grid to a GeoDataFrame using the geopandas library.

[0088] The specific steps are:

[0089] Import the required libraries:

[0090] geopandas: A Python library for working with geospatial data.

[0091] pandas: A Python library for data processing and analysis.

[0092] shapely.geometry.Polygon: Tools for creating polygons.

[0093] Define the range of longitude and latitude:

[0094] lon_range=(-180,180): longitude range from -180 degrees to 180 degrees.

[0095] lat_range=(-90,90): the latitude range is from -90 degrees to 90 degrees.

[0096] Define the step size for longitude and latitude:

[0097] step=2: The step size of longitude and latitude is 2 degrees. This means that each grid will be a 2x2 degree area.

[0098] Create a grid of longitude and latitude:

[0099] Use the pandas np.arange function to generate a sequence of longitude and latitude based on the given range and step size.

[0100] Create an empty GeoDataFrame:

[0101] grid_gdf = gpd.GeoDataFrame(): Creates an empty GeoDataFrame that will be used to store the polygons of each grid.

[0102] Create polygons and add to GeoDataFrame:

[0103] Use a loop to iterate over the grid of longitude and latitude.

[0104] For each (lon,lat) point, create a polygon whose corner points are (lon,lat), (lon+step,lat), (lon+step,lat+step), and (lon,lat+step).

[0105] Add this polygon to the GeoDataFrame of grid_gdf.

[0106] Finally, a GeoDataFrame containing a series of 2x2 degree grid polygons will be generated. Each polygon represents a 2x2 degree geographic area. The equal longitude and latitude grids (2 degrees * 2 degrees) generated by the above algorithm are as follows Figure 8 shown.

[0107] Spatial intersection of equal longitude and latitude grids and occultation event geospatial point objects can be achieved using Python's geopandas extension package, or using geospatial processing software such as qgis and ArcGIS. The final presentation of equal longitude and latitude grid (2 degrees * 2 degrees) spatial occultation detection coverage effect is as follows Fig. 9 shown.

[0108] (ii) Spatial equal-area grid and occultation event coverage assessment: divide the space into equal-area grids, spatially intersect the equal-area grids with the occultation event geographic spatial point objects, and obtain an equal-area grid spatial occultation detection coverage map. This allows intuitive observation of the occultation event coverage under the equal-area grid space division.

[0109] When evaluating and visualizing the spatial coverage of occultation detection events, the actual size of the grid decreases with increasing latitude, which causes errors in the evaluation of high-latitude occultation detection coverage. This is due to the shape and size of the Earth.

[0110] The Earth is a sphere, and its shape and size cause the distance between latitude and longitude to be different at different latitudes. The distance between longitude and latitude is the largest near the equator, and the smallest in the polar regions. Therefore, when we evaluate and visualize the coverage of occultation detection events, if we use equal longitude and latitude grids, the actual size of the grid will be smaller in high latitudes than in low latitudes, which may lead to evaluation errors.

[0111] In order to solve this problem, not only the reasonable setting of equal longitude and latitude grids is used to minimize the error, but also three evaluation items, namely spatial equal area network distribution, spatial heterogeneity and spatial distribution density, are added to reduce the error that may be caused by the longitude and latitude grid evaluation, forming a more sophisticated spatial coverage evaluation method for occultation detection events. At the same time, when performing visualization processing, a more suitable projection method or map representation method can be selected according to needs.

[0112] In this embodiment, an equal-area grid scheme is used to perform occultation detection coverage assessment and visualization, which ensures that the actual area size of the space grid is evenly distributed within each latitude, thereby reducing and avoiding the problem of high-latitude detection assessment errors.

[0113] The equal-area grid can be implemented using Python's geopandas geographic extension package. Taking a 200 km*200 km grid as an example, the algorithm principle is as follows:

[0114] 1. Calculate the latitude difference corresponding to 200 kilometers

[0115]

[0116] 2. Calculate the longitude difference corresponding to 200 kilometers at each latitude (where X is the latitude)

[0117]

[0118] According to the calculation of the above latitude difference and longitude difference, use python to construct a 200 km * 200 km equal area grid. The specific process is as follows:

[0119] Create a latitude list from 0° to 90°, create an empty longitude list, and store each longitude value corresponding to 200 kilometers at different latitudes (from 0-180 degrees); traverse each latitude in the latitude list, and use Python's geopandas library to create a geographic grid. Create a single grid object, and then define a polygon through a series of longitude and latitude coordinates. When creating a GeoDataFrame, the coordinate system is set to "EPSG:4326", which is a commonly used global coordinate system (WGS84). Merge all grid objects. The merged grid object ne_grid_200 is only a quarter of the sphere of 0-90 degrees north latitude and 0-180 degrees east longitude. Therefore, it needs to be mirrored north-south and east-west to form a global grid. The generated 200 km*200 km grid schematic diagram is as follows Fig.10 shown.

[0120] The method of spatially linking the equal-area grid and the occultation event geospatial point objects is consistent with the equal-longitude and latitude grid. Finally, an equal-area grid spatial occultation detection coverage map is formed, such as Fig.11 shown.

[0121] (iii) Assessment of spatial heterogeneity of occultation event coverage, and calculation of the heterogeneity standard deviation.

[0122] The spatial heterogeneity in this embodiment mainly refers to the uneven distribution of occultation objects in the global distribution and in a certain area.

[0123] Based on the previous evaluation results of occultation detection coverage in equal longitude and latitude or equal area grids, the spatial heterogeneity of occultation event coverage is described by calculating the standard deviation of the number of occultation events in each grid. The specific algorithm is:

[0124]

[0125] Where n is the number of grids, x i is the number of occultation detection events in each grid, is the average number of occultation detection times under all grids; S is the heterogeneity standard deviation, which is used to directly reflect the spatial heterogeneity of occultation event coverage. The larger the heterogeneity standard deviation, the more obvious the heterogeneity.

[0126] (iv) Assessment of spatial density of occultation event coverage: the spatial coverage density is obtained through calculation.

[0127] Based on the above equal latitude and longitude grids or given (irregular) geographic object ranges, spatial links are used to calculate the number of occultation events in each grid or within a given spatial range. At the same time, the geopandas geospatial extension package of Python can be used to obtain the actual area size of each grid or given space, so as to calculate the spatial density of occultation events.

[0128] Use geopandas to obtain the actual area size of the grid or a given space to form a regional face value, then count the number of occultation events in the area and calculate the spatial coverage density using the following formula.

[0129]

[0130] The equal longitude and latitude grid spatial occultation detection coverage map, equal area grid spatial occultation detection coverage map, heterogeneity standard deviation and spatial coverage density calculated and evaluated according to the above four aspects are used together as the precise spatial distribution assessment and together with the preliminary spatial distribution assessment form the final spatial distribution assessment result.

[0131] The fifth step is to combine the local time distribution assessment results and the spatial distribution assessment results to form the final integrated assessment results. The integrated assessment results can be used to intuitively and accurately evaluate the temporal and spatial coverage of atmospheric occultation detection in the overall area and local areas.

[0132] This embodiment, through the ingenious setting of precise spatial distribution evaluation, can more accurately evaluate the spatiotemporal coverage of the most concerned local area by distinguishing the overall area from the local area, and can intuitively see the distribution of occultation events and coverage effects through the generated equal longitude and latitude grid space occultation detection coverage map and equal area grid space occultation detection coverage map. At the same time, by treating the overall area and the local area differently, the amount of calculation can be effectively reduced to avoid waste.

[0133] This embodiment adds spatial equal-area grid distribution, spatial heterogeneity and spatial distribution density assessment to the precise spatial distribution assessment, thereby effectively reducing the error caused by the spatial equal-latitude and longitude grid distribution, while making the entire occultation event spatial distribution more accurate and more conducive to visualization, and enabling intuitive observation of the spatiotemporal coverage of occultation detection.

[0134] Compared with similar technologies that can only select one grid mode, in this embodiment, two grid modes can be switched for use, which is more intuitive and convenient.

[0135] Embodiment 2

[0136] In this embodiment, based on the first embodiment, the spatiotemporal coverage evaluation results of atmospheric occultation detection are visualized. After the fifth step of the first embodiment, the following contents are performed:

[0137] The sixth step is to display the integrated evaluation results in partitions according to the preset display strategy; the display interface includes at least two areas: the data area and the image area. The data area is used to display data information such as the standard deviation of heterogeneity and spatial coverage density, and the image area is used to display image information such as the local time frequency distribution map, the latitude local time distribution map, the equal longitude and latitude grid space occultation detection coverage map, and the equal area grid space occultation detection coverage map. It should be noted that for local areas, the local display will be made in the corresponding image area dynamic pop-up window.

[0138] The seventh step, the display strategy includes the display limitation of the local area of ​​focus. If the local area that needs attention is dynamic and continuous, such as the ocean route area, the aircraft navigation area, the long-distance moving range area, etc., the pop-up window used to display the image information of the local area will form a dynamic interface that moves along its trajectory. When the window is not popped up, its dynamic interface is reduced to a dot and moves on the track line of the overall area corresponding to the image area. When it is timed or when a change occurs, the window pops up and displays at the moved dot position. The moving trajectory of the dot in the overall area is consistent with the actual moving trajectory of the measured target, such as ships and airplanes. The dynamic local area changes dynamically with the movement of the measured target.

[0139] If it is set as a timer, or a certain local time is set as a focus time, the window of the local area will pop up after reaching the specific time point. When evaluating the local time distribution and spatial distribution of occultation events, not only the images corresponding to the current descending node are highlighted, but also all images in the most recent time period are retained, especially for the images of key time points. For example, if the local time of the descending node is 11:30, the corresponding frequency distribution diagram or dimension distribution diagram has obvious changes, or when this time point is selected as an important time point, the corresponding Figure 4 and Figure 5 will be highlighted and displayed. Figure 6 and Figure 7 Similarly, if the descending node local time 15:30 is an important time point or a changing time point, the corresponding Figure 6 and Figure 7 It will also be zoomed in and popped up.

[0140] In this embodiment, for a dynamically moving local area whose moving trajectory or total track length is L, the local area is approximated as a circle. If the diameter of the local area is less than or equal to 0.001 times of L, the spatiotemporal coverage assessment of the local area is displayed in a timed pop-up window, except when a pop-up window is manually clicked on a dot. The time period of the timed pop-up window can be set according to demand, generally within one tenth of the total time length of the moving trajectory or track. This setting can not only keep abreast of the latest assessment of the moving local area, but also will not affect the viewing of the overall area, and to a certain extent saves the update calculation amount.

[0141] In this embodiment, if the diameter of the local area is greater than 0.001 and less than 0.05 times of L, a timed pop-up window can generally be selected, or it can be set to adjust according to the curvature or difficulty of the moving trajectory or track. A large time interval can be set for a timed pop-up in some smooth areas with less difficulty to move; a small time interval can be set for a timed pop-up in areas with greater curvature and greater difficulty to move, or the pop-up display is kept until the area with greater curvature or greater difficulty to move is passed. The large time interval and small time interval here are both for the standard time interval. The standard time interval can be set in advance based on experience. The large time interval is greater than or equal to 2 to 2.5 times the standard time interval, and the small time interval is less than or equal to 0.5 times the standard time interval. In this way, personalized visualization of evaluation results can be performed according to specific detection needs.

[0142] This embodiment can better take into account the visualization needs of the overall area and the local area, especially the dynamic local area, and can be more intuitive and meet various practical needs for visualization, which is conducive to more intuitive and effective viewing of the spatiotemporal coverage assessment results of atmospheric occultation detection under specific application needs.

[0143] Embodiment 3

[0144] Different from the first embodiment, in the spatial equal longitude and latitude grid distribution evaluation of the spatial distribution of occultation events, after the equal longitude and latitude grids are divided, in addition to being able to intuitively view the coverage of occultation events through the equal longitude and latitude grid spatial occultation detection coverage map, there are also multiple preset equal longitude and latitude standard coverage maps of occultation events used to represent different distributions of occultation events, as well as the latitude and longitude coverage standard scores corresponding to each map.

[0145] The equal longitude and latitude grid space occultation detection coverage map obtained in Example 1 and the preset equal longitude and latitude standard coverage map of occultation events are compared for similarity, and the longitude and latitude coverage standard score corresponding to the equal longitude and latitude standard coverage map of occultation events with the highest similarity is selected as the longitude and latitude space coverage score corresponding to the current equal longitude and latitude grid space occultation detection coverage map used to represent the distribution of the current occultation event.

[0146] In this way, in addition to visually seeing the distribution of occultation events on the grid space of equal longitude and latitude, it is also possible to more accurately evaluate this distribution and obtain specific scores. This is conducive to combining the other three evaluation factors to more accurately reflect the spatial distribution of occultation events, making the final spatiotemporal coverage evaluation more accurate.

[0147] Similarly, the equal-area grid space occultation detection coverage map can also be compared with multiple preset equal-area standard coverage maps of occultation events. The area coverage standard score corresponding to the equal-area standard coverage map of the occultation event with the highest similarity will be used as the area space coverage score of the current equal-area grid space occultation detection coverage map, which is used to represent the evaluation of the corresponding actual occultation event in the spatial equal-area network distribution.

[0148] This embodiment provides an innovative and comprehensive evaluation system that can analyze the coverage of occultation events more accurately and specifically, especially for some local areas that are rarely visited and easily overlooked but have detection needs, such as the Qinghai-Tibet Plateau. Compared with the existing technology, the technical highlights and advantages of this solution can be expanded from the following aspects:

[0149] Region-specific assessment capabilities: This technical solution can conduct detailed occultation coverage analysis for specific regions, such as the Qinghai-Tibet Plateau. It goes beyond the global or overly generalized assessments of traditional methods and can provide customized coverage assessments based on the geographic and climatic characteristics of specific regions. This means that in-depth observational data support can be provided for regions of particular scientific and strategic significance.

[0150] Development of a normalized comprehensive index: The normalized comprehensive index proposed in the scheme can comprehensively consider multiple factors, such as spatial coverage, temporal coverage, and coverage uniformity, to evaluate the coverage results under different occultation constellation configurations. This index can provide a single, quantitative evaluation result, making the comparison between different constellation configuration schemes more intuitive and scientific.

[0151] Optimized spatial and temporal coverage analysis: Through the spatial and temporal coverage analysis of this technical solution, observation gaps can be identified and targeted improvements can be made in these areas. Temporal coverage analysis specifically considers the temporal distribution of occultation events, which is critical for understanding and predicting extreme weather events and long-term climate change.

[0152] Visualization of results: Using visualization tools in GIS software, the solution can convert complex data into intuitive heat maps and maps, which not only helps researchers and decision makers understand coverage conditions, but also helps the public understand this information.

[0153] Wide range of practical applications: Although this technical solution is designed with special consideration of the Qinghai-Tibet Plateau, its methodology and tools are highly versatile and can be applied to coverage assessment in any other region of the world, providing a new tool for global climate change research.

[0154] In summary, this embodiment provides an advanced assessment tool that can perform accurate occultation coverage analysis for key areas and optimize constellation configuration, providing strong technical support for global change research and extreme weather forecasting, and has significant social, scientific, and strategic value.

[0155] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the spatiotemporal coverage of atmospheric occultation detection, characterized in that: The following steps are involved: Step 1: obtaining the longitude and latitude data of the occultation event distribution, wherein the longitude and latitude data of the occultation event distribution includes the overall area data and the local area data required for attention; Step 2: Create a geospatial point object based on the longitude and latitude coordinates of the occultation event distribution data and establish a target coordinate system; Step 3: spatial intersection: intersect the geospatial point objects corresponding to the overall area data and the local area data with the geospatial point objects of the preset grid division in the target coordinate system, respectively, to form the overall area geospatial object and the local area geospatial object of a certain area range; Step 4: Perform local time distribution and spatial distribution of occultation events on the overall regional geographic space object and the local regional geographic space object, respectively, to form local time distribution evaluation results and spatial distribution evaluation results; Step 5: The local time distribution assessment results and the spatial distribution assessment results are combined to form the final integrated assessment results; The spatial distribution of the occultation events includes a preliminary spatial distribution assessment and a precise spatial distribution assessment; The precise spatial distribution assessment is to calculate and evaluate the spatial equal-area grid distribution, the spatial longitude-latitude grid distribution, the spatial heterogeneity and the spatial distribution density respectively, so as to obtain the precise spatial distribution assessment; The overall regional geographic spatial object only needs a preliminary spatial distribution assessment to obtain a spatial distribution assessment result; the local regional geographic spatial object needs to continue to conduct an accurate spatial distribution assessment based on the preliminary spatial distribution assessment to obtain a spatial distribution assessment result.

2. The method for evaluating the spatiotemporal coverage of atmospheric occultation detection according to claim 1, characterized in that: In the spatial equal-area grid distribution, the 200 km*200 km equal-area grid is used as the division grid in step three for spatial intersection.

3. The method for evaluating the spatiotemporal coverage of atmospheric occultation detection according to claim 1, characterized in that: In the spatial longitude and latitude grid distribution, the 2 degree * 2 degree equal longitude and latitude grid is used as the division grid in step three for spatial intersection.

4. The method for evaluating the spatiotemporal coverage of atmospheric occultation detection according to claim 1, characterized in that: The spatial heterogeneity of occultation event coverage is evaluated, and the heterogeneity standard deviation is calculated: in is the number of grids, is the number of occultation detection events in each grid, is the average number of occultation detection times under all grids; S is the heterogeneity standard deviation, which is used to directly reflect the spatial heterogeneity of occultation event coverage. The larger the heterogeneity standard deviation, the more obvious the heterogeneity.

5. The method for evaluating the spatiotemporal coverage of atmospheric occultation detection according to claim 1, characterized in that: The spatial density of occultation events is evaluated. The spatial coverage density is calculated as follows: 。 6. A visualization method for evaluating the spatiotemporal coverage of atmospheric occultation detection, characterized in that: The integrated evaluation result obtained by the atmospheric occultation detection spatiotemporal coverage evaluation method described in any one of claims 1 to 5 is visualized, and the visualization method includes the following contents: partition display according to a preset display strategy; wherein, on the display interface, at least two areas are included, the data area and the image area, the data area is used to display data information, and the image area is used to display image information. For local areas, a dynamic pop-up window will be displayed in the corresponding image area.

7. The method for visualizing the temporal and spatial coverage assessment of atmospheric occultation detection according to claim 6, characterized in that: The display strategy includes: if the local area that needs attention is dynamic and continuous, the pop-up window used to display the image information of the local area will form a dynamic interface that moves along its trajectory. When the window is not popped up, its dynamic interface is reduced to a dot and moves on the track line of the overall area corresponding to the image area. When the window needs to pop up for display, the window pops up and displays at the current dot position.

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