CN111458768A - Strong convection weather early warning method, computer equipment and storage medium - Google Patents
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Abstract
The invention is suitable for the technical field of strong convection weather monitoring, and provides a strong convection weather early warning method, which comprises the following steps: the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay; acquiring real-time troposphere stem delay according to the real-time meteorological parameters; the invention has the beneficial effects that: the method comprises the steps of receiving satellite observation data and real-time meteorological parameters such as air pressure and temperature in real time through a GNSS receiver, resolving dry and wet delay components of a troposphere in real time, calculating changes of atmospheric water vapor content in a reverse mode, determining an early warning threshold value according to the atmospheric water vapor content in historical data and an empirical model of actual measurement precipitation, achieving the purpose of monitoring strong convection weather in real time according to the changes of the atmospheric water vapor content and the real-time meteorological parameters, and having the advantages of real-time detection and high precision.
Description
Technical Field
The invention relates to the technical field of strong convection weather monitoring, in particular to a strong convection weather early warning method, computer equipment and a storage medium.
Background
The strong convection weather generally refers to weather such as short-term thunderstorm, strong wind, hail, strong precipitation and the like, has the characteristics of strong burstiness and locality, short life history, serious disasters and the like, and is a difficult point in weather forecast business. But the frequency of occurrence, casualties and property loss also make the problem research of great significance.
At present, the forecast of the strong convection weather is mainly realized by using an automatic weather station, a radar technology, a weather satellite and mesoscale numerical analysis. Since the 90 s, with the arrangement of a new generation of Doppler speed measuring radar, the detection capability of strong convection weather is greatly improved. In the 21 st century, the meteorological satellite detection technology in China is continuously developed, and the space-time resolution of satellite image data is greatly improved. In addition, the application of new data such as wind profile radar, microwave radiometer, automatic weather station and the like is greatly helpful for the research, monitoring and early warning of strong convection weather.
Analyzing the forming conditions of the strong convection weather by using a numerical simulation method by the Zhang Tuoling and the like; however, strong convection weather has strong short-time burstiness, so that it is difficult for single technologies such as conventional detection data and high-spatial-temporal-resolution mesoscale numerical simulation to accurately forecast and alarm such weather.
Strong convection weather often occurs with varying levels of moisture in the atmosphere. With the application of the GNSS technology in meteorology, the students such as Li also demonstrate the feasibility of the technology in rainstorm forecasting, and estimate the clock error of a precise satellite in real time through a GNSS Continuous Operation Reference Station (CORS) to complete PPP calculation, and obtain troposphere delay and the change of the water vapor content thereof in real time to carry out the short-term rainstorm forecasting.
In general, since the system causing strong convection weather belongs to a medium and small scale weather system, accurate forecasting by a single technology of a conventional meteorological observation network is difficult. The combination of fusion, data assimilation and scale numerical modes of various technical means is a development trend for short-term and close prediction of strong convection weather.
Disclosure of Invention
An embodiment of the present invention provides a strong convection weather early warning method, a computer device, and a storage medium, and aims to solve technical problems in the background art.
The embodiment of the invention is realized in such a way that the strong convection weather early warning method comprises the following steps:
the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay;
acquiring real-time troposphere dry delay according to the real-time meteorological parameters, and acquiring real-time troposphere wet delay according to the difference value of the real-time troposphere total delay and the real-time troposphere dry delay;
defining a conversion factor, wherein the conversion factor is a conversion coefficient of historical atmospheric degradable water content and real-time tropospheric moisture delay, acquiring an atmospheric weighted average temperature according to an existing empirical model, acquiring the conversion factor of the real-time tropospheric moisture delay and the atmospheric degradable water content, and acquiring real-time atmospheric water vapor content;
acquiring atmospheric water vapor content of a CORS station according to historical data, downloading data of the CORS station, acquiring precipitation data of local corresponding time, establishing an empirical model of atmospheric water vapor content change and actually measured precipitation, acquiring a preliminary early warning threshold value of strong convection weather according to the empirical model, and detecting the preliminary early warning threshold value by utilizing the actually measured data of multiple times of strong convection weather to acquire a final determined early warning threshold value;
and comparing the content of the atmospheric water vapor and the real-time meteorological parameters with an early warning threshold value, and pushing forecast information to a user when the content of the atmospheric water vapor exceeds the early warning threshold value.
As a further scheme of the invention: after acquiring CORS station observation data and precise orbit/clock error correction information SSR, processing the obtained data to remove data with overlarge errors.
As a still further scheme of the invention: after CORS station observation data and precision orbit/clock correction information SSR are obtained, real-time PPP resolving is carried out on the obtained data, and then real-time troposphere delay is obtained by adopting an anti-error Kalman filtering algorithm.
As a still further scheme of the invention: and the CORS station observation data and the precise orbit/clock error correction information SSR are respectively obtained through a TCP/IP protocol and an NTRIP protocol.
As a still further scheme of the invention: in the step of detecting the preliminary early warning threshold value by utilizing measured data of multiple times of strong convection weather, if the accuracy rate of detecting the preliminary early warning threshold value exceeds a set value, the preliminary early warning threshold value is directly determined to be determined as the early warning threshold value, and if the accuracy rate is lower than the set value, the early warning threshold value is adjusted through nonlinear fitting.
As a still further scheme of the invention: the real-time meteorological parameters are acquired by the air pressure and temperature sensors through network transmission.
It is another object of the embodiments of the present invention to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the strong convection weather warning method.
It is another object of an embodiment of the present invention to provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to execute the steps of the strong convection weather warning method.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of receiving satellite observation data and real-time meteorological parameters such as air pressure and temperature in real time through a GNSS receiver, resolving dry and wet delay components of a troposphere in real time, calculating changes of atmospheric water vapor content in a reverse mode, determining an early warning threshold value according to the atmospheric water vapor content in historical data and an empirical model of actual measurement precipitation, achieving the purpose of monitoring strong convection weather in real time according to the changes of the atmospheric water vapor content and the real-time meteorological parameters, and having the advantages of real-time detection and high precision.
Drawings
Fig. 1 is a flowchart of a strong convection weather early warning method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Specific implementations of the present invention are described in detail below with reference to specific embodiments.
As shown in fig. 1, a flowchart of a strong convection weather warning method according to an embodiment of the present invention includes the following steps:
the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay;
acquiring real-time troposphere dry delay according to the real-time meteorological parameters, wherein a model exists between the acquisition of the real-time troposphere dry delay and the real-time meteorological parameters, the model is introduced in the subsequent section, and real-time troposphere wet delay is obtained according to the difference value of the real-time troposphere total delay and the real-time troposphere dry delay;
defining a conversion factor, wherein the conversion factor is a conversion coefficient of historical atmospheric degradable water content and real-time tropospheric moisture delay, acquiring an atmospheric weighted average temperature according to an existing empirical model, acquiring the conversion factor of the real-time tropospheric moisture delay and the atmospheric degradable water content, and acquiring real-time atmospheric water vapor content;
acquiring atmospheric water vapor content of a CORS station according to historical data, downloading data of the CORS station, acquiring precipitation data of local corresponding time, establishing an empirical model of atmospheric water vapor content change and actually measured precipitation, acquiring a preliminary early warning threshold value of strong convection weather according to the empirical model, and detecting the preliminary early warning threshold value by utilizing the actually measured data of multiple times of strong convection weather to acquire a final determined early warning threshold value;
and comparing the content of the atmospheric water vapor and the real-time meteorological parameters with an early warning threshold value, and pushing forecast information to a user when the content of the atmospheric water vapor exceeds the early warning threshold value.
In the embodiment of the invention, satellite observation data and real-time meteorological parameters such as air pressure, temperature and the like are received in real time through a GNSS receiver, the dry and wet delay components of a troposphere are calculated in real time, the change of the content of atmospheric water vapor is calculated reversely, finally, a determined early warning threshold value is obtained according to an empirical model of the content of atmospheric water vapor and the actually measured precipitation in historical data, and the purpose of monitoring the strong convection weather in real time can be achieved according to the change of the content of atmospheric water vapor and the real-time meteorological parameters.
With respect to the model between the acquisition of real-time tropospheric stem delay and real-time meteorological parameters, the zenith tropospheric stem delay at the survey station location can be calculated by the Saastamoinen model because real-time tropospheric stem delay ZHD is closely related to barometric pressure, specifically:
in the formula (I), the compound is shown in the specification,
and h are latitude and elevation of the measuring station respectively, and P is surface air pressure.
The real-time tropospheric stem delay can be obtained by the model according to real-time meteorological parameters including the earth surface pressure.
For the conversion factor, the conversion factor is the conversion coefficient between the historical atmospheric degradable water quantity PWV and the real-time tropospheric wetting delay ZWD.
PWV ═ Π · ZWD, which can be expressed specifically as:
conversion coefficient
Which is related to the weighted average temperature of the troposphere.
The conversion coefficient pi can be directly calculated according to the station latitude and the annual product date:
wherein θ is the station latitude; t is tDThe number of the year is one;
for the same station, the conversion coefficient pi is only related to the year and the day, and the conversion coefficient pi is the empirical model.
As shown in fig. 1, as a preferred embodiment of the present invention, after acquiring the CORS station observation data and the precise orbit/clock error correction information SSR, the obtained data is further processed to remove the data with excessive errors.
Because the obtained data contains a large amount of drift data, the error of the drift data is large, and the final empirical model is not favorably established, so in the embodiment, the data with large errors needs to be removed.
As shown in fig. 1, as a preferred embodiment of the present invention, after acquiring CORS station observation data and precise orbit/clock correction information SSR, real-time PPP resolving is performed on the acquired data, and then real-time tropospheric delay is obtained by using an robust kalman filter algorithm.
Specifically, the state equation and observation equation of kalman filtering in Precise Point Positioning (PPP) are as follows:
Xk,k-1＝Φk,k-1Φk,k-1Xk-1+wk-1,wk-1～N(0,Qk-1)；
zk＝HkXk,k-1+vk,vk～N(0,Rk)；
in the formula, Xk,k-1、Xk-1Is a state vector of phik,k-1Is a state transition matrix; w is ak-1Is a dynamic noise vector; qk-1As a dynamic noise vector wk-1A covariance matrix of (a); z is a radical ofkIs an observation vector; hkIs a coefficient matrix; v. ofkTo observe the noise vector; rkFor observing noise vector vkCovariance matrix of (2).
By using the state equation and the observation equation, Kalman filtering recursion estimation can be carried out. The basic calculation process is summarized as two steps of prediction and filtering:
(1) prediction
Calculating a prediction error variance matrix:
(2) filtering
A gain array:
new information:
∑ calculating filter error equation matrixk＝[I-KkHk]∑k,k-1。
It can be seen that the filtering process is calculated in a continuous 'prediction-correction' recursion mode, the predicted value is calculated firstly, and then the predicted value is corrected according to new information obtained by the observed value and Kalman gain. The prediction value can be obtained from the filtering value, and the filtering value can be obtained from the prediction value, so that the prediction and the filtering interact, and the real-time estimation can be carried out without storing any observation data.
The robust Kalman filtering introduces robust equivalence weight and adaptive equivalence weight in the adjustment process, and realizes the adaptive filtering process of a dynamic system on the premise of robust estimation.
The gain matrix is changed to:
wherein the content of the first and second substances,
for anti-difference equivalence weights, they can be obtained by Huber, IGG1, IGG3 equivalenceRealizing a weight function;
is an adaptive equivalent weight matrix.
Therefore, the robust adaptive filtering result can be derived as follows:
the method determines the observation noise covariance and the state noise covariance by using a robust self-adaptive method, and can effectively control the influence of observation abnormity and dynamic model noise abnormity on state parameter estimation.
As shown in fig. 1, as a preferred embodiment of the present invention, the CORS station observation data and the precise orbit/clock correction information SSR are obtained by TCP/IP protocol and NTRIP protocol, respectively.
In this embodiment, the GNSS receiver actively acquires the data of the CORS station through the TCP/IP protocol, and decodes the RTCM format data stream to acquire data that can be recognized or processed, and the precise orbit/clock correction information SSR is acquired through the NTRIP protocol.
As shown in fig. 1, as a preferred embodiment of the present invention, in the step of detecting the preliminary early warning threshold by using the measured data of the strong convection weather, if the accuracy of the detected preliminary early warning threshold exceeds the set value, the preliminary early warning threshold is directly determined as the determined early warning threshold, and if the accuracy is lower than the set value, the early warning threshold is adjusted by nonlinear fitting.
The step of non-linear fit adjustment needs to be specified, and is better illustrated if the example is given.
As shown in FIG. 1, as a preferred embodiment of the present invention, the real-time weather parameters are obtained by the pressure and temperature sensors through network transmission.
An embodiment of the present invention further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the following steps:
the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay;
acquiring real-time troposphere dry delay according to the real-time meteorological parameters, and acquiring real-time troposphere wet delay according to the difference value of the real-time troposphere total delay and the real-time troposphere dry delay;
defining a conversion factor, obtaining the weighted average atmospheric temperature according to the existing empirical model, obtaining the conversion factor of the real-time tropospheric moisture delay and the atmospheric degradable water content, and obtaining the real-time atmospheric water vapor content;
acquiring atmospheric water precipitation of a CORS station according to historical data, downloading data of the CORS station, acquiring precipitation data of local corresponding time, establishing an empirical model of atmospheric water vapor content change and actually measured precipitation, acquiring a preliminary early warning threshold value of strong convection weather according to the empirical model, and detecting the preliminary early warning threshold value by utilizing the actually measured data of multiple times of strong convection weather to acquire a final determined early warning threshold value;
and comparing the atmospheric water vapor content and the real-time meteorological parameters with a determined early warning threshold value, and pushing forecast information to a user when the determined early warning threshold value is exceeded.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is caused to execute the following steps:
the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay;
acquiring real-time troposphere dry delay according to the real-time meteorological parameters, and acquiring real-time troposphere wet delay according to the difference value of the real-time troposphere total delay and the real-time troposphere dry delay;
defining a conversion factor, obtaining the weighted average atmospheric temperature according to the existing empirical model, obtaining the conversion factor of the real-time tropospheric moisture delay and the atmospheric degradable water content, and obtaining the real-time atmospheric water vapor content;
acquiring atmospheric water precipitation of a CORS station according to historical data, downloading data of the CORS station, acquiring precipitation data of local corresponding time, establishing an empirical model of atmospheric water vapor content change and actually measured precipitation, acquiring a preliminary early warning threshold value of strong convection weather according to the empirical model, and detecting the preliminary early warning threshold value by utilizing the actually measured data of multiple times of strong convection weather to acquire a final determined early warning threshold value;
and comparing the content of the atmospheric water vapor and the real-time meteorological parameters with an early warning threshold value, and pushing forecast information to a user when the content of the atmospheric water vapor exceeds the early warning threshold value.
Here, the early warning threshold is comprehensively judged according to historical data of the atmospheric water vapor content and the atmospheric pressure temperature near the weather station. Firstly, obtaining an early warning initial threshold value Y of strong convection weather through machine learning; secondly, performing linear regression analysis on the initial early warning initial threshold value Y according to the recent times of strong convection meteorological data, manually modifying and then determining a proper threshold value, specifically:
and (3) recalling historical atmospheric water vapor content data and temperature and pressure parameters in the current analysis area and performing data recall by using a machine learning method, and establishing a meteorological comprehensive index model:
K＝X1w1+X2w2+X3w3
wherein K is a meteorological index, X1、X2、X3Respectively, the atmospheric water vapor content, temperature, pressure parameters, w1、w2、w3Respectively, the weight of the atmospheric water vapor content, the temperature and the air pressure parameters. According to the obtained real-time water vapor content data and real-time meteorological parameters, calculating a meteorological comprehensive parameter K by using a weighted average model, and timely returning and comparing the meteorological comprehensive parameter K with a set early warning threshold value Y; and when K is more than or equal to Y, sending out early warning information, otherwise, not sending out the early warning information.
Those skilled in the art will appreciate that all or a portion of the processes in the methods of the embodiments described above may be implemented by computer programs that may be stored in a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, non-volatile memory may include read-only memory (ROM), programmable ROM (prom), electrically programmable ROM (eprom), electrically erasable programmable ROM (eeprom), or flash memory, volatile memory may include Random Access Memory (RAM) or external cache memory, RAM is available in a variety of forms, such as static RAM (sram), Dynamic RAM (DRAM), synchronous sdram (sdram), double data rate sdram (ddr sdram), enhanced sdram (sdram), synchronous link (sdram), dynamic RAM (rdram) (rdram L), direct dynamic RAM (rdram), and the like, and/or external cache memory.
The embodiment of the invention provides a strong convection weather early warning method, and provides a computer device and a computer readable storage medium based on the strong convection weather early warning method, wherein a GNSS receiver receives satellite observation data and real-time meteorological parameters such as air pressure and temperature in real time, calculates dry and wet delay components of a troposphere in real time and inversely calculates the change of the content of atmospheric water vapor, and finally obtains a determined early warning threshold value according to an empirical model of the content of atmospheric water vapor and actually measured precipitation in historical data, so that the purpose of monitoring the strong convection weather in real time can be achieved according to the change of the content of atmospheric water vapor and the real-time meteorological parameters, and the method has the advantages of real-time detection and high precision.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A strong convection weather early warning method is characterized by comprising the following steps:
the GNSS receiver acquires CORS station observation data and precise orbit/clock correction information SSR, and processes the acquired data to obtain real-time troposphere total delay;
acquiring real-time troposphere dry delay according to the real-time meteorological parameters, and acquiring real-time troposphere wet delay according to the difference value of the real-time troposphere total delay and the real-time troposphere dry delay;
defining a conversion factor, wherein the conversion factor is a conversion coefficient of historical atmospheric degradable water content and real-time tropospheric moisture delay, acquiring an atmospheric weighted average temperature according to an existing empirical model, acquiring the conversion factor of the real-time tropospheric moisture delay and the atmospheric degradable water content, and acquiring real-time atmospheric water vapor content;
acquiring atmospheric water vapor content of a CORS station according to historical data, downloading data of the CORS station, acquiring precipitation data of local corresponding time, establishing an empirical model of atmospheric water vapor content change and actually measured precipitation, acquiring a preliminary early warning threshold value of strong convection weather according to the empirical model, and detecting the preliminary early warning threshold value by utilizing the actually measured data of multiple times of strong convection weather to acquire a final determined early warning threshold value;
and comparing the content of the atmospheric water vapor and the real-time meteorological parameters with an early warning threshold value, and pushing forecast information to a user when the content of the atmospheric water vapor exceeds the early warning threshold value.
2. The strong convection weather early warning method according to claim 1, wherein after the CORS station observation data and the precise orbit/clock correction information SSR are obtained in the step, the obtained data are processed to remove data with excessive errors.
3. The strong convection weather early warning method according to claim 1 or 2, characterized in that after CORS station observation data and precise orbit/clock correction information SSR are obtained, real-time PPP resolving is carried out on the obtained data, and then real-time troposphere delay is obtained by adopting an anti-error Kalman filtering algorithm.
4. The strong convection weather early warning method according to claim 1, wherein the CORS station observation data and the precise orbit/clock correction information SSR are respectively obtained by a TCP/IP protocol and an NTRIP protocol.
5. The strong convection weather early warning method as claimed in claim 1, wherein in the step of detecting the preliminary early warning threshold value by using the measured data of the occurrence of the strong convection weather for a plurality of times, if the accuracy of detecting the preliminary early warning threshold value exceeds the set value, the preliminary early warning threshold value is directly determined as the determined early warning threshold value, and if the accuracy is lower than the set value, the early warning threshold value is adjusted by nonlinear fitting.
6. The strong convection weather warning method of claim 1, wherein the real-time weather parameters are obtained by pressure and temperature sensors through network transmission.
7. A computer device comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of the strong convection weather warning method of any one of claims 1 to 6.
8. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of the strong convection weather warning method as claimed in any one of claims 1 to 6.
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Cited By (8)
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| CN113406727A (en) * | 2021-08-18 | 2021-09-17 | 中国气象局气象探测中心 | Chip module special for water vapor treatment weather |
| CN114019584A (en) * | 2021-10-11 | 2022-02-08 | 武汉大学 | VRS resolving method for high-precision CORS network in large-altitude-difference area |
| CN114114469A (en) * | 2021-11-26 | 2022-03-01 | 广东电网有限责任公司广州供电局 | Strong convection weather early warning device and method |
| CN114415266A (en) * | 2021-12-31 | 2022-04-29 | 中国气象局气象探测中心 | Water vapor data processing method and device, electronic equipment and computer readable medium |
| CN114722642A (en) * | 2022-06-09 | 2022-07-08 | 山东大学 | Method and system for predicting physical parameter change after earthquake |
| CN114910982A (en) * | 2022-07-05 | 2022-08-16 | 中国电建集团西北勘测设计研究院有限公司 | Rainfall early warning model construction method based on Beidou technology |
| CN115629431A (en) * | 2022-12-22 | 2023-01-20 | 成都数之联科技股份有限公司 | Water vapor content prediction method, device, equipment and medium |
| CN115857057A (en) * | 2022-11-23 | 2023-03-28 | 长江水利委员会长江科学院 | Rainfall monitoring method based on GNSS PWV |
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