阅读说明：本技术 一种基于导航卫星系统的水汽观测方法 (Water vapor observation method based on navigation satellite system ) 是由 涂满红 李昌兴 雷勇 曹云昌 关彦华 虎文君 徐国元 王海深 梁静舒 于 2020-05-27 设计创作，主要内容包括：本申请公开了一种基于导航卫星系统的水汽观测方法,包括：接收导航卫星系统的卫星数据；接收气象观测仪的气象数据；处理所述卫星数据及所述气象数据,并获得延迟数据；建立加权平均温度计算模型；通过所述计算模型计算所述延迟数据,并获得计算结果；反演所述计算结果,并获得水汽数据。与现有技术相比,本申请具有如下有益效果：台站基于单一台站基于历元的实时水汽处理,获取每个历元实时的水汽产品,可以极大地提高水汽观测的时间分辨率,极大提高产品服务效率和能力；单站处理代替数据中心的批量数据处理,极大提高数据处理效率,同时也大大降低GNSS原始数据从台站传输到中心站的传输成本。(The application discloses a water vapor observation method based on a navigation satellite system, which comprises the following steps: receiving satellite data of a navigation satellite system; receiving meteorological data of a meteorological observer; processing the satellite data and the meteorological data and obtaining delay data; establishing a weighted average temperature calculation model; calculating the delay data through the calculation model, and obtaining a calculation result; and inverting the calculation result and obtaining water vapor data. Compared with the prior art, the method has the following beneficial effects: the station obtains the real-time water vapor product of each epoch based on the single station epoch-based real-time water vapor processing, so that the time resolution of water vapor observation can be greatly improved, and the service efficiency and the service capacity of the product are greatly improved; the single-station processing replaces the batch data processing of the data center, so that the data processing efficiency is greatly improved, and meanwhile, the transmission cost of the GNSS original data transmitted from the station to the central station is greatly reduced.)

1. A water vapor observation method based on a navigation satellite system is characterized by comprising the following steps:

receiving satellite data of a navigation satellite system;

receiving meteorological data of a meteorological observer;

processing the satellite data and the meteorological data and obtaining delay data;

establishing a weighted average temperature calculation model;

calculating the delay data through the calculation model, and obtaining a calculation result;

and inverting the calculation result and obtaining water vapor data.

2. The water vapor observation method based on the navigation satellite system according to claim 1, wherein the receiving the satellite data of the navigation satellite system comprises:

receiving preliminary satellite data of a navigation satellite system;

receiving satellite clock error data and precise orbit data;

and adjusting the preliminary satellite data according to the satellite clock error data and the precise orbit data, and obtaining the satellite data.

3. The moisture observation method based on the navigation satellite system according to claim 2, wherein the processing the satellite data and the meteorological data and obtaining the delay data comprises:

performing sliding window processing according to the first time interval and the second time processing radian to obtain troposphere zenith delay data;

wherein the tropospheric zenith delay data comprises: zenith static force delay and zenith non-static force delay data;

obtaining tropospheric zenith delay data comprises:

acquiring zenith static force delay data through a preset model and known air pressure;

and adding the zenith static force delay data and the estimated zenith non-static force delay data to obtain troposphere zenith delay data.

4. The water vapor observation method based on the navigation satellite system, according to claim 3, wherein the establishing of the weighted average temperature calculation model comprises:

analyzing data again according to weather in a preset time period, and acquiring the earth surface air temperature and the weighted average temperature at the grid point;

establishing a conversion grid model of the difference between the weighted average temperature and the earth surface temperature through spectrum analysis;

and selecting corresponding conversion parameters according to the grid points, and acquiring the weighted average temperature at the observation points by using the air temperature observation values of the observation points.

5. The method of claim 4, wherein inverting the computed results and obtaining steam data comprises:

removing a dry delay component from the troposphere zenith delay data to obtain a wet delay component;

converting the wet delay component to the water reducible amount of the observation point;

and converting the weighted average temperature obtained by calculation according to the air temperature observation at the observation position and the established weighted average temperature and earth surface temperature difference conversion model to obtain the atmospheric water vapor data at the observation position with time resolution superior to the third time.

6. The method of claim 5, wherein inverting the computed results and obtaining steam data further comprises:

eliminate the observation error that the difference in height brought between meteorological observation equipment and navigation satellite system antenna, include:

judge | h_g-h_s|≤20m；

When the result is positive, the result is,wherein the content of the first and second substances, T_g＝T_s+(h_g-h_s)；

when the result is negative, the program is executed,wherein the content of the first and second substances,

T_i＝T_s+·Δz_i，Δz_i＝20(-20)m for h_g＞h_s(h_g＜h_s)；

where the subscript g denotes the navigation satellite system antenna and the subscript s denotes the meteorological observation device. P, T, h respectively indicate pressure (hPa), temperature (K) and elevation (m). Acceleration of gravity g-9.8067 m/s²Dry air gas constant R_d＝287.058JK^-1kg^-1The rate of change of temperature with height is-6.5 K.km^-1。

7. The water vapor observation method based on the navigation satellite system according to claim 6, wherein the satellite data comprises position information, time information and wave velocity of the satellite; the meteorological data includes air temperature, humidity and air pressure.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable by the processor, wherein the processor implements the method of any one of claims 1-7 when executing the computer program.

9. A computer-readable storage medium, a non-transitory readable storage medium, having stored therein a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1-7.

10. A computer program product comprising computer readable code that, when executed by a computer device, causes the computer device to perform the method of any of claims 1-7.

Technical Field

The application relates to the field of meteorological monitoring, in particular to a water vapor observation method based on a navigation satellite system.

Background

The ground-based GNSS observation has the advantages of all weather, high precision, low cost and high time resolution, and the observation has good consistency on a time scale. And water vapor observation precision equivalent to that of a radiosonde and a foundation microwave radiometer can be obtained based on the foundation GNSS. As an independent observation means of moisture, the ground-based GNSS moisture has been widely used to calibrate the system error of other observation means (e.g., radiosonde), to improve the Numerical Weather Pattern (NWP), and to analyze the climate. The ground-based GNSS observation data can be used for a numerical prediction assimilation system, and vertical structure and short-term precipitation prediction of water vapor can be improved by assimilating atmospheric precipitation data or total delay data and the like, so that the method is particularly good for improving the falling area and strength prediction in the rainstorm process.

At present, the GNSS water vapor observation is that satellite navigation observation data and meteorological data are collected at a station, collected to a central station, processed and resolved and then provided for users, and the GNSS water vapor observation is not beneficial to real-time service of local water vapor.

Disclosure of Invention

The application mainly aims to provide a water vapor observation method based on a navigation satellite system, which comprises the following steps:

receiving satellite data of a navigation satellite system;

receiving meteorological data of a meteorological observer;

processing the satellite data and the meteorological data and obtaining delay data;

establishing a weighted average temperature calculation model;

calculating the delay data through the calculation model, and obtaining a calculation result;

and inverting the calculation result and obtaining water vapor data.

Optionally, receiving satellite data of the navigation satellite system comprises:

receiving preliminary satellite data of a navigation satellite system;

receiving satellite clock error data and precise orbit data;

and adjusting the preliminary satellite data according to the satellite clock error data and the precise orbit data, and obtaining the satellite data.

Optionally, the processing the satellite data and the meteorological data, and obtaining delay data comprises:

performing sliding window processing according to the first time interval and the second time processing radian to obtain troposphere zenith delay data;

wherein the tropospheric zenith delay data comprises: zenith static force delay and zenith non-static force delay data;

obtaining tropospheric zenith delay data comprises:

acquiring the zenith static force delay data through a preset model and known air pressure,

and adding the zenith static force delay data and the estimated zenith non-static force delay data to obtain troposphere zenith delay data.

Optionally, the establishing a weighted average temperature calculation model includes:

analyzing data again according to weather in a preset time period, and acquiring the earth surface air temperature and the weighted average temperature at the grid point;

establishing a conversion grid model of the difference between the weighted average temperature and the earth surface temperature through spectrum analysis;

and selecting corresponding conversion parameters according to the grid points, and acquiring the weighted average temperature at the observation points by using the air temperature observation values of the observation points.

Optionally, inverting the calculation result and obtaining water vapor data comprises:

removing a dry delay component from the troposphere zenith delay data to obtain a wet delay component;

converting the wet delay component to the water reducible amount of the observation point;

and converting the weighted average temperature obtained by calculation according to the air temperature observation at the observation position and the established weighted average temperature and earth surface temperature difference conversion model to obtain the atmospheric water vapor data at the observation position with time resolution superior to the third time.

Optionally, inverting the calculation result and obtaining water vapor data further comprises:

eliminate the observation error that the difference in height brought between meteorological observation equipment and navigation satellite system antenna, include:

judge | h_g-h_s|≤20m；

When the result is positive, the result is,wherein the content of the first and second substances, T_g＝T_s+(h_g-h_s)；

when the result is negative, the program is executed,wherein the content of the first and second substances,

T_i＝T_s+·Δz_i，Δz_i＝20(-20)m for h_g＞h_s(h_g＜h_s);

where the subscript g denotes the navigation satellite system antenna and the subscript s denotes the meteorological observation device. P, T, h respectively indicate pressure (hPa), temperature (K) and elevation (m). Acceleration of gravity g-9.8067 m/s²Dry air gas constant R_d＝287.058JK^{- 1}kg^-1The rate of change of temperature with height is-6.5 K.km^-1。

Optionally, the satellite data includes position information, time information, and wave velocity of the satellite; the meteorological data includes air temperature, humidity and air pressure.

The application also discloses a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of being executed by the processor, wherein the processor realizes the method of any one of the above items when executing the computer program.

The application also discloses a computer-readable storage medium, a non-volatile readable storage medium, having stored therein a computer program which, when executed by a processor, implements the method of any of the above.

The present application also discloses a computer program product comprising computer readable code which, when executed by a computer device, causes the computer device to perform the method of any of the above.

All methods in this application are executed or realize that the hardware is all realized on local single station, need not transmit data to other servers or high in the clouds, consequently compares with prior art, and this application has following beneficial effect:

according to the GNSS real-time observation data based on the station, by accessing a high-precision satellite orbit clock difference product, distributed high-timeliness, high-precision troposphere delay resolving and atmosphere water vapor content inversion at the station are realized, real-time provision of station-level water vapor products is realized, and the requirements of medium and small-scale extremely-strong convection weather system monitoring, typhoon monitoring and short-term forecasting are met.

The station obtains the real-time water vapor product of each epoch based on the single station epoch-based real-time water vapor processing, so that the time resolution of water vapor observation can be greatly improved, and the service efficiency and the service capacity of the product are greatly improved; the single-station processing replaces the batch data processing of the data center, so that the data processing efficiency is greatly improved, and meanwhile, the transmission cost of the GNSS original data transmitted from the station to the central station is greatly reduced.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:

FIG. 1 is a schematic flow chart of a method for observing moisture based on a navigation satellite system according to an embodiment of the present application;

FIG. 2 is a system diagram of a method for water vapor observation based on a navigation satellite system according to one embodiment of the present application;

FIG. 3 is a schematic diagram of a tropospheric zenith total delay estimation technique according to one embodiment of the present application;

FIG. 4 is a diagrammatic illustration of an air pressure elevation correction procedure in accordance with an embodiment of the present application;

FIG. 5 is a schematic diagram of a computer device according to one embodiment of the present application; and

FIG. 6 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Referring to fig. 1-2, an embodiment of the present application provides a method for observing moisture based on a navigation satellite system, including:

s1: receiving satellite data of a navigation satellite system;

s2: receiving meteorological data of a meteorological observer;

s3: processing the satellite data and the meteorological data and obtaining delay data;

s4: establishing a weighted average temperature calculation model;

s5: calculating the delay data through the calculation model, and obtaining a calculation result;

s6: and inverting the calculation result and obtaining water vapor data.

In this embodiment, the general technical route includes: storing and preprocessing station GNSS observation data and meteorological observation data; performing GNSS (global navigation satellite system) high-aging and high-precision PPP (Point-to-Point protocol) processing at the station to obtain a high-precision troposphere delay product; establishing a Tm (weighted average temperature) model at the station; and the high-precision atmospheric water vapor inversion is carried out at the station, and the water vapor product information station is grounded and output to a given server for real-time uploading, so that product support is provided for subsequent meteorological application.

In an embodiment of the present application, receiving satellite data of a navigation satellite system includes:

receiving preliminary satellite data of a navigation satellite system;

receiving satellite clock error data and precise orbit data;

and adjusting the preliminary satellite data according to the satellite clock error data and the precise orbit data, and obtaining the satellite data.

In this embodiment, the general technical route includes: accessing, storing and controlling the quality of a real-time high-precision satellite orbit clock error product; storing and preprocessing station GNSS observation data and meteorological observation data; performing GNSS (global navigation satellite system) high-aging and high-precision PPP (Point-to-Point protocol) processing at the station to obtain a high-precision troposphere delay product; establishing a Tm model at a station; and the high-precision atmospheric water vapor inversion is carried out at the station, and the water vapor product information station is grounded and output to a given server for real-time uploading, so that product support is provided for subsequent meteorological application.

And accessing a real-time high-precision GNSS satellite orbit clock error product from a national Beidou foundation enhancement system or Wuhan university by adopting an RTCM (real time measurement and control) protocol, storing the accessed stream by files according to a standard SP3 format, and removing rough errors and abnormal values of the accessed satellite orbit clock error product in the latest 6-hour arc segment according to a processing interval of 15 minutes.

Referring to fig. 3, in an embodiment of the present application, the processing the satellite data and the meteorological data and obtaining delay data includes:

performing sliding window processing according to the first time interval and the second time processing radian to obtain troposphere zenith delay data;

wherein the tropospheric zenith delay data comprises: zenith static force delay and zenith non-static force delay data;

obtaining tropospheric zenith delay data comprises:

acquiring the zenith static force delay data through a preset model and known air pressure,

and adding the zenith static force delay data and the estimated zenith non-static force delay data to obtain troposphere zenith delay data.

And (3) carrying out PPP sliding window processing by using a PPP module of PANDA software according to a processing interval of 15 minutes and a processing arc section of 6 hours to obtain a troposphere zenith delay product with time resolution superior to 5 minutes. The tropospheric Zenith Total Delay (ZTD) consists of two parts, zenith static delay ZHD (also known as zenith dry delay) and zenith non-static delay ZWD (also known as zenith wet delay). Given the station accurate air pressure, ZHD can be accurately determined (better than 1mm) by a model (e.g., the Saastamoinen model), whereas an air pressure error of 1hPa can result in an error of ZHD of approximately 3 mm. The prior ZHD at the stations can be calculated using the prior atmospheric pressure provided by prior temperature-atmospheric models (e.g., GPT and GPT2 models) (Boehm et al, 2006; Lagler et al, 2013) in the ZTD solution, estimating the ZWD as the parameter to be estimated, and summing the two as the final ZTD estimate. Since the dry and wet delay projection functions have small differences (although the difference of low altitude angles is increased, the influence can be reduced by the strategy of weighting the altitude angles of the observed values), the error of the prior ZHD can be mostly absorbed by the ZWD to be estimated, and therefore, even in the absence of meteorological observation at the observation station, the high-precision ZTD product can still be obtained by providing ZHD through the prior atmospheric temperature model.

In an embodiment of the present application, the establishing a weighted average temperature calculation model includes:

analyzing data again according to weather in a preset time period, and acquiring the earth surface air temperature and the weighted average temperature at the grid point;

establishing a conversion grid model of the difference between the weighted average temperature and the earth surface temperature through spectrum analysis;

and selecting corresponding conversion parameters according to the grid points, and acquiring the weighted average temperature at the observation points by using the air temperature observation values of the observation points.

The prior Tm model does not use temperature information to the station, and is therefore theoretically inferior to the Tm (weighted average temperature) -Ts (surface temperature) conversion model method. In this embodiment, the surface temperature and Tm at the grid point are solved according to the weather reanalysis data (e.g., ERA-Interim) of the last decade, and a suitable Tm-Ts conversion grid model is established through spectrum analysis. And the station selects corresponding conversion parameters according to the grid points, and finally obtains the Tm of the station by using the air temperature observation value observed by the station.

In an embodiment of the present application, inverting the calculation result and obtaining water vapor data includes:

removing a dry delay component from the troposphere zenith delay data to obtain a wet delay component;

converting the wet delay component to the water reducible amount of the observation point;

and converting the weighted average temperature obtained by calculation according to the air temperature observation at the observation position and the established weighted average temperature and earth surface temperature difference conversion model to obtain the atmospheric water vapor data at the observation position with time resolution superior to the third time.

The conversion from ZTD to atmospheric water-reducible content (PWV) is essentially two steps, first requiring the subtraction of the dry delay (ZHD) component from ZTD to obtain the wet delay component (ZWD), and then converting the ZWD to PWV at the station.

The air pressure accuracy obtained by the prior air temperature and air pressure model (such as GPT and GPT2 models) is about 5hPa generally, and the error of a few areas is more than 10hPa, so in order to obtain a high-accuracy ZWD product, a meteorological observation value is required to be adopted to calculate ZHD.

And after obtaining the high-precision wet component ZWD, converting the Tm obtained by calculation according to the air temperature observation at the station and the established Tm-Ts conversion model to obtain a high-precision atmospheric water vapor product at the station with the time resolution superior to 5 minutes, and transmitting the high-precision atmospheric water vapor product to a central server through a network.

Referring to fig. 4, in an embodiment of the present application, the inverting the calculation result and obtaining the water vapor data further includes:

eliminate the observation error that the difference in height brought between meteorological observation equipment and navigation satellite system antenna, include:

judge | h_g-h_s|≤20m；

When the result is positive, the result is,wherein the content of the first and second substances, T_g＝T_s+(h_g-h_s)；

when the result is negative, the program is executed,wherein the content of the first and second substances,

T_i＝T_s+·Δz_i，Δz_i＝20(-20)m for h_g＞h_s(h_g＜h_s)；

where the subscript g denotes the navigation satellite system antenna and the subscript s denotes the meteorological observation device. P, T, h respectively indicate pressure (hPa), temperature (K) and elevation (m). Acceleration of gravity g-9.8067 m/s²Dry air gas constant R_d＝287.058JK^{- 1}kg^-1The rate of change of temperature with height is-6.5 K.km^-1。

However, the meteorological observation equipment and the navigation satellite system antenna have certain height difference, the air pressure difference corresponding to the height difference of 10m near the earth surface exceeds 1hPa, and an error of nearly 3mm is introduced into ZHD. In order to obtain a ZWD product with the highest possible accuracy, the meteorological observations need to be corrected to the navigation satellite system antenna taking into account the effects of altitude differences. While changes in horizontal gradients due to differences in horizontal position have negligible effect. The flow for the air pressure correction is shown in fig. 4.

In an embodiment of the present application, the satellite data includes position information, time information, and wave velocity of a satellite; the meteorological data includes air temperature, humidity and air pressure.

In an embodiment of the application, station GNSS observation and meteorological observation are recorded into files according to a RINEX format, data preprocessing is carried out on the observation files according to a processing interval of 15 minutes and a processing arc segment of 6 hours, abnormal observation is eliminated by adopting a threshold control method for the meteorological observation files, and cycle slip detection and quality control are carried out by adopting a Turboexit method for the GNSS observation.

In an embodiment of the present application, the method further includes: and selecting a certain flood season typical station to carry out business test operation, and completing and submitting a test demonstration operation evaluation report.

High-precision data processing based on a ground-based GNSS station network generally has two modes, namely network solution and PPP. The network solution mode can uniformly process all data of the GNSS observation network without depending on high-precision satellite clock error products, and simultaneously, the parameters such as satellite clock error, troposphere delay and the like are solved. The mode has large processing and calculation load, and the observation abnormity of some stations in the station network easily pollutes the data processing result of the whole station network; the PPP mode needs high-precision satellite orbit and clock error products, the observation data of each observation station is independently processed, each observation station is independent, the resolving efficiency is high, and the operation is flexible. The method is particularly suitable for the rapid processing of the station network data with a huge number of stations. But the PPP mode requires high precision satellite orbits and clock error products compared to the web solution mode.

Referring to fig. 5, the present application further provides a computer device including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method of any one of the above methods when executing the computer program.

Referring to fig. 6, a computer-readable storage medium, a non-volatile readable storage medium, having stored therein a computer program which, when executed by a processor, implements any of the methods described above.

A computer program product comprising computer readable code which, when executed by a computer device, causes the computer device to perform the method of any of the above.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.