CN114910982B - Rainfall early warning model construction method based on Beidou technology - Google Patents

Rainfall early warning model construction method based on Beidou technology Download PDF

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CN114910982B
CN114910982B CN202210791019.7A CN202210791019A CN114910982B CN 114910982 B CN114910982 B CN 114910982B CN 202210791019 A CN202210791019 A CN 202210791019A CN 114910982 B CN114910982 B CN 114910982B
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李祖锋
赵庆志
尚海兴
狄圣杰
吕宝雄
王盼
付晓花
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Abstract

The invention provides a rainfall early warning model construction method based on Beidou technology, which comprises the following steps: obtaining total delay ZTD of the zenith troposphere based on the Beidou satellite navigation system observation data; acquiring the atmospheric precipitation PWV according to the ZTD data and the meteorological data; determining a plurality of forecasting factors; determining thresholds for the plurality of predictors; combining the plurality of forecasting factors to construct an early warning model; and evaluating the performance of the early warning model. According to the invention, the time sequence of the PWV and the ZTD is fitted by utilizing a polynomial fitting method, a percentile threshold method is introduced to determine thresholds of various forecasting factors, the precision of a rainfall early warning model is improved, various forecasting factors are combined, PWV values, PWV increment and increment rate and ZTD increment and increment rate are comprehensively considered, a rainfall prediction model combining the PWV and the ZTD based on the Beidou technology is constructed, the accuracy of rainfall prediction is improved, and the false rate of rainfall prediction is reduced.

Description

Rainfall early warning model construction method based on Beidou technology
Technical Field
The invention relates to a rainfall early warning model construction method based on the Beidou technology, and belongs to the field of meteorology.
Background
With global warming in recent years, extreme rainfall events are frequent at home and abroad, and serious influence is brought to social development and human life, wherein the extreme rainfall is one of typical destructive weather phenomena at home and abroad, and flood caused by the extreme rainfall can cause urban waterlogging, facility damage, casualties, economic losses and other influences.
The China is high, low, east and west in topography, the climate types are complex and various, and precipitation in each region is unevenly distributed. The storm event becomes one of the most serious and frequent weather disaster events in the weather disasters of China under the influence of climate and topography factors. Continuous long-term heavy rainfall data is extremely easy to cause various disaster events such as flood, dam break, river water flooding and the like, so that the early warning of the heavy rainfall event has important significance, and the establishment of an accurate rainfall early warning model becomes increasingly important.
At present, with the completion of global Beidou system networking, the Beidou technology is gradually applied to various industries at home and abroad, wherein the construction of a rainfall early warning model by using the Beidou technology gradually draws attention of researchers at home and abroad, but at present, little research is still available. At present, most students only carry out the time sequence change of the least square linear fit PWV and explore the response relation between the PWV and the rainfall event, and the rainfall prediction is carried out by setting a time change window and adopting the PWV increment and increment rate, but the rainfall model has the defects of obvious low accuracy and high false rate generally, and the essential reason is probably due to the fact that the rainfall event is caused by the combined action of various meteorological parameters, and if the accurate early warning of the rainfall event can not be realized by only using a single parameter or the increment and increment rate.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a rainfall early warning model construction method based on the Beidou technology, which constructs a rainfall early warning model based on the combination of the PWV and the ZTD of the Beidou technology by combining a PWV value, a PWV increment and an increment rate and a ZTD increment and an increment rate of various forecasting factors, improves the accuracy of rainfall prediction and reduces the false rate of rainfall prediction.
The invention provides a rainfall early warning model construction method based on Beidou technology, which comprises the following steps:
step one, obtaining total delay ZTD of a zenith troposphere based on the calculation of the observation data of a Beidou satellite navigation system;
step two, acquiring the atmospheric precipitation PWV according to the ZTD data and the meteorological data;
Step three, determining a plurality of forecasting factors;
determining thresholds of the plurality of forecasting factors;
step five, combining the plurality of forecasting factors to construct an early warning model;
and step six, evaluating the performance of the early warning model.
Preferably, in the first step, when the total delay ZTD of the zenith troposphere is obtained by calculation, the precise ephemeris and satellite clock error data are substituted into the observation equation to fix the satellite orbit and eliminate the satellite clock error term.
Preferably, in the first step, when the total delay ZTD of the zenith troposphere is obtained by resolving, a dual-frequency observation value is adopted to eliminate the influence of the ionosphere.
Preferably, in the second step, the method for obtaining the atmospheric precipitation PWV includes:
(1) The zenith dry delay ZHD is calculated:
(2) Calculating zenith wet delay ZWD:
ZWD＝ZTD-ZHD
(3) Calculating the atmospheric precipitation amount PWV:
Wherein P represents the air pressure at the BDS station, And H represents the latitude (rad) and the site height (km) of the BDS stations, K' 2、K3 and R V are constants, their values are 16.48 K.hPa -1、(3.776±0.014)×105K2·hPa-1 and 461 J.kg -1·K-1, respectively, ρ is the vapor density and Tm is the air weighted average temperature.
Preferably, in the third step, the plurality of predictors include an increment and an increment rate of ZTD, an increment and an increment rate of PWV, and a PWV value.
Preferably, in the fourth step, before determining the threshold values of the plurality of predictors, a time sequence of the atmospheric precipitation PWV and the zenith troposphere total delay ZTD is fitted.
Preferably, the method for fitting the time sequence of the atmospheric precipitation PWV and the zenith troposphere total delay ZTD is a polynomial fitting method.
Preferably, in the fourth step, the method for determining the threshold values of the plurality of predictors is a method using a percentile threshold value.
Preferably, the method of the percentile threshold is as follows:
(1) Arranging a forecasting factor X from small to large;
(2) Calculating (a+1) and marking the result as j+g, wherein a is the total number of the forecasting factor X value, j is an integer part, g is a decimal part, and P is a percentile;
(3) Acquiring a threshold value:
Wherein P value is the percentile corresponding to the percentile P, namely the threshold; x (j) represents the value of the jth predictor.
Preferably, in the sixth step, the evaluating the performance of the early warning model includes calculating an accuracy rate, a false rate, and a failure rate of the early warning model.
The invention has the beneficial effects that: fitting the time sequence of the PWV and the ZTD by using a polynomial fitting method, introducing a percentile threshold method to determine thresholds of various forecasting factors in the model construction, improving the precision of a rainfall early warning model, combining various forecasting factors, comprehensively considering PWV values, PWV increment and increment rate and ZTD increment and increment rate, constructing a rainfall prediction model based on the Beidou technology and combining the PWV and ZTD, improving the accuracy of rainfall prediction and reducing the false rate of rainfall prediction.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.
The invention provides a rainfall early warning model construction method based on Beidou technology, which comprises the following steps:
step one, obtaining total delay ZTD of a zenith troposphere based on the calculation of the observation data of a Beidou satellite navigation system;
Rinex file acquired by a Beidou satellite navigation system (BeiDou Navigation SATELLITE SYSTEM, BDS) receiver is resolved by using GNSS PPP software developed by the university of Wuhan by adopting a precise single point positioning (Precise Pointing Positioning, PPP) technology to acquire zenith troposphere total delay (Zenith Troposphere Delay, ZTD) data.
To eliminate the effects of the coordinate parameters, GNSS (Global Navigation SATELLITE SYSTEM ) observations of known stations are typically utilized, while the precise ephemeris and satellite clock correction data are substituted into the observation equation to achieve the goal of fixing the satellite orbit and eliminating the satellite clock correction term. To eliminate the effect of the ionosphere, dual frequency observations are typically employed.
The observation equation of the pseudo-range and carrier phase in the algorithm is as follows:
where V represents the observation value correction, i represents the corresponding observation epoch, j represents the satellite signal, c is the speed of light in vacuum, δt (i) is the receiver clock difference, δρ zd (i) and M (θ i (i)) are the total zenith tropospheric delay and the corresponding projection function, respectively, θ i (i) represents the satellite altitude angle, ε P and ε φ represent the combined observation value without modeling error effects such as multipath and observation noise, respectively, P j (i) and φ j (i) are the combined observation value with the ionospheric effects removed for the corresponding ith satellite epoch, λ is the corresponding wavelength, ρ j (i) represents the geometric distance between the satellite position at the time of signal transmission and the receiver position at the time of signal reception, and N j (i) is the ambiguity parameter of the combined observation value with the ionospheric effects removed.
Step two, acquiring the atmospheric precipitation PWV according to the ZTD data and the meteorological data;
If the BDS receiver is provided with a meteorological sensor, using meteorological data acquired in real time for inversion of PWV; if the BDS site is not equipped with a meteorological sensor, inverting PWV data according to meteorological data provided by the analysis data.
Firstly, using a Saastamoinen model to obtain zenith dry delay (Zenith Hydrostatic Delay, ZHD), wherein the calculation formula is as follows:
Wherein P represents the air pressure at the BDS station, And H represents the latitude (rad) and the site height (km) of the BDS station, respectively. Zenith Wet Delay (ZWD) was then obtained:
ZWD＝ZTD-ZHD#(4)
finally, PWV data of the GNSS site are obtained through calculation:
Where K 2′、K3 and R V are constants, their values are 16.48 KhPa -1、(3.776±0.014)×105K2·hPa-1 and 461J kg -1·K-1, respectively, ρ is the water vapor density and Tm is the air weighted average temperature.
Step three, determining a plurality of forecasting factors;
in one embodiment of the invention, the plurality of predictors determined include the increment and increment rate of ZTD, the increment and increment rate of PWV, and the PWV value.
Fitting the time sequence of the atmospheric precipitation PWV and total zenith troposphere delay ZTD by using a polynomial fitting method, wherein the specific fitting method can be summarized as the following steps:
(1) Drawing a scatter diagram by taking time as an abscissa and PWV or ZTD as an ordinate, and determining the degree n of a fitting polynomial;
(2) Calculation of And/>Wherein j=0, 1, …,2n, x i represents time, y i represents PWV or ZTD, and m represents the total number of data;
(3) And writing a normal equation set, and solving a coefficient a 0,a1,…an, wherein the normal equation set is expressed as a matrix:
(4) Determining a fitting polynomial P n (x):
the increment and increment rate of ZTD and the increment and increment rate of PWV are calculated as follows:
Datavalue＝Datamax-Datamin#(6)
ΔT＝T-t#(7)
Where Data value represents the increment of ZTD or PWV, data max represents the maximum value of ZTD or PWV closest to the time of rainfall before the rainfall, data min represents the minimum value of ZTD or PWV closest to the time of maximum, deltaT represents the interval time, T represents the time corresponding to the maximum value of ZTD or PWV, T represents the time corresponding to the minimum value of ZTD or PWV, and Data rate represents the increment rate of ZTD or PWV.
Determining thresholds of the plurality of forecasting factors;
The conventional method for determining the rainfall prediction threshold value is usually determined according to an empirical value, so that the determined threshold value often cannot adapt to the early warning model, and the accuracy of the early warning model is reduced. Compared with the traditional threshold value determining method, the method has universality and objectivity, so that the aim of improving the precision of the early warning model can be fulfilled, and the method can be realized through the following three steps:
(1) Arranging a forecasting factor X from small to large;
(2) Calculating (a+1) and marking the result as j+g, wherein a is the total number of the forecasting factor X value, j is an integer part, g is a decimal part, and P is a percentile;
(3) Acquiring a threshold value:
Wherein P value is the percentile corresponding to the percentile P, namely the threshold; x (j) represents the value of the jth predictor.
Step five, combining the plurality of forecasting factors to construct an early warning model;
the construction of the early warning model can use only one forecasting factor, and rainfall is predicted when the forecasting factor exceeds the corresponding threshold value determined in the fourth step; combinations of several predictors may also be selected, and rainfall is predicted when all of the selected predictors reach the corresponding thresholds, as shown in table 1, where "v" represents selecting the predictor and "x" represents not selecting the predictor.
TABLE 1
And step six, evaluating the performance of the early warning model.
In order to verify the performance and reliability of the rainfall early warning model, the accuracy of the model is to be verified in two aspects. Firstly, calculating the accuracy, the false rate and the failure rate of a rainfall early warning model through experimental data to evaluate the model accuracy, wherein the calculation modes of the accuracy, the false rate and the failure rate are as follows:
Wherein True R、FalseR、MissR represents accuracy, false rate and reporting missing rate, N t、Nf、Nm represents rainfall times of accurate model, false and reporting missing, and N a represents actual rainfall times.
And secondly, verifying by using actual data, and further evaluating the accuracy of the rainfall early warning model constructed by the invention by counting the number of actual rainfall occurrence times and the number of accurate model prediction times in a certain time period.

Claims (1)

1. The rainfall early warning model construction method based on the Beidou technology is characterized by comprising the following steps of:
step one, obtaining total delay ZTD of a zenith troposphere based on the calculation of the observation data of a Beidou satellite navigation system; when the total delay ZTD of the zenith troposphere is obtained through calculation, the precise ephemeris and satellite clock error data are substituted into an observation equation, so that the purposes of fixing a satellite orbit and eliminating a satellite clock error item are achieved; when the total delay ZTD of the zenith troposphere is obtained through calculation, a double-frequency observation value is adopted to eliminate the influence of the ionosphere;
step two, acquiring the atmospheric precipitation PWV according to the ZTD data and the meteorological data; the method for acquiring the atmospheric precipitation PWV comprises the following steps of:
(1) The zenith dry delay ZHD is calculated:
(2) Calculating zenith wet delay ZWD:
(3) Calculating the atmospheric precipitation amount PWV:
Wherein P represents the air pressure at the BDS station, And H represents the latitude (rad) and the site height (km) of the BDS site respectively,
、/>And/>Are constants with values of 16.48/>, respectively、(3.776±0.014)×105/>And 461/>，/>Is the density of water vapor, and Tm is the weighted average temperature of the atmosphere;
determining a plurality of forecasting factors, wherein the plurality of forecasting factors comprise the increment and increment rate of ZTD, the increment and increment rate of PWV and PWV values;
Determining thresholds of the plurality of forecasting factors; before determining the threshold values of the plurality of forecasting factors, fitting the time sequence of the atmospheric precipitation PWV and the total zenith troposphere delay ZTD, wherein the fitting method is a polynomial fitting method; the method for determining the thresholds of the plurality of predictors is a method for utilizing a percentile threshold, wherein the method for determining the percentile threshold is as follows:
(1) Arranging a forecasting factor X from small to large;
(2) Calculating (a+1) and marking the result as j+g, wherein a is the total number of the forecasting factor X value, j is an integer part, g is a decimal part, and P is a percentile;
(3) Acquiring a threshold value:
In the method, in the process of the invention, A percentile corresponding to the percentile P, i.e. a threshold; x (j) represents the value of the jth predictor;
step five, combining the plurality of forecasting factors to construct an early warning model;
And step six, evaluating the performance of the early warning model, wherein the evaluating the performance of the early warning model comprises calculating the accuracy rate, the false rate and the failure report rate of the early warning model.
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CN115857057B (en) * 2022-11-23 2023-11-07 长江水利委员会长江科学院 Rainfall monitoring method based on GNSS PWV
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110610595A (en) * 2019-08-01 2019-12-24 江苏科博空间信息科技有限公司 Geological disaster early warning method based on Beidou water vapor inversion
CN111458768A (en) * 2020-03-27 2020-07-28 山东大学 Strong convection weather early warning method, computer equipment and storage medium
CN114282721A (en) * 2021-12-22 2022-04-05 中科三清科技有限公司 Pollutant forecast model training method and device, electronic equipment and storage medium
CN114488349A (en) * 2022-01-04 2022-05-13 中国科学院空天信息创新研究院 Construction method of local short-term rainfall forecast model based on GNSS-PWV multi-factor

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US20120119944A1 (en) * 2010-05-30 2012-05-17 Trimble Navigation Limited Gnss atmospheric estimation with ambiguity fixing

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110610595A (en) * 2019-08-01 2019-12-24 江苏科博空间信息科技有限公司 Geological disaster early warning method based on Beidou water vapor inversion
CN111458768A (en) * 2020-03-27 2020-07-28 山东大学 Strong convection weather early warning method, computer equipment and storage medium
CN114282721A (en) * 2021-12-22 2022-04-05 中科三清科技有限公司 Pollutant forecast model training method and device, electronic equipment and storage medium
CN114488349A (en) * 2022-01-04 2022-05-13 中国科学院空天信息创新研究院 Construction method of local short-term rainfall forecast model based on GNSS-PWV multi-factor

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