CN114297939B - Troposphere delay prediction method and system suitable for Antarctic region - Google Patents
Troposphere delay prediction method and system suitable for Antarctic region Download PDFInfo
- Publication number
- CN114297939B CN114297939B CN202111671425.1A CN202111671425A CN114297939B CN 114297939 B CN114297939 B CN 114297939B CN 202111671425 A CN202111671425 A CN 202111671425A CN 114297939 B CN114297939 B CN 114297939B
- Authority
- CN
- China
- Prior art keywords
- delay
- troposphere delay
- troposphere
- forecast
- value
- 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
-
- 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
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention belongs to the technical field of troposphere delay prediction, and provides a troposphere delay prediction method and a troposphere delay prediction system suitable for a south pole area in order to solve the problems that an existing empirical troposphere model and a GPT3 model are not suitable for the south pole area and the accuracy of troposphere delay values is low. The method comprises the steps of obtaining space information of a target station and a troposphere delay calculation value of a zenith direction in a forecast time period; obtaining the deviation between a troposphere delay forecast value in the zenith direction of the target station and a calculated value in a forecast time period by using a space domain troposphere delay model, and further accumulating the deviation with the corresponding calculated value to obtain the troposphere delay forecast value in the zenith direction of the target station in the forecast time period; the space domain troposphere delay model is formed by training the space information of the training station, a troposphere delay calculation value in the zenith direction of the training station in the forecast time period and the deviation between the troposphere delay calculation value and a corresponding forecast value. The high-precision troposphere delay can be provided, and the precision of the inversion of the atmospheric degradable water content is further improved.
Description
Technical Field
The invention belongs to the technical field of troposphere delay prediction, and particularly relates to a troposphere delay prediction method and a troposphere delay prediction system suitable for a south pole.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
GNSS (Global Navigation Satellite System) technology is widely used in meteorology and space physics. Troposphere delay is an important error source in satellite positioning, and high-time-resolution and high-precision effective data are provided for inverting the water reducible quantity in the atmosphere. However, the existing empirical troposphere delay model has low accuracy of troposphere delay values provided in the Antarctic region, and influences the inversion accuracy of the degradable water volume. Therefore, a high-precision troposphere delay prior value is provided, the inversion precision of the atmospheric degradable water volume in the south Pole region is improved, and the GNSS technology is promoted to be widely applied in the field of meteorology.
Because actual measurement meteorological parameters are difficult to obtain in practical application, empirical troposphere models are widely applied, such as UNB series models, EGNOS models and Global Pressure and Temperature (GPT) series models. These empirical models are based on local standard atmosphere orGlobal weather re-analysis data is established and therefore does not perform well in local areas. Johannes
And the global barometric temperature series model (GPT) is established by using the monthly average values of the barometric pressure and temperature data provided by ERA-Interim and a spherical harmonic function model and is used for calculating the GNSS tropospheric delay in the global range, wherein the accuracy of the GPT2 model in the aspects of estimating the meteorological parameters in the south Pole region and the tropospheric delay correction value is still limited. The accuracy of the GPT2w model in the south Pole region for estimating meteorological parameters decreases with the increase of the height, the estimation accuracy of the tropospheric delay correction in the zenith direction is in centimeter level, and the accuracy of the GPT2w model is influenced without considering the daily change. The GPT3 enhanced model is a physical model constructed based on measured meteorological parameters and meteorological parameters estimated by the model, and a zenith wet delay sequence estimated by the GPT3 model is always too smooth, so that high-precision tropospheric delay cannot be provided, and the GPT3 enhanced model is not suitable for south Pole areas.
In summary, the inventor finds that the current empirical troposphere model and the GPT3 model are not suitable for the Antarctic region, the troposphere delay value precision is low, and the atmospheric degradable water yield precision inverted by troposphere delay correction in the Antarctic region is influenced finally.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a tropospheric delay prediction method and a tropospheric delay prediction system suitable for a south Pole region, which can provide high-precision tropospheric delay, thereby improving the accuracy of inversion of atmospheric water reducible volume by using tropospheric delay correction.
In order to achieve the purpose, the invention adopts the following technical scheme:
a first aspect of the present invention provides a tropospheric delay prediction method suitable for use in the south pole region, comprising:
acquiring spatial information of a target station and a troposphere delay calculation value in the zenith direction in a forecast time period;
obtaining the deviation between a troposphere delay forecast value in the zenith direction of the target station and a calculated value in a forecast time period by using a space domain troposphere delay model, and further accumulating the deviation with the corresponding calculated value to obtain the troposphere delay forecast value in the zenith direction of the target station in the forecast time period;
the space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and deviation between the troposphere delay calculation value and a corresponding forecast value.
As an embodiment, the calculating of the predicted tropospheric delay values in the zenith direction at the training station in the forecast period includes:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period with the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
In one embodiment, in the process of training the time domain tropospheric delay one-day prediction model, the length of the historical period is determined by an autocorrelation function of a tropospheric delay deviation sequence in the zenith direction of the training station.
As an embodiment, the time domain tropospheric delay one-day prediction model is an LSTM model.
As one embodiment, the spatial domain troposphere delay model is constructed by an RBF neural network.
In one embodiment, the tropospheric delay calculation is calculated from a global barometric temperature series model.
A second aspect of the invention provides a tropospheric delay prediction system suitable for use in the south Pole region, comprising:
the information acquisition module is used for acquiring spatial information of the target station and a troposphere delay calculation value in the zenith direction in a forecast time period;
the troposphere delay prediction module is used for obtaining the deviation between a troposphere delay prediction value in the zenith direction of the target station in the prediction time period and a calculated value by utilizing a space domain troposphere delay model, and then accumulating the deviation with the corresponding calculated value to obtain the troposphere delay prediction value in the zenith direction of the target station in the prediction time period;
the space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and deviation between the troposphere delay calculation value and a corresponding forecast value.
As an embodiment, in the tropospheric delay prediction module, in the training of the spatial domain tropospheric delay model, the calculation of the predicted tropospheric delay values in the zenith direction at the training station within the prediction period includes:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period with the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
A third aspect of the invention provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the tropospheric delay prediction method applicable in the south Pole region as described above.
A fourth aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the tropospheric delay prediction method for the south Pole area as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a troposphere delay prediction method and a troposphere delay prediction system suitable for a south Pole region, wherein a spatial domain troposphere delay model is utilized to obtain a deviation between a troposphere delay prediction value and a calculated value in the zenith direction of a target station within a prediction time period, and further obtain the troposphere delay prediction value in the zenith direction of the target station within the prediction time period; the troposphere delay prediction value in the zenith direction of the training station in the prediction time period is obtained by using the time domain troposphere delay single-day prediction model, the problems that the existing empirical troposphere model and the GPT3 model are not suitable for the Antarctic region and the troposphere delay value is low in precision are solved, the difference deviation in time and space is compensated, and the accuracy of a troposphere delay prediction result is improved, so that the accuracy of inverting the atmospheric water-degradable quantity by using troposphere delay correction is improved, the troposphere delay prediction model is particularly suitable for the Antarctic region, and has a high reference value for extreme weather prediction.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic diagram of a tropospheric delay prediction method for the south pole area according to an embodiment of the present invention;
FIG. 2 is a flow chart of an example tropospheric delay prediction for the Antarctic region applicable to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a tropospheric delay prediction system suitable for use in the south pole area according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Interpretation of terms:
GNSS: global Navigation Satellite System, Global Navigation Satellite System.
PPP: precision Point Positioning.
NGL: nevada geodic Laboratory, Nevada Geodetic Laboratory.
GPT: global Pressure and Temperature, Global barometric Temperature model.
LSTM: Long-Short Term Memory, Long-Short Term Memory network.
RBF: radial Bias Function, Radial basis Function neural network.
Example one
With reference to fig. 1 and fig. 2, the present embodiment provides a tropospheric delay prediction method suitable for the south pole area, which specifically includes the following steps:
s101: and acquiring spatial information of the target station and a troposphere delay calculation value in the zenith direction in a forecast time period.
Specifically, in step S101, the spatial information here includes information of longitude, latitude, and elevation.
In step S101, the tropospheric delay calculation is calculated from a global barometric temperature series model.
The global barometric pressure temperature series model GPT is used for calculating GNSS tropospheric delay in a global scope and comprises GPT models, GPT2 models, GPT2w models and GPT3 models.
The tropospheric delay calculations GPT3_ ZTD are calculated using the GPT3 model as an example, and are described below in connection with fig. 2.
Specifically, based on the location information of the GNSS target station, meteorological parameters (including, for example, temperature, barometric pressure, and vapor pressure), meteorological parameter decrementing factors, and elevation correction parameters provided using the GPT3 model are used. And calculating a troposphere delay calculation value GPT3_ ZTD in the zenith direction at the GNSS target station by adopting a Sasta model and an Askne & Nordius model respectively. Wherein the calculated tropospheric delay in the zenith direction includes dry delay (ZHD) and wet delay (ZWD).
S102: and obtaining the deviation between the troposphere delay forecast value in the zenith direction of the target station and the calculated value in the forecast time period by using the space domain troposphere delay model, and further accumulating the deviation with the corresponding calculated value to obtain the troposphere delay forecast value in the zenith direction of the target station in the forecast time period.
The space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and deviation between the troposphere delay calculation value and a corresponding forecast value.
Specifically, in step S102, the spatial domain troposphere delay model is constructed by an RBF neural network. The input parameters of the space domain troposphere delay model are longitude, latitude and elevation of the observation station and troposphere delay calculation value GPT3_ ZTD in the forecast period, and the output parameters are deviation D _ LSTM _ ZTD between the troposphere delay forecast value and the calculation value in the forecast period.
Thus, the troposphere delay forecast value LSTM _ RBF _ ZTD in the forecast time period can be obtained by using the deviation D _ LSTM _ ZTD between the troposphere delay forecast value and the calculated value of the forecast time period obtained by the space domain troposphere delay model and adding the troposphere delay calculated value GPT3_ ZTD in the same time period.
It should be noted here that the spatial domain troposphere delay model can also be constructed by using other existing neural network models, and those skilled in the art can specifically select the model according to actual situations, and the details are not described here.
Specifically, in step S102, the calculating process of the predicted tropospheric delay value in the zenith direction at the training station in the forecast period includes:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period with the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
In this embodiment, the tropospheric delay standard sequence is a high-precision tropospheric delay product GNSS _ ZTD sequence over an observation period. The tropospheric delay product (GNSS _ ZTD) for each GNSS station is obtained from the NGL center with a precision that is verified to be comparable to the IGS center tropospheric delay product (IGS _ ZTD). Therefore, the deviation sequence (GPT3_ Bias) of the GPT3_ ZTD and the observation period is calculated by taking the GNSS _ ZTD sequence of the observation period as a reference value.
It should be noted that, those skilled in the art can specifically select the tropospheric delay criterion sequence according to actual situations, and the details are not described herein.
In a specific implementation, in the process of training the time domain troposphere delay single-day forecasting model, the length of the historical time period is determined by an autocorrelation function of a troposphere delay deviation sequence in the zenith direction of the training station.
For example: the correlation period of the GPT3_ Bias sequence was analyzed step by step using an autocorrelation function.
The parameters of the input layer of the time domain troposphere delay one-day forecasting model are deviation data (D _ GNSS _ ZTD) between GPT3_ ZTD and GNSS _ ZTD in the historical period, and the output layer is deviation data (D _ GNSS _ ZTD) between GPT3_ ZTD and GNSS _ ZTD in the forecasting period.
In fig. 2, the time domain tropospheric delay one-day prediction model is the LSTM model. The number of hidden layers of the LSTM model and the number of neurons in each hidden layer are set. Inputting training samples, and constructing a prediction model LSTM according to the set network parameters. And estimating GPT3_ Bias data in a forecast period based on the trained LSTM model, and adding GPT3_ ZTD in the same period to obtain an LSTM _ ZTD forecast value.
It is understood that the space-time domain troposphere delay one-day forecasting model can also be constructed by using other existing neural network models, and those skilled in the art can specifically select the model according to actual situations, and the details are not described here.
Although there are many empirical tropospheric delay models at present, there are two major drawbacks, one is that the accuracy of the data source is limited by the region, resulting in tropospheric atmospheric delay models that are not suitable for south Pole areas; secondly, the correction value provided by the tropospheric delay model is too smooth and not consistent with the time-space characteristic of the actual tropospheric delay variation. In addition, the existing tropospheric delay prediction models in the south Pole region are mainly directed to the West of Antarctic, while for the east of Antarctic, due to the few stations, they have not been discussed and studied. In order to overcome the problem that the latest empirical troposphere model GPT3 is poor in accuracy in estimating troposphere delay in the south pole region, the troposphere delay prediction model in the south pole region is constructed, and two neural network algorithms of long short-term memory (LSTM) and Radial Basis Function (RBF) are introduced to compensate the difference deviation of the troposphere delay correction (GPT3_ ZTD) estimated by the high-precision troposphere delay product (GNSS _ ZTD) and the GPT3 model in time and space respectively. Meanwhile, the daily change of the GPT3_ ZTD is simulated by adopting a daily modeling and prediction strategy, and the method has higher reference value for extreme weather prediction.
It is understood that, for the difference deviation compensation in time and space, besides the two neural network algorithms of Long Short Term Memory (LSTM) and Radial Basis Function (RBF), other existing neural network algorithms can be used by those skilled in the art, and will not be described in detail herein.
Example two
Referring to fig. 3, the present embodiment provides a tropospheric delay prediction system suitable for the south pole area, which specifically includes the following modules:
(1) the information acquisition module is used for acquiring spatial information of the target station and a troposphere delay calculation value in the zenith direction in a forecast time period;
(2) the troposphere delay prediction module is used for obtaining the deviation between a troposphere delay prediction value in the zenith direction of the target station in the prediction time period and a calculated value by utilizing a space domain troposphere delay model, and then accumulating the deviation with the corresponding calculated value to obtain the troposphere delay prediction value in the zenith direction of the target station in the prediction time period;
the space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and deviation between the troposphere delay calculation value and a corresponding forecast value.
In a specific embodiment, in the tropospheric delay prediction module, in the training of the tropospheric delay model in the spatial domain, the calculation of the predicted tropospheric delay value in the zenith direction at the training station in the prediction period includes:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period with the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described again here.
EXAMPLE III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the tropospheric delay prediction method applicable in the south pole area as described above.
Example four
The present embodiment provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the tropospheric delay prediction method for the south Pole region as described above.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A tropospheric delay prediction method suitable for use in the south Pole region, comprising:
acquiring spatial information of a target station and a troposphere delay calculation value in the zenith direction in a forecast time period;
obtaining the deviation between a troposphere delay forecast value in the zenith direction of the target station and a calculated value in a forecast time period by using a space domain troposphere delay model, and further accumulating the deviation with the corresponding calculated value to obtain the troposphere delay forecast value in the zenith direction of the target station in the forecast time period;
the space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and a deviation between the troposphere delay calculation value and a corresponding forecast value;
the calculation process of the troposphere delay forecast value in the zenith direction of the training station in the forecast period comprises the following steps:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period with the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
2. The tropospheric delay prediction method for the south Pole area of claim 1 wherein during training of the time domain tropospheric delay one-day prediction model, the length of the history period is determined by the autocorrelation function of the tropospheric delay variation sequences in the zenith direction of the training station.
3. The tropospheric delay prediction method for the antarctic region of claim 1 wherein the time domain tropospheric delay one-day prediction model is an LSTM model.
4. The tropospheric delay prediction method for the antarctic region of claim 1 wherein the spatial domain tropospheric delay model is constructed from an RBF neural network.
5. A method for tropospheric delay prediction in the antarctic region of claim 1 wherein the calculated tropospheric delay is calculated from a global barometric temperature series model.
6. A tropospheric delay prediction system suitable for use in the south Pole region, comprising:
the information acquisition module is used for acquiring spatial information of the target station and a troposphere delay calculation value in the zenith direction in a forecast time period;
the troposphere delay prediction module is used for obtaining the deviation between a troposphere delay prediction value in the zenith direction of the target station in the prediction time period and a calculated value by utilizing a space domain troposphere delay model, and then accumulating the deviation with the corresponding calculated value to obtain the troposphere delay prediction value in the zenith direction of the target station in the prediction time period;
the space domain troposphere delay model is formed by training spatial information of a training station, a troposphere delay calculation value in the zenith direction of the training station in a forecast time period and deviation between the troposphere delay calculation value and a corresponding forecast value;
in the troposphere delay prediction module, in the process of training the troposphere delay model in the spatial domain, the calculation process of the troposphere delay prediction value in the zenith direction at the training station in the forecast period includes:
calculating a troposphere delay deviation sequence of the training station in the zenith direction in the historical period based on the comparison of the troposphere delay calculation value of the training station in the zenith direction in the historical period and the troposphere delay standard sequence;
then, obtaining a troposphere delay forecast value of the training station in the zenith direction in a forecast time period by using a time domain troposphere delay one-day forecast model; wherein the time domain tropospheric delay one-day prediction model is trained based on a tropospheric delay variation sequence in a zenith direction within a historical period of a training station.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a tropospheric delay prediction method for the south Pole area according to any one of claims 1 to 5.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps in the tropospheric delay prediction method for the south Pole area of any one of claims 1 to 5 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111671425.1A CN114297939B (en) | 2021-12-31 | 2021-12-31 | Troposphere delay prediction method and system suitable for Antarctic region |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111671425.1A CN114297939B (en) | 2021-12-31 | 2021-12-31 | Troposphere delay prediction method and system suitable for Antarctic region |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114297939A CN114297939A (en) | 2022-04-08 |
| CN114297939B true CN114297939B (en) | 2022-09-16 |
Family
ID=80972844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111671425.1A Active CN114297939B (en) | 2021-12-31 | 2021-12-31 | Troposphere delay prediction method and system suitable for Antarctic region |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114297939B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ES2356821T3 (en) * | 2003-04-17 | 2011-04-13 | The Secretary Of State For Defence | CORRECTION OF ERRORS INDUCED BY THE TROPOSPHERE IN GLOBAL POSITIONING SYSTEMS. |
| US8665146B2 (en) * | 2007-07-10 | 2014-03-04 | Electronic Navigation Research Institute | Calculation method of the amount of zenith troposphere delay, and a correcting method of troposphere delay of satellite positioning signal |
| CN106022470B (en) * | 2016-04-29 | 2019-01-29 | 东南大学 | A kind of tropospheric delay correction method based on BP-EGNOS Fusion Model |
| CN110031877B (en) * | 2019-04-17 | 2020-05-26 | 山东科技大学 | GRNN model-based regional NWP troposphere delay correction method |
| CN111382507B (en) * | 2020-03-04 | 2021-06-01 | 山东大学 | Global troposphere delay modeling method based on deep learning |
-
2021
- 2021-12-31 CN CN202111671425.1A patent/CN114297939B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114297939A (en) | 2022-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109543353B (en) | Three-dimensional water vapor inversion method, device, equipment and computer readable storage medium | |
| US20180077534A1 (en) | Systems and Methods for Graph-Based Localization and Mapping | |
| Zhang et al. | Precipitable water vapor fusion: An approach based on spherical cap harmonic analysis and Helmert variance component estimation | |
| Liu et al. | An analysis of GPT2/GPT2w+ Saastamoinen models for estimating zenith tropospheric delay over Asian area | |
| CN106324620A (en) | Tropospheric zenith delay method based not on real-time measurement of surface meteorological data | |
| CN105738934B (en) | The quick fixing means of URTK fuzzinesses of additional atmospheric information dynamic constrained | |
| CN109145344A (en) | A kind of experience ZTD model refinement method based on sounding data | |
| US20220043180A1 (en) | Method and system of real-time simulation and forecasting in a fully-integrated hydrologic environment | |
| CN110244387A (en) | A kind of method, apparatus, equipment and storage medium based on Atmospheric Precipitable Water prediction rainy weather | |
| CN110488332B (en) | Positioning information processing method and device based on network RTK technology | |
| Fan et al. | A comparative study of four merging approaches for regional precipitation estimation | |
| CN115049013A (en) | Short-term rainfall early warning model fusion method combining linearity and SVM | |
| CN111123345A (en) | GNSS measurement-based empirical ionosphere model data driving method | |
| CN114297939B (en) | Troposphere delay prediction method and system suitable for Antarctic region | |
| CN109884666A (en) | A kind of tropospheric delay correction method based on data assimilation | |
| CN117574622A (en) | Troposphere modeling method and device | |
| CN116609859A (en) | Weather disaster high-resolution regional mode forecasting system and method | |
| KR102500534B1 (en) | Recurrent neural network based water resource information generating device and method | |
| KR101941132B1 (en) | Apparatus and method for extending available area of regional ionosphere map | |
| Wongchuig et al. | Toward Discharge Estimation for Water Resources Management with a Semidistributed Model and Local Ensemble Kalman Filter Data Assimilation | |
| Hu et al. | An accurate height reduction model for zenith tropospheric delay correction using ECMWF data | |
| Ma et al. | Establishment of regional tropospheric delay model in Australia | |
| Zhang et al. | A New GPT2w Model Improved by PSO‐LSSVM for GNSS High‐Precision Positioning | |
| CN116663432B (en) | Hundred-meter height wind speed forecast correction downscaling method and device | |
| CN117251520B (en) | Method and device for identifying biodiversity key region and electronic equipment |
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 |