Data fusion method and system based on ERA5 reanalysis data and satellite observation data

Abstract

The present application discloses a data fusion method and system based on ERA5 reanalysis data and satellite observation data, and relates to the technical field of meteorological data fusion. The method comprises: firstly converting the altitude coordinates of the reanalysis data set from air pressure values ​​to corresponding altitude values, so as to facilitate subsequent matching and fusion; then dividing the reanalysis data set after the altitude coordinate conversion into a grid model according to a preset resolution, and calculating and fusion the parameters of the reanalysis data and the observation data according to the space-time grid as a unit; for each space-time grid, firstly calculating the mean value and standard deviation of the reanalysis data of the space-time grid, and then using the satellite observation data obtained by matching, calculating the mean value and standard deviation of the observation data, and finally determining the fusion data grid model; the present application adopts satellite observation data, and through the design of a reasonable data fusion algorithm, better corrects the ERA5 reanalysis data set, which is more in line with the actual atmospheric change characteristics.

Description

Technical Field

[0001] The present application relates to the technical field of meteorological data fusion, and in particular to a data fusion method and system based on ERA5 reanalysis data and satellite observation data. Background Art

[0002] Based on long-term global observation data and calculated by assimilation methods, the global atmospheric parameter reanalysis dataset can reasonably characterize the climate average state of different parameters of the global atmosphere. Among the many reanalysis data products, the ERA5 reanalysis dataset is one of the most recognized in the industry. The dataset covers the earth with a grid of 0.25°×0.25°. The dataset contains more than 200 parameters and provides a large number of hourly atmospheric, land and ocean climate variables. The data is based on improved three-dimensional variational technology and has the advantages of high temporal and spatial resolution, fast update and many parameters. These data cover the earth's atmosphere from the surface to 0.01hPa (about 80km above sea level) and are divided into 137 model layers in the vertical direction. ERA5 combines model data with observational data from all over the world to form a global, complete and consistent dataset.

[0003] However, the accuracy of the ERA5 reanalysis dataset depends on the observational data integrated by the assimilation system. At 0km-30km, a large number of sounding stations distributed around the world can provide it with a large amount of long-term measured data. Therefore, the ERA5 reanalysis dataset is very close to the sounding data results in this altitude range. However, over areas with sparse sounding stations (for example, high latitudes in the northern and southern hemispheres and ocean areas), especially above the stratosphere, the accuracy of the ERA5 dataset is still questionable. Therefore, in actual use, the ERA5 reanalysis dataset needs to be corrected based on additional observational data.

[0004] The SABER / TIMED satellite can observe the temperature, atmospheric density and pressure parameters within the altitude range of 30km-80km, and has accumulated 20 years of observation data. Through comparison with other observation data and application in atmospheric wave research, the SABER / TIMED satellite observation data show the characteristics of high data accuracy, good temporal continuity and comprehensive spatial coverage. However, the academic research based on this data is mostly for the comparative verification of individual parameter models and the correction of parameters for short-term or local models. The correction results are not universal and difficult to be put into industrial design and testing in fields such as aerospace.

[0005] In summary, it is necessary to design a reasonable data fusion algorithm to correct the ERA5 reanalysis dataset based on SABER / TIMED satellite observation data so that the results of the reanalysis dataset are more consistent with the actual atmospheric change characteristics. Summary of the invention

[0006] The purpose of this application is to provide a data fusion method and system based on ERA5 reanalysis data and satellite observation data, which can better realize the correction of the ERA5 reanalysis data set and make the corrected ERA5 reanalysis data set closer to the measured value.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a data fusion method based on ERA5 reanalysis data and satellite observation data, comprising the following steps:

[0009] Obtain the ERA5 reanalysis data set and satellite observation data set; the height coordinates of the ERA5 reanalysis data are pressure values, and the height coordinates of the satellite observation data are altitude values.

[0010] The height coordinates of the ERA5 reanalysis dataset are converted from pressure values ​​to corresponding altitude values ​​to obtain the ERA5 reanalysis dataset after height coordinate conversion.

[0011] The ERA5 reanalysis data set after the altitude coordinate conversion is divided into a reanalysis data gridding model including several spatiotemporal grids according to the preset resolution.

[0012] For any spatiotemporal grid in the reanalysis data gridding model, the mean and standard deviation of the reanalysis data of the spatiotemporal grid are calculated based on the EAR5 reanalysis data in the spatiotemporal grid.

[0013] For any spatiotemporal grid in the reanalysis data gridding model, the satellite observation data in the satellite observation dataset are matched according to the position of the spatiotemporal grid, and the mean and standard deviation of the observation data of the spatiotemporal grid are calculated.

[0014] The fused data gridding model is determined based on the mean of satellite observation data, the mean of reanalysis data, the standard deviation of reanalysis data and the standard deviation of satellite observation data in each spatiotemporal grid.

[0015] Optionally, convert the height coordinates of the ERA5 reanalysis dataset from pressure values ​​to corresponding altitude values ​​according to the following formula:

[0016]

[0017] Where P is the air pressure value in the ERA5 reanalysis data set, H is the corresponding altitude value, P0 = 1013.25 hPa.

[0018] Optionally, the resolution of the reanalysis data gridding model in the horizontal direction is 2.5°×2.5° and the resolution in the vertical height direction is 500m.

[0019] Optionally, the reanalysis data mean for the spatiotemporal grid is calculated according to the following formula:

[0020]

[0021] in, represents the mean of the reanalysis data in the spatiotemporal grid, n is the number of ERA5 reanalysis data in the spatiotemporal grid, E i is the ith ERA5 reanalysis data in the spatiotemporal grid.

[0022] The standard deviation of the reanalysis data for the space-time grid is calculated according to the following formula:

[0023]

[0024] Among them, S E Represents the standard deviation of the reanalysis data for a space-time grid.

[0025] Optionally, for any spatiotemporal grid in the reanalysis data gridding model, satellite observation data in the satellite observation dataset are matched according to the position of the spatiotemporal grid, and the mean value and standard deviation of the observation data of the spatiotemporal grid are calculated, specifically including the following steps:

[0026] For any spatiotemporal grid in the reanalysis data gridding model, several satellite observation sites are matched in the horizontal direction based on the proximity principle.

[0027] Based on the matched several satellite observation sites in the horizontal direction, several satellite observation data are matched in the vertical height direction.

[0028] According to several satellite observation data matched in the vertical height direction, the mean value and standard deviation of the observation data of the space-time grid are calculated.

[0029] Optionally, the mean of the observed data for the spatiotemporal grid is calculated according to the following formula:

[0030]

[0031] in, represents the mean value of the observation data of the space-time grid, m is the number of satellite observation data matched to the space-time grid, O j is the jth satellite observation data in the space-time grid.

[0032] The standard deviation of the observed data of the space-time grid is calculated according to the following formula:

[0033]

[0034] Among them, S O Represents the standard deviation of the observation data for the space-time grid.

[0035] Optionally, the fused data gridding model includes a plurality of spatiotemporal grids, and the spatiotemporal grids in the fused data gridding model correspond one to one with the spatiotemporal grids in the reanalysis data gridding model; determining the fused data gridding model according to the satellite observation data mean, the reanalysis data mean, the reanalysis data standard deviation and the satellite observation data standard deviation of each spatiotemporal grid specifically includes the following steps:

[0036] For any spatiotemporal grid in the fusion data gridding model, the data fusion mean of the spatiotemporal grid is determined according to the mean of satellite observation data and the mean of reanalysis data of the corresponding spatiotemporal grid in the reanalysis data gridding model.

[0037] The data fusion standard deviation of the space-time grid is determined based on the standard deviation of the satellite observation data and the standard deviation of the reanalysis data corresponding to the space-time grid in the reanalysis data gridding model.

[0038] The fusion data gridding model is determined based on the data fusion mean and data fusion standard deviation of each space-time grid.

[0039] Optionally, the data fusion mean of the spatiotemporal grid is determined according to the following formula:

[0040]

[0041] Among them, A is the data fusion mean of the space-time grid, represents the mean of the reanalysis data for the space-time grid, represents the mean value of the observed data of the space-time grid, a and b are fusion coefficients, a = 1-Δh / n, b = Δh / n, Δh is the height difference between the calculated height and 30 km, n = 40 / dh, dh is the resolution in the vertical height direction.

[0042] The data fusion standard deviation of the space-time grid is determined according to the following formula:

[0043] S=a·S E +b·S O .

[0044] Among them, S is the data fusion standard deviation of the space-time grid, S E represents the standard deviation of the reanalysis data at the spatiotemporal grid, S O Represents the standard deviation of the observation data for the space-time grid.

[0045] In the second aspect, the present application provides a data fusion system based on ERA5 reanalysis data and satellite observation data, including the following modules:

[0046] The reanalysis data set acquisition module is used to obtain the ERA5 reanalysis data set; the altitude coordinates of the ERA5 reanalysis data are air pressure values.

[0047] The satellite observation data set acquisition module is used to acquire the satellite observation data set; the height coordinates of the satellite observation data are altitude values.

[0048] The altitude coordinate conversion module is used to convert the altitude coordinates of the ERA5 reanalysis dataset from pressure values ​​to corresponding altitude values, and obtain the ERA5 reanalysis dataset after altitude coordinate conversion.

[0049] The gridding processing module is used to divide the ERA5 reanalysis data set after the height coordinate conversion into a reanalysis data gridding model including several spatiotemporal grids according to a preset resolution.

[0050] The reanalysis data parameter calculation module is used to calculate the reanalysis data mean and reanalysis data standard deviation of any space-time grid in the reanalysis data gridding model according to the EAR5 reanalysis data in the space-time grid.

[0051] The observation data parameter calculation module is used to match the satellite observation data in the satellite observation dataset according to the position of the space-time grid for any space-time grid in the reanalysis data gridding model, and calculate the observation data mean and observation data standard deviation of the space-time grid.

[0052] The fusion data parameter determination module is used to determine the fusion data gridding model according to the satellite observation data mean, reanalysis data mean, reanalysis data standard deviation and satellite observation data standard deviation of each spatiotemporal grid.

[0053] Optionally, the observation data parameter calculation module includes the following units:

[0054] The satellite observation site matching unit is used to match several satellite observation sites in the horizontal direction based on the proximity principle for any spatiotemporal grid in the reanalysis data gridding model.

[0055] The satellite observation data matching unit is used to match a number of satellite observation data in the vertical height direction based on a number of satellite observation sites matched in the horizontal direction.

[0056] The observation data parameter calculation unit is used to calculate the observation data mean and observation data standard deviation of the space-time grid according to a number of satellite observation data matched in the vertical height direction.

[0057] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0058] The present application provides a data fusion method and system based on ERA5 reanalysis data and satellite observation data, the method comprising: first converting the altitude coordinates of the acquired ERA5 reanalysis data set from air pressure values ​​to corresponding altitude values, obtaining the ERA5 reanalysis data set after altitude coordinate conversion, so as to facilitate subsequent matching and fusion; then dividing the ERA5 reanalysis data set after altitude coordinate conversion into a reanalysis data grid model according to a preset resolution, calculating and fusion the parameters of the reanalysis data and the observation data according to the space-time grid; for each space-time grid, first calculating the space-time grid according to each EAR5 reanalysis data in the space-time grid; The gridded reanalysis data mean and reanalysis data standard deviation are calculated by using the satellite observation data obtained by position matching of the space-time grid, and finally the fused data gridding model is determined. This application adopts satellite observation data and designs a reasonable data fusion algorithm to better correct the ERA5 reanalysis data set, and calculates the fused data gridding model of the ERA5 reanalysis data set in the global 30km-70km altitude range. The results of the fused data gridding model will be more in line with the actual atmospheric change characteristics and more suitable for industrial design and testing in aerospace and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 This is a flow chart of a data fusion method based on ERA5 reanalysis data and satellite observation data in one embodiment of the present application.

[0061] Figure 2 Schematic diagram of the global distribution of satellite observation points in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0062] Figure 3 This is a flowchart of step S5 in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0063] Figure 4 A schematic diagram comparing the gridded model results of ERA5 reanalysis data and the gridded model results of fused data at the 45°N, 25°E spatiotemporal grid in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0064] Figure 5A schematic diagram comparing the ERA5 reanalysis data gridding model and the fusion data gridding model at the 30°S, 140°E spatiotemporal grid in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0065] Figure 6 This is a flowchart of step S6 in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0066] Figure 7 A schematic diagram of the comparison of the ERA5 reanalysis data gridding model, the fused data gridding model and the measured data at the 45°N, 25°E spatiotemporal grid in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0067] Figure 8 A schematic diagram of the comparison of the ERA5 reanalysis data gridding model, the fused data gridding model and the measured data at the 30°S, 140°E spatiotemporal grid in a data fusion method based on ERA5 reanalysis data and satellite observation data provided in this application.

[0068] Fig. 9 A schematic diagram of functional modules of a data fusion system based on ERA5 reanalysis data and satellite observation data provided in one embodiment of the present application.

[0069] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0072] In an exemplary embodiment, Figure 1 As shown, a data fusion method based on ERA5 reanalysis data and satellite observation data is provided, including the following steps S1 to S6:

[0073] S1. Obtain the ERA5 reanalysis data set and the satellite observation data set; the height coordinates of the ERA5 reanalysis data are air pressure values, and the height coordinates of the satellite observation data are altitude values. Specifically in this embodiment, the globally gridded ERA5 reanalysis data are collected at each time of each decade from 1991 to 2020, with a horizontal resolution of 2.5°×2.5° and an altitude range of 137 pressure layers. Each altitude is expressed in air pressure values, which are approximately 0km-80km above sea level. At the same time, the global observation data from 2002 to 2020 within the time range near this moment observed by the SABER / TIMED satellite are collected. Figure 2 The spatial distribution of satellite observation points is given.

[0074] S2. Convert the height coordinates of the ERA5 reanalysis data set from pressure values ​​to corresponding altitude values, and obtain the ERA5 reanalysis data set after the height coordinate conversion. To facilitate subsequent matching, it is necessary to convert the pressure coordinates of the vertical profiles of each parameter in the ERA5 reanalysis data set into altitude value coordinates. In this embodiment, the height coordinates of the ERA5 reanalysis data set are converted from pressure values ​​to corresponding altitude values ​​according to the following formula:

[0075]

[0076] Where P is the air pressure value in the ERA5 reanalysis dataset, H is the corresponding altitude value, P0 = 1013.25hPa. Because air pressure changes with latitude, the upper limit of the altitude value of the ERA5 reanalysis dataset will vary with time and region, usually above 75km.

[0077] S3. The ERA5 reanalysis data set after the height coordinate conversion is divided into a reanalysis data gridding model including several space-time grids according to the preset resolution. Specifically, the vertical resolution of the ERA5 reanalysis data in the altitude range of 30km-70km is interpolated to 500m. The same processing method is used at all grid points. The global temperature, atmospheric density and pressure profiles with a horizontal resolution of 2.5°×2.5° and a vertical resolution of 500m are calculated. That is, the resolution of the reanalysis data gridding model in the horizontal direction is 2.5°×2.5°, and the resolution in the vertical height direction is 500m.

[0078] S4. For any spatiotemporal grid in the reanalysis data gridding model, the mean value and standard deviation of the reanalysis data of the spatiotemporal grid are calculated according to each EAR5 reanalysis data in the spatiotemporal grid. In this embodiment, the mean value of the reanalysis data of the spatiotemporal grid is calculated according to the following formula:

[0079]

[0080] in, represents the mean of the reanalysis data in the spatiotemporal grid, n is the number of ERA5 reanalysis data in the spatiotemporal grid, E i is the ith ERA5 reanalysis data in the spatiotemporal grid.

[0081] The standard deviation of the reanalysis data for the space-time grid is calculated according to the following formula:

[0082]

[0083] Among them, S E Represents the standard deviation of the reanalysis data for a space-time grid.

[0084] S5. For any spatiotemporal grid in the reanalysis data gridding model, match the satellite observation data in the satellite observation data set according to the position of the spatiotemporal grid, and calculate the mean and standard deviation of the observation data of the spatiotemporal grid. Figure 3 In the flowchart shown, step S5 specifically includes the following steps:

[0085] S51. For any spatiotemporal grid in the reanalysis data gridding model, match several satellite observation sites in the horizontal direction based on the principle of proximity. Specifically, match the satellite observation sites in each spatiotemporal grid with a horizontal resolution of 2.5°×2.5°. The principle of proximity is adopted when matching satellite observation sites. For example, the longitude and latitude of a spatiotemporal grid is The matching range includes longitudes in (λ i -1.25°, λ i +1.25°) and latitude Satellite observation sites within range.

[0086] S52: Based on the matched plurality of satellite observation sites in the horizontal direction, match a plurality of satellite observation data in the vertical height direction.

[0087] S53, according to the several satellite observation data matched in the vertical height direction, calculate the observation data mean and observation data standard deviation of the space-time grid. Calculate the observation data mean of the space-time grid according to the following formula:

[0088]

[0089] in, represents the mean value of the observation data of the space-time grid, m is the number of satellite observation data matched to the space-time grid, O j is the jth satellite observation data in the space-time grid.

[0090] The standard deviation of the observed data of the space-time grid is calculated according to the following formula:

[0091]

[0092] Among them, S O Represents the standard deviation of the observation data for the space-time grid.

[0093] According to the mean and standard deviation of the observation data of each space-time grid, a global 30km-70km gridded climatological model obtained from SABER / TIMED satellite observations can be obtained. Figure 4-Figure 5 The height profiles of the ERA5 temperature, atmospheric density and pressure climatological statistics calculated by step S4 and the height profiles of the SABER / TIMED observation temperature, atmospheric density and pressure climatological statistics calculated by step S5 are compared at different time and space grids (45°N, 25°E) and (30°S, 140°E). The dotted lines represent the ERA5 model values, and the solid lines represent the SABER / TIMED satellite observation model values. It can be seen that below 40km, the difference between the two model parameters is not large, but above 40km, the temperature difference between the two model values ​​can reach about 10K.

[0094] S6. Determine the fusion data gridding model based on the satellite observation data mean, reanalysis data mean, reanalysis data standard deviation and satellite observation data standard deviation of each spatiotemporal grid. The fusion data gridding model includes several spatiotemporal grids, and the spatiotemporal grids in the fusion data gridding model correspond to the spatiotemporal grids in the reanalysis data gridding model. Figure 6 In the flowchart shown, step S6 specifically includes the following steps:

[0095] S61. For any spatiotemporal grid in the fusion data gridding model, determine the data fusion mean of the spatiotemporal grid according to the satellite observation data mean and the reanalysis data mean of the corresponding spatiotemporal grid in the reanalysis data gridding model. In this embodiment, the data fusion mean of the spatiotemporal grid is determined according to the following formula:

[0096]

[0097] Among them, A is the data fusion mean of the space-time grid, represents the mean of the reanalysis data for the space-time grid, It represents the mean of the observed data of the space-time grid, a and b are the fusion coefficients, a=1-Δh / n, b=Δh / n, Δh is the height difference between the calculated height and 30km, n=40 / dh, dh is the resolution in the vertical height direction, and 40 in the numerator represents the height difference between 30km and 70km, which can be adjusted according to the difference in calculated height.

[0098] S62, according to the satellite observation data standard deviation and the reanalysis data standard deviation of the corresponding spatiotemporal grid in the reanalysis data gridding model, determine the data fusion standard deviation of the spatiotemporal grid. In this embodiment, the data fusion standard deviation of the spatiotemporal grid is determined according to the following formula:

[0099] S=a·S E +b·S O .

[0100] Among them, S is the data fusion standard deviation of the space-time grid, S E represents the standard deviation of the reanalysis data at the spatiotemporal grid, S O Represents the standard deviation of the observation data for the space-time grid.

[0101] S63. Determine a fusion data gridding model according to the data fusion mean and data fusion standard deviation of each space-time grid.

[0102] The gridded model of ERA5 reanalysis data and the gridded model of fusion data are compared with the observations (measured data) of SABER / TIMED in 2021. Figure 4-5 At the same space-time grid (45°N, 25°E) and (30°S, 140°E), the comparison results of each space-time grid are as follows: Figure 7-Figure 8 In the figure, the solid line on the left represents the grid model results of ERA5 reanalysis data, the dashed line represents the grid model results of fusion data, the dotted line represents the measured data, the solid line on the right represents the difference between the grid model results of ERA5 reanalysis data and the measured data, and the dashed line on the right represents the difference between the grid model results of fusion data and the measured data.

[0103] According to the attached figure, for temperature, there is little difference between the ERA5 model and the fusion model in the altitude range of 30km-40km; at altitudes above 40km, the fusion model is closer to the measured values ​​than the ERA5 model. For atmospheric density and pressure, the fusion model is closer to the measured values ​​than the ERA5 model. The above results show that, on the whole, in different typical areas, the temperature, atmospheric density and pressure calculated by the above fusion method proposed in this application are more consistent with actual observations than the ERA5 model results.

[0104] Based on the same inventive concept, the embodiment of the present application also provides a data fusion system based on ERA5 reanalysis data and satellite observation data for implementing the above data fusion method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method embodiment, so the specific limitations in one or more data fusion system embodiments based on ERA5 reanalysis data and satellite observation data provided below can refer to the limitations of the data fusion method based on ERA5 reanalysis data and satellite observation data above, and will not be repeated here.

[0105] In an exemplary embodiment, Fig. 9 As shown, a data fusion system based on ERA5 reanalysis data and satellite observation data is provided, including:

[0106] The reanalysis data set acquisition module is used to obtain the ERA5 reanalysis data set; the altitude coordinates of the ERA5 reanalysis data are air pressure values.

[0107] The satellite observation data set acquisition module is used to acquire the satellite observation data set; the height coordinates of the satellite observation data are altitude values.

[0108] The altitude coordinate conversion module is used to convert the altitude coordinates of the ERA5 reanalysis dataset from pressure values ​​to corresponding altitude values, and obtain the ERA5 reanalysis dataset after altitude coordinate conversion.

[0109] The gridding processing module is used to divide the ERA5 reanalysis data set after the height coordinate conversion into a reanalysis data gridding model including several spatiotemporal grids according to a preset resolution.

[0110] The reanalysis data parameter calculation module is used to calculate the reanalysis data mean and reanalysis data standard deviation of any space-time grid in the reanalysis data gridding model according to the EAR5 reanalysis data in the space-time grid.

[0111] The observation data parameter calculation module is used to match the satellite observation data in the satellite observation dataset according to the position of the space-time grid for any space-time grid in the reanalysis data gridding model, and calculate the observation data mean and observation data standard deviation of the space-time grid.

[0112] The fusion data parameter determination module is used to determine the fusion data gridding model according to the satellite observation data mean, reanalysis data mean, reanalysis data standard deviation and satellite observation data standard deviation of each spatiotemporal grid.

[0113] Some modules may also have sub-units for implementing their functions. For example, the observation data parameter calculation module includes the following units:

[0114] The satellite observation site matching unit is used to match several satellite observation sites in the horizontal direction based on the proximity principle for any spatiotemporal grid in the reanalysis data gridding model.

[0115] The satellite observation data matching unit is used to match a number of satellite observation data in the vertical height direction based on a number of satellite observation sites matched in the horizontal direction.

[0116] The observation data parameter calculation unit is used to calculate the observation data mean and observation data standard deviation of the space-time grid according to a number of satellite observation data matched in the vertical height direction.

[0117] certainly, Fig. 9 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Fig. 9 One or at least two components of the system shown.

[0118] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile 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 non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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 data fusion method based on ERA5 reanalysis data and satellite observation data is implemented.

[0119] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0124] Those of ordinary skill 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0125] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0126] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A data fusion method based on ERA5 reanalysis data and satellite observation data, characterized in that: include: Obtain an ERA5 reanalysis data set and a satellite observation data set; the height coordinates of the ERA5 reanalysis data are pressure values, and the height coordinates of the satellite observation data are altitude values; Converting the altitude coordinates of the ERA5 reanalysis data set from pressure values ​​to corresponding altitude values ​​to obtain an ERA5 reanalysis data set after altitude coordinate conversion; The ERA5 reanalysis data set after the height coordinate conversion is divided into a reanalysis data gridding model including a plurality of spatiotemporal grids according to a preset resolution; For any spatiotemporal grid in the reanalysis data gridding model, calculate the reanalysis data mean and reanalysis data standard deviation of the spatiotemporal grid according to each EAR5 reanalysis data in the spatiotemporal grid; For any spatiotemporal grid in the reanalysis data gridding model, matching the satellite observation data in the satellite observation data set according to the position of the spatiotemporal grid, and calculating the mean value and standard deviation of the observation data of the spatiotemporal grid; The fused data gridding model is determined based on the mean of satellite observation data, the mean of reanalysis data, the standard deviation of reanalysis data and the standard deviation of satellite observation data in each spatiotemporal grid.

2. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 1, characterized in that: The height coordinates of the ERA5 reanalysis dataset are converted from pressure values ​​to corresponding altitude values ​​according to the following formula: Where P is the air pressure value in the ERA5 reanalysis data set, H is the corresponding altitude value, P0 = 1013.25 hPa.

3. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 1, characterized in that: The reanalysis data gridding model has a horizontal resolution of 2.5°×2.5° and a vertical resolution of 500 m.

4. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 1, characterized in that: The mean of the reanalysis data for the space-time grid is calculated according to the following formula: in, represents the mean of the reanalysis data in the spatiotemporal grid, n is the number of ERA5 reanalysis data in the spatiotemporal grid, E i is the ith ERA5 reanalysis data in the spatiotemporal grid; The standard deviation of the reanalysis data for the space-time grid is calculated according to the following formula: Among them, S E Represents the standard deviation of the reanalysis data for a space-time grid.

5. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 1, characterized in that: For any spatiotemporal grid in the reanalysis data gridding model, matching the satellite observation data in the satellite observation data set according to the position of the spatiotemporal grid, and calculating the mean value and standard deviation of the observation data of the spatiotemporal grid, specifically including: For any spatiotemporal grid in the reanalysis data gridding model, a number of satellite observation sites are matched in the horizontal direction based on the proximity principle; Based on the matched satellite observation sites in the horizontal direction, several satellite observation data are matched in the vertical height direction; According to a plurality of satellite observation data matched in the vertical height direction, the observation data mean and the observation data standard deviation of the space-time grid are calculated.

6. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 5, characterized in that: The mean of the observed data of the space-time grid is calculated according to the following formula: in, represents the mean value of the observation data of the space-time grid, m is the number of satellite observation data matched to the space-time grid, O j is the jth satellite observation data in the space-time grid; The standard deviation of the observed data of the space-time grid is calculated according to the following formula: Among them, S O Represents the standard deviation of the observation data for the space-time grid.

7. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 1, characterized in that: The fused data gridding model includes a plurality of spatiotemporal grids, and the spatiotemporal grids in the fused data gridding model correspond one to one with the spatiotemporal grids in the reanalysis data gridding model; the fused data gridding model is determined according to the satellite observation data mean, the reanalysis data mean, the reanalysis data standard deviation and the satellite observation data standard deviation of each spatiotemporal grid, specifically including: For any spatiotemporal grid in the fusion data gridding model, determine the data fusion mean of the spatiotemporal grid according to the satellite observation data mean and the reanalysis data mean of the corresponding spatiotemporal grid in the reanalysis data gridding model; Determining the data fusion standard deviation of the space-time grid according to the satellite observation data standard deviation and the reanalysis data standard deviation of the corresponding space-time grid in the reanalysis data gridding model; The fusion data gridding model is determined based on the data fusion mean and data fusion standard deviation of each space-time grid.

8. The data fusion method based on ERA5 reanalysis data and satellite observation data according to claim 7, characterized in that: The data fusion mean of the spatiotemporal grid is determined according to the following formula: Among them, A is the data fusion mean of the space-time grid, represents the mean of the reanalysis data for the space-time grid, represents the mean value of the observed data of the space-time grid, a and b are the fusion coefficients, a = 1-Δh / n, b = Δh / n, Δh is the height difference between the calculated height and 30km, n = 40 / dh, dh is the resolution in the vertical height direction; The data fusion standard deviation of the spatiotemporal grid is determined according to the following formula: S=a·S E +b·S O ; Among them, S is the data fusion standard deviation of the space-time grid, S E represents the standard deviation of the reanalysis data at the spatiotemporal grid, S O Represents the standard deviation of the observation data for the space-time grid.

9. A data fusion system based on ERA5 reanalysis data and satellite observation data, characterized in that: include: Reanalysis dataset acquisition module, used to obtain ERA5 reanalysis dataset; The altitude coordinates of the ERA5 reanalysis data are air pressure values; A satellite observation data set acquisition module is used to acquire a satellite observation data set; the height coordinate of the satellite observation data is an altitude value; An altitude coordinate conversion module is used to convert the altitude coordinate of the ERA5 reanalysis data set from the air pressure value to the corresponding altitude value to obtain the ERA5 reanalysis data set after the altitude coordinate conversion; A gridding processing module, used for dividing the ERA5 reanalysis data set after the height coordinate conversion into a reanalysis data gridding model including a plurality of spatiotemporal grids according to a preset resolution; A reanalysis data parameter calculation module, for calculating the reanalysis data mean and reanalysis data standard deviation of any spatiotemporal grid in the reanalysis data gridding model according to each EAR5 reanalysis data in the spatiotemporal grid; An observation data parameter calculation module is used to match the satellite observation data in the satellite observation data set according to the position of any spatiotemporal grid in the reanalysis data gridding model, and calculate the observation data mean and observation data standard deviation of the spatiotemporal grid; The fusion data parameter determination module is used to determine the fusion data gridding model according to the satellite observation data mean, reanalysis data mean, reanalysis data standard deviation and satellite observation data standard deviation of each spatiotemporal grid.

10. The data fusion system based on ERA5 reanalysis data and satellite observation data according to claim 9, characterized in that: The observation data parameter calculation module includes: A satellite observation site matching unit, for matching a plurality of satellite observation sites in a horizontal direction based on a proximity principle for any spatiotemporal grid in the reanalysis data gridding model; A satellite observation data matching unit, used to match a number of satellite observation data in a vertical height direction based on a number of satellite observation sites matched in a horizontal direction; The observation data parameter calculation unit is used to calculate the observation data mean and observation data standard deviation of the space-time grid according to a number of satellite observation data matched in the vertical height direction.

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