CN109117555A - A kind of Bevis model refinement method of sub-region right mean temperature - Google Patents
A kind of Bevis model refinement method of sub-region right mean temperature Download PDFInfo
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Abstract
The invention discloses a kind of Bevis model refinement methods of sub-region right mean temperature, comprising the following steps: S1: pre-processing survey station sounding data, obtains the true value T of weighted meanm0With the true value T of surface temperatures0；S2: the calculated value T of weighted mean is obtained using Bevis modelm；S3: consider weighted mean calculated value TmThe Rule of Geographical Distribution and variation annual period, increase periodic term, longitude, latitude and elevation correction term on the basis of Bevis model, establish nonlinear equation；S4: determining each term coefficient of nonlinear equation with least square method, and determination finally improves model equation and verifies its precision.The present invention effectively increases computational accuracy.
Description
Technical field
The present invention relates to Global Navigation System fields, more particularly to a kind of Bevis model refinement method.
Background technique
Effective supplement of the ground GNSS technology as conventional detection Atmospheric Precipitable Water (PWV) method has round-the-clock, high
Precision, near real-time, high-spatial and temporal resolution, and do not need many advantages, such as calibrating instrument.It is detected using GNSS technology big
Air water vapour, depends on the accurate transformation of troposphere wet stack emission (ZWD) to PWV, and currently used method is to utilize TmCalculate ZWD
To PWV convert conversion parameter, by GNSS be finally inversed by come ZWD obtain Atmospheric Precipitable Water, therefore how to obtain in high precision
Tm, it is one of the key problem of GNSS meteorology.Utilize temperature, air pressure, the vapour pressure in the survey station overhead that sounding data obtains
Can directly be calculated accurate weighted mean, however most of the time we be that can not accurately obtain survey station overhead
The meteorological datas such as temperature, air pressure, vapour pressure, this undoubtedly limits the use of Ground-Based GPS detection steam method.Bevis exists
T is found after analyzing 13, North America radiosonde station, 8718 Sounding DatassAnd TmWith very strong linear dependence, and give
The linear regression formula T of suitable North America mid latitudes is gone outm=aTs+ b, TmAnd TsUnit be all kelvin, the formula
Root-mean-square error is 4.74k, is the current ground GNSS detection widely used formula of steam.
However, the computational accuracy of Bevis model in the prior art is also lower.
Summary of the invention
Goal of the invention: the object of the present invention is to provide a kind of sub-region right mean temperatures that can be improved computational accuracy
Bevis model refinement method.
Technical solution: to reach this purpose, the invention adopts the following technical scheme:
The Bevis model refinement method of sub-region right mean temperature of the present invention, comprising the following steps:
S1: pre-processing survey station sounding data, obtains the true value T of weighted meanm0With the true value of surface temperature
Ts0；
S2: the calculated value T of weighted mean is obtained using Bevis modelm；
S3: consider weighted mean calculated value TmThe Rule of Geographical Distribution and annual period variation, in Bevis model
On the basis of increase periodic term, longitude, latitude and elevation correction term, establish nonlinear equation；
S4: determining each term coefficient of nonlinear equation with least square method, and determination finally improves model equation and verifies it
Precision.
Further, in the step S2, the calculated value T of weighted mean is calculated by formula (1)m:
Tm=aTs0+bl (1)
In formula (1), a is the coefficient of surface temperature item, b1For constant.
Further, the nonlinear equation established in the step S3 are as follows:
In formula (2), TmFor weighted mean calculated value, JD is Julian date, a0For constant term, a1For longitude term coefficient, a2
For latitude term coefficient, a3For elevation term coefficient, a4For the amplitude of periodic term, a5For the coefficient of surface temperature item, b is periodic function
Fitting first phase value, Log is longitude, and Lat is latitude, and H is elevation.
Further, the true value T of the weighted mean in the step S1m0It is obtained by formula (3):
In formula (3), e is the vapour pressure at the respective heights of survey station overhead, and T is the absolute temperature of survey station overhead respective heights.
Further, the true value T of the weighted mean in the step S1m0It is obtained by formula (4):
In formula (4), zi2For the height value on i-th layer of upper layer of sounding data, zi1For the height value of i-th layer of lower layer of sounding data,
eiFor the vapour pressure in i-th layer of survey station overhead, TiFor the absolute temperature in i-th layer of survey station overhead.
It is and traditional the utility model has the advantages that the invention discloses a kind of Bevis model refinement method of sub-region right mean temperature
Bevis model is compared, and computational accuracy is effectively increased.
Detailed description of the invention
Fig. 1 is the hum pattern that sounding data provides in the specific embodiment of the invention；
Fig. 2 is distribution situation figure of each sounding station in regional of the specific embodiment of the invention；
Fig. 3 is that the average daily Bias of 2 regional of model obtained using present embodiment the method and Rms is changed
Figure.
Specific embodiment
Technical solution of the present invention is further introduced with attached drawing With reference to embodiment.
Present embodiment discloses a kind of Bevis model refinement method of sub-region right mean temperature, including following
Step:
S1: pre-processing survey station sounding data, obtains the true value T of weighted meanm0With the true value of surface temperature
Ts0。
Present embodiment uses the radiosonde data of 76 survey stations of 2006-2013 in regional, and Fig. 2 is
Distribution situation figure of each survey station in regional.By taking 57494 websites as an example, sounding data provides the atmosphere of different isobaric surface layers
Significant level and stratification of wind data, as shown in Figure 1.Atmospheric characteristic layer parameter includes geopotential unit (HGHT), temperature (TEMP), dew point
The element of these detections of temperature (DWPT), relative humidity (RELH).
S2: the calculated value T of weighted mean is obtained using Bevis modelm。
S3: consider weighted mean calculated value TmThe Rule of Geographical Distribution and annual period variation, in Bevis model
On the basis of increase periodic term, longitude, latitude and elevation correction term, establish nonlinear equation.
S4: determining each term coefficient of nonlinear equation with least square method, and determination finally improves model equation and verifies it
Precision.
In step S2, the calculated value T of weighted mean is calculated by formula (1)m:
Tm=aTs0+bl (1)
In formula (1), a is the coefficient of surface temperature item, b1For constant.
The nonlinear equation established in step S3 are as follows:
In formula (2), TmFor weighted mean calculated value, JD is Julian date, a0For constant term, a1For longitude term coefficient, a2
For latitude term coefficient, a3For elevation term coefficient, a4For the amplitude of periodic term, a5For the coefficient of surface temperature item, b is periodic function
Fitting first phase value, Log be longitude (°), Lat be latitude (°), H be elevation (m).
The true value T of weighted mean tropospheric temperaturem0It can be by the vapour pressure e and absolute temperature T in survey station overhead along zenith direction
Integrated value obtain, as shown in formula (3):
In formula (3), e is the vapour pressure at the respective heights of survey station overhead, and T is the absolute temperature of survey station overhead respective heights.
Since atmosphere vapour is substantially distributed within hemisphere 12km, radio balloon can provide ground to 30
The sounding contour line of the meteorological elements such as temperature, the humidity of more km atmosphere, therefore formula (3) can be reduced to formula (4):
In formula (4), zi2For the height value on i-th layer of upper layer of sounding data, zi1For the height value of i-th layer of lower layer of sounding data,
eiFor the vapour pressure in i-th layer of survey station overhead, TiFor the absolute temperature in i-th layer of survey station overhead.
Using formula (4), the sounding data of collected regional 76 survey stations of 2006-2013 is calculated, is obtained
Each survey station corresponding T dailym0And Ts0Mean value.
Present embodiment uses the sounding data of collected 86 survey stations of regional 2014-2015, obtains
To each survey station corresponding T dailymAnd TsMean value.It is fitted according to formula (2), obtains the regional for taking annual periodicity into account
TmModel (model 2).
For the precision of analysis model 2, average deviation (Bias) and root-mean-square error is utilized in present embodiment
(Rms) as the precision index of evaluation model 2, wherein Bias indicates accuracy, the i.e. departure degree of model and true value；Rms
Precision is indicated, for measuring the reliability and stability of model.
Their calculating formula is respectively as follows:
Wherein:It is the weighted mean tropospheric temperature being calculated by formula (2) formula,It is sounding data along zenith
The weighted mean tropospheric temperature approximation true value that direction integral obtains, N are observation station number.
86 survey stations of 2014-2015 regional, 1 year Sounding Data is chosen, is located in advance in the same manner
Reason, obtains corresponding TmWith TsValue as test samples, test, obtained a result such as 1 institute of table to Bevis model and model 2
Show:
The various model accuracy contrast tables of table 1: two
From table 1 and Fig. 3 it can be seen that
(1)TmAnd TsCorrelativity in addition to being influenced by geographic factor, also influenced by Seasonal, in Bevis
After increasing periodic term in model, the precision of model is improved, improves 25% compared with traditional Bevis model；
(2) residual error of model 2 shows certain characteristic annual period, due to increasing periodic term, the annual period of residual error
Characteristic is weakened, and consideration, which continues growing periodic function item, can further increase the precision of model.
Claims (5)
1. a kind of Bevis model refinement method of sub-region right mean temperature, it is characterised in that: the following steps are included:
S1: pre-processing survey station sounding data, obtains the true value T of weighted meanm0With the true value T of surface temperatures0；
S2: the calculated value T of weighted mean is obtained using Bevis modelm；
S3: consider weighted mean calculated value TmThe Rule of Geographical Distribution and annual period variation, on the basis of Bevis model
Upper increase periodic term, longitude, latitude and elevation correction term, establish nonlinear equation；
S4: determining each term coefficient of nonlinear equation with least square method, and determination finally improves model equation and verifies its precision.
2. the Bevis model refinement method of sub-region right mean temperature according to claim 1, it is characterised in that: described
In step S2, the calculated value T of weighted mean is calculated by formula (1)m:
Tm=aTs0+b1 (1)
In formula (1), a is the coefficient of surface temperature item, b1For constant.
3. the Bevis model refinement method of sub-region right mean temperature according to claim 1, it is characterised in that: described
The nonlinear equation established in step S3 are as follows:
In formula (2), TmFor weighted mean calculated value, JD is Julian date, a0For constant term, a1For longitude term coefficient, a2For latitude
Spend term coefficient, a3For elevation term coefficient, a4For the amplitude of periodic term, a5For the coefficient of surface temperature item, b is the quasi- of periodic function
First phase value is closed, Log is longitude, and Lat is latitude, and H is elevation.
4. the Bevis model refinement method of sub-region right mean temperature according to claim 1, it is characterised in that: described
The true value T of weighted mean in step S1m0It is obtained by formula (3):
In formula (3), e is the vapour pressure at the respective heights of survey station overhead, and T is the absolute temperature of survey station overhead respective heights.
5. the Bevis model refinement method of sub-region right mean temperature according to claim 1, it is characterised in that: described
The true value T of weighted mean in step S1m0It is obtained by formula (4):
In formula (4), zi2For the height value on i-th layer of upper layer of sounding data, zi1For the height value of i-th layer of lower layer of sounding data, eiFor
The vapour pressure in i-th layer of survey station overhead, TiFor the absolute temperature in i-th layer of survey station overhead.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902346A (en) * | 2019-01-24 | 2019-06-18 | 东南大学 | Sub-region right mean temperature information acquisition method neural network based |
| CN110205909A (en) * | 2019-07-04 | 2019-09-06 | 交通运输部公路科学研究所 | A kind of pavement structure flexure based on bitumen layer equivalent temperature means target temperature correction |
| CN110378540A (en) * | 2019-08-02 | 2019-10-25 | 桂林理工大学 | A kind of Weighted Atmospheric Temperature Used calculation method suitable for Beibu Bay, guangxi area |
| CN111274707A (en) * | 2020-02-05 | 2020-06-12 | 东南大学 | Weighted average temperature calculation method based on reanalysis data and wireless sounding data |
| CN111352173A (en) * | 2020-02-17 | 2020-06-30 | 东南大学 | Weighted average temperature calculation method based on spatial position |
| CN111753408A (en) * | 2020-06-09 | 2020-10-09 | 南京信息工程大学 | Weather-considered GNSS atmospheric weighted average temperature calculation method |
| 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 |
| CN116881619A (en) * | 2022-12-01 | 2023-10-13 | 中国矿业大学(北京) | Atmospheric weighted average temperature refinement method taking surface air temperature and water vapor pressure into consideration |
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Cited By (13)
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| CN109902346A (en) * | 2019-01-24 | 2019-06-18 | 东南大学 | Sub-region right mean temperature information acquisition method neural network based |
| CN110205909A (en) * | 2019-07-04 | 2019-09-06 | 交通运输部公路科学研究所 | A kind of pavement structure flexure based on bitumen layer equivalent temperature means target temperature correction |
| CN110378540B (en) * | 2019-08-02 | 2023-05-09 | 桂林理工大学 | Atmospheric weighted average temperature calculation method suitable for northern Guangxi Bay region |
| CN110378540A (en) * | 2019-08-02 | 2019-10-25 | 桂林理工大学 | A kind of Weighted Atmospheric Temperature Used calculation method suitable for Beibu Bay, guangxi area |
| CN111274707A (en) * | 2020-02-05 | 2020-06-12 | 东南大学 | Weighted average temperature calculation method based on reanalysis data and wireless sounding data |
| CN111274707B (en) * | 2020-02-05 | 2022-11-25 | 东南大学 | Weighted average temperature calculation method based on reanalysis data and wireless sounding data |
| WO2021164480A1 (en) * | 2020-02-17 | 2021-08-26 | 东南大学 | Spatial location-based weighted mean temperature calculation method |
| CN111352173A (en) * | 2020-02-17 | 2020-06-30 | 东南大学 | Weighted average temperature calculation method based on spatial position |
| CN111753408A (en) * | 2020-06-09 | 2020-10-09 | 南京信息工程大学 | Weather-considered GNSS atmospheric weighted average temperature calculation method |
| CN111753408B (en) * | 2020-06-09 | 2023-05-09 | 南京信息工程大学 | GNSS atmosphere weighted average temperature calculation method taking weather into consideration |
| CN116881619A (en) * | 2022-12-01 | 2023-10-13 | 中国矿业大学(北京) | Atmospheric weighted average temperature refinement method taking surface air temperature and water vapor pressure into consideration |
| CN116881619B (en) * | 2022-12-01 | 2024-05-14 | 中国矿业大学(北京) | Atmospheric weighted average temperature refinement method taking surface air temperature and water vapor pressure into consideration |
| 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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