CN111274707B - Weighted average temperature calculation method based on reanalysis data and wireless sounding data - Google Patents
Weighted average temperature calculation method based on reanalysis data and wireless sounding data Download PDFInfo
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Abstract
The invention discloses a weighted average temperature calculation method based on reanalysis data and wireless sounding data, which corrects a factor model established according to EAR5 data by using a weighted average temperature true value of a sounding site, establishes a correction factor model of the sounding site and a correction factor model of the whole area, and can calculate the weighted average temperature of the non-sounding site. Compared with the traditional model established only by using wireless sounding data, the method improves the space-time resolution and the calculation accuracy of the weighted average temperature.
Description
Technical Field
The invention belongs to the field of global navigation systems, and particularly relates to a weighted average temperature calculation method based on reanalysis data and wireless sounding data.
Background
Water vapor is a main component of the atmosphere, and the total water vapor content (PWV) in the zenith direction of the survey station is calculated by inverting meteorological parameters in the atmosphere mainly by using ground-based GNSS meteorology at present. The calculation accuracy of the PWV mainly depends on the accuracy of GNSS data for calculating Zenith Wet Delay (ZWD) and the accuracy of a conversion coefficient pi in the process of converting the ZWD into the PWV, and the weighted average temperature T of the atmosphere m To calculate the important parameter of the conversion coefficient pi. The accuracy of the zenith wet retardation conversion to water vapor therefore depends primarily on T m The accuracy of (2). Conventional calculation of T m The method generally establishes a weighted average temperature model based on observation data of wireless sounding, but the resolution of wireless sounding data is 12 hours, the time-space resolution is lower, the established model is higher only in places with sounding data, and the accuracy is lower in places where the sounding data are difficult to obtain.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method capable of calculating weighted average temperature in a region, and compared with a traditional model established by only utilizing wireless sounding data, the method improves the space-time resolution and the calculation accuracy.
The technical scheme is as follows: the invention adopts the following technical scheme:
the weighted average temperature calculation method based on the reanalysis data and the wireless sounding data comprises the following steps:
s1: acquiring sounding data of each sounding site in a target area, wherein the sounding data comprises: the top water vapor pressure and temperature of each layer in the atmospheric layering; calculating the weighted average temperature true value of each exploration station and the weighted average temperature true value T of the nth exploration station m0,n Comprises the following steps:
wherein i =1,2,L, I is the number of atmospheric layers observed for sounding, e i,n 、T i,n 、Vh i,n Respectively the water vapor pressure, the temperature and the thickness of the top of the ith atmospheric layer,
n is the total number of the exploration stations in the target area;
s2: acquiring ERA5 reanalysis data of each sounding site, and interpolating to obtain temperature T of each layer in the atmosphere ERA5j,n And water vapour pressure e ERA5j,n And the temperature T of the ground 0,n And water vapour pressure e 0,n (ii) a Calculating the weighted average temperature T of the reanalysis data of each sounding site ERA5,n ：
J =1,2,L, J is the number of atmospheric layers obtained by interpolation; vh ERA5j,n Is the thickness of the j-th atmosphere after interpolation;
s3: establishing a factor model between the ground temperature and the weighted average temperature of the reanalysis data for each sounding site:
T′ ERA5,n ＝a 1,n +b 1,n T 0,n
wherein a is 1,n Is constant coefficient，b 1,n Is the surface temperature coefficient; calculating T obtained by S2 ERA5,n Temperature T of ground 0,n Substituting the formula into the formula, and fitting to obtain a 1,n And b 1,n A value of (d);
s4: and establishing a correction factor model between the weighted average temperature true value and the re-analysis data weighted average temperature for each sounding site:
T′ m0,n ＝c 1,n +d 1,n T′ ERA5,n
wherein c is 1,n Modifying the constant coefficient of the factor model for the nth sounding site, d 1,n Re-analyzing the data coefficients; calculating T obtained by S1 m0,n And reanalysis data weighted average temperature T 'obtained by calculation according to the model established in S3' ERA5,n Substituting the formula into the formula, and fitting to obtain c 1,n And d 1,n A value of (d);
s5: establishing an integral correction factor model in the target region:
T m0 ＝K 1 +K 2 T ERA5 ；
weighted average temperature truth value T of each sounding site in the target area m0,n And reanalyzing the data weighted average temperature T ERA5,n As T m0 And T ERA5 Substituting the formula and fitting to obtain K 1 And K 2 A value of (d);
s6: calculating and reanalyzing the weighted average temperature T of the data according to S2 according to the real-time ERA5 data for the position with the sounding site in the target area ERA5,n Then, calculating the weighted average temperature by using the correction factor model in the S4 as a calculation result;
for the position of the non-sounding site in the target area, firstly acquiring ERA5 reanalysis data at the position of the non-sounding site in the target area, establishing a factor model between the ground temperature and the weighted average temperature of the reanalysis data according to the steps S2 and S3, fitting the coefficient, and obtaining a factor model T at the position of the non-sounding site ERA5 ＝a′+b′T 0 (ii) a Acquiring real-time ERA5 data at a non-sounding site, and interpolating to obtain ground temperature T 0 Substituting the temperature into the factor model to obtain a reanalyzed data weighted average temperature T' ERA5 Prepared from T' ERA5 Substituting the correction factor model established in S5 to calculate a weighted average temperature T m0 As a result of the calculation.
And in the step S2, the insert value obtains the temperature and the water vapor pressure value of the 26 layers in the atmosphere of 150hpa-975 hpa.
In step S2, interpolation is performed by using an inverse distance weighting method.
In the step S3, a factor model between the ground temperature and the weighted average temperature of the reanalysis data is established for each sounding site, wherein the factor model also comprises the ground water vapor pressure, and the factor model comprises the following steps:
T′ ERA5,n ＝a 2,n +b 2,n T 0,n +c n e 0,n ，
wherein a is 2,n Is a constant coefficient, b 2,n Is the surface temperature coefficient, c is the coefficient of the ground water pressure; calculating T obtained by S2 ERA5,n Temperature T of ground 0,n Surface vapor pressure e 0,n Substituting the formula into the formula, and fitting to obtain a 2,n 、b 2,n And the value of c.
The factor model at the position of the non-sounding site in the step S6 further comprises ground water vapor pressure, and the factor model is as follows:
T ERA5 ＝a′ 2 +b′ 2 T 0 +c′e 0 ，
wherein e 0 And the ground water vapor pressure at the non-sounding station is obtained.
Has the advantages that: compared with the traditional model established only by using the wireless sounding data, the method improves the space-time resolution and the accuracy of calculating the weighted average temperature at the position of the non-sounding station.
Drawings
FIG. 1 is a diagram of a distribution of grid data and sounding sites in a Jiangsu area and nearby areas in accordance with an embodiment of the present invention;
FIG. 2 is a comparison graph of the weighted average temperature true value of the Hangzhou site in 2018, the weighted average temperature calculated by the single-factor model, and the weighted average temperature calculated by the two-factor model in the embodiment of the present invention;
FIG. 3 is a comparison graph of the precision of the integral model of Jiangsu regions before and after single factor model correction of five sounding sites in 2018 in the embodiment of the invention;
FIG. 4 is a comparison graph of the precision of the integral model of Jiangsu regions before and after the two-factor model of five sounding sites is corrected in 2018 in the embodiment of the invention;
fig. 5 is a comparison graph of models at 2018, month 1, day 7, day 0 to month 1, day 16, day 0;
FIG. 6 is a comparison diagram of models in the Chongqing area in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described below with reference to the accompanying drawings.
The invention discloses a method for calculating weighted average temperature based on reanalysis data and wireless sounding data, and the steps of the method are described by taking Jiangsu and surrounding areas as examples.
There are 5 exploration sites in and near Jiangsu region, including Xuzhou, she yang, nanjing, baoshan and Hangzhou, as shown in FIG. 1.
S1: acquiring sounding data of each sounding site in a target area, wherein the sounding data comprises: the top water vapor pressure and temperature of each layer in the atmospheric layering; calculating the weighted average temperature true value of each exploration station and the weighted average temperature true value T of the nth exploration station m0,n Comprises the following steps:
wherein i =1,2,L, I is the number of atmospheric layers observed for sounding, e i,n 、T i,n 、Vh i,n Respectively the water vapor pressure, the temperature and the thickness of the top of the ith atmospheric layer,
n is the total number of the exploration stations in the target area;
s2: acquiring ERA5 reanalysis data of each sounding site, and interpolating to obtain temperature T of each layer in the atmosphere ERA5j,n And water vapour pressure e ERA5j,n And the temperature T of the ground 0,n And water vapour pressure e 0,n ；
ERA5 is a fifth generation reanalyzed product by ECMWF (european mid-range weather forecast center), with spatial resolution of 0.25 ° x 0.25 °, temporal resolution of 1 hour, and higher temporal resolution.
Calculating the weighted average temperature T of the reanalysis data of each sounding site ERA5,n ：
J =1,2,L, J is the number of atmospheric layers obtained by interpolation; vh ERA5j,n Is the thickness of the j-th atmosphere after interpolation;
in this embodiment, an inverse distance weighting method is used for interpolation, the 150hpa-975hpa atmosphere is divided into 26 layers, that is, J =26, and the temperature and the water vapor pressure of each layer are obtained by interpolation.
S3: and (3) establishing a factor model between the ground temperature and the weighted average temperature of the reanalysis data for each sounding site, wherein a single factor model can be established:
T′ ERA5,n ＝a 1,n +b 1,n T 0,n (2)
wherein a is 1,n Is a constant coefficient, b 1,n Is the surface temperature coefficient; calculating T obtained by S2 ERA5,n Temperature T of ground 0,n Substituting the formula into the formula, and fitting to obtain a 1,n And b 1,n A value of (d);
a two factor model can also be established:
T′ ERA5,n ＝a 2,n +b 2,n T 0,n +c n e 0,n (3)
wherein a is 2,n Is a constant coefficient, b 2,n Is the surface temperature coefficient, c is the surface water vaporThe coefficient of compression; similar to the one-factor model, T calculated from S2 ERA5,n Temperature T of ground 0,n Surface vapor pressure e 0,n Substituting the formula into the formula, and fitting to obtain a 2,n 、b 2,n And the value of c.
The single factor model coefficients of the five sites in this example are shown in table 1:
table 1: sounding site single factor model coefficient table
The two-factor model coefficients for the five sites are shown in table 2:
table 2: sounding site dual-factor model coefficient table
In order to verify the accuracy of the factor model, a weighted average temperature true value calculated according to formula (1) by the Hangzhou website 2018 sounding data is selected to be compared with the single factor model and the double factor model established above, and the comparison result is shown in fig. 2. As can be seen from fig. 2, there is a certain difference between the reanalyzed data weighted average temperature calculated using the above factor model and the weighted average temperature true value calculated according to equation (1). This difference is eliminated by the following correction factor model.
S4: and establishing a correction factor model between the weighted average temperature true value and the re-analysis data weighted average temperature for each sounding site: t' m0,n ＝c 1,n +d 1,n T′ ERA5,n (4)
Wherein c is 1,n Modifying the constant coefficient of the factor model for the nth sounding site, d 1,n Re-analyzing the data coefficients; calculating T obtained by S1 m0,n And reanalysis data weighted average temperature T 'obtained by calculation according to the single factor model established in S3' ERA5,n Substituting the formula into the formula, and fitting to obtain c 1,n And d 1,n A value of (d);
if T' ERA5,n The correction factor model is obtained by calculation according to the two-factor model in S3, and the correction factor model after coefficient fitting is as follows: t' m0,n ＝c 2,n +d 2,n T′ ERA5,n (5)
The coefficients of the modified model for the five sounding sites in this embodiment are shown in tables 3 and 4:
table 3: single factor model correction coefficient table for sounding site
Table 4: sounding site dual-factor model correction coefficient table
In order to verify the accuracy of the single-factor weighted average temperature model and the correction factor model, the method calculates the temperature of 2018 by using the single-factor model of each station
T calculated separately from the sounding data m0 Comparing, calculating the root mean square error (black bar chart in figure 3); then, the correction coefficient in Table 3 is used to perform the correction, and the correction is further compared with T m0 Comparing, calculating the root mean square error (white bar chart of figure 3); the literature: zhu Mingchen, hu Wusheng correlation analysis of Tm-Ts in Jiangsu region and establishment of linear model thereof [ J]The surveying and mapping project comprises 2018, volume (6) 14-18, wireless sounding data of five sounding stations (Xuzhou, she yang, nanjing, baoshan and Hangzhou) near Jiangsu in 11 years from 2005 to 2015 are collected, linear models of weighted average temperature and surface temperature of the five stations are respectively established, gray in figure 3 is the root mean square error of comparison between the weighted average temperature calculated by the models and the sounding data, and N is the number of comparison samples. As can be seen from fig. 3, for five sounding sites in Jiangsu and peripheral areas, the results after the single factor model established by sounding data on ERA5 data is corrected are all better than the results before the correction, and the corrected resultsThe accuracy of the local model is equivalent to or even slightly superior to that of the local model of each site in the Jiangsu area, which is established only by using sounding data. Considering that the ERA5 model uses hourly data modeling, the modifier model of the present invention is more practical than the sounding station 12-hour data.
Fig. 4 compares the rms error of the two-factor model before (black) and after (white) correction, and the accuracy of the corrected model for each station is improved. The two-factor model (white) works better than the single-factor model (grey and diagonal fill). FIG. 5 selects a comparison graph of single-factor and double-factor models of Hangzhou station from 7 days to 16 days 1 month to 2018 and an ERA5 and an exploration station, wherein the double-factor model is obviously superior to the single-factor model, and the modified model is superior to the T of the model before modification and the T of exploration m0 Compared with the precision, the precision is slightly improved, and therefore the corrected dual-factor model added with the ground temperature steam pressure has the highest precision.
S5: establishing an integral correction factor model in the target region:
T m0 ＝K 1 +K 2 T ERA5 (6)
weighted average temperature truth value T of each sounding site in the target area m0,n And reanalyzing the data weighted average temperature T ERA5,n As T m0 And T ERA5 Substituting the formula and fitting to obtain K 1 And K 2 A value of (d); here weighted average temperature truth T for all stations in the area m0,n And reanalyzing the data weighted average temperature T ERA5,n And (5) obtaining a correction factor model of the whole region by fitting.
S6: calculating and reanalyzing the weighted average temperature T of the data according to S2 according to the real-time ERA5 data for the position with the sounding site in the target area ERA5,n Then, calculating the weighted average temperature by using the correction factor model in the S4 as a calculation result;
for the position of the non-sounding site in the target area, firstly, ERA5 reanalysis data at the position of the non-sounding site in the target area are obtained, a factor model between the ground temperature and the weighted average temperature of the reanalysis data is established according to the steps S2 and S3, and the coefficient is fitted to obtain the factor model at the position of the non-sounding site.
Likewise, a single factor model at a non-sounding site location may be established here:
T ERA5 ＝a′+b′T 0 (7)
a two factor model at a non-sounding site location may also be established:
T ERA5 ＝a′ 2 +b′ 2 T 0 +c′e 0 (8)
acquiring real-time ERA5 data at a non-sounding site, and interpolating to obtain ground temperature T 0 And water vapour pressure e 0 Substituting into the single factor model or the two factor model to obtain reanalyzed data weighted average temperature T' ERA5 。
Mixing T' ERA5 Substituting the correction factor model established in S5 to calculate a weighted average temperature T m0 As a result of the calculation.
In order to verify the model accuracy of the non-exploration area, the experiment uses exploration data of Anqing exploration stations (30.62 degrees N,116.97 degrees E) nearest to Jiangsu area) in the areas of 30 degrees N-34.5 degrees N and 115 degrees E-122 degrees E for verification. Firstly, establishing a weighted average temperature single factor model T of an Anqing site according to the methods in S2 and S3 by utilizing ERA5 data of Anqing in 2008-2017 ERA5 ＝a′+b′T 0 Obtaining a weighted average temperature T calculated by a one-factor model ERA5 Then, 5 exploration stations of Xuzhou, zhuyang, nanjing, baoshan and Hangzhou are utilized to establish a correction factor model of the whole target area according to S5, and the coefficient obtained by fitting is as follows: k1= -3.5807, K2=1.0145. Substituting ERA5 data of Anqing in 2018 into weighted average temperature single factor model of Anqing site to obtain T ERA5 Then, in the formula (6), a calculated value of the weighted average temperature is obtained. Verification was performed using the exploration data of the station in ansqing of 2018 as a true value. FIG. 6 shows the 2018 Anqing site single factor model, the modified model, the Jiangsu region integral model and T m0 And (5) comparing true values. The experimental results are shown in table 5, and the ERA5 data is calculated as the single factor model T of the Anqing station m The root mean square error with the sounding truth value is 2.6572K, and parameter correction is usedThe root mean square error of the post-processing is 2.6406K, which is better than the literature: zhu Mingchen, hu Wusheng correlation analysis of Tm-Ts in Jiangsu region and establishment of linear model thereof [ J]Mapping project 2018, volume (6): 14-18. Root mean square error of Jiangsu region model 2.6801K, further verified high spatial resolution grid T m Regional applicability of the model.
Table 5: precision comparison of single factor model, modified single factor model and Jiangsu region overall model
Claims (5)
1. The weighted average temperature calculation method based on reanalysis data and wireless sounding data is characterized by comprising the following steps of:
s1: acquiring sounding data of each sounding site in a target area, wherein the sounding data comprises: the top water vapor pressure and temperature of each atmospheric layer; calculating the weighted average temperature true value of each exploration station and the weighted average temperature true value T of the nth exploration station m0,n Comprises the following steps:
wherein i =1,2,L, I is the number of atmospheric layers observed for sounding, e i,n 、T i,n 、Vh i,n Respectively the water vapor pressure, the temperature and the thickness of the top of the ith atmospheric layer,
n is the total number of the exploration stations in the target area;
s2: acquiring ERA5 reanalysis data of each sounding site, and interpolating to obtain temperature T of each layer in the atmosphere ERA5j,n And water vapour pressure e ERA5j,n And groundTemperature T of noodle 0,n And water vapour pressure e 0,n (ii) a Calculating the weighted average temperature T of the reanalysis data of each sounding site ERA5,n ：
J =1,2,L, J is the number of atmospheric layers obtained by interpolation; vh ERA5j,n Is the thickness of the j-th atmosphere after interpolation;
s3: establishing a factor model between the ground temperature and the weighted average temperature of the reanalysis data for each sounding site:
T′ ERA5,n ＝a 1,n +b 1,n T 0,n
wherein a is 1,n Is a constant coefficient, b 1,n Is the surface temperature coefficient; calculating T obtained by S2 ERA5,n Temperature T of ground 0,n Substituting the formula into the formula, and fitting to obtain a 1,n And b 1,n A value of (d);
s4: and establishing a correction factor model between the weighted average temperature true value and the re-analysis data weighted average temperature for each sounding site:
T′ m0,n ＝c 1,n +d 1,n T′ ERA5,n
wherein c is 1,n Modifying the constant coefficient of the factor model for the nth sounding site, d 1,n Re-analyzing the data coefficients; calculating T obtained by S1 m0,n And reanalysis data weighted average temperature T 'obtained by calculation according to the model established in S3' ERA5,n Substituting the formula into the formula, and fitting to obtain c 1,n And d 1,n A value of (d);
s5: establishing an integral correction factor model in the target region: t is m0 ＝K 1 +K 2 T ERA5 ；
Weighted average temperature truth value T of each sounding site in the target area m0,n And reanalyzing the data weighted average temperature T ERA5,n As T m0 And T ERA5 Substituting the formula and fitting to obtain K 1 And K 2 A value of (d);
s6: calculating and reanalyzing the weighted average temperature T of the data according to S2 according to the real-time ERA5 data for the position with the sounding site in the target area ERA5,n Then, calculating the weighted average temperature by using the correction factor model in the S4 as a calculation result;
for the position of the non-sounding site in the target area, firstly acquiring ERA5 reanalysis data at the position of the non-sounding site in the target area, establishing a factor model between the ground temperature and the weighted average temperature of the reanalysis data according to the steps S2 and S3, fitting the coefficient, and obtaining a factor model T at the position of the non-sounding site ERA5 ＝a′+b′T 0 (ii) a Acquiring real-time ERA5 data at a non-sounding site, and interpolating to obtain ground temperature T 0 Substituting the temperature into the factor model to obtain a reanalyzed data weighted average temperature T' ERA5 Prepared from T' ERA5 Substituting the correction factor model established in S5 to calculate a weighted average temperature T m0 As a result of the calculation.
2. The method of claim 1, wherein the interpolation in step S2 is performed to obtain a temperature and a water vapor pressure of 26 layers of the atmosphere of 150hpa to 975 hpa.
3. The method of claim 1, wherein the interpolation in step S2 is performed by inverse distance weighting.
4. The weighted average temperature calculation method according to claim 1, wherein the model of the factor between the ground temperature and the weighted average temperature of the reanalysis data, which is established for each sounding site in step S3, further includes the ground water vapor pressure, and the model of the factor is: t' ERA5,n ＝a 2,n +b 2,n T 0,n +c n e 0,n Wherein a is 2,n Is a constant coefficient, b 2,n Is the surface temperature coefficient, and c is the coefficient of the ground water pressure; calculating T obtained by S2 ERA5,n And the temperature T of the ground 0,n Surface vapor pressure e 0,n Substituting the formula into the formula, and fitting to obtain a 2,n 、b 2,n And the value of c.
5. The method of claim 1, wherein the factor model at the position of the non-sounding site in step S6 further includes a ground water vapor pressure, and the factor model is: t is a unit of ERA5 ＝a′ 2 +b′ 2 T 0 +c′e 0 Wherein e is 0 And the ground water vapor pressure at the non-sounding station is obtained.
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