CN113639893B - Near-earth weighted average temperature information acquisition method based on multiple meteorological factors - Google Patents
Near-earth weighted average temperature information acquisition method based on multiple meteorological factors Download PDFInfo
- Publication number
- CN113639893B CN113639893B CN202110725482.7A CN202110725482A CN113639893B CN 113639893 B CN113639893 B CN 113639893B CN 202110725482 A CN202110725482 A CN 202110725482A CN 113639893 B CN113639893 B CN 113639893B
- Authority
- CN
- China
- Prior art keywords
- weighted average
- average temperature
- value
- neural network
- altitude
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
- G01K13/02—Thermometers specially adapted for specific purposes for measuring temperature of moving fluids or granular materials capable of flow
- G01K13/024—Thermometers specially adapted for specific purposes for measuring temperature of moving fluids or granular materials capable of flow of moving gases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/08—Adaptations of balloons, missiles, or aircraft for meteorological purposes; Radiosondes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Environmental Sciences (AREA)
- Health & Medical Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Aviation & Aerospace Engineering (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a near-earth weighted average temperature information acquisition method based on multiple meteorological factors. The method comprises the following steps: acquiring global radio detection empty station observation data, and extracting approximate truth values of weighted average temperatures on different height surfaces; secondly, acquiring ERA-5 global reanalysis data, and establishing a polynomial model of weighted average temperature, altitude and annual product day of each grid point according to a least square method; calculating a preliminary predicted value of the weighted average temperature of the sounding site by using a polynomial model, and calculating a residual error between the preliminary predicted value and an approximate true value; then, establishing T BP neural network compensation models with different network structures, and training to obtain T groups of neural network model parameters; and finally, calculating T groups of residual prediction values of the weighted average temperature of the target position by utilizing T groups of neural network model parameters, averaging the residual prediction values to obtain a final residual prediction value, and adding a preliminary prediction value calculated by the polynomial model to obtain a final prediction value of the near-earth weighted average temperature.
Description
Technical Field
The invention relates to the field of meteorological application of a global navigation satellite system, in particular to a global near-earth weighted average temperature information acquisition method based on a plurality of measured meteorological factors.
Background
The method for inverting the Water-reducing volume (PWV) by utilizing GNSS is an important application of the satellite navigation positioning technology in the field of meteorology. Utilizing GNSS inversion PWV benefits from the tropospheric Delay that is created by GNSS satellite signals as they are affected by water vapor when traversing the atmosphere, and the portion of the tropospheric Delay that maps to the Zenith direction is called the Zenith Wet Delay (ZWD). The PWV and the ZWD have an approximately linear relation, namely PWV is Π ZWD, wherein the ZWD can be solved in real time through data processing software, but a undetermined parameter, namely Weighted Mean Temperature (T) is contained in a conversion coefficient Π m ) Thus, accurate T is obtained in time m The method is the key for realizing PWV by utilizing GNSS real-time inversion. Currently acquired T m The main methods of (1) include a constant method, a numerical integration method andand (4) modeling. Constant method will T m Set to a constant, but actual T m The change is very complex, and a constant method can bring large PWV inversion errors; the numerical integration method mainly calculates T through the measured atmospheric vertical profile data or the atmospheric vertical profile data extracted by re-analyzing the data through a discrete numerical integration formula m However, the actually measured atmospheric profile data and the reanalysis data cannot be acquired by an ordinary user in real time, and therefore cannot be used for the real-time inversion of the PWV; the calculation of the weighted average temperature by using a model method is a more common alternative scheme at present. However, most of the current weighted average temperature models can only calculate the weighted average temperature value of the earth surface, the application range of the models is very limited, and the model precision is low for the areas with complicated topographic features. For example, the traditional Bevis formula does not consider the position and seasonal difference of the relation between the weighted average temperature and the meteorological factor, and does not consider the influence of altitude on the model precision, so the model precision is poor; although the altitude and the temperature are considered in the NN-II model, the strong correlation between the weighted average temperature and the vapor pressure is not considered, and the model accuracy also needs to be improved.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for acquiring near-earth weighted average temperature information based on multiple meteorological factors, which weakens model errors and improves model precision compared with a polynomial model and an NN-II model of a single meteorological factor.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that: a method for acquiring near-earth weighted average temperature information based on multiple meteorological factors comprises the following steps:
(1) acquiring observation data of the globally distributed radio detection stations, extracting observation values of potential height, vapor pressure and air temperature on each atmospheric vertical profile, calculating approximate truth values of weighted average temperatures of different height surfaces on the detection stations, and recording the approximate truth values as
(2) Acquiring ERA-5 global reanalysis data, and extractingPotential height, air temperature and specific humidity information of each grid point on different isobaric surfaces are calculated, and weighted average temperature values of each grid point on different isobaric surface layers are calculated
Establishing by least square method
Altitude h g A polynomial model of year and day doy;
(3) calculating the initial prediction values of the weighted average temperatures of different height surfaces on the exploration station for modeling according to the polynomial model, and recording the initial prediction values as
And calculating the residual error of the preliminary predicted value
Approximate truth value for weighted average temperature
And preliminary predicted value
A difference of (d);
(4) establishing T BP neural network compensation models with different network structures, and inputting parameters into latitude of a sounding site for modeling
Altitude h g Annual accumulation day doy, and air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
Residual with output parameter as preliminary predicted value
Training to obtain T groups of neural network model parameters;
(5) acquiring initial forecast value information of latitude, altitude, year, date, air temperature, water vapor pressure and polynomial model calculation of a target position, calculating by using the neural network model parameters in the step (4) to obtain T groups of residual forecast values, averaging the residual forecast values according to an equal-weight principle to obtain a final residual forecast value
Adding the initial predicted value of the weighted average temperature of the target position calculated by the polynomial model to obtain the final predicted value T of the weighted average temperature of the ground m 。
Preferably, in step (1), the calculation formula of the weighted average temperature approximate truth values of different altitude surfaces on the atmospheric vertical profile observed by the sounding station is as follows:
in the formula (I), the compound is shown in the specification,
T i 、h i and
T i+1 、h i+1 the vapor pressure, absolute temperature and altitude value of the ith layer and the (i + 1) th layer are respectively, and N is the layer number of the isobaric surface.
Preferably, in the step (2), the horizontal resolution of the grid of the ERA-5 global reanalysis data is 1 ° × 1 °, and potential heights, air temperatures and specific humidity information of 37 isobaric surfaces from the earth surface to the top of the convection layer are included, wherein the water vapor pressure of each isobaric surface layer is converted from specific humidity and atmospheric pressure, and the conversion formula is as follows:
in the formula, P w P and R are respectively the water vapor pressure, the atmospheric pressure and the specific humidity on the same equal pressure surface;
when the weighted average temperature value of each isobaric surface is calculated by utilizing ERA-5 global reanalysis data, the calculation surface is the altitude of each isobaric surface, but not the ground surface, and the formula for calculating the weighted average temperature values of different isobaric surfaces is the same as the formula (1).
Preferably, in the step (2), the polynomial model formula of the weighted average temperature of each grid point is as follows:
in the formula (I), the compound is shown in the specification,
is a weighted average temperature value, A, of the grid points at sea level height 0 For the remainder, A 1 、A 2 As a coefficient of a trigonometric function term related to the chronological order, d 1 、d 2 Is the initial phase of the annual and semiannual cycles, doy is the product of the year,
altitude of grid is h g Weighted average temperature value of (h) g Delta is the rate of decrease of the weighted average temperature at that grid point with height, for altitude.
Preferably, in the step (3), when the preliminary predicted value of the weighted average temperature of the sounding site for modeling is calculated according to the polynomial model, 4 grid points closest to the sounding site are found out, the polynomial model is used to calculate a weighted average temperature value at the same altitude as that of the sounding site on each grid point, and then the weighted average temperature values of the 4 grid points are subjected to bilinear interpolation to obtain the weighted average temperature value of the sounding site.
Preferably, in the step (4), T BP neural network compensation models are constructed, each BP neural network compensation model is a three-layer network structure of 6 × P × 1, wherein the input parameters include the latitude of the sounding site for modeling
Altitude h g Annual accumulation day doy, air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
Residual error with output layer as preliminary predicted value
And respectively training each BP neural network compensation model to obtain T groups of neural network model parameters.
Preferably, in the step (5), the trained neural network model parameters are used to calculate T groups of residual prediction values of the target position weighted average temperature, and the final residual prediction value is calculated by averaging
The calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
and (4) representing a residual prediction value obtained by calculation by using the ith BP neural network model parameter, wherein T is the number of BP neural network compensation models.
Preferably, in the step (5), the target position weights the final predicted value T of the average temperature m The calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,
a preliminary prediction of the weighted average temperature of the target location calculated for the polynomial model.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention discloses a near-earth weighted average temperature information acquisition method based on multiple meteorological factors, which adopts a mode of mutually fusing a plurality of BP neural network compensation models to improve the generalization of the models; by taking a plurality of actually measured meteorological factors as model input, the model precision is improved. Compared with a polynomial model or an NN-II model of a single meteorological factor, the model precision of the method is greatly improved.
Drawings
FIG. 1 is a global radio sounding space station profile for training in an embodiment of the present invention;
FIG. 2 is a flow chart of a method of an embodiment of the present invention;
FIG. 3 is a global radio sounding station profile for model testing in accordance with an embodiment of the present invention;
fig. 4 is a comparison of the accuracy of different models at various height planes in the embodiment of the present invention.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the accompanying drawings and the detailed description.
The specific embodiment discloses a method for acquiring near-earth weighted average temperature information based on multiple meteorological factors, wherein the specific embodiment adopts meteorological data observed in 2011-2015 by 209 radio sounding stations in global distribution, fig. 1 shows global distribution conditions of all stations, and observation data of the sounding stations are organized into an independent file according to all stations, and the independent file comprises observation time, station position information, potential height reported by instruments, air temperature, water vapor pressure and other information. The flow of the method is shown in fig. 2, and the method comprises the following steps:
(1) acquiring observation data of the globally distributed radio detection stations, extracting observation values of potential height, vapor pressure and air temperature on each atmospheric vertical profile, calculating approximate truth values of weighted average temperatures of different height surfaces on the detection stations, and recording the approximate truth values as
(2) Acquiring ERA-5 global reanalysis data, extracting potential height, air temperature and specific humidity information of each grid point on different isobaric surfaces, and calculating weighted average temperature values of each grid point on different isobaric surface layers
Establishing by least squares
Altitude h g A polynomial model of year and day doy;
(3) calculating the initial prediction values of the weighted average temperatures of different height surfaces on the exploration station for modeling according to the polynomial model, and recording the initial prediction values as
And calculating the residual error of the preliminary predicted value
Approximate truth value for weighted average temperature
And preliminary predicted value
A difference of (d);
(4) establishing T BP neural network complements with different network structuresThe input parameters of the compensation model are the latitude of the exploration station used for modeling
Altitude h g Annual accumulation day doy, air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
Residual with output parameter as preliminary predicted value
Training to obtain T groups of neural network model parameters;
(5) acquiring initial forecast value information of latitude, altitude, year, date, air temperature, water vapor pressure and polynomial model calculation of a target position, calculating by using the neural network model parameters in the step (4) to obtain T groups of residual forecast values, averaging the residual forecast values according to an equal-weight principle to obtain a final residual forecast value
Adding the initial predicted value of the weighted average temperature of the target position calculated by the polynomial model to obtain the final predicted value T of the weighted average temperature of the ground m 。
In the step (1), a calculation formula of weighted average temperature approximate truth values of different height surfaces on the atmospheric vertical profile observed by the sounding station is as follows:
in the formula (I), the compound is shown in the specification,
T i 、h i and
T i+1 、h i+1 the vapor pressure, absolute temperature and altitude value of the ith layer and the (i + 1) th layer are respectively, and N is the layer number of the isobaric surface.
In the step (2), the grid horizontal resolution of ERA-5 global reanalysis data is 1 degree multiplied by 1 degree, and potential height, air temperature and specific humidity information of 37 isobaric surfaces from the earth surface to the top of the convection layer are included, wherein the water vapor pressure of each isobaric surface layer is converted from specific humidity and atmospheric pressure, and the conversion formula is as follows:
in the formula, P w P and R are respectively the water vapor pressure, the atmospheric pressure and the specific humidity on the same equal pressure surface;
when the weighted average temperature value of each isobaric surface is calculated by utilizing ERA-5 global reanalysis data, the calculation surface is the altitude of each isobaric surface, but not the ground surface, and the formula for calculating the weighted average temperature values of different isobaric surfaces is the same as the formula (1).
In the step (2), the polynomial model formula of the weighted average temperature of each grid point is as follows:
in the formula (I), the compound is shown in the specification,
is a weighted average temperature value of the grid points at sea level height, A 0 For the remainder, A 1 、A 2 As a coefficient of a trigonometric function term related to the chronological order, d 1 、d 2 Is the initial phase of the annual and semiannual cycles, doy is the product of the year,
altitude of grid is h g Weighted average temperature value of (h) g Delta is the rate of decrease of the weighted average temperature at that grid point with height, for altitude.
In the step (3), when calculating the preliminary prediction value of the weighted average temperature of the sounding site for modeling according to the polynomial model, 4 grid points nearest to the sounding site are found out, the polynomial model is used to calculate the weighted average temperature value of each grid point at the same altitude as the sounding site, and then the weighted average temperature values of the 4 grid points are subjected to bilinear interpolation to obtain the weighted average temperature value of the sounding site.
In the step (4), T BP neural network compensation models are constructed, each BP neural network compensation model is a three-layer network structure of 6 × P × 1, wherein the input parameters include latitudes of sounding sites used for modeling
Altitude h g Annual accumulation day doy, air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
The output layer is the residual error of the preliminary predicted value
And respectively training each BP neural network compensation model to obtain T groups of neural network model parameters.
In the step (5), T groups of residual prediction values of the weighted average temperature of the target position are calculated by using the trained neural network model parameters, and the final residual prediction value is calculated by adopting an averaging mode
The calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
and (4) representing a residual prediction value obtained by calculation by using the ith BP neural network model parameter, wherein T is the number of BP neural network compensation models.
In the step (5), the final predicted value T of the weighted average temperature of the target position m The calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,
a preliminary prediction of the weighted average temperature of the target location calculated for the polynomial model.
In order to compare the prediction performances of a polynomial model, an NN-II model of a single meteorological factor and a multi-meteorological factor model, the meteorological data observed by 576 sounding stations which are globally distributed in 2016-2018 are adopted for verification, the distribution condition of the sounding stations is shown in figure 3, and the average deviation MD and the root mean square error RMSE are adopted as model evaluation indexes, wherein MD represents the accuracy, namely the deviation degree of a model value from a true value; RMSE represents accuracy, which is a measure of reliability and stability of the model. Their calculation formulas are respectively:
in the formula (I), the compound is shown in the specification,
and
respectively represent model values andand the true value n is the number of the test samples. Table 1 gives the accuracy comparison for the different models.
TABLE 12016-comparison of MD and RMSE between different models in 2018
To further compare the predicted effect of the three models at different altitudes, the RMSE of the three models was made to vary with altitude, as shown in fig. 4. As can be seen from table 1 and fig. 4:
(1) compared with a polynomial model without actually measured meteorological factors and an NN-II model only adopting a single meteorological factor as input, the prediction precision of the fusion model based on the multiple meteorological factors is highest, and is respectively improved by about 43% and 18% compared with the former two models;
(2) compared with a polynomial model without actually measured meteorological factors and an NN-II model only adopting a single meteorological factor as input, the advantage of the fusion model based on the multiple meteorological factors in the prediction performance is mainly shown in the elevation direction, namely the precision of the fusion model on different altitude height surfaces is superior to that of the other two models.
Claims (7)
1. A method for acquiring near-earth weighted average temperature information based on multiple meteorological factors is characterized by comprising the following steps:
(1) acquiring observation data of the globally distributed radio detection stations, extracting observation values of potential height, vapor pressure and air temperature on each atmospheric vertical profile, calculating approximate truth values of weighted average temperatures of different height surfaces on the detection stations, and recording the approximate truth values as
(2) Acquiring ERA-5 global reanalysis data, and extracting potential height and gas of each grid point on different equal pressure surfacesThe temperature and the specific humidity information are used for calculating the weighted average temperature value of each grid point on different isobaric surface layers
Establishing by least square method
Altitude h g A polynomial model of year and day doy;
(3) calculating the initial prediction values of the weighted average temperatures of different height surfaces on the exploration station for modeling according to the polynomial model, and recording the initial prediction values as
And calculating the residual error of the preliminary predicted value
Approximate truth value for weighted average temperature
And preliminary predicted value
A difference of (d);
(4) establishing T BP neural network compensation models with different network structures, and inputting parameters into latitude of a sounding site for modeling
Altitude h g Annual accumulation day doy, air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
The output parameter isResidual error of preliminary predicted value
Training to obtain T groups of neural network model parameters;
(5) acquiring the latitude, altitude, accumulated days, air temperature, water vapor pressure and preliminary predicted value information calculated by a polynomial model of a target position, calculating by utilizing the parameters of the neural network model in the step (4) to obtain T groups of residual predicted values, averaging the residual predicted values according to the equal weight principle to obtain the final residual predicted value
Adding the initial predicted value of the weighted average temperature of the target position calculated by the polynomial model to obtain the final predicted value T of the weighted average temperature of the ground m ；
In the step (1), a calculation formula of weighted average temperature approximate truth values of different height surfaces on the atmospheric vertical profile observed by the sounding station is as follows:
in the formula (I), the compound is shown in the specification,
T i 、h i and
T i+1 、h i+1 the vapor pressure, absolute temperature and altitude value of the ith layer and the (i + 1) th layer are respectively, and N is the layer number of the isobaric surface.
2. The method for obtaining near-earth weighted average temperature information based on multiple meteorological factors according to claim 1, wherein in the step (2), the horizontal resolution of the grid of ERA-5 global reanalysis data is 1 ° × 1 °, potential heights, air temperatures and specific humidity information of 37 isobaric surfaces from the earth surface to the top of the convection layer are included, wherein the water vapor pressure of each isobaric surface layer is converted from the specific humidity and the atmospheric pressure, and the conversion formula is as follows:
in the formula, P w P and R are respectively the water vapor pressure, the atmospheric pressure and the specific humidity on the same equal pressure surface;
when the weighted average temperature value of each isobaric surface is calculated by utilizing ERA-5 global reanalysis data, the calculation surface is the altitude of each isobaric surface, but not the ground surface, and the formula for calculating the weighted average temperature values of different isobaric surfaces is the same as the formula (1).
3. The multi-meteorological-factor based near-earth weighted average temperature information acquisition method according to claim 1, wherein in the step (2), the polynomial model formula of the weighted average temperature of each grid point is as follows:
in the formula (I), the compound is shown in the specification,
is a weighted average temperature value, A, of the grid points at sea level height 0 For the remainder, A 1 、A 2 As a coefficient of a trigonometric function term related to the chronological order, d 1 、d 2 Is the initial phase of the annual and semiannual cycles, doy is the product of the year,
altitude of grid is h g Weighted average temperature value of (h) g Is the altitude, δ is the gridThe rate of decrease of the weighted average temperature with height at the mesh point.
4. The method according to claim 1, wherein in step (3), when calculating the preliminary predicted value of the weighted average temperature of the sounding site for modeling according to the polynomial model, 4 grid points closest to the sounding site are found, the polynomial model is used to calculate the weighted average temperature value at the same altitude as the sounding site on each grid point, and then the weighted average temperature values of the 4 grid points are bilinearly interpolated to obtain the weighted average temperature value of the sounding site.
5. The method for obtaining the multiple meteorological factor-based near-earth weighted average temperature information according to claim 1, wherein in the step (4), T BP neural network compensation models are constructed in total, each BP neural network compensation model is a three-layer network structure of 6 × P × 1, and the input parameters include latitudes of sounding sites for modeling
Altitude h g Annual accumulation day doy, air temperature T at corresponding altitude s Water vapor pressure P w Preliminary prediction values for sum polynomial model calculations
Residual error with output layer as preliminary predicted value
And respectively training each BP neural network compensation model to obtain T groups of neural network model parameters.
6. The method for obtaining the multiple meteorological factor-based near-earth weighted average temperature information according to claim 1, wherein in the step (5), the trained neural network model parameters are used for calculationCalculating the final residual prediction value by averaging the T groups of residual prediction values of the weighted average temperature of the target position
The calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
and (4) representing a residual prediction value obtained by calculation by using the ith BP neural network model parameter, wherein T is the number of BP neural network compensation models.
7. The multiple meteorological factor-based approximate weighted average temperature information acquisition method according to claim 6, wherein in the step (5), the final predicted value T of the target position weighted average temperature m The calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,
a preliminary prediction of the weighted average temperature of the target location calculated for the polynomial model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110725482.7A CN113639893B (en) | 2021-06-29 | 2021-06-29 | Near-earth weighted average temperature information acquisition method based on multiple meteorological factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110725482.7A CN113639893B (en) | 2021-06-29 | 2021-06-29 | Near-earth weighted average temperature information acquisition method based on multiple meteorological factors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113639893A CN113639893A (en) | 2021-11-12 |
| CN113639893B true CN113639893B (en) | 2022-09-30 |
Family
ID=78416254
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110725482.7A Active CN113639893B (en) | 2021-06-29 | 2021-06-29 | Near-earth weighted average temperature information acquisition method based on multiple meteorological factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113639893B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116027459B (en) * | 2022-12-30 | 2024-05-14 | 桂林理工大学 | Calculation method of atmospheric weighted average temperature based on numerical weather forecast data |
| CN117390875B (en) * | 2023-10-27 | 2024-03-12 | 长安大学 | Construction method of atmosphere weighted average temperature model |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102156007B (en) * | 2011-02-14 | 2013-02-20 | 国家粮食局科学研究院 | Grain condition prediction system, grain condition control system and grain bulk safety state judging method |
| CN103033833B (en) * | 2012-12-13 | 2014-12-10 | 中国航天科工信息技术研究院 | Method of correcting troposphere delaying errors |
| CN109145344A (en) * | 2018-03-06 | 2019-01-04 | 东南大学 | A kind of experience ZTD model refinement method based on sounding data |
| CN108680268B (en) * | 2018-03-20 | 2020-07-31 | 东南大学 | Bevis model improvement method for area weighted average temperature based on sounding data |
| CN109902346B (en) * | 2019-01-24 | 2023-04-07 | 东南大学 | Neural network-based regional weighted average temperature information acquisition method |
| 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 |
| CN111539109B (en) * | 2020-04-24 | 2021-05-21 | 桂林理工大学 | Real-time high-precision global multi-dimensional troposphere zenith delay grid model construction method |
| CN112034490B (en) * | 2020-10-10 | 2022-03-22 | 山东科技大学 | NWP inversion troposphere delay improvement method |
-
2021
- 2021-06-29 CN CN202110725482.7A patent/CN113639893B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113639893A (en) | 2021-11-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110031877B (en) | GRNN model-based regional NWP troposphere delay correction method | |
| CN113639893B (en) | Near-earth weighted average temperature information acquisition method based on multiple meteorological factors | |
| CN109902346B (en) | Neural network-based regional weighted average temperature information acquisition method | |
| CN111401602B (en) | Assimilation method for satellite and ground rainfall measurement values based on neural network | |
| CN107180128B (en) | Weighted average temperature calculation method suitable for low-latitude areas in China | |
| CN108920414B (en) | Novel method for calculating local zenith troposphere wet delay by utilizing meteorological data | |
| CN110378540B (en) | Atmospheric weighted average temperature calculation method suitable for northern Guangxi Bay region | |
| CN109543353A (en) | Three-dimensional water vapor retrieval method, apparatus, equipment and computer readable storage medium | |
| CN106324620A (en) | Tropospheric zenith delay method based not on real-time measurement of surface meteorological data | |
| CN111539109B (en) | Real-time high-precision global multi-dimensional troposphere zenith delay grid model construction method | |
| CN111382507B (en) | Global troposphere delay modeling method based on deep learning | |
| CN112034490B (en) | NWP inversion troposphere delay improvement method | |
| CN109917424B (en) | Residual error correction method for troposphere delay in NWP (N-WP) inversion under multi-factor constraint | |
| CN109145344A (en) | A kind of experience ZTD model refinement method based on sounding data | |
| CN115755103B (en) | Robust self-adaptive GNSS water vapor chromatography method | |
| CN111352173A (en) | Weighted average temperature calculation method based on spatial position | |
| CN111368406A (en) | Continuous depth datum plane construction method | |
| CN116609859A (en) | Weather disaster high-resolution regional mode forecasting system and method | |
| CN111126466B (en) | Multi-source PWV data fusion method | |
| CN113534194B (en) | Troposphere temperature and humidity profile inversion method combining GNSS and wind lidar | |
| CN113960635A (en) | Tropospheric delay correction method taking daily change into account | |
| CN117113828A (en) | Numerical forecast correction method based on ship-based navigation observation | |
| CN111538943B (en) | Novel high-space-time resolution global ZTD vertical section grid model construction method | |
| Zhang et al. | A new four-layer inverse scale height grid model of china for zenith tropospheric delay correction | |
| CN113361190B (en) | CMONOC-ZTD-based troposphere delay correction method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |