CN114357770A - A tropospheric chromatography method - Google Patents
A tropospheric chromatography method Download PDFInfo
- Publication number
- CN114357770A CN114357770A CN202210005801.1A CN202210005801A CN114357770A CN 114357770 A CN114357770 A CN 114357770A CN 202210005801 A CN202210005801 A CN 202210005801A CN 114357770 A CN114357770 A CN 114357770A
- Authority
- CN
- China
- Prior art keywords
- water vapor
- tropospheric
- layered
- data
- tomographic
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004587 chromatography analysis Methods 0.000 title description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 149
- 238000003325 tomography Methods 0.000 claims abstract description 44
- 238000012950 reanalysis Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 claims 1
- 238000005259 measurement Methods 0.000 claims 1
- 230000003068 static effect Effects 0.000 claims 1
- 230000002123 temporal effect Effects 0.000 abstract description 9
- 238000001556 precipitation Methods 0.000 abstract description 2
- 238000004365 square wave voltammetry Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 239000005436 troposphere Substances 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 239000000047 product Substances 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000005305 interferometry Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Environmental & Geological Engineering (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
本发明提供一种对流层层析方法,包括在现有GNSS测量的水汽数据的基础上,加入静止卫星风云4A获得的分层大气可降水量观测值数据,所述风云4A获得的分层大气可降水量观测值包括低层水汽观测值、中层水汽观测值和高层水汽观测值,并合理确定GNSS测量的水汽数据与风云4A获得的分层大气可降水量观测值数据这两类水汽数据的权比,得到一种更准确的对流层层析模型,并在该对流层层析模型下进行对流层层析。本发明将高时空分辨率的风云卫星分层水汽数据附加到层析模型,能够大大降低传统层析模型的不适定性,构建更准确的对流层层析模型,从而得到较好的对流层水汽密度分布。
The present invention provides a tropospheric tomography method, which comprises adding, on the basis of the water vapor data measured by the existing GNSS, the layered atmospheric precipitable water observation value data obtained by the geostationary satellite Fengyun 4A, and the layered atmosphere obtained by the Fengyun 4A can be Precipitation observations include low-level water vapor observations, middle-level water vapor observations, and high-level water vapor observations, and reasonably determine the weight ratio of the water vapor data measured by GNSS and the layered atmospheric precipitable water vapor data obtained by Fengyun 4A. , obtain a more accurate tropospheric tomography model, and perform tropospheric tomography under the tropospheric tomography model. The present invention attaches the stratified water vapor data of the Fengyun satellite with high temporal and spatial resolution to the tomographic model, which can greatly reduce the ill-posedness of the traditional tomographic model and build a more accurate tropospheric tomographic model, thereby obtaining a better tropospheric water vapor density distribution.
Description
技术领域technical field
本发明属于气象预报领域,具体涉及大气水汽探测和一种对流层的层析方法。The invention belongs to the field of meteorological forecasting, and particularly relates to atmospheric water vapor detection and a tropospheric tomographic method.
背景技术Background technique
水汽在大气中含量虽然较少,但是它是全球能量收支平衡中的重要因素,而由于水汽的相变效应,具有雨、雾、云、雪等各种状态,是引起天气变化的重要因素,同时也是灾害性天气形成和演变的主要驱动力。在天气预报中,对大气中水汽含量、水汽的变化以及时间和空间分布情况的了解决定了短时临近预报的成败,对于大气中湿度场的分析会直接影响到数值预报的准确性。对流层是地球大气层中与地面相接的最底层,其大气密度最高,集中了超过90%以上的大气水汽。在空间大地测量中,对流层延迟已逐渐成为现代空间测地技术的重要研究方向,而由于对流层水汽在时空上的不稳定特性,使得水汽引起的信号延迟成为影响空间大地测量精度的主要因素之一。因此探测获取高精度的水汽含量对于做出更加准确的气象预报和加强极端天气灾害的预警以及空间大地测量研究的发展具有重要的意义。Although the content of water vapor in the atmosphere is relatively small, it is an important factor in the global energy balance. Due to the phase transition effect of water vapor, it has various states such as rain, fog, cloud, and snow, which is an important factor causing weather changes. , and is also the main driving force for the formation and evolution of severe weather. In weather forecasting, the understanding of the water vapor content in the atmosphere, the changes of water vapor, and the temporal and spatial distribution determines the success or failure of short-term nowcasting, and the analysis of the humidity field in the atmosphere will directly affect the accuracy of numerical forecasting. The troposphere is the lowest layer of the Earth's atmosphere that is in contact with the ground. It has the highest atmospheric density and concentrates more than 90% of atmospheric water vapor. In space geodesy, tropospheric delay has gradually become an important research direction of modern space geodetic technology. Due to the unstable characteristics of tropospheric water vapor in space and time, the signal delay caused by water vapor has become one of the main factors affecting the accuracy of space geodesy. . Therefore, detecting and obtaining high-precision water vapor content is of great significance for making more accurate meteorological forecasts and strengthening the early warning of extreme weather disasters, as well as the development of space geodetic research.
传统大气水汽探测方法有:水汽辐射计、无线电探空仪以及气象卫星对地观测等等,而利用全球导航定位(GNSS)技术遥感大气水汽含量则是九十年代发展起来的新兴大气探测技术。当卫星信号穿过对流层的时候,会与所接触的大气介质相互作用,进而产生在时间和传播路径上的延迟效应,即产生折射。通过建立延迟量与大气折射率之间的函数关系,从而估计出重要的气象参数---大气可降水量(PWV)。作为传统大气探测方法的有力补充,GNSS探测水汽同时具有覆盖全球、实时连续性、全天候以及高精度等优点,是现代水汽探测的重要手段。由于天顶可降水量只包含对流层水汽含量的一维信息,无法直接反映大气水汽含量的垂直分布信息,因此限制了其在气象研究中的应用。1992年,基于医学领域的断层扫描技术,Bevis首次提出了对流层层析技术,即利用沿着从各个方向穿透模型空间的不同射线的斜路径可降水量(SWV)来重建对流层水汽密度图像。基于GNSS对流层层析技术可有效获取高精度、高时空密度的水汽四维分布,同时具有用低廉、操作简单、全天候监测等诸多优势,为掌握对流层水汽的时空变化提供了可能。可以说,研究对流层层析技术对于加强全球大气水汽多维动态变化监测、推动现代气象预报业务发展等方面具有重要的科学意义和应用价值。The traditional atmospheric water vapor detection methods include: water vapor radiometer, radiosonde and meteorological satellite earth observation, etc., and the use of global navigation and positioning (GNSS) technology to remotely sense atmospheric water vapor content is an emerging atmospheric detection technology developed in the 1990s. When a satellite signal passes through the troposphere, it interacts with the atmospheric medium it comes in contact with, resulting in a delay effect in time and propagation path, known as refraction. By establishing the functional relationship between the retardation and the atmospheric refractive index, an important meteorological parameter, precipitable water (PWV), is estimated. As a powerful supplement to traditional atmospheric detection methods, GNSS detection of water vapor has the advantages of global coverage, real-time continuity, all-weather and high precision, and is an important means of modern water vapor detection. Because the zenith precipitable water content only contains one-dimensional information of tropospheric water vapor content, it cannot directly reflect the vertical distribution information of atmospheric water vapor content, so its application in meteorological research is limited. In 1992, based on the tomography technology in the medical field, Bevis first proposed the tropospheric tomography technique, that is, the tropospheric water vapor density image is reconstructed using the oblique path precipitable water vapour (SWV) along different rays penetrating the model space from all directions. The GNSS-based tropospheric tomography technology can effectively obtain the four-dimensional distribution of water vapor with high precision and high spatiotemporal density. At the same time, it has many advantages such as low cost, simple operation, and all-weather monitoring, which provides the possibility for grasping the spatiotemporal changes of tropospheric water vapor. It can be said that the study of tropospheric tomography technology has important scientific significance and application value for strengthening the monitoring of multi-dimensional dynamic changes of global atmospheric water vapor and promoting the development of modern meteorological forecasting services.
对流层层析模型的不适定性问题是影响层析结果精度的主要原因,目前该问题常用的解决方法主要包括以下几类:The ill-posed problem of the tropospheric tomography model is the main reason that affects the accuracy of the tomographic results. At present, the commonly used solutions to this problem mainly include the following categories:
优化层析网格划分模型:层析网格的划分不再是粗略地规则划分,而是根据地面GNSS台站和卫星星座的分布,或是结合地形等因素对层析网格进行自适应划分,使得网格尽可能多地被GNSS信号线穿过,降低层析模型的不适定性。但由于GNSS信号线的“倒锥形”分布特征,此方法作用有限,仍然存在大量网格未被GNSS信号线穿过。Optimized tomographic grid division model: The division of tomographic grids is no longer roughly ruled, but adaptively divided according to the distribution of ground GNSS stations and satellite constellations, or combined with terrain and other factors , so that the grid is traversed by the GNSS signal lines as much as possible to reduce the ill-posedness of the tomographic model. However, due to the "inverted cone" distribution characteristics of GNSS signal lines, this method has limited effect, and there are still a large number of grids that are not crossed by GNSS signal lines.
添加经验性约束条件:依据水汽在对流层中分布的特征,添加经验性的约束条件,主要包括水平约束、垂直约束以及边界约束等,参与层析方程的解算,以获得较为可靠的层析解。然而由于大气系统存在不稳定性,经验约束常常与实际水汽分布不符合,就会导致层析解失真,难以获得可靠的层析结果。Add empirical constraints: According to the distribution characteristics of water vapor in the troposphere, add empirical constraints, mainly including horizontal constraints, vertical constraints and boundary constraints, and participate in the solution of tomographic equations to obtain more reliable tomographic solutions . However, due to the instability of the atmospheric system, the empirical constraints are often inconsistent with the actual water vapor distribution, which will lead to distortion of the tomographic solution, and it is difficult to obtain reliable tomographic results.
融合多源水汽信息:将非GNSS水汽信息作为强约束引入层析模型,可减弱由经验性约束条件不准确带来的解算结果失真效应。目前已被引入的包括:全球再分析或预报的水汽资料、合成孔径雷达干涉测量的PWV差分数据、中分辨率成像光谱仪以及大气红外探测仪等卫星传感器获取的PWV影像。这些外部水汽信号均是穿过了层析模型顶部到模型底部网格的完整PWV信号,可以改善层析模型的不适定性,但改善程度有限,同时容易增加层析方程组的冗余。Fusion of multi-source water vapor information: The non-GNSS water vapor information is introduced into the tomographic model as a strong constraint, which can reduce the distortion effect of the solution results caused by inaccurate empirical constraints. The ones that have been introduced so far include: global reanalysis or forecast water vapor data, PWV differential data from synthetic aperture radar interferometry, PWV images acquired by satellite sensors such as the Moderate Resolution Imaging Spectrometer and the Atmospheric Infrared Sounder. These external water vapor signals are all complete PWV signals passing through the grid from the top of the tomographic model to the bottom of the model, which can improve the ill-posedness of the tomographic model, but the improvement is limited, and it is easy to increase the redundancy of the tomographic equations.
因此,传统层析模型存在大量未被信号穿过的网格,层析方程组不适定性严重,层析反演结果误差较大。融合外部水汽信息的层析模型非GNSS水汽信息作为强约束引入层析模型,可减弱由经验性约束条件不准确带来的解算结果失真效应。目前的研究主要使用的是穿过了层析模型顶部到模型底部网格的完整PWV信号。这种信号包含了其穿刺过的垂直方向上所有网格水汽未知数的总和,可以改善层析模型的不适定性,但改善程度有限,而且容易增加层析方程组的冗余。Therefore, the traditional tomographic model has a large number of grids that are not passed through by the signal, the tomographic equations are seriously ill-posed, and the tomographic inversion result has a large error. The non-GNSS water vapor information of the tomographic model fused with the external water vapor information is introduced into the tomographic model as a strong constraint, which can reduce the distortion effect of the solution results caused by the inaccurate empirical constraints. The current study mainly uses the complete PWV signal that passes through the grid from the top of the tomographic model to the bottom of the model. This signal contains the sum of the water vapor unknowns of all grids in the vertical direction it pierces, which can improve the ill-posedness of the tomographic model, but the improvement is limited, and it is easy to increase the redundancy of the tomographic equations.
中国发明专利CN201711033706.8公开了一种基于函数基的三维水汽探测方法,包括步骤:一、观测数据接收和解算;二、大气对流层参数解算;三、卫星信号斜路径上大气水汽含量SWV的计算;四、建立函数基观测方程;五、构建先验约束方程;六、建立基于函数基的三维水汽层析模型;七、确定函数基三维水汽层析模型中各类参数权比;八、基于函数基的三维水汽层析模型的待估参数解算及结果显示。该发明以函数基观测方程为基础,建立新的函数基层析模型,通过引入函数基观测方程,保证待估水汽密度参数的空间连续性,降低待估参数的个数,增强层析模型结构的稳定性,确保重构水汽结果的精度和可靠性。该专利的方法降低了层析未知数的个数,有效地改善了层析方程组的不适定性。但该专利采用函数基来描述大气水汽在水平空间上的连续性,然而实际水汽分布复杂多变,并不能完全用一组函数来描述,因而存在较大的误差。Chinese invention patent CN201711033706.8 discloses a three-dimensional water vapor detection method based on function basis, including steps: 1. Receive and calculate observation data; 2. Calculation of atmospheric troposphere parameters; Calculation; 4. Establish a function-based observation equation; 5. Establish a priori constraint equation; 6. Establish a 3D water vapor tomography model based on a function basis; 7. Determine the weight ratios of various parameters in the function-based 3D water vapor tomography model; 8. Calculation of parameters to be estimated and display of results for a three-dimensional water vapor tomography model based on function basis. Based on the function-based observation equation, the invention establishes a new function-based analytical model, and by introducing the function-based observation equation, the spatial continuity of the water vapor density parameters to be estimated is guaranteed, the number of parameters to be estimated is reduced, and the structure of the tomographic model is enhanced. Stability ensures the accuracy and reliability of reconstructed water vapor results. The method of this patent reduces the number of chromatographic unknowns and effectively improves the ill-posedness of the chromatographic equations. However, this patent uses a function basis to describe the continuity of atmospheric water vapor in horizontal space. However, the actual water vapor distribution is complex and changeable, and cannot be completely described by a set of functions, so there is a large error.
因此,本领域需要一种新的对流层层析方法。Therefore, there is a need in the art for a new tropochromatographic method.
发明内容SUMMARY OF THE INVENTION
为了解决上述问题,建立更加精确的对流层层析建模方法和理论,本发明在吸收现有附加外部约束水汽层析优点的基础上,开发了一种融合高时空分辨率的静止卫星分层水汽数据对流层层析方法,该方法首次使用了高时空分辨率的风云静止卫星分层水汽数据作为层析约束条件,较大程度地降低了层析模型的不适定性,可以获得更加接近于实际水汽分布的层析产品。In order to solve the above problems and establish a more accurate tropospheric tomographic modeling method and theory, the present invention develops a geostationary satellite stratified water vapor integrated with high spatial and temporal resolution on the basis of absorbing the advantages of the existing additional externally constrained water vapor tomography Data tropospheric tomography method, which uses the Fengyun geostationary satellite stratified water vapor data with high spatial and temporal resolution for the first time as the tomographic constraint, which greatly reduces the ill-posedness of the tomographic model, and can obtain a water vapor distribution that is closer to the actual chromatography products.
本发明提供一种对流层层析方法,包括在现有GNSS测量的水汽数据的基础上,加入静止卫星风云4A获得的分层大气可降水量观测值数据,所述风云4A获得的分层大气可降水量观测值包括低层水汽观测值、中层水汽观测值和高层水汽观测值,并合理确定GNSS测量的水汽数据与风云4A获得的分层大气可降水量观测值数据这两类水汽数据的权比,得到一种更准确的对流层层析模型,并在该对流层层析模型下进行对流层层析。The present invention provides a tropospheric tomography method, which comprises adding, on the basis of the water vapor data measured by the existing GNSS, the layered atmospheric precipitable water observation value data obtained by the geostationary satellite Fengyun 4A, and the layered atmosphere obtained by the Fengyun 4A can be Precipitation observations include low-level water vapor observations, middle-level water vapor observations, and high-level water vapor observations, and reasonably determine the weight ratio of the water vapor data measured by GNSS and the layered atmospheric precipitable water vapor data obtained by Fengyun 4A. , obtain a more accurate tropospheric tomography model, and perform tropospheric tomography under the tropospheric tomography model.
在一种具体的实施方式中,利用探空资料分别对所述两类水汽数据进行评估,将得到的中误差的比值作为权比,并采用乘法代数重建技术迭代求解所述对流层层析模型中的层析方程组。In a specific embodiment, the two types of water vapor data are evaluated respectively by using sounding data, the ratio of the obtained intermediate errors is used as a weight ratio, and a multiplicative algebraic reconstruction technique is used to iteratively solve the tropospheric tomographic model. chromatographic equations.
在一种具体的实施方式中,乘法代数重建技术迭代所需的初始水汽密度场由美国气象环境预报中心提供。In a specific embodiment, the initial water vapor density field required for the iteration of the multiplicative algebraic reconstruction technique is provided by the US Weather and Environmental Prediction Center.
在一种具体的实施方式中,建立所述更准确的对流层层析模型包括如下步骤:In a specific embodiment, establishing the more accurate tropospheric tomography model includes the following steps:
沿着GNSS接收机到GNSS卫星信号线路径l,斜路径可降水量SWV与水汽密度Nw之间的关系用积分表示为:Along the path l of the GNSS receiver to the GNSS satellite signal line, the relationship between the oblique path precipitable water volume SWV and the water vapor density Nw is expressed as an integral:
对每一条SWV信号线的积分近似为:The integral for each SWV signal line is approximated by:
SWV(i)=∑jAS(i,j)x(j) (2)SWV(i)=∑ j AS(i,j)x(j) (2)
其中,ΔS(i,j)为第i条信号线穿过第j个网格的截距,x(j)为第j个网格的水汽密度未知数;Among them, ΔS(i, j) is the intercept of the i-th signal line passing through the j-th grid, and x(j) is the unknown water vapor density of the j-th grid;
将风云4A反演的分层PWV数据附加到层析模型构成约束条件进行联合层析反演;风云4A分层PWV数据同样按照式(2)建立方程,但由于风云4A是基于sigma压力坐标确定分层PWV数据的边界,需要将其转换为层析模型的几何高度;采用欧洲中期天气预报中心提供的第五代全球大气再分析地表气压确定分层PWV数据边界处的气压值 The layered PWV data inverted by Fengyun 4A is added to the constraints of the tomographic model for joint tomographic inversion; the layered PWV data of Fengyun 4A is also established according to equation (2), but because Fengyun 4A is determined based on sigma pressure coordinates The boundary of the layered PWV data needs to be converted into the geometric height of the tomographic model; the fifth-generation global atmospheric reanalysis surface pressure provided by the European Centre for Medium-Range Weather Forecasts is used to determine the pressure value at the boundary of the layered PWV data
其中σk为sigma系数,具体为1.0,0.9,0.7和0.3;为第i个风云4A水汽图像像素中心点的第五代全球大气再分析地表气压,即ERA5地表气压;where σ k is the sigma coefficient, specifically 1.0, 0.9, 0.7 and 0.3; The fifth generation global atmospheric reanalysis surface pressure for the i-th Fengyun 4A water vapor image pixel center point, that is, the ERA5 surface pressure;
然后利用ERA5分层位势数据插值得到各分层PWV数据边界处的位势,使用下式近似得到相应的几何高度H:Then use the ERA5 layered potential data to interpolate to obtain the potential at the boundary of each layered PWV data, and use the following approximation to obtain the corresponding geometric height H:
H=Φ/g (4)H=Φ/g (4)
其中Φ为各分层PWV数据边界处的位势,g为重力加速度;where Φ is the potential at the boundary of each layered PWV data, and g is the gravitational acceleration;
最后,引入水平约束、垂直约束和顶层约束,得到融合风云4A分层水汽的对流层层析函数模型:Finally, the horizontal constraints, vertical constraints and top-level constraints are introduced to obtain the tropospheric tomographic function model that integrates the stratified water vapor of Fengyun 4A:
其中,BGNSS、BFY和V均为系数矩阵;X为水汽密度未知数向量;SWVGNSS为GNSS SWV观测值;LPWFY为风云4A分层PWV观测值,包括低层水汽观测值、中层水汽观测值和高层水汽观测值;然后再合理确定风云4A分层PWV数据与GNSS测量的水汽数据的权比。Among them, B GNSS , B FY and V are coefficient matrices; X is the water vapor density unknown vector; SWV GNSS is the GNSS SWV observation value; LPW FY is the Fengyun 4A layered PWV observation value, including the low layer water vapor observation value and the middle layer water vapor observation value and high-level water vapor observations; and then reasonably determine the weight ratio of the Fengyun 4A layered PWV data and the water vapor data measured by GNSS.
本发明的优点:Advantages of the present invention:
本发明所述方法通过融合风云分层水汽模型提高对流层层析结果精度。本发明将高时空分辨率的风云卫星分层水汽数据附加到层析模型,能够大大降低传统层析模型的不适定性,构建更准确的对流层层析模型,从而得到较好的对流层水汽密度分布。本发明提出的融合风云分层水汽层析模型相对于传统模型能显著提高层析解精度。因此,本发明能够有效的弥补传统模型的理论缺陷,更加精确地确定水汽密度场结构。The method of the invention improves the accuracy of tropospheric tomography results by integrating the wind-cloud layered water vapor model. The present invention attaches the Fengyun satellite layered water vapor data with high temporal and spatial resolution to the tomographic model, which can greatly reduce the ill-posedness of the traditional tomographic model and build a more accurate tropospheric tomographic model, thereby obtaining a better tropospheric water vapor density distribution. Compared with the traditional model, the integrated wind-cloud layered water vapor tomography model proposed by the present invention can significantly improve the tomographic solution accuracy. Therefore, the present invention can effectively make up for the theoretical defects of the traditional model, and determine the water vapor density field structure more accurately.
此外,本发明与专利CN201711033706.8相比,该专利不在水平空间上进行层析网格划分,而本发明在水平空间划分为均匀的网格;该专利通过减少层析未知数个数改善层析方程不适定性,而本发明则通过增加实际观测约束,即增加方程个数来改善不适定性。In addition, compared with the patent CN201711033706.8, the present invention does not divide the tomographic grid in the horizontal space, while the present invention divides the horizontal space into a uniform grid; the patent improves the tomographic grid by reducing the number of tomographic unknowns The equation is ill-posed, and the present invention improves the ill-posedness by increasing the actual observation constraint, that is, increasing the number of equations.
附图说明Description of drawings
图1为融合风云4A分层水汽的对流层层析模型示意图。Fig. 1 is a schematic diagram of the tropospheric tomography model incorporating the stratified water vapor of Fengyun 4A.
图2为湖南省区域使用传统模型进行水汽层析反演的均方根误差分布图。Figure 2 shows the distribution of root mean square errors of water vapor tomography inversion using traditional models in Hunan Province.
图3为湖南省区域使用本发明所述融合有风云分层水汽数据的新模型进行水汽层析反演的均方根误差分布图。Fig. 3 is a distribution diagram of root mean square error of water vapor tomography inversion using the new model of the present invention incorporating the wind-cloud layered water vapor data in Hunan Province.
图4为湖南怀化探空站的数据与传统层析方法以及本发明中的新层析方法的水汽密度垂直廓线对比图。FIG. 4 is a comparison diagram of the vertical profile of water vapor density between the data of the Huaihua Sounding Station in Hunan Province and the traditional tomographic method and the new tomographic method of the present invention.
图5为湖南郴州探空站的数据与传统层析方法以及本发明中的新层析方法的水汽密度垂直廓线对比图。FIG. 5 is a comparison diagram of the vertical profile of water vapor density between the data of the Chenzhou Sounding Station in Hunan Province and the traditional tomographic method and the new tomographic method of the present invention.
具体实施方式Detailed ways
对流层:大气圈中最靠近地球表面的一层,集中了约75%的大气的质量和90%以上的水汽质量。Troposphere: The layer of the atmosphere closest to the Earth's surface, containing about 75% of the atmosphere's mass and more than 90% of the water vapor mass.
对流层层析:根据射线扫描得到的对流层延迟信息进行反演计算,重建被测对流层范围内水汽密度分布规律的图像。Tropospheric tomography: Inversion calculation is performed based on the tropospheric delay information obtained by ray scanning, and an image of the distribution law of water vapor density in the measured troposphere is reconstructed.
GNSS:全球定位导航系统,包括中国北斗、欧洲伽利略、俄罗斯格洛纳斯以及美国GPS四大系统。GNSS: Global Positioning and Navigation System, including China's Beidou, Europe's Galileo, Russia's GLONASS and the United States GPS four systems.
本发明解决了对流层层析建模中融合气象卫星水汽产品改善层析模型的不适定性问题。在对流层层析模型建立的过程中,现有技术一般是通过规则的方格块对层析区域进行离散化,并假定在一定时间内每个方格块的水汽密度均匀分布且保持不变。然后根据GNSS信号穿刺的网格,建立对应网格未知数与信号线斜路径可降水量(SWV)之间的函数关系,进而组成层析方程组反演出对流层水汽密度。然而由于地面GNSS测站位置固定,以及卫星星座分布全方位的原因,造成GNSS信号线呈现倒锥形分布的特点,这导致层析模型中存在大量未被信号线穿过的网格,进而使得层析方程组产生不适定性问题。为降低层析方程组的不适定性,获取更接近于实际水汽分布的层析解,可以通过添加外部多源水汽信号进行联合层析反演。目前融合多源水汽信息的层析模型主要使用的是穿过了层析模型顶部到模型底部网格的垂直PWV信号,即天顶总可降水量(TPW),这种信号包含了其穿刺过的垂直方向上所有网格水汽未知数的总和。通过外部TPW信号建立的约束条件可以在改善层析模型的不适定性,但改善程度有限,而且容易与位于相同平面网格内的GNSS TPW产生矛盾,增加层析方程组的冗余。The invention solves the ill-posed problem of improving the tomographic model by integrating the meteorological satellite water vapor product in the tropospheric tomographic modeling. In the process of establishing a tropospheric tomography model, the prior art generally discretizes the tomographic region through regular grid blocks, and assumes that the water vapor density of each grid block is uniformly distributed and remains unchanged within a certain period of time. Then, according to the grid punctured by the GNSS signal, the functional relationship between the corresponding grid unknown and the signal line oblique path precipitable water (SWV) is established, and then a tomographic equation system is formed to invert the tropospheric water vapor density. However, due to the fixed location of ground GNSS stations and the omnidirectional distribution of satellite constellations, the GNSS signal lines have the characteristics of an inverted cone distribution, which leads to a large number of grids in the tomographic model that are not crossed by the signal lines, which makes the The system of tomographic equations creates ill-posed problems. In order to reduce the ill-posedness of the tomographic equations and obtain a tomographic solution closer to the actual water vapor distribution, a joint tomographic inversion can be performed by adding external multi-source water vapor signals. The current tomographic model that integrates multi-source water vapor information mainly uses the vertical PWV signal that passes through the grid from the top of the tomographic model to the bottom of the model. The sum of all gridded water vapor unknowns in the vertical direction of . The constraints established by the external TPW signal can improve the ill-posedness of the tomographic model, but the improvement is limited, and it is easy to conflict with the GNSS TPW located in the same plane grid, increasing the redundancy of the tomographic equations.
本发明在吸收现有附加外部约束水汽层析优点的基础上,开发了一种融合高时空分辨率的静止卫星分层水汽数据对流层层析方法,该方法首次使用了高时空分辨率的风云静止卫星分层水汽数据作为层析约束条件,较大程度地降低了层析模型的不适定性,可以获得更加接近于实际水汽分布的层析产品。On the basis of absorbing the advantages of the existing additional externally constrained water vapor tomography, the present invention develops a tropospheric tomography method that integrates geostationary satellite layered water vapor data with high spatial and temporal resolution. The satellite stratified water vapor data, as a tomographic constraint, greatly reduces the ill-posedness of the tomographic model, and can obtain a tomographic product that is closer to the actual water vapor distribution.
本专利针对传统层析模型的不足进行改进,使用了高时空分辨率的风云静止卫星分层水汽数据作为层析约束条件,较大程度地降低了层析模型的不适定性,可以获得更加接近于实际水汽分布的层析产品。其原理与过程如下:This patent improves the shortcomings of the traditional tomographic model, and uses the Fengyun geostationary satellite layered water vapor data with high temporal and spatial resolution as the tomographic constraint, which greatly reduces the ill-posedness of the tomographic model, and can obtain closer to the Chromatography product with actual water vapor distribution. The principle and process are as follows:
沿着GNSS接收机到GNSS卫星信号线路径l,SWV与水汽密度Nw之间的关系可用积分表示为:Along the GNSS receiver to GNSS satellite signal line path l, the relationship between SWV and water vapor density Nw can be expressed as an integral:
SWV=∮lNwdl (1)SWV=∮ l N w d l (1)
基于一系列在层析空间各个方向上的SWV,对流层层析技术可以反演对流层水汽密度的空间分布。传统的层析方法是在层析区域建立格网模型,并假设在层析时段内每个体素内水汽密度是不变且均匀分布,则层析模型中的每一个网格都为一个水汽密度未知数。因此对每一条SWV信号线的积分可以近似为:Based on a series of SWVs in all directions of the tomographic space, the tropospheric tomography technique can invert the spatial distribution of tropospheric water vapor density. The traditional tomography method is to establish a grid model in the tomography area, and assuming that the water vapor density in each voxel is constant and uniformly distributed during the tomography period, each grid in the tomography model is a water vapor density. Unknown. So the integral for each SWV signal line can be approximated as:
SWV(i)=∑jΔS(i,j)x(j) (2)SWV(i)=∑j ΔS(i, j )x(j) (2)
其中,ΔS(i,j)为第i条信号线穿过第j个网格的截距,x(j)为第j个网格的水汽密度未知数。Among them, ΔS(i, j) is the intercept of the i-th signal line passing through the j-th grid, and x(j) is the unknown water vapor density of the j-th grid.
风云四号卫星(风云4A)是由中国航天科技集团公司第八研究院(上海航天技术研究院)研制的第二代地球静止轨道(GEO)定量遥感气象卫星,采用三轴稳定控制方案,其连续、稳定运行将大幅提升我国静止轨道气象卫星探测水平。2018年5月8日零时起,中国以及亚太地区用户可正式接收“风云四号”A星数据。Fengyun-4 satellite (Fengyun-4A) is a second-generation geostationary orbit (GEO) quantitative remote sensing meteorological satellite developed by the Eighth Research Institute of China Aerospace Science and Technology Corporation (Shanghai Institute of Aerospace Technology). It adopts a three-axis stabilization control scheme. Continuous and stable operation will greatly improve the detection level of my country's geostationary meteorological satellites. From 0:00 on May 8, 2018, users in China and the Asia-Pacific region can officially receive the data of "Fengyun-4" A star.
本发明在传统层析模型的基础上,提出将风云4A卫星反演的分层PWV数据附加到层析模型构成约束条件进行联合层析反演。风云4A分层PWV数据同样按照式(2)建立方程,但由于风云4A是基于sigma压力坐标确定分层PWV数据的边界,需要将其转换为层析模型的几何高度。本发明采用欧洲中期天气预报中心(ECMWF)提供的第五代全球大气再分析(ERA5)地表气压确定分层PWV数据边界处的气压值 Based on the traditional tomographic model, the present invention proposes to add the layered PWV data inverted by the Fengyun 4A satellite to the tomographic model to form constraints for joint tomographic inversion. The layered PWV data of Fengyun 4A also establishes an equation according to formula (2), but since Fengyun 4A determines the boundary of layered PWV data based on sigma pressure coordinates, it needs to be converted into the geometric height of the tomographic model. The present invention adopts the fifth-generation global atmospheric reanalysis (ERA5) surface pressure provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) to determine the pressure value at the boundary of the layered PWV data
其中σk为sigma系数,具体为1.0,0.9,0.7和0.3;为第i个风云4A水汽图像像素中心点的ERA5地表气压。where σ k is the sigma coefficient, specifically 1.0, 0.9, 0.7 and 0.3; is the ERA5 surface pressure at the center point of the i-th Fengyun 4A water vapor image pixel.
然后利用ERA5分层位势数据插值得到各分层PWV数据边界处的位势,使用下式近似得到相应的几何高度H:Then use the ERA5 layered potential data to interpolate to obtain the potential at the boundary of each layered PWV data, and use the following approximation to obtain the corresponding geometric height H:
H=Φ/g (4)H=Φ/g (4)
其中Φ为各分层PWV数据边界处的位势,g为重力加速度。where Φ is the potential at the boundary of each layered PWV data, and g is the gravitational acceleration.
最后,引入水平约束、垂直约束和顶层约束,可得到融合风云4A分层水汽的对流层层析函数模型:Finally, by introducing horizontal constraints, vertical constraints and top-level constraints, the tropospheric tomographic function model incorporating the stratified water vapor of Fengyun 4A can be obtained:
其中,BGNSS、BFY和V均为系数矩阵;X为水汽密度未知数向量;SWVGNSS为GNSS SWV观测值;LPWFY为风云4A分层PWV观测值,包括低层水汽(LPW-LOW)、中层水汽(LPW-MID)和高层水汽(LPW-HIGH)。图1为融合风云4A分层水汽的对流层层析模型示意图。Among them, B GNSS , B FY and V are coefficient matrices; X is the water vapor density unknown vector; SWV GNSS is the GNSS SWV observation value; LPW FY is the Fengyun 4A layered PWV observation value, including low-level water vapor (LPW-LOW), Water vapor (LPW-MID) and high-level water vapor (LPW-HIGH). Fig. 1 is a schematic diagram of the tropospheric tomography model incorporating the stratified water vapor of Fengyun 4A.
考虑到风云4A分层PWV数据与GNSS测量的水汽数据存在精度上的不一致,因此还需要合理确定两类数据的权比。本发明利用探空资料分别对两类水汽数据进行评估,将得到的中误差的比值作为权比。乘法代数重建技术(MART)采用迭代的方式来重建图像,避免了矩阵求逆,具有在短时间内收敛良好的优点,是解决对流层层析方程不适定问题的常用方法。本发明采用MART迭代求解层析方程组,迭代所需的初始水汽密度场由美国气象环境预报中心提供。Considering the inconsistency in accuracy between the Fengyun 4A layered PWV data and the water vapor data measured by GNSS, it is necessary to reasonably determine the weight ratio of the two types of data. The present invention uses sounding data to evaluate two types of water vapor data respectively, and uses the ratio of the obtained mid-error as the weight ratio. Multiplicative Algebraic Reconstruction Technology (MART) uses an iterative method to reconstruct images, avoids matrix inversion, and has the advantage of good convergence in a short period of time. The invention adopts MART to iteratively solve the tomographic equation system, and the initial water vapor density field required for the iteration is provided by the American Meteorological Environment Prediction Center.
基于湖南省连续运行跟踪站网(HNCORS)观测数据,利用本发明提出的融合风云分层水汽层析方法实现了湖南省区域的大范围水汽层析反演,结果表明该方法能够显著提升水汽层析结果精度。图2为湖南省区域使用传统模型进行水汽层析反演的均方根误差分布图,图3为湖南省区域使用本发明所述融合有风云分层水汽数据的新模型进行水汽层析反演的均方根误差分布图。图2和图3均为其模型的层析结果与ERA5再分析数据对比的均方根误差(RMSE误差)。Based on the observation data of Hunan Province Continuous Operation Tracking Station Network (HNCORS), a large-scale water vapor tomography inversion in Hunan Province is realized by using the method of merging wind-cloud layered water vapor tomography proposed in the present invention. The results show that this method can significantly improve the water vapor layer. The accuracy of the analysis results. Fig. 2 is the distribution diagram of root mean square error of using traditional model to perform water vapor tomographic inversion in Hunan Province, and Fig. 3 is that Hunan Province uses the new model of the present invention that is integrated with wind-cloud layered water vapor data to perform water vapor tomographic inversion The root mean square error distribution of . Figures 2 and 3 are the root mean square error (RMSE error) of the tomographic results of its model compared with the ERA5 reanalysis data.
从图2和图3的原图可见,图2整体呈现为橙色,图3整体呈现为蓝色。具体观察图中湖南各地的数据可知,在传统模型中,湖南省的大部分面积中与ERA5再分析数据对比的RMSE误差为1.7~2.5g/m3之间,且大面积集中在1.8~2.3g/m3之间。而本发明提供的新模型得到层析结果在湖南省的大部分面积中与ERA5再分析数据对比的RMSE误差为0.2~1.7g/m3之间,且尤其集中在0.5~1.3g/m3之间。由此可知,在大多数层析区域,新模型和新方法得到了比传统模型更小的RMSE,说明新方法能够整体提升层析结果的质量。As can be seen from the original images of Figures 2 and 3, Figure 2 is shown in orange as a whole, and Figure 3 is shown in blue as a whole. Looking at the data in various parts of Hunan in the figure, we can see that in the traditional model, the RMSE error compared with the ERA5 reanalysis data in most areas of Hunan Province is between 1.7 and 2.5 g/m 3 , and the large area is concentrated at 1.8 to 2.3 g/m. between g/m 3 . However, the RMSE error of the tomographic results obtained from the new model provided by the present invention compared with the ERA5 reanalysis data in most areas of Hunan Province is between 0.2 and 1.7 g/m 3 , and is especially concentrated in 0.5 to 1.3 g/m 3 . between. It can be seen that in most of the tomographic regions, the new model and the new method obtained a smaller RMSE than the traditional model, indicating that the new method can improve the quality of the tomographic results as a whole.
图4和图5分别展示了湖南怀化和湖南郴州两地的层析与两个探空站的水汽密度垂直廓线对比结果。从图4和图5中明显可见,新模型和新方法的结果与探空水汽廓线更符合。统计结果显示,与湖南怀化探空站对比,传统模型和方法与新模型和方法的RMSE分别为0.99和0.52g/m3,RMSE降低幅度达到47.47%;与湖南郴州探空站对比,RMSE由传统模型的1.47g/m3减少到0.95g/m3,RMSE降低幅度为35.37%。由此可见,新模型和新方法能够显著提升层析结果的精度,得到更加符合实际的三维水汽分布。Figures 4 and 5 show the comparison results of the tomography and the vertical profiles of the water vapor density at the two sounding stations in Huaihua, Hunan and Chenzhou, Hunan, respectively. It is evident from Figures 4 and 5 that the results of the new model and the new method are more in line with the sounding water vapour profiles. The statistical results show that, compared with the Huaihua sounding station in Hunan, the RMSE of the traditional model and method and the new model and method are 0.99 and 0.52 g/m 3 respectively, and the RMSE reduction rate reaches 47.47%; compared with the Chenzhou sounding station in Hunan, the RMSE is reduced by The 1.47 g/m 3 of the conventional model was reduced to 0.95 g/m 3 , and the RMSE was reduced by 35.37%. It can be seen that the new model and new method can significantly improve the accuracy of the tomographic results and obtain a more realistic three-dimensional water vapor distribution.
传统对流层层析模型仅添加经验性的水平约束、垂直约束及顶层约束,层析结果不理想。将具有高空间分辨率的卫星完整PWV信号作为约束条件,在一定程度上降低了层析方程组的不适定性,提高了层析结果的精度。然而完整PWV信号对层析模型的不适定性改善程度有限,而且容易增加层析方程组的冗余,使得层析模型的建模精度仍然不高。另外,目前还缺乏将同时具有较高空间和时间分辨率的静止卫星分层水汽数据应用到层析模型中的研究。本发明将高时空分辨率的风云卫星分层水汽数据附加到层析模型,能够大大降低传统层析模型的不适定性,构建更准确的对流层层析模型,从而得到较好的对流层水汽密度分布。如在实际案例中应用本发明提出的方法能较好的反演水汽密度空间分布变化信息,实现空间上任一点水汽密度值的准确反演,相较于传统方法层析结果精度得到了整体的提高。基于大量的对比分析,本发明提出的融合风云分层水汽层析模型相对于传统模型能显著提高层析解精度。因此,本发明能够有效的弥补传统模型的理论缺陷,更加精确地确定水汽密度场结构。The traditional tropospheric tomography model only adds empirical horizontal constraints, vertical constraints and top-level constraints, and the tomographic results are not ideal. Taking the complete satellite PWV signal with high spatial resolution as the constraint condition reduces the ill-posedness of the tomographic equations to a certain extent and improves the accuracy of the tomographic results. However, the improvement of the ill-posedness of the tomographic model by the complete PWV signal is limited, and it is easy to increase the redundancy of the tomographic equations, so that the modeling accuracy of the tomographic model is still not high. In addition, there is still a lack of research on applying geostationary satellite stratified water vapor data with high spatial and temporal resolution to tomographic models. The present invention attaches the Fengyun satellite layered water vapor data with high temporal and spatial resolution to the tomographic model, which can greatly reduce the ill-posedness of the traditional tomographic model and build a more accurate tropospheric tomographic model, thereby obtaining a better tropospheric water vapor density distribution. For example, applying the method proposed by the present invention in an actual case can better invert the change information of the spatial distribution of water vapor density, realize the accurate inversion of the water vapor density value at any point in space, and improve the overall accuracy of the tomographic results compared with the traditional method. . Based on a large number of comparative analyses, the integrated wind-cloud layered water vapor tomography model proposed by the present invention can significantly improve the tomographic solution accuracy compared with the traditional model. Therefore, the present invention can effectively make up for the theoretical defects of the traditional model, and determine the water vapor density field structure more accurately.
以上结合附图详细描述了本发明实施例的可选实施方式,但本发明实施例并不限于上述实施方式中的具体细节,在本发明实施例的技术构思范围内,可以对本发明实施例的技术方案进行多种简单变型,这些简单变型均属于本发明实施例的保护范围。The optional embodiments of the embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the embodiments of the present invention are not limited to the specific details of the above-mentioned embodiments. The technical solution undergoes a variety of simple modifications, and these simple modifications all belong to the protection scope of the embodiments of the present invention.
Claims (4)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210005801.1A CN114357770B (en) | 2022-01-04 | 2022-01-04 | A tropospheric chromatography method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210005801.1A CN114357770B (en) | 2022-01-04 | 2022-01-04 | A tropospheric chromatography method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114357770A true CN114357770A (en) | 2022-04-15 |
CN114357770B CN114357770B (en) | 2022-10-11 |
Family
ID=81106642
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210005801.1A Active CN114357770B (en) | 2022-01-04 | 2022-01-04 | A tropospheric chromatography method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114357770B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114814999A (en) * | 2022-06-24 | 2022-07-29 | 山东大学 | Evaluation method and system for BDS-3 water vapor inversion accuracy at different latitudes |
CN116643294A (en) * | 2023-06-01 | 2023-08-25 | 中南大学 | Ionosphere disturbance detection method, device and medium based on double coefficients and double sequences |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103267970A (en) * | 2013-05-24 | 2013-08-28 | 重庆绿色智能技术研究院 | A Method and System for Atmospheric Water Vapor Detection Based on Beidou GPS Combined Tomography |
CN107843943A (en) * | 2017-10-30 | 2018-03-27 | 西安科技大学 | A kind of three-dimensional water vapor detecting method based on function base |
CN109580003A (en) * | 2018-12-18 | 2019-04-05 | 成都信息工程大学 | A kind of stationary weather satellite Thermal Infrared Data estimation Air Close To The Earth Surface temperature methods |
CN110703357A (en) * | 2019-04-30 | 2020-01-17 | 国家气象中心 | Global medium-term numerical forecast GRAPES_GFS |
CN111881581A (en) * | 2020-07-29 | 2020-11-03 | 中国测绘科学研究院 | Method and system for establishing three-dimensional water vapor grid model |
CN112083453A (en) * | 2020-09-15 | 2020-12-15 | 中南大学 | A tropospheric chromatography method involving water vapor spatiotemporal parameters |
CN113811795A (en) * | 2019-05-13 | 2021-12-17 | 古野电气株式会社 | Vapor observation system and vapor observation method |
-
2022
- 2022-01-04 CN CN202210005801.1A patent/CN114357770B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103267970A (en) * | 2013-05-24 | 2013-08-28 | 重庆绿色智能技术研究院 | A Method and System for Atmospheric Water Vapor Detection Based on Beidou GPS Combined Tomography |
CN107843943A (en) * | 2017-10-30 | 2018-03-27 | 西安科技大学 | A kind of three-dimensional water vapor detecting method based on function base |
CN109580003A (en) * | 2018-12-18 | 2019-04-05 | 成都信息工程大学 | A kind of stationary weather satellite Thermal Infrared Data estimation Air Close To The Earth Surface temperature methods |
CN110703357A (en) * | 2019-04-30 | 2020-01-17 | 国家气象中心 | Global medium-term numerical forecast GRAPES_GFS |
CN113811795A (en) * | 2019-05-13 | 2021-12-17 | 古野电气株式会社 | Vapor observation system and vapor observation method |
CN111881581A (en) * | 2020-07-29 | 2020-11-03 | 中国测绘科学研究院 | Method and system for establishing three-dimensional water vapor grid model |
CN112083453A (en) * | 2020-09-15 | 2020-12-15 | 中南大学 | A tropospheric chromatography method involving water vapor spatiotemporal parameters |
Non-Patent Citations (2)
Title |
---|
吴昊等: "联合地基GNSS及空基GNSS掩星探测水汽三维分布", 《导航定位与授时》 * |
张文渊等: "附加高水平分辨率PWV约束的GNSS水汽层析算法", 《武汉大学学报信息科学版》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114814999A (en) * | 2022-06-24 | 2022-07-29 | 山东大学 | Evaluation method and system for BDS-3 water vapor inversion accuracy at different latitudes |
CN116643294A (en) * | 2023-06-01 | 2023-08-25 | 中南大学 | Ionosphere disturbance detection method, device and medium based on double coefficients and double sequences |
CN116643294B (en) * | 2023-06-01 | 2024-02-09 | 中南大学 | Ionosphere disturbance detection method, device and medium based on double coefficients and double sequences |
Also Published As
Publication number | Publication date |
---|---|
CN114357770B (en) | 2022-10-11 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2022161229A1 (en) | Error model calibration method and apparatus, electronic device, error model-based positioning method and apparatus, terminal, computer-readable storage medium, and program product | |
CN111273335A (en) | Ionosphere tomography method based on vertical measurement data constraint | |
Chen et al. | Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model | |
CN114357770B (en) | A tropospheric chromatography method | |
CN107402391B (en) | A Navigation Satellite Constellation Compatibility and Interoperability Analysis Method | |
CN107843943B (en) | A three-dimensional water vapor detection method based on function basis | |
CN109001382A (en) | A kind of regional atmospheric steam method of real-time and system based on CORS | |
Zhang et al. | Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique | |
Yao et al. | A novel, optimized approach of voxel division for water vapor tomography | |
Zhang et al. | A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography | |
CN111881581A (en) | Method and system for establishing three-dimensional water vapor grid model | |
Zhang et al. | GNSS-RS tomography: Retrieval of tropospheric water vapor fields using GNSS and RS observations | |
CN111126466B (en) | Multi-source PWV data fusion method | |
Yao et al. | GGOS tropospheric delay forecast product performance evaluation and its application in real-time PPP | |
CN112034490A (en) | An improved method for NWP inversion of tropospheric delay | |
Chen et al. | Tomographic reconstruction of water vapor density fields from the integration of GNSS observations and Fengyun-4A products | |
Zhao et al. | An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area | |
CN115857057A (en) | A method of rainfall monitoring based on GNSS PWV | |
Haji-Aghajany et al. | The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy | |
CN115616637B (en) | A Navigation and Positioning Method for Urban Complex Environment Based on 3D Grid Multipath Modeling | |
Zhang et al. | AN IMPROVED TROPOSPHERIC TOMOGRAPHY METHOD BASED ON THE DYNAMIC NODE PARAMETERIZED ALGORITHM. | |
Yang et al. | GNSS water vapor tomography based on Kalman filter with optimized noise covariance | |
CN112711022B (en) | GNSS chromatography-assisted InSAR (interferometric synthetic aperture radar) atmospheric delay correction method | |
CN116559912A (en) | Construction method of space-based occultation atmosphere inversion system fused with GNSS horizontal gradient | |
Izanlou et al. | Enhanced troposphere tomography: integration of GNSS and remote sensing data with optimal vertical constraints |
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 |