Quantitative estimation method and system for occultation data error and related equipment
By acquiring observation data from different sources, performing normalization processing and variance calculation, the error of GNSS occultation data is directly estimated. This solves the problems of resource consumption and error superposition caused by relying on numerical prediction models in existing technologies, and achieves higher accuracy and reliability in error estimation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 航天天目(重庆)卫星科技有限公司
- Filing Date
- 2025-12-23
- Publication Date
- 2026-05-15
AI Technical Summary
Existing methods for estimating errors in GNSS occultation data rely on complex numerical weather prediction models, which result in high computational resource consumption and the superposition of forecast errors, affecting the accuracy of the estimation results.
By acquiring observational data from different sources, normalizing the data, and calculating the variance between each pair of data, the error of the occultation data can be directly estimated, avoiding reliance on forecast field data provided by numerical weather prediction models.
It improves the accuracy and reliability of error estimation results, reduces the impact of errors from single-source data, and is adaptable to different types of observation data.
Smart Images
Figure CN122045544A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of meteorological observation data processing technology, and in particular to a method, system and related equipment for quantitative estimation of occultation data error. Background Technology
[0002] GNSS (Global Navigation Satellite System) occultation detection technology provides high vertical resolution atmospheric parameter profiles globally, making it a crucial data source for numerical weather prediction and climate monitoring. In the GNSS occultation data assimilation process, the quantitative estimation of observation errors is a key factor determining the assimilation effect, directly impacting the weighting of occultation data within the assimilation system. A larger error / variance ratio results in a lower weight for the observation data in the assimilation system, thus reducing its influence on the analysis field. Quantitatively estimating the errors in GNSS occultation observations has significant practical value for the assimilation application of occultation data.
[0003] Currently, existing error estimation methods often rely on forecast field data provided by numerical weather prediction models as a reference for comparison. Using forecast field data as a reference standard, the error is calculated by comparing it with occultation data. This method depends on the complex forecasting process of numerical weather prediction models, consuming enormous computational resources, and the forecast error is further superimposed on the observation error estimation result. Summary of the Invention
[0004] To overcome the problem that quantitative estimation of observation errors requires reliance on complex numerical weather prediction models, which consumes a great deal of computational resources, and that errors from the prediction process are further superimposed on the estimation results of observation errors, this invention provides a method, system, and related equipment for quantitative estimation of occultation data errors.
[0005] Firstly, in order to solve the above-mentioned technical problems, the present invention provides a method for quantitatively estimating occultation data errors, comprising: Acquire observational data from different sources; Normalize the observation data from different sources to determine the normalized data; Calculate the variance between each pair of normalized data and determine the variance result; Based on the variance results, the observation error estimation results of the occultation data are determined.
[0006] Secondly, the present invention provides a quantitative estimation system for occultation data errors, comprising: The observation data acquisition module is used to acquire observation data from different sources; The normalization module is used to normalize observation data from different sources and determine the normalized data. The variance result determination module is used to calculate the variance between each pair of normalized data and determine the variance result. The observation result determination module is used to determine the observation error estimation results of occultation data based on the variance results.
[0007] Thirdly, the present invention provides a computing device, including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the above-described method for quantitative estimation of occultation data error.
[0008] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform the steps of the above-described method for quantitative estimation of occultation data errors.
[0009] The beneficial effects of this invention are: acquiring observational data from different sources, normalizing the observational data, calculating the variance between each pair of data, and finally quantitatively estimating the observational error of the occultation data based on the variance results. This application no longer relies on forecast field data provided by numerical weather prediction models, thus avoiding the introduction of errors in the forecast field data into the final quantitative estimation result of the observational error. Furthermore, this method is adaptable to different types of observational data, and by utilizing the variance between each pair of data, it reduces the impact of errors from single-source observational data, improving the accuracy and reliability of the error estimation results. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0011] Figure 1 This is a flowchart illustrating a method for quantitatively estimating occultation data errors according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a quantitative estimation system for occultation data error according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computing device according to an embodiment of the present disclosure. Detailed Implementation
[0012] The following embodiments are further explanations and supplements to the present invention and do not constitute any limitation on the present invention.
[0013] The following describes, with reference to the accompanying drawings, a method, system, and related equipment for quantitative estimation of occultation data errors according to embodiments of the present invention.
[0014] like Figure 1 As shown, this embodiment of the invention provides a method for quantitatively estimating occultation data errors, including: S1. Obtain observation data from different sources.
[0015] S2. Normalize the observation data from different sources to determine the normalized data.
[0016] S3. Calculate the variance between each pair of normalized data and determine the variance result.
[0017] S4. Based on the variance results, determine the estimation results of the observation error of the occultation data.
[0018] In this embodiment, observational data from different sources are acquired, normalized, and then the variance between each pair of data is calculated. Finally, the observational error of the occultation data is quantitatively estimated based on the variance results. This application no longer relies on forecast field data provided by numerical weather prediction models, thus avoiding the introduction of errors in the forecast field data into the final quantitative estimation result of the observational error. At the same time, this method is adaptable to different types of observational data, and by utilizing the variance between each pair of data, it reduces the impact of errors in observational data from a single source, thereby improving the accuracy and reliability of the error estimation results.
[0019] In this embodiment, the observation data from different sources cannot be fused due to their large differences in magnitude. Therefore, normalization is used to adjust the data of different heights and magnitudes to a uniform scale, which helps to eliminate the interference of different units on the analysis results, highlight the differences between the datasets, and lay the foundation for subsequent error estimation.
[0020] In this embodiment, the normalization process can be performed using a conventional normalization formula, for example... in, Represents normalized data. This indicates the observation data that needs to be normalized. , These represent the minimum and maximum values in the observation data that need to be used as a baseline. For example, when normalizing radiosonde data, ERA5 data can be used as the baseline. This represents sounding data. , This indicates the minimum and maximum values in the ERA5 data that need to be used as a baseline.
[0021] Optionally, observational data from different sources include radiosonde data, occultation data, ERA5 data, and FNL data; Then, observational data from different sources are obtained, including: If the time difference between radiosonde data and occultation data is less than the first threshold and the spatial distance is less than the second threshold, then the radiosonde data will be used as the target radiosonde data, and the occultation data will be used as the target occultation data. The ERA5 data and FNL data are matched to the occultation point position by interpolation to determine the target ERA5 data and target FNL data. The target sounding data, target occultation data, target ERA5 data, and target FNL data were used as observation data.
[0022] In this embodiment, sounding data, occultation data, ERA5 data, and FNL data are four types of observational and reanalysis data commonly used in atmospheric science. They each have their own characteristics in terms of data source, spatiotemporal resolution, and application scenarios, as detailed below: (1) Radiosonde Data Source: Real-time measurement of atmospheric vertical profile data via sensors (such as thermometers, hygrometers, barometers, and GPS locators) carried by weather balloons.
[0023] Generation method: Global weather stations release weather balloons at regular intervals (usually 00Z and 12Z) every day, and the data is transmitted to the ground station via radio.
[0024] (2) Radio Occultation Data Source: Parameters such as atmospheric temperature, humidity, and air pressure are retrieved by utilizing the refraction delay caused by GPS / LEO satellite signals passing through the Earth's atmosphere.
[0025] Generation method: Atmospheric parameters are calculated by using the geometric relationship and signal delay of occultation events (satellite signals are refracted by the atmosphere).
[0026] (3) ERA5 data Source: Reanalysis data generated by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the data assimilation system (4D-Var) that merges observational data with model forecasts.
[0027] Generation method: Combining observation data from multiple sources such as satellites, radiosondes, and ground stations, the global atmospheric state is simulated through numerical models.
[0028] (4) FNL data Source: Reanalysis data generated by the National Center for Environmental Prediction (NCEP) based on assimilation analysis results from the Global Forecast System (GFS).
[0029] Generation method: Use 3D-Var or 4D-Var assimilation technology to fuse observation data and model forecasts to generate an initial field for numerical forecasting.
[0030] This embodiment may also include a reference dataset that does not directly provide occultation observations. Instead, it uniformly uses the local refractive index observation operator and the one-dimensional bending angle observation operator to calculate the atmospheric refractive index and bending angle, ensuring the consistency of physical quantities. This serves as supplementary comparative data for estimating the observation error of the final occultation data.
[0031] In this embodiment, the first threshold and the second threshold are set according to the actual situation. Preferably, in this embodiment, the first threshold is 3 hours and the second threshold is 300km. That is, the radiosonde data and the occultation data are screened according to the standard of time difference <3 hours and spatial distance <300km.
[0032] In this embodiment, the interpolation methods include time linear interpolation, horizontal spatial bilinear interpolation, and vertical spatial cubic spline interpolation. Taking FNL data as an example, the process is as follows: First, the location of the occultation point (time, latitude, longitude, and altitude) is obtained. Then, the FNL data (time, latitude, longitude, barosphere, and variables) is obtained. For the occultation point, the two time points in the FNL data that are closest to the time provided by the occultation point location are found. For the FNL data at these two time points, bilinear interpolation is performed to obtain the horizontal interpolation. Then, for each vertical layer of the horizontal interpolation, cubic spline interpolation is performed to obtain the vertical interpolation. Finally, time linear interpolation is performed on the vertical interpolation to obtain the time interpolation value, and the time interpolation value is output to obtain the matched target FNL data (time, latitude, longitude, and altitude corresponding to the occultation point location).
[0033] Optionally, the target sounding data, target occultation data, target ERA5 data, and target FNL data are used as observation data, and the data also include: Anomaly detection is performed on the target sounding data and target occultation data, and the first abnormal data is removed; the anomaly detection includes data range detection and data consistency detection. Perform a reasonableness check on the target ERA5 data and the target FNL data, and remove the second abnormal data; the reasonableness check includes data anomaly detection.
[0034] In this embodiment, since the observation data comes from different sources, abnormal data is removed through anomaly detection and rationality detection to further improve the rationality of the data and improve the accuracy of the final observation error estimation result.
[0035] Optionally, anomaly detection includes: Data that exceeds the first preset range in the target sounding data and target occultation data are removed; Data fitting was performed on the target sounding data and the target occultation data, and data that deviated from the fitted line were removed.
[0036] In this embodiment, the first preset range is set according to the actual situation. For example, if the sounding data is temperature, the reasonable range for tropospheric temperature is -90°C to +40°C, the reasonable range for stratospheric temperature is -80°C to 0°C, and the reasonable range for mesosphere temperature is 0°C to -100°C. If the temperature obtained in the troposphere is 100°C, it is obviously abnormal data and needs to be removed.
[0037] In this embodiment, the fitting method can be polynomial fitting, spline function fitting, exponential fitting, etc., so it will not be described in detail.
[0038] Optionally, the reasonableness test includes: Remove data from the target ERA5 data and target FNL data that exceeds the second preset range.
[0039] In this embodiment, the second preset range is set according to the actual situation. For example, if ERA5 data is a wind field, the reasonable range for near-ground wind speed is 0-30m / s, and the reasonable range for upper-level wind field is 0-100m / s. If the wind speed obtained near the ground is 50m, it is obviously abnormal data and needs to be removed.
[0040] Optionally, the variance between each pair of normalized data is calculated to determine the variance result, including: Based on the normalized data corresponding to the target sounding data, target occultation data, target ERA5 data, and target FNL data, the variance is calculated using the first formula, which is: ; in, , , These represent the variance results for the first group, the second group, and the third group, respectively. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target ERA5 data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target FNL data and the normalized data corresponding to the target sounding data.
[0041] The derivation process of the first formula in this embodiment is as follows: Suppose we have three independent datasets I, J, and K, and the goal is to determine the variance of the observation error in dataset I. Here, we use datasets J and K as controls. First, we calculate the variance of the error between datasets I and J: ; in, This represents the error between the data at each position in dataset I and dataset J. , These represent individual data points in datasets I and J, respectively. Let represent the variance of the error between the data points at each position in dataset I and dataset J, and let n represent the total amount of data in dataset I or J. express The average value, , This represents the variance of datasets I and J, This represents the error covariance between dataset I and dataset J.
[0042] Similarly, the variance of the error between datasets I and K, and the variance of the error between datasets J and K can also be calculated.
[0043] Assuming that the errors of datasets I, J, and K are independent of each other, their variances can be disregarded. Based on this, the following mathematical expression can be derived: .
[0044] Based on the above formula, the variance between any two pairs of variables can be calculated: .
[0045] Optionally, based on the variance results, the observation error estimation results of the target occultation data are determined, including: Based on the variance results, the observation error estimation result of the target occultation data is determined using the second formula, whereby: in, This represents the estimated observation error result for the target occultation data.
[0046] In this embodiment, the observation error estimation result is calculated by combining observation data from different sources, which can greatly reduce the error introduced by data from a single source and greatly improve the accuracy of the observation error estimation result.
[0047] Optionally, another specific embodiment is used to illustrate a method for quantitatively estimating occultation data errors, as follows: Taking the estimation of the curvature angle observation error of the Tianmu-1 constellation occultation data in a certain region as an example, the specific implementation process is as follows: 1. Data Input: Acquire occultation data, sounding data, ERA5 data (0.25°×0.25° resolution), and FNL data of the target Tianmu-1.
[0048] 2. Spatiotemporal Matching: Spatiotemporal matching of occultation data and radiosonde data. The matching criteria for the two types of data are a time difference within 3 hours and a spatial radial distance of less than 300 kilometers. The ERA5 / FNL data is mapped to the observation spatiotemporal points of the Tianmu-1 occultation data using a combined algorithm of "temporal linear interpolation + horizontal bilinear interpolation + vertical cubic spline interpolation".
[0049] 3. Quality control: Perform data range checks on radiosonde data to remove outliers that exceed the physically reasonable range (such as temperature anomalies); eliminate the impact of small-scale disturbances on the occultation profile error estimation through double-weighted consistency checks; and verify the rationality of ERA5 / FNL data.
[0050] 4. Observation operator calculation: If there is a reference dataset that does not directly provide occultation observations, the bending angle data is derived from the raw physical quantities such as temperature and air pressure in the radiosonde data and ERA5 / FNL data through a one-dimensional bending angle observation operator, and is used as auxiliary data for the final bending angle observation error.
[0051] 5. Normalization Processing: Based on the spatiotemporally matched ERA5 data, the occultation data, sounding data, and FNL data are normalized according to the normalization algorithm formula.
[0052] Triangle cap estimation: Substitute the normalized relevant data into the first and second formulas to calculate the estimated value of the curvature error variance of the Tianmu-1 occultation data.
[0053] like Figure 2 As shown, the present invention provides a quantitative estimation system for occultation data errors, comprising: The observation data acquisition module is used to acquire observation data from different sources; The normalization module is used to normalize observation data from different sources and determine the normalized data. The variance result determination module is used to calculate the variance between each pair of normalized data and determine the variance result. The observation result determination module is used to determine the observation error estimation results of occultation data based on the variance results.
[0054] Optionally, observational data from different sources include radiosonde data, occultation data, ERA5 data, and FNL data; The observation data acquisition module is specifically used for: If the time difference between radiosonde data and occultation data is less than the first threshold and the spatial distance is less than the second threshold, then the radiosonde data will be used as the target radiosonde data, and the occultation data will be used as the target occultation data. The ERA5 data and FNL data are matched to the occultation point position by interpolation to determine the target ERA5 data and target FNL data. The target sounding data, target occultation data, target ERA5 data, and target FNL data were used as observation data.
[0055] Optionally, the observation data acquisition module is also used for: Anomaly detection is performed on the target sounding data and target occultation data, and the first abnormal data is removed; the anomaly detection includes data range detection and data consistency detection. Perform a reasonableness check on the target ERA5 data and the target FNL data, and remove the second abnormal data; the reasonableness check includes data anomaly detection.
[0056] Optionally, the observation data acquisition module is specifically used for: Data that exceeds the first preset range in the target sounding data and target occultation data are removed; Data fitting was performed on the target sounding data and the target occultation data, and data that deviated from the fitted line were removed.
[0057] Optionally, the observation data acquisition module is specifically used for: Remove data from the target ERA5 data and target FNL data that exceeds the second preset range.
[0058] Optionally, the variance result determination module is specifically used for: Based on the normalized data corresponding to the target sounding data, target occultation data, target ERA5 data, and target FNL data, the variance is calculated using the first formula, which is: ; in, , , These represent the variance results for the first group, the second group, and the third group, respectively. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target ERA5 data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target FNL data and the normalized data corresponding to the target sounding data.
[0059] Optionally, the observation result determination module is specifically used for: Based on the variance results, the observation error estimation result of the target occultation data is determined using the second formula, whereby: in, This represents the estimated observation error result for the target occultation data.
[0060] A computing device according to an embodiment of this disclosure includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned quantitative estimation of occultation data error. That is, a computing device according to an embodiment of this disclosure may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the quantitative estimation of occultation data error shown in any embodiment of this disclosure by calling the computer program.
[0061] In one alternative embodiment, a computing device is provided, such as Figure 3 As shown, Figure 3 The computing device 4000 shown includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the computing device 4000 may further include a transceiver 4004, which can be used for data interaction between the computing device and other computing devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of this computing device 4000 does not constitute a limitation on the embodiments of this disclosure.
[0062] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
[0063] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.
[0064] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.
[0065] The memory 4003 stores application code (computer program) that executes the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.
[0066] The computing device can also be a terminal device, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.
[0067] It should be noted that, Figure 3 The computing device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.
[0068] This disclosure provides an embodiment of a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned quantitative estimation of occultation data errors.
[0069] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.
[0070] In an exemplary embodiment, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computing device to perform the aforementioned quantitative estimation of occultation data errors.
[0071] Computer program code for performing the operations of this disclosure can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0072] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0073] The computer-readable storage medium provided in this disclosure can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0074] The computer-readable storage medium described above carries one or more programs, which, when executed by the computing device, cause the computing device to perform the method shown in the above embodiments.
[0075] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.
[0076] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.
[0077] Those skilled in the art will recognize that this disclosure can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this disclosure can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.
[0078] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.
Claims
1. A method for quantitatively estimating occultation data errors, characterized in that, include: Acquire observational data from different sources; The observation data from different sources are normalized to determine the normalized data; Calculate the variance between each pair of normalized data and determine the variance result; Based on the variance results, the observation error estimation results of the occultation data are determined.
2. The method according to claim 1, characterized in that, The observational data from different sources include radiosonde data, occultation data, ERA5 data, and FNL data; Then, observational data from different sources are obtained, including: If the time difference between the radiosonde data and the occultation data is less than a first threshold and the spatial distance is less than a second threshold, then the radiosonde data is used as the target radiosonde data, and the occultation data is used as the target occultation data. The ERA5 data and the FNL data are matched to the occultation point position by interpolation to determine the target ERA5 data and the target FNL data. The target sounding data, target occultation data, target ERA5 data, and target FNL data are used as observation data.
3. The method according to claim 2, characterized in that, The method of using the target sounding data, target occultation data, target ERA5 data, and target FNL data as observation data also includes: Anomaly detection is performed on the target sounding data and the target occultation data, and the first abnormal data is removed; wherein, the anomaly detection includes data range detection and data consistency detection; Perform a reasonableness check on the target ERA5 data and the target FNL data, and remove the second abnormal data; the reasonableness check includes data anomaly detection.
4. The method according to claim 3, characterized in that, The anomaly detection includes: Data that exceeds a first preset range in the target sounding data and the target occultation data are removed. Data fitting is performed on the target sounding data and the target occultation data, and data that deviates from the fitted line are discarded.
5. The method according to claim 3, characterized in that, The rationality check includes: Remove data from the target ERA5 data and target FNL data that exceeds the second preset range.
6. The method according to claim 2, characterized in that, The calculation of the variance between each pair of normalized data and the determination of the variance result include: Based on the normalized data corresponding to the target sounding data, the target occultation data, the target ERA5 data, and the target FNL data, the variance is calculated using a first formula, wherein the first formula is: ； in, , , These represent the variance results for the first group, the second group, and the third group, respectively. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target ERA5 data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target FNL data. This represents the variance between the normalized data corresponding to the target occultation data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target ERA5 data and the normalized data corresponding to the target sounding data. This represents the variance between the normalized data corresponding to the target FNL data and the normalized data corresponding to the target sounding data.
7. The method according to claim 6, characterized in that, The determination of the observation error estimation result of the target occultation data based on the variance result includes: Based on the variance results, the observation error estimation result of the target occultation data is determined by the second formula, wherein the second formula is: in, This represents the estimation result of the observation error of the target occultation data.
8. A quantitative estimation system for occultation data errors, characterized in that, include: The observation data acquisition module is used to acquire observation data from different sources; The normalization module is used to normalize the observation data from different sources and determine the normalized data; The variance result determination module is used to calculate the variance between each pair of normalized data and determine the variance result. The observation result determination module is used to determine the observation error estimation result of the occultation data based on the variance result.
9. A computing device, comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of a quantitative estimation method for occultation data error as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the steps of a quantitative estimation method for occultation data errors as described in any one of claims 1-7.