CN114895381A - Ground flash grading early warning method based on double-linear polarization radar - Google Patents
Ground flash grading early warning method based on double-linear polarization radar Download PDFInfo
- Publication number
- CN114895381A CN114895381A CN202210808046.0A CN202210808046A CN114895381A CN 114895381 A CN114895381 A CN 114895381A CN 202210808046 A CN202210808046 A CN 202210808046A CN 114895381 A CN114895381 A CN 114895381A
- Authority
- CN
- China
- Prior art keywords
- aragonite
- ground
- thunderstorm
- radar
- linear polarization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Environmental & Geological Engineering (AREA)
- Probability & Statistics with Applications (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a ground flash grading early warning method based on a dual-linear polarization radar, which comprises the following steps: s1, obtaining the probability of the aragonite on each radar observation distance library; s2, identifying whether the corresponding position on each distance library is aragonite or not, and inverting the quality of the aragonite by combining reflectivity factors; s3, identifying the convection region as a potential thunderstorm region; and S4, calculating the height of the echo peak, the vertical accumulated liquid water content and the quality of the aragonite above the specific stable layer height in the thunderstorm area. The quality of the aragonite above the height of a specific temperature layer in the thunderstorm range, the vertically accumulated liquid water content and the echo peak height are objectively identified, so that the height of the lightning frequency or the existence of the lightning of the thunderstorm is judged, the lightning frequency proximity prediction is realized for the first time, and information is provided for the predictor to issue the early warning.
Description
Technical Field
The invention relates to the technical field of weather forecast, in particular to a ground flash grading early warning method based on a dual-linear polarization radar.
Background
Lightning is a discharge phenomenon in a thunderstorm and has serious disastrous effects, usually accompanied by hail, tornados, strong winds and strong precipitation. The lightning is ground flash and cloud flash, wherein the ground flash is the discharge of thunderstorm cloud to the ground, and the economic activity and the life and property safety of people are directly influenced. With the development of economy and modern science and technology, the economic loss caused by the land flashover is more serious. Lightning strikes are statistically responsible for losses of up to more than 10 billion dollars each year around the world. Industries affected by thunderstorms are numerous, such as aerospace, national defense, communication, computers, power transmission, petrochemical engineering and the like, and more industries are concerned with the countryside. Therefore, the forecast of the lightning is very important to the society, the economy and the public.
With increasing researchers focusing on the forecasting of the lightning. Lightning forecasting based on a numerical mode is developed, although a mesoscale mode can reproduce thermal and dynamic processes of a thunderstorm through technical means, the forecasting capability of the current mode on lightning is very limited, mainly because the knowledge on the electrification in a real thunderstorm is relatively limited, and the electrification and discharge parameterization scheme in the mode cannot accurately describe the actual electrification and discharge conditions. In addition, the mode depends on the correct micro physical process for forecasting the lightning, and the parameterization scheme is mainly based on foreign observation and has obvious difference with the process of China.
Observations represented by ground automatic stations, lightning detection networks, satellites and the like are continuously built, and a large number of observation facts are provided for researchers. Research relying on these observations is constantly being conducted and makes a very important contribution to understanding the characteristics and influencing mechanisms of thunderstorm activity in the area. Ground automatic station data, while providing real-time precipitation observations, do not observe the structural features of a thunderstorm in the vertical direction, nor do they observe the thunderstorm between stations. Although the lightning detection network can provide a large range of space-time information of lightning, the lightning detection network has a large limitation on understanding the structure and micro physical information of the thunderstorm. The satellite data has the advantages that the characteristics of a wide range of thunderstorm activities can be observed, but the observation space-time resolution of a specific area needs to be improved, the characteristics of the thunderstorm life history, movement, vertical structure, daily change and the like are difficult to reveal due to the influence of signal attenuation. The Doppler weather radar can observe a three-dimensional structure with high space-time resolution of a thunderstorm, and the current ground-to-lightning approach prediction method is mainly based on the observation of an echo structure by the radar and mainly depends on that the reflectivity factor of a specific temperature layer (0-20 ℃) reaches a threshold value (30-40 dBZ), and then lightning triggering is predicted. These radar echo structure based nowcasting methods are capable of predicting the lightning frequency. In addition, because the main contribution of thunderstorm electrification comes from the micro-physical process, and the methods are not linked with the micro-physical characteristics, the forecasting accuracy rate is difficult to be further improved,
in recent years, more S-band doppler radars are upgraded to dual-polarization radars (SPOL) in China. Not only providing a reflectivity factor (Z) H ) Radial velocity and spectral width, and also provides differential reflectivity (Z) DR ) Proportional differential phase (K) DP ) And correlation coefficient (p) hv ). These variables can be used to analyze information about the phase, concentration, size and shape of the water condensate particles in the thunderstorm, and can be used to describe the internal micro-physical characteristics of the thunderstorm. Based on the current situation, a ground-lightning grading early warning method based on a dual-linear polarization radar is urgently needed to be developed on the basis of utilizing thunderstorm micro-physical characteristic inversion.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a ground flash grading early warning method based on a dual-linear polarization radar, aiming at the defects of the prior art, and the ground flash grading early warning method comprises the following steps:
s1, inputting the observation information of the dual linear polarization radar on each distance library into the Bayes classification method condition similarity probability density function of the formula (1), Z H -Z DR 、LK DP And cross-correlation coefficient ρ HV In the model (a) is addedAfter weighted averaging, obtaining the probability of the aragonite on each radar observation distance library;
wherein SF represents a normalization parameter in the formula,
a posterior conditional probability density function representing the aragonite over each range bin,
representing a function of the prior probability density,
representing a conditional similarity probability density function;
s2, judging whether the corresponding position on each distance library is aragonite or not according to the probability of the aragonite, and inverting the quality of the aragonite by combining reflectivity factors;
s3, identifying a convection region as a potential thunderstorm region according to a radar echo texture structure by using an objective convection layer cloud classification method;
and S4, calculating the quality of the aragonite with the echo peak height, the vertically accumulated liquid water content and the height of the specific stable layer in the thunderstorm area, comparing the quality with preset conditions, carrying out early warning, and dividing the early warning of the ground flashover into low frequency, medium frequency, high frequency and no ground flashover.
The further preferable technical scheme of the invention is that in the step S1, when the conditional similarity probability density function is established, the data of the observation area of the aragonite is uniformly indicated by the double-linear polarization radar observation multivariate.
Preferably, when identifying whether the corresponding position on each distance library is aragonite in step S2, interpolating the sounding temperature to the radar observation coordinate height, inputting the height to the prior probability function of the bayesian classification method, and constraining the identification result by using the temperature information and the probability of aragonite in the vertical height.
Preferably, the specific method of constraint is to place the temperature information observed by the radar on each distance library into the vertical prior probability density function for calculation to obtain the corresponding probability result, and multiply it by the probability of the aragonite on each distance library in step S1 to obtain the final constraint result.
Preferably, the inverse formula of the shot mass in step S2 is as follows (2):
wherein z represents a linear reflectivity factor.
Preferably, the calculation formula of the vertical accumulated liquid water content in step S4 is as follows (3):
wherein Z is i Represents the reflectance factor, Δ h Is Z i And Z i+1 The height in between.
Preferably, the preset condition in step S4 is obtained by:
calculating the occurrence frequency of the ground flashes of each thunderstorm on radar observation data to obtain 4-class data sets of the high, medium, low and no ground flashes of the thunderstorm, wherein the low-frequency ground flashes are defined as the ground flashes less than or equal to 1 time/6 minutes, the medium-frequency ground flashes are defined as the ground flashes of 2-12 times/6 minutes, and the high-frequency ground flashes are defined as the ground flashes more than or equal to 13 times/6 minutes;
and (4) statistically analyzing the content of aragonite particles, the echo peak height and the vertical accumulated liquid water content VIL in each data set thunderstorm, constructing an early warning model, and determining preset conditions at all levels of ground flash frequencies.
Has the advantages that: according to the method, a conditional similarity probability density function is established by utilizing observation data of the dual-linear polarization radar, and the sounding temperature is interpolated to the radar observation coordinate height for constraint, so that the aragonite on each radar observation distance library is identified, and the quality of the aragonite is inverted on the basis; the quality of the aragonite above the height of a specific temperature layer in the thunderstorm range, the vertically accumulated liquid water content and the echo peak height are objectively identified, so that the thunderstorm occurrence lightning frequency is judged to be high or low or whether the thunderstorm occurs, the high-low ground-flashover prediction of the ground-flashover frequency is realized for the first time, and information is provided for the prediction personnel to issue early warning.
Drawings
FIG. 1 is a flow chart of a flash rate grading pre-warning method of the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the embodiments.
Example (b): a ground flash grading early warning method based on a dual-linear polarization radar is shown in figure 1 and comprises the following steps:
s1, calculating the ground flash generation frequency of each thunderstorm on radar observation data to obtain 4-class data sets of the ground flash generation frequency high, medium, low and no ground flash thunderstorms; wherein the flashes of earth less than or equal to 1 per 6 minutes are defined as flashes of low frequency, flashes of earth between 2 and 12 per 6 minutes are defined as flashes of medium frequency, flashes of earth greater than or equal to 13 per 6 minutes are defined as flashes of high frequency.
And (4) statistically analyzing the hail and aragonite particle content, echo peak height and vertical accumulated liquid water content VIL in each data set thunderstorm, constructing an early warning model, and determining preset conditions under the flash frequency of each stage.
S2, inputting the observation information of the dual linear polarization radar on each distance library into the Bayes classification method conditional similarity probability density function of the formula (1) and Z H -Z DR 、LK DP And cross-correlation coefficient ρ HV In the model (2), the probability of the aragonite on each radar observation distance library is obtained after weighted averaging;
the conditional similarity probability density function is:
wherein SF represents a normalization parameter in the formula,
a posterior conditional probability density function representing the aragonite over each range bin,
representing a function of the prior probability density,
representing a conditional similarity probability density function; and when the conditional similarity probability density function is established, selecting the data of the observation area of the uniform indication aragonite of the double-linear polarization radar observation multivariable.
S3, identifying whether the corresponding position on each distance library is a shot or not, interpolating the sounding temperature to the radar observation coordinate height when identifying whether the corresponding position on each distance library is a shot or not, and constraining the identification result by utilizing the temperature information and the probability of the vertical height of the shot. The specific method of constraint is to put the temperature information observed by the radar on each distance library into the probability density function in the vertical direction for calculation to obtain the corresponding probability result, and multiply the probability with the probability of the aragonite on each distance library in the step S1 to obtain the final constraint result.
Inverting the quality of the shot in combination with the reflectivity factor; the inverse formula for the quality of aragonite is:
wherein z represents a linear reflectivity factor.
S4, identifying the convection region as a potential thunderstorm region by using an objective convection layer cloud classification method;
and S5, calculating the quality of the aragonite with the echo peak height, the vertically accumulated liquid water content and the height of the specific stable layer in the thunderstorm area, comparing the quality with preset conditions, carrying out early warning, and dividing the early warning of the ground flashover into low frequency, medium frequency, high frequency and no ground flashover.
The calculation formula of the vertical accumulated liquid water content is as follows:
wherein Z is i Represents the reflectance factor, Δ h Is Z i And Z i+1 The height in between.
As noted above, while the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A ground flash grading early warning method based on a dual-linear polarization radar is characterized by comprising the following steps:
s1, inputting the observation information of the dual linear polarization radar on each distance library into the Bayes classification method condition similarity probability density function of the formula (1), Z H -Z DR 、LK DP And cross-correlation coefficient ρ HV In the model (2), the probability of the aragonite on each radar observation distance library is obtained after weighted averaging;
wherein SF represents a normalization parameter in the formula,
a posterior conditional probability density function representing the aragonite over each range bin,
representing a function of the prior probability density,
representing a conditional similarity probability density function;
s2, judging whether the corresponding position on each distance library is aragonite or not according to the probability of the aragonite, and inverting the quality of the aragonite by combining reflectivity factors;
s3, identifying a convection region as a potential thunderstorm region according to a radar echo texture structure by using an objective convection layer cloud classification method;
and S4, calculating the quality of the aragonite with the echo peak height, the vertically accumulated liquid water content and the height of the specific stable layer in the thunderstorm area, comparing the quality with preset conditions, carrying out early warning, and dividing the early warning of the ground flashover into low frequency, medium frequency, high frequency and no ground flashover.
2. The ground-flash grading pre-warning method based on the dual-linear polarization radar as claimed in claim 1, wherein in the step S1, when the conditional similarity probability density function is established, the data of the observed area of the aragonite is uniformly indicated by the observation multivariate of the dual-linear polarization radar is selected.
3. The ground-based lightning grading early-warning method based on the dual-linear polarization radar as claimed in claim 1, wherein when identifying whether the corresponding position on each distance library is aragonite or not in step S2, the sounding temperature is interpolated to the height of the radar observation coordinate, the interpolated sounding temperature is input to the prior probability function of the bayesian classification method, and the identification result is constrained by using the temperature information and the probability of the aragonite at the vertical height.
4. The ground-flash grading pre-warning method based on the dual-linear polarization radar as claimed in claim 3, wherein the constraint is implemented by putting the temperature information observed by the radar on each distance bin into a vertical prior probability density function for calculation to obtain a corresponding probability result, and multiplying the probability result by the probability of the aragonite on each distance bin in the step S1 to obtain a final constraint result.
5. The ground-flash grading early warning method based on the dual-linear polarization radar as claimed in claim 1, wherein the inverse formula of the aragonitic mass in the step S2 is as follows (2):
wherein z represents a linear reflectivity factor.
6. The ground flash grading pre-warning method based on the dual-linear polarization radar as claimed in claim 1, wherein the calculation formula of the vertical accumulated liquid water content in the step S4 is as follows (3):
wherein Z is i Represents the reflectance factor, Δ h Is Z i And Z i+1 The height in between.
7. The ground-flash grading pre-warning method based on the dual-linear polarization radar as claimed in claim 1, wherein the preset conditions in step S4 are obtained by:
calculating the occurrence frequency of the ground flashes of each thunderstorm on radar observation data to obtain 4-class data sets of the high, medium, low and no ground flashes of the thunderstorm, wherein the low-frequency ground flashes are defined as the ground flashes less than or equal to 1 time/6 minutes, the medium-frequency ground flashes are defined as the ground flashes of 2-12 times/6 minutes, and the high-frequency ground flashes are defined as the ground flashes more than or equal to 13 times/6 minutes;
and (3) statistically analyzing the content of the aragonite particles, the echo peak height and the vertical accumulated liquid water content VIL in the thunderstorm of each data set, constructing an early warning model, and determining preset conditions at all levels of the flash frequency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210808046.0A CN114895381B (en) | 2022-07-11 | 2022-07-11 | Ground flash grading early warning method based on double-linear polarization radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210808046.0A CN114895381B (en) | 2022-07-11 | 2022-07-11 | Ground flash grading early warning method based on double-linear polarization radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114895381A true CN114895381A (en) | 2022-08-12 |
| CN114895381B CN114895381B (en) | 2022-09-30 |
Family
ID=82730069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210808046.0A Active CN114895381B (en) | 2022-07-11 | 2022-07-11 | Ground flash grading early warning method based on double-linear polarization radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114895381B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303368B (en) * | 2023-04-24 | 2023-07-21 | 中国人民解放军国防科技大学 | Dual-polarization radar body scan data interpolation method, device, equipment and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6035057A (en) * | 1997-03-10 | 2000-03-07 | Hoffman; Efrem H. | Hierarchical data matrix pattern recognition and identification system |
| CN110749871A (en) * | 2019-11-05 | 2020-02-04 | 南京大学 | Parameter estimation method of dual-polarization weather radar |
| CN110852245A (en) * | 2019-11-07 | 2020-02-28 | 中国民航大学 | Dual-polarization meteorological radar precipitation particle classification method based on discrete attribute BNT |
| CN113933845A (en) * | 2021-10-18 | 2022-01-14 | 南京气象科技创新研究院 | Ground hail reduction identification and early warning method based on dual-linear polarization radar |
-
2022
- 2022-07-11 CN CN202210808046.0A patent/CN114895381B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6035057A (en) * | 1997-03-10 | 2000-03-07 | Hoffman; Efrem H. | Hierarchical data matrix pattern recognition and identification system |
| CN110749871A (en) * | 2019-11-05 | 2020-02-04 | 南京大学 | Parameter estimation method of dual-polarization weather radar |
| CN110852245A (en) * | 2019-11-07 | 2020-02-28 | 中国民航大学 | Dual-polarization meteorological radar precipitation particle classification method based on discrete attribute BNT |
| CN113933845A (en) * | 2021-10-18 | 2022-01-14 | 南京气象科技创新研究院 | Ground hail reduction identification and early warning method based on dual-linear polarization radar |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303368B (en) * | 2023-04-24 | 2023-07-21 | 中国人民解放军国防科技大学 | Dual-polarization radar body scan data interpolation method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114895381B (en) | 2022-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Goudenhoofdt et al. | Generation and verification of rainfall estimates from 10-yr volumetric weather radar measurements | |
| CN111260111A (en) | Runoff forecast improvement method based on meteorological big data | |
| CN106772696A (en) | For the total method for dodging translation proxy humidity of assimilation of Severe Convective Weather Forecasting | |
| CN114895381B (en) | Ground flash grading early warning method based on double-linear polarization radar | |
| Chen et al. | A flexible Bayesian approach to bias correction of radar-derived precipitation estimates over complex terrain: Model design and initial verification | |
| CN115542431A (en) | Convection current birth monitoring method based on foundation cloud radar and satellite data | |
| CN117518299B (en) | Classified strong convection proximity probability forecasting method, system, equipment and terminal | |
| Ruzanski et al. | Scale filtering for improved nowcasting performance in a high-resolution X-band radar network | |
| CN113311416A (en) | Mountain region small watershed radar quantitative precipitation estimation technology | |
| CN113933845A (en) | Ground hail reduction identification and early warning method based on dual-linear polarization radar | |
| CN116384733A (en) | Power transmission line risk early warning method based on weather radar in strong convection weather | |
| Zeng et al. | An investigation of convective features and ZR relationships for a local extreme precipitation event | |
| Zhao et al. | Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting | |
| Yoshida et al. | Early detection of convective echoes and their development using a Ka-band radar network | |
| Cui et al. | Association of lightning occurrence with precipitation cloud column structure at a fixed position | |
| CN115932762A (en) | Weather numerical prediction radar base data quality control method | |
| Carrió et al. | Exploring the benefits of a Hi-EnKF system to forecast an extreme weather event. The 9th October 2018 catastrophic flash flood in Mallorca | |
| Pejcic et al. | Polarimetric radar-based methods for evaluation of hydrometeor mixtures in numerical weather prediction models | |
| van den Heuvel et al. | Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps | |
| CN110727719A (en) | Lightning positioning data assimilation method based on dynamic relaxation approximation | |
| CN116702659B (en) | Rapid forecasting method and system for sea wave crushing parameters | |
| Yang et al. | Can CINRAD Radar with VCP-21 Mode Capture the Accumulated Rainfall Pattern and Intensity of Fast-moving Storms? | |
| Neirynck et al. | Mesoscale weather systems and associated potential wind power variations in a mid-latitude sea strait (Kattegat) | |
| CN117805826B (en) | Minute precipitation estimation method and system based on MIM network and radar jigsaw | |
| Harjupa et al. | RDCA Index Based Updraft Area And Its Verification Using Polarimetric Doppler Radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |