CN114910982A - Rainfall early warning model construction method based on Beidou technology - Google Patents
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Abstract
The invention provides a rainfall early warning model construction method based on a Beidou technology, which comprises the following steps of: resolving and obtaining zenith troposphere total delay ZTD based on observation data of a Beidou satellite navigation system; acquiring the PWV of the atmospheric degradable water yield according to the ZTD data and meteorological data; determining various forecasting factors; determining a threshold value for the plurality of predictor factors; combining the multiple forecasting factors to construct an early warning model; and evaluating the performance of the early warning model. According to the rainfall prediction method, a polynomial fitting method is used for fitting the time sequences of the PWV and the ZTD, a percentile threshold value method is introduced to determine the threshold values of various forecasting factors, the precision of the rainfall early warning model is improved, the various forecasting factors are combined, the PWV value, the PWV increment and increment rate and the 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 rainfall prediction accuracy is improved, and the rainfall prediction false rate is reduced.
Description
Technical Field
The invention relates to a rainfall early warning model construction method based on a Beidou technology, and belongs to the field of meteorology.
Background
With global warming in recent years, extreme rainfall events occur frequently at home and abroad, which bring serious influences 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 inland inundation, facility damage, casualties, economic loss and other influences.
The Chinese landscape is high in the west and low in the east, the climate types are complex and various, and the precipitation distribution in each region is uneven. Under the influence of climate and terrain factors, rainstorm events become one of the most serious and frequent meteorological disaster events in China. The continuous long-term heavy rainfall data is easy to cause various disaster events such as flooding, dam break, river flooding and the like, so that the early warning for the heavy rainfall event has important significance, and the construction of an accurate rainfall early warning model becomes increasingly important.
At present, with the completion of networking of a global Beidou system, the Beidou technology is gradually applied to various industries at home and abroad, wherein the construction of a rainfall early warning model by utilizing the Beidou technology gradually draws attention of researchers at home and abroad, but at present, few researches are carried out. At present, most researchers only carry out least square linear fitting on the time sequence change of the PWV and explore the response relation between the PWV and the rainfall event, and rainfall prediction is carried out by setting a time change window according to the increment and the increment of the PWV, but the rainfall model has the defects of low accuracy and high false rate, which are obvious, and the essential reason of the rainfall model is that the occurrence of the rainfall event is caused by the combined action of a plurality of meteorological parameters, and if only a single parameter or the increment and the increment of the rainfall event are utilized, accurate early warning of the rainfall event cannot be realized.
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 Beidou technology and combining PWV and ZTD by combining various forecasting factors of PWV value, PWV increment and increment rate and ZTD increment and increment rate, 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 a Beidou technology, which comprises the following steps of:
resolving and obtaining zenith troposphere total delay ZTD based on observation data of a Beidou satellite navigation system;
step two, acquiring PWV (atmospheric degradable water volume) according to the ZTD data and meteorological data;
determining various forecasting factors;
step four, determining the threshold values of the various forecasting factors;
combining the multiple forecasting factors to construct an early warning model;
and sixthly, evaluating the performance of the early warning model.
Preferably, in the first step, when the zenith troposphere total delay ZTD is obtained by calculation, the ephemeris and the satellite clock error data are substituted into an observation equation to fix the satellite orbit and eliminate the satellite clock error term.
Preferably, in the first step, when the zenith troposphere total delay ZTD is obtained by calculation, a dual-frequency observation value is adopted to eliminate the influence of the ionosphere.
Preferably, in the second step, the method for acquiring the amount of atmospheric water reducible PWV includes:
(1) calculate zenith dry delay ZHD:
(2) calculating zenith wet retardation ZWD:
ZWD＝ZTD-ZHD
(3) calculating the PWV of the atmospheric water reducible quantity:
wherein P represents the air pressure at the BDS station,
and H represent latitude (rad) and station height (km), K 'of the BDS station, respectively' 2 、K 3 And R V Is a constant, and their values are respectively 16.48K hPa -1 、(3.776±0.014)×10 5 K 2 ·hPa -1 And 461 J.kg -1 ·K -1 ρ is the water vapor density and Tm is the atmospheric weighted average temperature.
Preferably, in the third step, the plurality of forecasting factors include increment and increment rate of ZTD, increment and increment rate of PWV, and PWV value.
Preferably, in the fourth step, before determining the threshold values of the various forecasting factors, the time sequence of the atmospheric water reducible quantity PWV and the zenith troposphere total delay ZTD is fitted.
Preferably, the method of fitting the time sequences of the total delay of the atmospheric water reducible volume PWV and the zenith troposphere ZTD is a polynomial fitting method.
Preferably, in the fourth step, the method for determining the thresholds of the plurality of forecasting factors is a method using percentile thresholds.
Preferably, the method of the percentile threshold comprises:
(1) arranging a forecasting factor X from small to large;
(2) calculating (a +1) P%, and recording the result as j + g, wherein a is the total number of the prediction 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 formula, P value Is a percentile corresponding to percentile P, i.e. a threshold value; x (j) represents the value of the jth predictor.
Preferably, in the sixth step, evaluating the performance of the early warning model includes calculating an accuracy rate, a false rate, and a false negative rate of the early warning model.
The invention has the beneficial effects that: the time sequences of the PWV and the ZTD are fitted by utilizing a polynomial fitting method, a percentile threshold value method is introduced in model construction to determine the threshold values of various forecasting factors, the precision of a rainfall early warning model is improved, the various forecasting factors are combined, the PWV value, the PWV increment and increment rate and the 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 rainfall prediction accuracy is improved, and the rainfall prediction false rate is reduced.
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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 described clearly and completely with reference to the accompanying drawings.
The invention provides a rainfall early warning model construction method based on a Beidou technology, which comprises the following steps of:
resolving and obtaining zenith troposphere total delay ZTD based on observation data of a Beidou satellite navigation system;
rinex files acquired by a Beidou Satellite Navigation System (BDS) receiver are resolved by utilizing GNSS PPP software developed by Wuhan university and adopting a Precision Pointing Positioning (PPP) technology to acquire Zenith Troposphere Delay (ZTD) data.
In order to eliminate the influence of the coordinate parameters, the GNSS (Global Navigation Satellite System) observation values of the known stations are usually utilized, and the precise ephemeris and the Satellite clock error data are substituted into the observation equation to fix the Satellite orbit and eliminate the Satellite clock error term. To eliminate the ionospheric effects, dual-frequency observations are typically employed.
The observation equation of the pseudo range and the carrier phase in the algorithm is as follows:
in the formula, V represents the correction number of the observed value, i represents the corresponding observed epoch, j represents the satellite signal, c is the speed of light in vacuum, δ t (i) is the receiver clock error, δ ρ zd (i) And M (θ) i (i) Respectively, the zenith tropospheric total delay and the corresponding projection function, theta i (i) Representing the satellite altitude, epsilon P And ε φ Representing the influence of unmodeled errors, P, of multipath and observation noise, respectively j (i) And phi j (i) Combined observations with ionospheric effects removed for the corresponding ith satellite epoch, λ being the corresponding wavelength, ρ j (i) Geometric distance, N, between the position of the satellite representing the moment of transmission of the signal and the position of the receiver at the moment of reception of the signal j (i) An ambiguity parameter for the combined observations that eliminates ionospheric effects.
Step two, acquiring PWV (atmospheric degradable water volume) according to the ZTD data and meteorological data;
if the BDS receiver is provided with a meteorological sensor, using real-time acquired meteorological data for PWV inversion; and if the BDS site is not provided with a meteorological sensor, carrying out the inversion of the PWV data according to the meteorological data provided by the reanalysis data.
Firstly, a Saastamoinen model is used for obtaining Zenith drostatic Delay (ZHD), and the calculation formula is as follows:
wherein P represents the air pressure at the BDS station,
and H represents the latitude (rad) and the height (km) of the BDS station, respectively. The Zenith Wet retardation (Zenith Wet Delay, ZWD) was then obtained:
ZWD＝ZTD-ZHD#(4)
and finally, calculating to obtain PWV data of the GNSS station:
in the formula, K 2 ′、K 3 And R V Is a constant, and their values are respectively 16.48K hPa -1 、(3.776±0.014)×10 5 K 2 ·hPa -1 And 461 J.kg -1 ·K -1 ρ is the water vapor density and Tm is the atmospheric weighted average temperature.
Determining various forecasting factors;
in one embodiment of the invention, the plurality of predictor factors determined include the increment and increment rate of ZTD, the increment and increment rate of PWV, and the value of PWV.
Fitting the time sequences of the PWV and the ZTD of the zenith troposphere total delay by utilizing 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 fitting the polynomial;
(2) computing
And
wherein j is 0,1, …,2n, x i Represents time, y i Representing PWV or ZTD, and m represents the total number of data;
(3) writing out a normal equation set to obtain a coefficient a 0 ,a 1 ,…a n The normal system of equations is represented in a matrix as:
(4) determining a fitting polynomial P n (x)：
The incremental change and incremental rate of ZTD and the incremental change and incremental rate of PWV are calculated as follows:
Data value ＝Data max -Data min #(6)
ΔT＝T-t#(7)
in the formula, Data value Representing increments of ZTD or PWV, Data max Representing the maximum value of ZTD or PWV closest to the time of rainfall before rainfall, Data min Representing the minimum value of the closest ZTD or PWV before the maximum value, DeltaT representing interval time, T representing the time corresponding to the ZTD or PWV maximum value, T representing the time corresponding to the ZTD or PWV minimum value, Data rate Representing the rate of increase of ZTD or PWV.
Step four, determining the threshold values of the various forecasting factors;
the traditional method for determining the rainfall prediction threshold value is generally determined according to empirical values, and the determined threshold value is often not suitable for the early warning model, so that the accuracy of the early warning model is reduced. Compared with the traditional threshold value determining method, the method has universality and objectivity, and further can achieve the purpose of improving the precision of the early warning model, and can be realized through the following three steps:
(1) arranging a forecasting factor X from small to large;
(2) calculating (a +1) × P%, and recording the result as j + g, wherein a is the total number of the values of the forecasting factors X, j is an integer part, g is a decimal part, and P is a percentile;
(3) acquiring a threshold value:
in the formula, P value Is a percentile corresponding to percentile P, i.e. a threshold value; x (j) represents the value of the jth predictor.
Combining the multiple forecasting factors to construct an early warning model;
the construction of the early warning model can only use one forecasting factor, and when the forecasting factor exceeds the corresponding threshold determined in the fourth step, the rainfall is predicted; it is also possible to select a combination of several forecast factors, and predict rainfall when the selected forecast factors reach corresponding threshold values, as shown in table 1, five possible combinations are shown, where "√" represents selection of the forecast factor and "xx" represents non-selection of the forecast factor.
TABLE 1
And sixthly, evaluating the performance of the early warning model.
In order to verify the performance and reliability of the rainfall early warning model, the precision of the model is verified from two aspects. Firstly, the accuracy, the false rate and the missing report rate of the rainfall early warning model are calculated through experimental data to evaluate the model precision, and the calculation modes of the accuracy, the false rate and the missing report rate are as follows:
in the formula, True R 、False R 、Miss R Respectively representing the accuracy, the false rate, the missing report rate, N t 、N f 、N m Respectively representing the rainfall times of accurate, false and missing reports of the model, N a Representing the number of actual rain occurrences.
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 times of actual rainfall occurrence and the times of accurate model prediction in a certain time period.
Claims (10)
1. A rainfall early warning model construction method based on the Beidou technology is characterized by comprising the following steps:
resolving and obtaining zenith troposphere total delay ZTD based on observation data of a Beidou satellite navigation system;
step two, acquiring PWV (atmospheric degradable water volume) according to the ZTD data and meteorological data;
determining various forecasting factors;
step four, determining the threshold values of the various forecasting factors;
combining the multiple forecasting factors to construct an early warning model;
and sixthly, evaluating the performance of the early warning model.
2. The method for constructing the rainfall early warning model based on the Beidou technology according to claim 1, wherein in the first step, when the zenith troposphere total delay ZTD is obtained through calculation, precise ephemeris and satellite clock error data are substituted into an observation equation so as to achieve the purposes of fixing the satellite orbit and eliminating the satellite clock error item.
3. The method for building the rainfall early warning model based on the Beidou technology according to claim 1, wherein in the first step, when the zenith troposphere total delay ZTD is obtained through calculation, a dual-frequency observation value is adopted to eliminate the influence of an ionosphere.
4. The Beidou technology-based rainfall early warning model construction method according to claim 1, wherein in the second step, the method for obtaining the PWV comprises the following steps:
(1) calculate zenith dry delay ZHD:
(2) calculating zenith wet retardation ZWD:
ZWD＝ZTD-ZHD
(3) calculating the PWV of the atmospheric water reducible quantity:
wherein P represents the air pressure at the BDS station,
and H represent latitude (rad) and station height (km), K 'of the BDS station, respectively' 2 、K 3 And R V Are constants, their values are respectively 16.48K hPa -1 、(3.776±0.014)×10 5 K 2 ·hPa -1 And 461 J.kg -1 ·K -1 ρ is the water vapor density and Tm is the atmospheric weighted average temperature.
5. The Beidou technology-based rainfall early warning model construction method of claim 1, wherein in the third step, the plurality of forecast factors include a rate of increase and increase of ZTD, a rate of increase and increase of PWV, and a value of PWV.
6. The method for constructing the rainfall early warning model based on the Beidou technology according to claim 1, wherein in the fourth step, before the threshold values of the various forecasting factors are determined, the time sequences of the atmospheric water reducible quantity PWV and the zenith troposphere total delay ZTD are fitted.
7. The Beidou technology-based rainfall early warning model construction method according to claim 6, wherein the method for fitting the time sequences of the atmospheric water-reducible quantity PWV and the zenith troposphere total delay ZTD is a polynomial fitting method.
8. The method for constructing the Beidou technology-based rainfall early warning model according to claim 1, wherein in the fourth step, the method for determining the thresholds of the plurality of forecast factors is a percentile threshold method.
9. The Beidou technology-based rainfall early warning model construction method according to claim 8, wherein the percentile threshold value method comprises the following steps:
(1) arranging a forecasting factor X from small to large;
(2) calculating (a +1) P%, and recording the result as j + g, wherein a is the total number of the prediction 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 formula, P value Is a percentile corresponding to percentile P, i.e. a threshold value; x (j) represents the value of the jth predictor.
10. The Beidou technology-based rainfall early warning model construction method according to any one of claims 1 to 9, wherein in the sixth step, evaluating performance of the early warning model comprises calculating accuracy, false rate and missing report rate of the early warning model.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN115993668A (en) * | 2023-03-22 | 2023-04-21 | 成都云智北斗科技有限公司 | Polynomial correction and neural network-based PWV reconstruction method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120092213A1 (en) * | 2008-08-19 | 2012-04-19 | Trimble Navigation Limited | Gnss atmospheric estimation with federated ionospheric filter |
| 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 |
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120092213A1 (en) * | 2008-08-19 | 2012-04-19 | Trimble Navigation Limited | Gnss atmospheric estimation with federated ionospheric filter |
| 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 |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
| CN115993668A (en) * | 2023-03-22 | 2023-04-21 | 成都云智北斗科技有限公司 | Polynomial correction and neural network-based PWV reconstruction method and system |
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