Method, device and equipment for budgeting uncertainty of sounding temperature measurement

Abstract

The embodiment of the invention provides a sounding temperature measurement uncertainty budgeting method, device and equipment, and is applied to the technical field of high-altitude meteorological detection. The method comprises the following steps: acquiring and quantifying error sources including the temperature, the thermal peak, the albedo, the ventilation and the equipment calibration detected by the current sonde; calculating a temperature correction value, a thermal peak correction value, a radiation error correction value, a ventilation correction value, a statistical uncertainty, a thermal peak uncertainty, an albedo uncertainty and a ventilation uncertainty according to the quantized error source; correcting the temperature detected by the current sonde according to the temperature correction value, the thermal peak correction value, the radiation error correction value, the ventilation correction value and equipment calibration; and based on a preset synthesis rule, calculating the temperature uncertainty according to the statistical uncertainty, the thermal peak uncertainty, the albedo uncertainty, the ventilation uncertainty and the standby calibration. In this way, the reliability of the sounding temperature measurement uncertainty budget can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of high-altitude meteorological detection technology, and specifically to a method, device and equipment for budgeting uncertainty in sounding temperature measurement. Background Art

[0002] With the completion of the BeiDou-3 global network, BeiDou-based sounding systems have gradually replaced L-band radar sounding. However, in practical applications, many current sounding processes have shortcomings. For example, if a differential positioning method is used to improve Beidou positioning accuracy, the error is corrected through data from the base station and the test station. However, it has deficiencies in error separation, dynamic correction, and multi-source data fusion. The correction model is not dynamic enough and cannot adapt to the high-speed movement environment of the sounding instrument. If a Kalman filter sounding data correction method is used, but the nonlinear error of the sensor is not considered, the pressure data correction based on the neural network is based on the training set relying on historical sounding data, and the real-time performance is poor. The temperature correction error at high altitudes above 300hPa exceeds 1K (WMO requires <0.4K). If a rocket launch method is used, the sounding instrument is transported to a specified altitude and released, and the atmospheric environmental elements are measured during the descent. The detection mode is parachute-sonde separation from the rocket at a predetermined altitude. When the atmospheric dynamic pressure at high altitude gradually meets the parachute opening conditions, the falling speed gradually decreases, and the system gradually stabilizes. However, due to the different existing detection modes, the error sources are different, including drift error, sensor hysteresis effect, thermal influence source, space and speed The influence of the sounding instrument motion state, etc., cannot effectively locate and calculate the error of the balloon sounding mode; if dynamic calibration (such as wind tunnel simulation of high-speed airflow) is used, complex correction models (aerodynamic thermodynamics) are used, but they cannot be applied to the balloon sounding mode. Balloon sounding relies on laboratory static calibration, and the dynamic environment may introduce additional errors; if Monte Carlo simulation or covariance fitting analysis and other methods are used to eliminate systematic errors, the existing sounding mode has a large proportion of systematic errors (such as trajectory error, thermal model error, etc.), but they cannot be applied to the balloon sounding mode. The main source of balloon sounding error is random error, which can be reduced by multiple releases, ground calibration and other methods; if the aerodynamic heating correction model uncertainty is quantified, and data processing methods such as high-frequency sampling data noise reduction processing based on high-speed motion are used, they cannot be applied to the balloon sounding mode. Balloon sounding requires trajectory correction for horizontal drift motion, which introduces position uncertainty, and long time series need to consider instrument drift calibration (such as temperature and humidity sensor baseline changes).

[0003] In summary, based on the balloon sounding mode, the current sounding process has the following problems in practical application: 1. Complex sources of uncertainty: sounding data are interfered by multiple factors such as satellite signal multipath effects, sensor response delays, and stratospheric turbulence, and existing methods are difficult to quantify and analyze; 2. Insufficient timeliness of correction: static correction models (such as the least squares method) cannot adapt to the dynamic error changes caused by the high-speed ascent of the sounding instrument (typical speed: 5-8m / s); 3. Poor cross-system compatibility: existing correction devices rely on a single data source (such as only using Beidou sounding data) and lack the ability to integrate multiple sources with ground-based vertical remote sensing such as microwave radiometers and wind profilers.

[0004] Among them, the professional terms involved in this disclosure include: Global Navigation Satellite System Radiosonde (GNSS RS) refers to a radiosonde carried by a balloon (balloon) that provides longitude and latitude information based on satellite navigation positioning, and cooperates with a ground (or networked) receiver to complete the comprehensive detection of meteorological elements such as atmospheric temperature, pressure, relative humidity, wind direction and wind speed from the ground to high altitude (or in the three stages of ascent, leveling and descent). Radiosonde Measurement Uncertainty (RSMU) refers to the deviation range between the quantitative radiosonde measurement results and the true value, including random errors and systematic errors of parameters such as temperature, humidity, pressure, and wind speed caused by multiple factors such as radiosonde sensor errors, signal transmission delays, and atmospheric refraction. It is usually expressed as a 95% confidence interval (2σ standard deviation). The specific value varies depending on the different physical variables of the radiosonde measurement data in the high-altitude vertical layer. The Error Correction Model (ECM) automatically adjusts the error correction range and time window (typical values: 30 seconds to 5 minutes) based on typical errors such as radiosonde altitude, motion speed, and temperature radiation effects. It requires quantitative evaluation through a comprehensive error method to form a corresponding error correction algorithm. The Uncertainty Budget (UB) is a systematic analysis method used to identify, quantify, and summarize all possible sources of error in the measurement process. These error sources include systematic errors, random errors, calibration errors, instrument performance errors, etc. Through the uncertainty budget, the impact of each error source on the measurement result can be evaluated, and the total uncertainty of the measurement result can be ultimately determined. The Law of Propagation of Uncertainty (LPU) is a general formula for calculating the combined standard uncertainty. When the input quantities are correlated, their covariance needs to be considered. Summary of the Invention

[0005] The present disclosure provides a sounding temperature measurement uncertainty budget method, apparatus, device and storage medium.

[0006] According to a first aspect of the present disclosure, a method for budgeting uncertainty in sounding temperature measurement is provided. The method comprises:

[0007] Identify and quantify sources of error in current radiosonde measurements, including temperature, thermal peaks, albedo, ventilation, and instrument calibration;

[0008] Calculate corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo, and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values, and ventilation correction values; and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty, and ventilation uncertainty;

[0009] Correcting the temperature currently detected by the radiosonde according to the temperature correction value, the thermal peak correction value, the radiation error correction value, the ventilation correction value, and the equipment calibration;

[0010] Based on a preset synthesis rule, temperature uncertainty is calculated according to the statistical uncertainty, the thermal peak uncertainty, the albedo uncertainty, the ventilation uncertainty and the equipment calibration.

[0011] According to the above aspects and any possible implementation, there is further provided an implementation, wherein the calculation of the temperature correction value and the statistical uncertainty includes:

[0012] The quantified temperature is subjected to high and low filtering and disturbance error filtering, and the temperature correction value and statistical uncertainty are calculated.

[0013] According to the above aspects and any possible implementation, further provided is an implementation, wherein the calculation of the thermal peak correction value and the thermal peak uncertainty includes:

[0014] The quantified thermal peaks are filtered, and the thermal peak correction value and thermal peak uncertainty are calculated.

[0015] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the calculation of the radiation error correction value and the albedo uncertainty includes:

[0016] Get the time, longitude and latitude, and air pressure of the current radiosonde detection;

[0017] Calculate the solar altitude angle according to the time and longitude and latitude;

[0018] The radiation error correction value and the albedo uncertainty are calculated based on the quantified albedo, the preset radiation difference table, the air pressure and the solar altitude angle.

[0019] According to the above aspects and any possible implementation, further provided is an implementation, wherein the calculation of the ventilation correction value and the ventilation uncertainty includes:

[0020] Get the ascent rate detected by the current radiosonde;

[0021] Based on the ascent rate, the ventilation correction value and ventilation uncertainty are calculated.

[0022] According to the above aspects and any possible implementation, further provided is an implementation, wherein obtaining and quantifying the error source includes:

[0023] Obtain and identify systematic errors and random errors, and determine the sources of errors;

[0024] The error sources are quantified based on a preset quantization rule.

[0025] According to the above aspects and any possible implementation, a further implementation is provided, wherein the preset synthesis rule includes:

[0026]

[0027] Among them, u i is the uncertainty of the ith error source.

[0028] According to a second aspect of the present disclosure, a device for estimating uncertainty in sounding temperature measurement is provided. The device comprises:

[0029] An acquisition module is used to acquire and quantify error sources, including temperature, thermal peak, albedo, ventilation, and equipment calibration detected by the current radiosonde;

[0030] a calculation module for calculating corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo, and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values, and ventilation correction values; and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty, and ventilation uncertainty;

[0031] a correction module, configured to correct the temperature currently detected by the radiosonde according to the temperature correction value, the heat peak correction value, the radiation error correction value, the ventilation correction value, and the equipment calibration;

[0032] The calculation module is further configured to calculate the temperature uncertainty based on a preset synthesis rule, the statistical uncertainty, the thermal peak uncertainty, the albedo uncertainty, the ventilation uncertainty, and the equipment calibration.

[0033] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0034] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0035] The embodiment of the present application provides a sounding temperature measurement uncertainty budget method, which can obtain and quantify the error sources, including the temperature, thermal peak, albedo, ventilation and equipment calibration detected by the current sounding instrument; then calculate the corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values and ventilation correction values, and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty and ventilation uncertainty; then calculate the current sounding temperature measurement uncertainty budget method based on the temperature correction values, thermal peak correction values, radiation error correction values, ventilation correction values and equipment calibration. The temperature detected by the radiosonde is corrected. Based on preset synthesis rules, the temperature uncertainty is calculated based on statistical uncertainty, thermal peak uncertainty, albedo uncertainty, ventilation uncertainty, and backup calibration. This allows for the construction of a systematic analytical framework, a dynamic adaptive correction model, to identify, quantify, and summarize all sources of error in the measurement process and estimate the uncertainty of the radiosonde temperature measurement. This not only improves the reliability of the measurement results but also ensures the long-term stability and traceability of the data. Furthermore, the uncertainty budget is used not only to assess the reliability of the measurement results but also to verify the measurement results, improve the measurement method, and control data quality. Specifically, the rationality of the uncertainty budget is verified by comparison with independent measurement results. By analyzing the uncertainty budget, the main error sources are identified and measures are taken to reduce them. In data processing and analysis, the uncertainty budget is used to assess data quality and make necessary corrections. The uncertainty budget is used to verify the reliability of the measurement results and confirm the rationality of the uncertainty estimate by comparison with independent measurement results. Furthermore, the uncertainty budget provides a better understanding of the uncertainty of the measurement results, thereby providing high-quality data support for scientific research and climate monitoring.

[0036] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0038] Figure 1 A flow chart of a sounding temperature measurement uncertainty budget method according to an embodiment of the present disclosure is shown;

[0039] Figure 2 FIG2 shows a block diagram of a sounding temperature measurement uncertainty budget device according to an embodiment of the present disclosure;

[0040] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0041] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0042] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0043] In the present disclosure, a systematic analysis framework, namely a dynamic adaptive correction model, can be built to identify, quantify and summarize all sources of error in the measurement process, and budget the uncertainty of the radiosonde temperature measurement, which not only improves the reliability of the measurement results, but also provides a guarantee for the long-term stability and traceability of the data; at the same time, it is not only used to evaluate the reliability of the measurement results, but also to verify the measurement results, improve the measurement method and control the data quality. Specifically, by comparing with independent measurement results, the rationality of the uncertainty budget is verified; by analyzing the uncertainty budget, the main sources of error are identified, and measures are taken to reduce the error; in data processing and analysis, the uncertainty budget is used to evaluate the quality of the data and make necessary corrections; among them, the uncertainty budget is used to verify the reliability of the measurement results, and by comparing with independent measurement results, the rationality of the uncertainty estimate is confirmed, and through the uncertainty budget, the uncertainty of the measurement results can be better understood, thereby providing high-quality data support for scientific research and climate monitoring.

[0044] Figure 1 A flow chart of a sounding temperature measurement uncertainty budget method 100 according to an embodiment of the present disclosure is shown.

[0045] At block 110 , error sources are obtained and quantified, including temperature, thermal peaks, albedo, ventilation, and equipment calibration of the current radiosonde measurement.

[0046] In some embodiments, due to the various sources of error during sounding, such as instrument error, human error, environmental error, sample error, method error, and data error, when performing a sounding temperature measurement uncertainty budget, it is necessary to obtain, identify, and quantify these error sources to improve the reliability of the sounding temperature measurement uncertainty budget. For example, when obtaining the error sources, all possible error sources should be listed as much as possible.

[0047] In some embodiments, obtaining and quantifying the error source specifically includes:

[0048] Obtain and identify systematic errors and random errors, and determine the sources of errors;

[0049] Quantize the error sources based on preset quantization rules.

[0050] In some embodiments, when obtaining the error sources, the error sources can be framework-based and divided into systematic errors and random errors so as to avoid missing possible error sources and obtain all the error sources required for estimating the uncertainty of the radiosonde temperature measurement as much as possible.

[0051] In some embodiments, all acquired error sources are identified and each error source is quantified, such as by evaluating the size of each error source through experiments, models, or literature data, thereby determining the construction of a dynamic adaptive correction model to improve the reliability of the uncertainty budget of the sounding temperature measurement.

[0052] In some embodiments, by identifying and quantifying the sources of errors and determining the temperature, thermal peaks, albedo, ventilation and equipment calibration detected by the current radiosonde as the sources of errors, real-time separation and quantification of multi-source errors can be achieved, such as achieving the goal of an error identification response time of less than 2 seconds.

[0053] In box 120, the corresponding correction results and uncertainties are calculated based on the quantified temperature, thermal peak, albedo and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values and ventilation correction values, and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty and ventilation uncertainty.

[0054] In some embodiments, the calculation of the temperature correction value and the statistical uncertainty specifically includes:

[0055] The quantified temperature is subjected to high and low filtering and disturbance error filtering, and the temperature correction value and statistical uncertainty are calculated.

[0056] In some embodiments, the detected temperature can be Gaussian filtered, and the presence of light can be determined by calculating the first data day height angle, so that different filtering windows can be used to filter the disturbance error, and then the temperature correction value and statistical uncertainty can be calculated to improve the reliability of the uncertainty budget of the sounding temperature measurement.

[0057] In some embodiments, the calculation of the thermal peak correction value and the thermal peak uncertainty specifically includes:

[0058] The quantified thermal peaks are filtered, and the thermal peak correction value and thermal peak uncertainty are calculated.

[0059] In some embodiments, the radiosonde may be subjected to thermal peak filtering and processed for local abnormal high value correction, thereby recalculating the thermal peak correction value and thermal peak uncertainty to improve the reliability of the radiosonde temperature measurement uncertainty budget.

[0060] In some embodiments, the calculation of the radiation error correction value and the albedo uncertainty specifically includes:

[0061] Get the time, longitude and latitude, and air pressure of the current radiosonde detection;

[0062] Calculate the solar altitude angle according to time and longitude and latitude;

[0063] The radiation error correction value and albedo uncertainty are calculated based on the quantified albedo, the preset radiation difference table, air pressure, and solar altitude angle.

[0064] In some embodiments, the radiosonde's current time, latitude, longitude, and air pressure can be combined with a radiometric error correction table (i.e., an initialized radiometric correction difference table) to calculate the radiometric error correction value and albedo uncertainty. This calculation can be performed every second to improve the reliability of the uncertainty budget for the radiosonde temperature measurement.

[0065] In some embodiments, the calculation of the ventilation correction value and the ventilation uncertainty specifically includes:

[0066] Get the ascent rate detected by the current radiosonde;

[0067] Based on the ascent rate, calculate the ventilation correction value and ventilation uncertainty.

[0068] In some embodiments, the ascent rate of the sounding instrument during detection can be calculated by the second-by-second difference in potential height, and the ventilation correction coefficient can be calculated based on the ascent rate. The influence of the ambient wind is corrected in the wind-related calculation step, and then the ventilation correction value and ventilation uncertainty are calculated to improve the reliability of the uncertainty budget of the sounding temperature measurement.

[0069] In block 130 , the temperature currently detected by the radiosonde is corrected based on the temperature correction value, the heat peak correction value, the radiation error correction value, the ventilation correction value, and the equipment calibration.

[0070] In some embodiments, the temperature currently detected by the radiosonde may be corrected by combining filtering results (temperature correction values), radiation error correction, ventilation correction, thermal peak correction, and equipment calibration.

[0071] At block 140 , temperature uncertainty is calculated based on a preset synthesis rule from statistical uncertainty, thermal peak uncertainty, albedo uncertainty, ventilation uncertainty, and device calibration.

[0072] In some embodiments, the temperature uncertainty may be calculated by combining statistical uncertainty, thermal peak uncertainty, equipment calibration, albedo uncertainty, and ventilation uncertainty, ie, the uncertainties of all error sources are synthesized to obtain a total uncertainty.

[0073] In some embodiments, the above-mentioned preset synthesis rules specifically include:

[0074]

[0075] Among them, u i is the uncertainty of the ith error source.

[0076] In some embodiments, the synthesis method, i.e., the preset synthesis rule, is generally based on the error propagation formula, for example, for independent error sources, the total uncertainty u t otal, that is, the temperature uncertainty can be calculated by the above formula.

[0077] In some embodiments, by constructing a dynamic, adaptive correction model in the manner described above to correct the current sonde temperature and calculate temperature uncertainty, the temperature measurement error at an altitude of 30 km can be reduced from 0.8 K to 0.4 K. Furthermore, based on the above-described uncertainty budgeting method for sonde temperature measurements, a lightweight, embedded correction device can be developed, achieving the goals of less than 5 W power consumption and less than 100 g weight.

[0078] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0079] It can obtain and quantify the error sources, including the temperature, thermal peak, albedo, ventilation and equipment calibration detected by the current radiosonde; then calculate the corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo and ventilation; the correction results include temperature correction value, thermal peak correction value, radiation error correction value and ventilation correction value, and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty and ventilation uncertainty; then correct the temperature detected by the current radiosonde based on the temperature correction value, thermal peak correction value, radiation error correction value, ventilation correction value and equipment calibration; then Based on preset synthesis rules, temperature uncertainty is calculated from statistical uncertainty, thermal peak uncertainty, albedo uncertainty, ventilation uncertainty, and backup calibration. This allows for the construction of a systematic analytical framework—a dynamic, adaptive correction model—to identify, quantify, and summarize all sources of error during the measurement process, thereby budgeting the uncertainty of radiosonde temperature measurements. This not only improves the reliability of the measurement results but also ensures the long-term stability and traceability of the data. Furthermore, the uncertainty budget is used not only to assess the reliability of the measurement results but also to verify the results, improve measurement methods, and control data quality. Specifically, the rationality of the uncertainty budget is verified by comparison with independent measurement results. By analyzing the uncertainty budget, the main sources of error are identified, and measures are taken to reduce them. In data processing and analysis, the uncertainty budget is used to assess data quality and make necessary corrections. The uncertainty budget is used to verify the reliability of the measurement results and confirm the rationality of the uncertainty estimate by comparison with independent measurement results. Furthermore, the uncertainty budget provides a better understanding of the uncertainty of the measurement results, thereby providing high-quality data support for scientific research and climate monitoring.

[0080] What is more noteworthy is that the above-mentioned sounding temperature measurement uncertainty budget method can be applied not only to scenarios where the sounding instrument is carried by a weather balloon from the ground to 49km to conduct high-altitude climate observations, but also to scenarios where the sounding instrument is carried by rockets, airplanes, airships and other carriers to conduct high-altitude climate observations.

[0081] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0082] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0083] Figure 2 FIG. 2 shows a block diagram of a sounding temperature measurement uncertainty budget device 200 according to an embodiment of the present disclosure. Figure 2 As shown, the device 200 includes:

[0084] An acquisition module 210 is used to acquire and quantify error sources, including temperature, thermal peak, albedo, ventilation, and equipment calibration detected by the current radiosonde;

[0085] A calculation module 220 is configured to calculate corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo, and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values, and ventilation correction values; and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty, and ventilation uncertainty;

[0086] The correction module 230 is used to correct the temperature currently detected by the radiosonde according to the temperature correction value, the heat peak correction value, the radiation error correction value, the ventilation correction value and the equipment calibration;

[0087] The calculation module 220 is further configured to calculate the temperature uncertainty based on a preset synthesis rule, statistical uncertainty, thermal peak uncertainty, albedo uncertainty, ventilation uncertainty, and equipment calibration.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0089] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0091] Figure 3 A block diagram of an exemplary electronic device 300 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0092] The electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM 302 or a computer program loaded from a storage unit 308 into a RAM 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0093] Multiple components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0094] The computing unit 301 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 308.

[0095] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0096] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0101] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0102] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0103] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A sounding temperature measurement uncertainty budget method, characterized in that: include: Identify and quantify sources of error in current radiosonde measurements, including temperature, thermal peaks, albedo, ventilation, and instrument calibration; Calculate corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo, and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values, and ventilation correction values; and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty, and ventilation uncertainty; Correcting the temperature currently detected by the radiosonde according to the temperature correction value, the thermal peak correction value, the radiation error correction value, the ventilation correction value, and the equipment calibration; Based on a preset synthesis rule, temperature uncertainty is calculated according to the statistical uncertainty, the thermal peak uncertainty, the albedo uncertainty, the ventilation uncertainty and the equipment calibration.

2. The method according to claim 1, characterized in that The calculation of the temperature correction value and the statistical uncertainty includes: The quantified temperature is subjected to high and low filtering and disturbance error filtering, and the temperature correction value and statistical uncertainty are calculated.

3. The method according to claim 1, characterized in that The calculation of the thermal peak correction value and the thermal peak uncertainty includes: The quantified thermal peaks are filtered, and the thermal peak correction value and thermal peak uncertainty are calculated.

4. The method according to claim 1, wherein The calculation of the radiation error correction value and the albedo uncertainty includes: Get the time, longitude and latitude, and air pressure of the current radiosonde detection; Calculate the solar altitude angle according to the time and longitude and latitude; The radiation error correction value and the albedo uncertainty are calculated based on the quantified albedo, the preset radiation difference table, the air pressure and the solar altitude angle.

5. The method according to claim 1, wherein The calculation of the ventilation correction value and the ventilation uncertainty includes: Get the ascent rate detected by the current radiosonde; Based on the ascent rate, the ventilation correction value and ventilation uncertainty are calculated.

6. The method according to claim 1, characterized in that The acquisition and quantification of error sources include: Obtain and identify systematic errors and random errors, and determine the sources of errors; Based on a preset quantization rule, the error source is quantified.

7. The method according to any one of claims 1 to 6, characterized in that The preset synthesis rules include: Among them, u i is the uncertainty of the ith error source.

8. A sounding temperature measurement uncertainty budget device, characterized in that: include: An acquisition module is used to acquire and quantify error sources, including temperature, thermal peak, albedo, ventilation, and equipment calibration detected by the current radiosonde; a calculation module for calculating corresponding correction results and uncertainties based on the quantified temperature, thermal peak, albedo, and ventilation; the correction results include temperature correction values, thermal peak correction values, radiation error correction values, and ventilation correction values; and the uncertainties include statistical uncertainty, thermal peak uncertainty, albedo uncertainty, and ventilation uncertainty; a correction module, configured to correct the temperature currently detected by the radiosonde according to the temperature correction value, the heat peak correction value, the radiation error correction value, the ventilation correction value, and the equipment calibration; The calculation module is further configured to calculate the temperature uncertainty based on a preset synthesis rule, the statistical uncertainty, the thermal peak uncertainty, the albedo uncertainty, the ventilation uncertainty, and the equipment calibration.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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