CN111753408A - Weather-considered GNSS atmospheric weighted average temperature calculation method - Google Patents

Weather-considered GNSS atmospheric weighted average temperature calculation method Download PDF

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CN111753408A
CN111753408A CN202010518633.7A CN202010518633A CN111753408A CN 111753408 A CN111753408 A CN 111753408A CN 202010518633 A CN202010518633 A CN 202010518633A CN 111753408 A CN111753408 A CN 111753408A
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王明华
唐旭
金双根
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Nanjing University of Information Science and Technology
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Abstract

The invention discloses a weather-considered GNSS atmospheric weighted average temperature calculation method which comprises the steps of collecting historical sounding data, preprocessing the data, classifying the data according to weather, respectively constructing a linear model between atmospheric weighted average temperature and earth surface temperature according to the classified data, and calculating different weather lower values according to the model and actual measurement. The differences of linear models under different weathers are fully considered, the calculation accuracy of the weighted average atmospheric temperature can be further improved, and the improvement of the estimation accuracy of the GNSS atmospheric degradable water yield is further promoted. The method can be used in daily GNSS water vapor estimation services, and can also be used for reprocessing historical GNSS data so as to improve the accuracy of the historical GNSS water vapor data.

Description

Weather-considered GNSS atmospheric weighted average temperature calculation method
Technical Field
The invention relates to the field of surveying and mapping science and technology/navigation satellite meteorology, in particular to a weather-considered GNSS atmospheric weighted average temperature calculation method.
Background
The atmospheric weighted average temperature is a key parameter in the process of acquiring the atmospheric water-reducing amount by utilizing Global Navigation Satellite System (GNSS) observation data, and the precision of the atmospheric weighted average temperature directly determines the precision of converting the zenith direction GNSS signal wet delay into the atmospheric water-reducing amount. The atmospheric weighted average temperature can be calculated by a definitional formula or an empirical model. The calculation of the atmospheric weighted average temperature by the definitional formula requires knowledge of atmospheric temperature and water vapor pressure vertical profile information, which results in limited application, so that empirical model calculation is often adopted in practical application, generally, a linear relation between the atmospheric weighted average temperature and the surface air temperature is established, and then the atmospheric weighted average temperature is calculated according to an observed value of the surface air temperature. The research finds that the linear relation between the atmospheric weighted average temperature and the surface air temperature is not constant and is related to both time and space, and the geographic characteristic (space) and the seasonal characteristic (time) of the linear relation are considered in the prior method, so that the calculation accuracy of the atmospheric weighted average temperature is improved to a certain extent. Different weather causes different vertical distribution of atmospheric temperature and vapor pressure, so that the linear relation between the atmospheric weighted average temperature and the surface air temperature is influenced. The current atmospheric weighted average temperature linear model does not take into account the weather information problem.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for calculating the GNSS atmospheric weighted average temperature considering weather so as to further improve the calculation accuracy of the atmospheric weighted average temperature and further promote the improvement of the estimation accuracy of the GNSS atmospheric water reducible quantity.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a weather-considered GNSS atmospheric weighted average temperature calculation method, which comprises the following steps:
step 1, collecting historical exploration data for constructing a Ts-Tm linear model; wherein Tm is the atmospheric weighted average temperature, and Ts is the surface air temperature;
step 2, preprocessing historical sounding data;
step 3, classifying the preprocessed sounding data according to weather; the method comprises the following specific steps:
acquiring rainfall information at the time and position corresponding to the preprocessed sounding data, and dividing all the preprocessed sounding data into rainless data and rained data according to whether rainfall occurs on the same day by taking days as units;
step 4, constructing a Ts-Tm model according to weather; the method comprises the following specific steps:
respectively constructing a Ts-Tm linear model according to the data of rainy days and the data of rainy days; the linear model of Ts-Tm is in the form of
Tm＝a·Ts+b (1)
Wherein, a is a proportionality coefficient, namely the slope of the linear model, and b is the intercept of the linear model;
the atmospheric weighted mean temperature Tm is defined as
Figure BDA0002531079930000021
Wherein, PwWater vapor pressure, T temperature, h height, h0For measuring the height of the station, h1Is the atmospheric layer top height; when calculating Tm from the sounding data, a discrete form of the formula (2) is used
Figure BDA0002531079930000022
In the formula, N is the number of atmospheric layers divided by the sounding data, one layer is defined between two adjacent observations in the ascending process of the balloon, and delta hiThe thickness of the ith layer of atmosphere is the difference between the adjacent two observation heights; pwiIs the average atmospheric water vapor pressure, T, of the ith layeriIs the average temperature of the ith layer;
calculating the weighted average temperature Tm of the atmosphere according to each sounding profile, establishing an equation with the observed value of the surface air temperature Ts, and writing the equation into the form of an error equation as follows
Figure BDA0002531079930000023
Wherein n is the total number of observed values, vj、TsjAnd TmjRespectively, the residual error of the jth observation equation, the surface air temperature and the atmosphere weighted average temperature, wherein j is 12, …, n; a and b corresponding to the Ts-Tm linear model are obtained by the following formula
Figure BDA0002531079930000024
The coefficient (a) is obtained for the data of no rain days and the data of rain days1,b1) And (a)2,b2) Obtaining the linear calculation model of the weighted average temperature of the atmosphere in the same area in the rainy days and no rainy days
Figure BDA0002531079930000025
Step 5, calculating the weighted average temperature of the atmosphere under different weather conditions; the method comprises the following specific steps:
and (3) correspondingly selecting a rainy day model or a no-rainy day model according to whether the actual weather has rainfall during application, and calculating the weighted average atmospheric temperature according to the actually observed earth surface temperature Ts and the formula (6).
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, in the step 1, the historical sounding data is derived from the historical sounding data of a long-time sequence of an actual application area or weather reanalysis data is adopted to replace the sounding data.
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, in the step 2, the preprocessing refers to removing the data of the atmospheric pressure layer which does not meet the preset requirement and the data containing the gross error.
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, the step 2 specifically comprises the following steps:
the preset required data meet the following requirements: 1) observations of sounding data at the station height (i.e. first layer data) must exist; 2) the highest air pressure observed value is less than or equal to 300 hPa; 3) when the barometric pressure observations are greater than 1000hPa at the stations, the data must include observations of barometric pressures of 1000, 850, 700, 500, and 300 hPa; 4) when the barometric pressure observations at the stations are less than 1000hPa, but greater than 850hPa, then observations at barometric pressures of 850, 700, 500, and 300hPa must be included in the data; 5) when the barometric pressure observations at the stations are less than 850hPa, but greater than 700hPa, then observations at pressures of 700, 500, and 300hPa must be included in the data; 6) when the air pressure observed value at the station is less than 700hPa but more than 500hPa, the data must include the observation of 500hPa and 300 hPa; if any one of the above conditions is not met, discarding the whole profile data;
and performing gross error detection on the sounding profile data meeting the conditions that Tm-Ts is more than 10K and Ts-Tm is more than 30K.
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, in the step 3, rainfall information is acquired through recording data of the ground automatic weather station.
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, in step 4, PwiThe average value of the water pressure and the air pressure at the bottom and the top of the ith layer is obtained, TiThe average is obtained from the temperature of the bottom and the top of the ith layer.
As a further optimization scheme of the weather-considered GNSS atmospheric weighted average temperature calculation method, in step 4, the water vapor pressure PwFrom the water-steam mixing ratio mxCalculated from the atmospheric pressure P and the calculation formula is as follows
Figure BDA0002531079930000031
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the method provided by the invention can be used for calculating the atmospheric weighted average temperature in consideration of weather information, namely, respectively establishing an atmospheric weighted average temperature model for rainy days and rainy days, and adopting different models for different weathers, compared with the traditional weather-indistinguishable calculation model, the method provided by the invention can be used for remarkably improving the calculation accuracy of the atmospheric weighted average temperature (the accuracy is improved by 3% -15%), and thus, a basis is provided for further improving the estimation accuracy of the GNSS atmospheric water-reducing capacity. The method can carry out water-vapor conversion again on the existing GNSS data, can obviously improve the product precision, and can better serve scientific research and production.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a relationship between the surface air temperature and the atmospheric weighted average temperature of a Yunnan Kunming exploration station (station number: 56778).
FIG. 3 is a linear model of the Ts-Tm of various classes (Kunming station).
FIG. 4 is a comparison of the computational accuracy of the method of the present invention (with the weather model) and the conventional method (without distinguishing the weather model).
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
FIG. 1 is a flow chart of the method of the present invention. The method mainly comprises data collection, data preprocessing, data classification and model establishment and use.
The method comprises the steps of collecting historical sounding data, preprocessing the data, classifying the data according to weather, respectively constructing a linear model between the atmospheric weighted average temperature (Tm) and the earth surface temperature (Ts) according to the classified data, and calculating Tm values under different weathers according to the model and the actually measured Ts. The method comprises the following specific steps:
step 1: and collecting historical sounding data.
Historical exploration data of a long-time sequence (more than 1 year) of an actual application region are collected and used for constructing a Ts-Tm linear model. The exploration data can also be replaced by weather reanalysis data, such as weather reanalysis data products of organizations such as European Medium weather forecast center (ECMWF) and American national Environment forecast center (NCEP).
Step 2: and (4) preprocessing data.
And eliminating the data which do not meet the requirements of the air pressure layer and the data containing the gross errors. In use, the data is required to satisfy: 1) observations of sounding data at the station height (i.e. first layer data) must exist; 2) the highest air pressure observed value is less than or equal to 300 hPa; 3) when the barometric pressure observations are greater than 1000hPa at the stations, the data must include observations of barometric pressures of 1000, 850, 700, 500, and 300 hPa; 4) when the barometric pressure observations at the stations are less than 1000hPa, but greater than 850hPa, then observations at barometric pressures of 850, 700, 500, and 300hPa must be included in the data; 5) when the barometric pressure observations at the stations are less than 850hPa, but greater than 700hPa, then observations at pressures of 700, 500, and 300hPa must be included in the data; 6) when the barometric pressure observations at the stations are less than 700hPa, but greater than 500hPa, then observations at pressures of 500hPa and 300hPa must be included in the data. If any of the above conditions is not met, the entire profile data is discarded.
And (3) performing gross error detection on the sounding profile data meeting the conditions that Tm-Ts is more than 10K and Ts-Tm is more than 30K, and if unreasonable data records such as abnormal temperature records and the like are found, considering that gross errors exist in the data and rejecting the data.
And step 3: the data is classified by weather.
Acquiring rainfall information of the corresponding time and position of the sounding data, and dividing all preprocessed data into data of no-rain days and data of rain days according to whether rainfall occurs in the same day by taking the day as a unit. Rainfall information can be acquired from sources such as recording data of the ground automatic weather station.
And 4, step 4: and constructing a Ts-Tm model according to weather.
And respectively constructing a Ts-Tm linear model according to the data of the rainless days and the data of the rainy days. The linear model of Ts-Tm is in the form of
Tm＝a·Ts+b (1)
Where a is the scaling factor, i.e., the slope of the linear model, and b is the intercept of the linear model.
The atmospheric weighted mean temperature Tm is defined as
Figure BDA0002531079930000051
Wherein, PwWater vapor pressure, T temperature, h0For measuring the height of the station, h1Is the atmospheric layer top height. Calculating T from sounding datamWhen the discrete form of the formula (2) in the embodiment is used
Figure BDA0002531079930000052
In the formula, N is the number of atmospheric layers divided by the sounding data, and defines that one layer exists between two adjacent observations in the ascending process of the balloon. Δ hiThe thickness of the atmosphere of the ith layer, namely the difference between the adjacent two observed heights. PwiThe average atmospheric water vapor pressure of the ith layer can be obtained by averaging the water vapor pressures of the bottom and the top of the ith layer. T isiThe average temperature of the ith layer can be obtained by averaging the temperatures of the bottom and the top of the ith layer. The water vapor pressure P of each layer is not directly provided by the sounding data observation filewThe value of which can be determined by the steam mixing ratio mxCalculated from the atmospheric pressure P and the calculation formula is as follows
Figure BDA0002531079930000053
Calculating the weighted average temperature Tm of the atmosphere according to each sounding profile, establishing an equation with the observed value of the surface air temperature Ts, and writing the equation into the form of an error equation as follows
Figure BDA0002531079930000054
Wherein n is the total number of observed values, vj、TsjAnd TmjRespectively, the residual error of the jth observation equation, the surface air temperature and the atmospheric weighted average temperature, j is 1,2, …, n; a and b corresponding to the Ts-Tm linear model are obtained by the following formula
Figure BDA0002531079930000061
The coefficient (a) is obtained for the data of no rain days and the data of rain days1,b1) And (a)2,b2) Obtaining the linear calculation model of the weighted average temperature of the atmosphere in the same area in the rainy days and no rainy days
Figure BDA0002531079930000062
And 5: and calculating Tm in different weather.
And (3) correspondingly selecting a rainy day model or a no-rainy day model according to whether the actual weather has rainfall during application, and calculating the weighted average atmospheric temperature according to the actually observed earth surface temperature Ts and a formula (7).
FIG. 2 is a relationship between the surface air temperature and the atmospheric weighted average temperature of a Yunnan Kunming exploration station (station number: 56778). The exploration data time is 2005-2016, data without rain (5271 data points) are corresponded to black points, and data with rain (3368 data points) are corresponded to gray points.
Firstly, collecting sounding data of Kunming sounding station (56778) in 2005-2018, wherein the data in 2005-2016 are used for establishing a linear model of atmospheric weighted average temperature and surface air temperature, and the sounding data in 2017-2018 are used for verifying the accuracy of the established model.
Secondly, preprocessing the collected sounding data, and rejecting data which do not meet the requirement of the number of observation layers and contain gross errors in the sounding data.
Thirdly, collecting rainfall information of Kunming exploration stations 2005-2018, and classifying the original exploration data according to the rainfall data: the data are divided into data in rainy days and data in rainy days. Data for rainy days and no rainy days in Kunming station in 2005-2016 are shown in FIG. 2.
Fourthly, respectively establishing a Ts-Tm linear model for data of no rain days and data of rain days of 2005-2016 in the Kunming station, wherein the model obtained by the data of no rain days is
Tm＝0.41·Ts+161.13 (8)
The model obtained with the data in rainy days is Tm 0.62 · Ts +98.44 (9)
For the purpose of subsequent comparison, a Ts-Tm linear model was constructed using all data (including data without rain and data with rain), i.e., a model that does not distinguish weather was
Tm＝0.46·Ts+146.86 (10)
Three types of models are shown in fig. 3.
And finally, selecting a corresponding model according to different weather (rainy or non-rainy) in actual application to calculate the weighted average temperature of the atmosphere.
In FIG. 2, it is shown that there is a certain difference between the Ts-Tm data point distribution patterns in the rainy day and the rainy day, which indicates that there is a certain difference in the Ts-Tm linear relationship in different weather. Fig. 3 shows various Ts-Tm linear models (kunming station), where the solid line is a model created by data in rainy days, the dotted line is a model created by data in rainy days, and the dotted line is a model created by all data (including data in rainy days and data in rainy days). Fig. 3 shows that the Ts-Tm linear model established from the data of no raining is significantly different from the Ts-Tm linear model established from the data of rainy days, and that the two models are also clearly different from the model of no distinguishing weather (Ts-Tm linear model established from all data, total model in fig. 3), thus confirming that the present invention is necessary to model and use rainy days separately. In addition, by taking the sounding data of 2017 and 2018 as the true value, the accuracy evaluation is performed on the method (model for distinguishing weather) and the traditional method (model for not distinguishing weather), and fig. 4 is a calculation accuracy comparison between the method (model for distinguishing weather) and the traditional method (model for not distinguishing weather). The black bars represent the Root Mean Square (RMS) that does not distinguish the difference between the calculated and true values of the weather model. The grey bars represent the RMS of the difference between the calculated and true values of the process of the invention. And modeling adopts 2005 + 2016 sky detection data, and calculates RMS by taking 2017 + 2018 sky detection data as a true value. The abscissa 1 indicates the absence of rain and 2 indicates the presence of rain. Fig. 4 shows that, in no-rain days or rain days, compared with the conventional method, the calculated value of the method is closer to the true value, and the RMS is respectively reduced by 3.8% (no-rain days) and 13.9% (rain days), which indicates that the method can improve the calculation accuracy of the atmospheric weighted average temperature.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (7)

1. A weather-considered GNSS atmospheric weighted average temperature calculation method is characterized by comprising the following steps:
step 1, collecting historical exploration data for constructing a Ts-Tm linear model; wherein Tm is the atmospheric weighted average temperature, and Ts is the surface air temperature;
step 2, preprocessing historical sounding data;
step 3, classifying the preprocessed sounding data according to weather; the method comprises the following specific steps:
acquiring rainfall information at the time and position corresponding to the preprocessed sounding data, and dividing all the preprocessed sounding data into rainless data and rained data according to whether rainfall occurs on the same day by taking days as units;
step 4, constructing a Ts-Tm model according to weather; the method comprises the following specific steps:
respectively constructing a Ts-Tm linear model according to the data of rainy days and the data of rainy days; the linear model of Ts-Tm is in the form of
Tm is a · Ts + b (1), where a is a proportionality coefficient, i.e., the slope of the linear model, and b is the intercept of the linear model;
the atmospheric weighted mean temperature Tm is defined as
Figure FDA0002531079920000011
Wherein, PwWater vapor pressure, T temperature, h height, h0For measuring the height of the station, h1Is the atmospheric layer top height; when calculating Tm from the sounding data, a discrete form of the formula (2) is used
Figure FDA0002531079920000012
In the formula, N is the number of atmospheric layers divided by the sounding data, one layer is defined between two adjacent observations in the ascending process of the balloon, and delta hiThe thickness of the ith layer of atmosphere is the difference between the adjacent two observation heights; pwiIs the average atmospheric water vapor pressure, T, of the ith layeriIs the average temperature of the ith layer;
calculating the weighted average temperature Tm of the atmosphere according to each sounding profile, establishing an equation with the observed value of the surface air temperature Ts, and writing the equation into the form of an error equation as follows
Figure FDA0002531079920000013
Wherein n is the total number of observed values, vj、TsjAnd TmjRespectively, the residual error of the jth observation equation, the surface air temperature and the atmospheric weighted average temperature, j is 1,2, …, n; a and b corresponding to the Ts-Tm linear model are obtained by the following formula
Figure FDA0002531079920000021
The coefficient (a) is obtained for the data of no rain days and the data of rain days1,b1) And (a)2,b2) Obtaining the linear calculation model of the weighted average temperature of the atmosphere in the same area in the rainy days and no rainy days
Figure FDA0002531079920000022
Step 5, calculating the weighted average temperature of the atmosphere under different weather conditions; the method comprises the following specific steps:
and (3) correspondingly selecting a rainy day model or a no-rainy day model according to whether the actual weather has rainfall during application, and calculating the weighted average atmospheric temperature according to the actually observed earth surface temperature Ts and the formula (6).
2. The weather-aware GNSS atmospheric weighted average temperature calculation method as claimed in claim 1, wherein in step 1, the historical sounding data is derived from historical sounding data of a long time sequence of actual application areas or weather re-analysis data is adopted to replace the sounding data.
3. The method as claimed in claim 1, wherein the preprocessing in step 2 is to remove data that the atmospheric pressure layer does not meet the preset requirement and data that contains gross errors.
4. The method as claimed in claim 3, wherein the step 2 comprises:
the preset required data meet the following requirements: 1) observations of sounding data at the station height (i.e. first layer data) must exist; 2) the highest air pressure observed value is less than or equal to 300 hPa; 3) when the barometric pressure observations are greater than 1000hPa at the stations, the data must include observations of barometric pressures of 1000, 850, 700, 500, and 300 hPa; 4) when the barometric pressure observations at the stations are less than 1000hPa, but greater than 850hPa, then observations at barometric pressures of 850, 700, 500, and 300hPa must be included in the data; 5) when the barometric pressure observations at the stations are less than 850hPa, but greater than 700hPa, then observations at pressures of 700, 500, and 300hPa must be included in the data; 6) when the air pressure observed value at the station is less than 700hPa but more than 500hPa, the data must include the observation of 500hPa and 300 hPa; if any one of the above conditions is not met, discarding the whole profile data;
and performing gross error detection on the sounding profile data meeting the conditions that Tm-Ts is more than 10K and Ts-Tm is more than 30K.
5. The weather-aware GNSS atmospheric weighted average temperature calculation method as claimed in claim 1, wherein in step 3, rainfall information is obtained by recording data from a ground automatic weather station.
6. The method as claimed in claim 1, wherein P in step 4 is PwiThe average value of the water pressure and the air pressure at the bottom and the top of the ith layer is obtained, TiThe average is obtained from the temperature of the bottom and the top of the ith layer.
7. The weather-aware GNSS atmospheric weighted average temperature calculation method according to claim 1, wherein in step 4, the water vapor pressure PwFrom the water-steam mixing ratio mxCalculated from the atmospheric pressure P and the calculation formula is as follows
Figure FDA0002531079920000031
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CN113804318B (en) * 2021-10-11 2022-08-26 南京信息工程大学 Data fusion method for obtaining weighted average temperature and computing device
CN115184967A (en) * 2022-09-14 2022-10-14 中国石油大学(华东) GNSS correction method for scanning water vapor data of microwave radiometer

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