2. 西安科技大学测绘科学与技术学院, 陕西 西安 710054
2. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
对流层作为地球空间环境中最重要的组成部分之一，蕴含着整个地球大气层中几乎所有的大气水汽，是与人类活动联系最为密切的大气圈层。对流层中的水汽虽然占比很少，却是一种非常重要的温室气体，其在变化过程中吸收或释放大量热能，促进对流天气系统的形成和演变，在不同时空尺度的大气变化过程中起着主导作用。大气水汽受季节、地形及气候条件等因素影响，空间分布不均匀，随时空变化快，难以对其进行精确监测和反演。通常，灾害性天气短临预报的难点在于现有气象观测手段无法对水汽的时空分布和变化(特别是垂向分布和变化)进行实时准确地监测和追踪。因此，研究掌握区域/全球水汽时空快速、长期变化有助于深入了解短临极端天气和长期气候变化特征，可为监测和预报短临降雨、台风等多种极端天气及重大旱涝灾害事件提供重要的数据支持。
电磁波信号在穿过对流层时，会受到大气效应的影响，导致卫星信号产生折射和弯曲，称为对流层延迟，其在测站天顶方向的投影称为天顶对流层延迟(zenith tropospheric delay, ZTD)。作为GNSS定位技术应用中的重要误差源之一，ZTD包括天顶静力学延迟(zenith hydrostatic delay，ZHD)和天顶湿延迟(zenith wet delay，ZWD)两部分。其中，ZHD可通过经典模型(实测气象参数模型)联合地面实测气象参数精确模型化; 而ZWD尽管在ZTD中占比较小，但受水汽时空变化影响较大，难以准确模型化。因此，研究掌握GNSS测站附近的水汽时空变化有助于了解对流层延迟对卫星信号的影响，可为GNSS数据解算提供更为精确的对流层延迟初值。
传统水汽观测手段包括无线电探空仪、卫星遥感、微波辐射计等。但上述水汽探测手段由于系统设计的局限性存在地面分布不均匀、观测不连续、时间分辨率低、数据质量良莠不齐及海洋区域数据极其匮乏等缺点，成为研究强对流等极端天气和大尺度异常气候事件发生发展机理的瓶颈问题。利用GNSS信号穿过对流层的延迟量，结合地表实测气象参数可反演得到测站上空附近的大气可降水量(precipitable water vapor，PWV)，由此衍生出GNSS气象学这一新的学科。随着GNSS技术的不断发展，GNSS水汽探测技术凭借其连续运行、低成本、高精度、高时空分辨率、不受天气影响等优点在极端天气监测和气候变化研究等方面取得了快速发展。近年来，全球范围内建立了数以十万计的连续运行参考站，改善了传统水汽观测站分布不均、数量较少、空间分辨率不高的现状。此外，空基GNSS气象学的发展有效解决了海洋区域观测数据匮乏的问题。
本文分别对GNSS水汽反演及其相关应用方面进行介绍，主要包括GNSS二维和三维水汽反演，以及GNSS水汽在定位、短临降雨及旱涝监测、数值同化预报等方面的应用现状和进展。
1 GNSS二维水汽监测二维水汽监测主要是针对大气可降水量PWV的监测。
1.1 GNSS水汽监测1992年，Bevis首次提出了GPS气象学的概念[1]，其后随着GNSS技术的迅速发展，基于GNSS技术的大气水汽监测得到国内外众多学者的关注。
GNSS气象学的产生与发展方面，文献[2]推导出ZWD与PWV之间的关系，为PWV反演奠定了理论基础，随后众多学者验证了GPS技术遥感大气水汽的可行性[3-6]。依据测站所在位置，可将GPS气象学分为地基GPS气象学与空基GPS气象学。地基GPS气象学是指利用安置在地面上的GPS接收机接收到的观测数据和实测地面气象参数精确计算PWV等要素[7]; 空基GPS气象学是利用搭载在低轨卫星上的GPS接收机接收卫星信号并估算相关气象要素[8]。随着GPS[9]、GLONASS[10]、Galileo[11]的发展及我国北斗三号导航卫星系统(BDS-3)全球组网的完成[12]，GPS气象学逐渐演变为GNSS气象学。由于空基GNSS水汽监测受制于卫星星座分布及数量影响，且水汽获取依赖于水汽廓线垂直积分，其精度相对较低[13]。因此，当前GNSS水汽监测主要还是以地基GNSS为主，本文后续关于GNSS水汽监测及相关应用进展的介绍主要围绕地基GNSS水汽展开。
在multi-GNSS水汽监测方面，文献[14]证明了基于GPS计算的PWV与无线电探空和微波辐射计获取的PWV精度相当。在国内，文献[15-17]验证了利用GPS获取PWV的可行性，其PWV反演精度可达1~3 mm[7, 5, 18-19]。除GPS外，文献[20]利用BDS获得与GPS精度相当的PWV。由于GLONASS与Galileo系统的自身缺陷，仅利用单一的GLONASS或Galileo监测水汽的研究相对较少，其在稳定性与精确度方面存在一定缺陷[21]。多系统提供更多的对流层延迟观测值，因此，GNSS水汽监测由单系统向multi-GNSS的方向发展，文献[21]证实了multi-GNSS技术可提升水汽反演的有效性、稳定性及精确度。此外，相关学者分别对GPS+GLONASS[22]、GPS+BDS[23-24]及四系统组合[25]的水汽监测能力进行了评估，发现双系统及四系统组合解算PWV精度优于3 mm，且较单系统具有更高的稳定性、精度及可用性。
在实时GNSS水汽监测方面，多数研究主要利用国际GNSS服务(International GNSS Service，IGS)中心提供的事后精密星历进行高精度水汽反演，但精密星历存在13 d左右的延迟[26]。此外，快速和超快速精密星历仍会存在17 h[27]和3 h[28]的延迟，无法满足实时水汽监测的现实需求。得益于IGS启动的实时领航项目(real time pilot project，RTPP)，用户在2013年4月1日后可在全球范围内获取实时的GPS卫星轨道和钟差校正数据[29]，使得GNSS实时水汽监测成为可能。随着RTPP项目的发展，利用GNSS系统进行实时水汽监测得到众多学者的广泛研究。文献[30]证实了利用GPS实时数据可获取精度优于3 mm的PWV。此外，在基于GLONASS[31]、BDS[32]、Galileo[33]卫星系统的实时水汽监测方面也有一定进展，水汽监测精度与事后GPS反演的PWV精度基本相当。在多系统实时水汽监测方面，相关学者分别利用GPS+BDS[34]、GPS+GLONASS[31]及四大全球卫星导航系统组合[35]进行了实时水汽试验，证实了多系统实时水汽精度与事后水汽精度相当，相比于单系统，其可信度更高且避免了可能存在的粗差影响。
1.2 其他水汽监测手段水汽监测除利用GNSS技术外，还包括以无线电探空仪[36]、太阳光度计[37]及微波辐射计[38]为主导的传统站点水汽监测，气象方面专用的卫星遥感水汽监测[39-40]，以及利用数值同化生成再分析资料的水汽监测[41-42]。
在传统站点水汽监测方面，无线电探空仪是精度最高的水汽探测方式之一，但其测量成本高、工作量大，获取的水汽信息时间分辨率也较低[37, 43]。微波辐射计获取的水汽数据时间分辨率高，但数据质量受环境影响大[38]。太阳光度计只能测量太阳辐射路径的水汽，在转换为大气水汽时会有精度损失[37]。通过与GNSS PWV对比，无线电探空仪、微波辐射计、太阳光度计获取的PWV的RMS分别为2.60[44]、1.78[45]、1.66 mm[46]。但由于仪器本身和水汽探测方式的限制，传统站点水汽探测方法存在时间分辨率较低、易受天气影响、成本高等缺点[36, 47]。
在卫星遥感水汽监测方面，主要是根据卫星水汽敏感通道的辐射观测数据包含整个大气垂直廓线中水汽信息的原理来反演PWV信息[48-49]。常见的有搭载在Terra和Aqua卫星上的中分辨率成像光谱仪(moderate resolution imaging spectroradiometer, MODIS)[50]、搭载在中国风云三号(FY-3)卫星上的中分辨率光谱成像仪(medium resolution spectral image, MERSI)[51]、搭载在风云四号(FY-4)上的多通道扫描辐射计(advance geostationary radiation imager, AGRI)[40]，以及搭载在哨兵三号(Sentinel-3)系列卫星上的海陆色度仪(ocean and land colour instrument，OCLI)[52]等。上述遥感卫星能提供范围较广、周期性较长、空间分辨率较高的PWV产品，为获取水汽信息提供了一种高效、快速的观测技术，但受云层的影响，卫星反演的PWV数据精度较低[53]。通过与GNSS PWV对比，Terra MODIS、FY-3A MERSI、FY-4A AGRI、Sentinel-3A OCLI获取的PWV的RMS分别为6.02[50]、5.64[51]、4.78[40]、3.154 mm[52]。
在利用数值同化技术进行水汽监测方面，融合了地面站点观测数据、卫星遥感数据等资料的数值同化技术凭借其高精度和高时空分辨率的优势成为获取PWV的另一种有效手段。常见的再分析资料包括欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts，ECMWF)提供的再分析数据集ERA-Interim[54]和ERA5[55]、美国国家环境预报中心(National Centers for Environmental Prediction，NCEP)提供的NCEP系列[56]和CFSR数据集[57]及美国国家航空航天局提供的现代回顾分析研究和应用(modern era retrospective-analysis for research and applications，MERRA)系列数据集[58]。计算模式、同化方案及同化数据等方面的差异使得这些再分析数据的时空分辨率、数据质量各不相同(表 1)，通过与GNSS PWV对比，ERA5、ERA-Interim、NCEP1、NCEP2、CFSR、MERRA、MERRA-2提供的PWV数据集的RMS分别为1.77[42]、1.21[59]、3.75[60]、5.84、4.13[61]、2.75、2.12 mm[42]。整体而言，ERA5和MERRA再分析数据提供的PWV精度较好。
| 数据集 | 数据时段 | 最高时空分辨率 | 融合算法(同化) | 发布机构 |
| ERA-Interim | 1979—2019-08-31 | 6 h，0.125°×0.125° | CY31R1-4DVAR | ECMWF |
| ERA5 | 1950至今 | 1 h，0.25°×0.25° | CY41R2-4DVAR | ECMWF |
| NCEP1 | 1948至今 | 6 h，2.5°×2.5° | GDAS | NCEP |
| NCEP2 | 1979至今 | 6 h，2.5°×2.5° | GDAS | NCEP |
| CFSR | 1979—2010 | 1 h，0.313°×0.313° | GEOS-5 | NCEP |
| MERRA-1 | 1979—2016-03-01 | 1 h，0.5°×0.667° | GEOS-5 DAS | NASA |
| MERRA-2 | 1980至今 | 1 h，0.5°×0.625° | GEOS 5.12.4 | NASA |
1.3 GNSS联合多技术水汽监测方面
仅利用单一GNSS或其他技术可以获取高精度的PWV，但存在数据缺失[62]、产品偏差[44]、时空分辨率不兼顾[63]等缺点。随着全球各种水汽观测数据的公开和共享，增强水汽信息的多技术融合研究得到快速发展，主要包括GNSS校正卫星数据和多源水汽融合两个方面。在GNSS校正卫星水汽监测方面，主要是通过建立遥感卫星和GNSS水汽间的函数关系，进一步提升遥感水汽产品的质量，实现其高空间分辨率的全球水汽探测优势。文献[64]在评估MODIS水汽反演质量时，提出应用前进行GNSS线性校准的建议，随后相关学者陆续提出了考虑季节变化[65-66]、气候特征[67]及区域高度[68]等不同方式的卫星水汽产品校正模型，有效促进了卫星遥感水汽产品在气象业务方面的再应用。此外，直接引入地基GNSS改进的卫星水汽反演算法也在Aqua和Terra[69]、FY-3B[70]和Sentinel-3A[52]等多颗卫星中得到验证和应用，质量可提升约10%~15%。
在多源水汽融合监测方面，根据水汽融合表达方式可分为显式和隐式两类。在显式融合方面，现阶段已试验评估多种适用于不同区域、尺度及特征的方法。最新研究成果包括城市级高斯过程(Gaussian processes, GP)融合模型[71]与时空分步融合方法[72]、高山地区的增强型自适应反射率时空融合模型[73]、大尺度区域的球冠谐(spherical cap harmonic, SCH)方法[74]与分区混合模型融合[63]等，均能有效改善单一数据反演中的时空不连续问题。但显式融合过程需要考虑多源数据的系统性差异及非等精度问题，多采用系统偏差校准[73, 75]或自适应权比估计方法[63, 74]进行解决，较等权补充[76]具有更好的可靠性。隐式融合则是直接通过人工智能、机器学习等数据挖掘技术获取高时空分辨率的水汽信息[77]。文献[75]采用神经网络纠正和优化多源水汽偏差，基于广义回归神经网络实现了无偏的时空融合。文献[78]则利用空间降尺度融合思想，通过将BP神经网络建立的粗糙尺度辅助因子与GNSS校准产品的关系模型应用于精细尺度辅助因子，提升了GNSS校准产品的精度和空间分辨率。
2 GNSS三维水汽监测三维水汽监测主要是采用水汽层析技术，进行三维湿折射率和水汽密度廓线反演，其中水汽层析观测方程的构建及解算是三维水汽监测的关键所在。
2.1 水汽层析观测方程构建水汽层析观测方程构建的好坏直接影响水汽反演廓线信息的精度。本节对水汽层析观测方程构建方法、层析区域网格划分及层析建模数据分别选取3个方面介绍其研究现状和进展。
在层析观测方程构建方面，主要是基于分块[79]、节点[80]及自适应节点[81]等方法构建研究区域层析模型参数化表达的观测方程。基于分块法是指在构建模型时人为引入像素体边界，在进行射线追踪和走时计算时较方便，但人为引入边界会导致模型参数不连续，且只能将数值异常区域表示为块状[79]。基于节点法是将研究区内节点值作为待求变量，任意位置的数值由其周围的8个邻近节点数据内插得到，该方法所构建的区域模型参数连续，减少了人为设置边界的影响，其结果优于分块参数化方法[80]。自适应节点参数化方法是指通过多种网络技术动态确定不同历元的层析模型边界和节点，其精度优于传统的节点参数化方法，且准确度显著提高[81]。此外，文献[82]还提出一种水平参数化层析方法，用于描述水汽在水平方向上的连续变化。
在层析区域网格划分方面，主要包括层析区域高度选择、水平网格划分及垂直约束确定3部分。在层析区域高度选择上，传统方法常根据经验选取层析区域的垂直高度，如15[79]、12[83]、10[80]或8 km[84]不等。但研究区域不同，大气水汽在不同高度上分布差异很大，层析区域高度选择过高会造成层析模型的过度参数化[84]，选择过低会导致层析结果精度较差[85]。针对上述难题，文献[85]提出了非经验选取垂直高度的原则，即通过水汽随高度的实际分布情况确定层析区域的最优高度。在水平网格划分上，传统方法采用水平网格等间距划分的方法[79]，该方法导致不同网格中包含的实测信息差异大。文献[84]提出了基于移动研究区域与改善设计矩阵的方法提高网格射线穿过率，但实际操作较困难。文献[86]提出了非均匀对称水平网格划分方法，降低了空白网格数。在垂直约束建立上，文献[81]基于水汽分布的指数递减规律建立了层析模型上下层的关系，但该方式所构建的垂直约束解算结果精度较低。文献[87]提出了基于短期探空信息拟合函数构建层析区域垂直约束的方法，并基于无线电探空数据验证了该方法对层析结果具有一定的改进作用。
在层析建模数据选取方面，由于卫星和接收机几何位置的特定性，很多位于层析区域底层和侧面的网格没有射线穿过[88]，因此仅利用从层析区域顶部穿过的射线会导致水汽反演质量不高[89]。文献[90]提出了利用多组观测历元数据进行内插的方法，提高了层析区域观测网的空间密度。文献[84]利用在水平方向上搜索射线穿过网格最大数的方法确定最优水平网格分布。上述研究在一定程度上提高了射线利用率，但均未涉及如何有效利用侧面射线的问题。为改进上述现状，文献[91-92]分别利用UNB3m模型和CIRA-Q湿大气气候模型对层析区域侧面穿出射线的水汽含量进行估计。文献[93-94]利用经验指数负相关函数，对从研究区域侧面穿出射线中位于层析区域内的射线进行几何线性估计。尽管上述研究对层析区域侧面穿出射线进行了尝试，但利用指数函数等模型估计从研究区域侧面穿出射线中位于层析区域内的水汽含量时，并未对其合理性进行验证。因此，相关学者通过附加辅助层析区域[95]、引入水汽单位指数[96-97]、构建水汽比例因子模型[98]、顾及边界入射信号[99]等方法构建层析区域侧面穿出射线的水汽观测方程，较明显地降低了网格空格率，改善了水汽反演精度。
2.2 水汽层析模型解算水汽层析方程的解算质量对层析结果精度具有重要影响。
在层析模型权比确定方面，文献[79]给出了经验性设置迭代终止阈值确定权比的方法，但在实际应用中，不同类型信息权比确定与观测方程的结构关系很大，有时仅调节权值不能得到很好的反演效果[100]。随后众多学者提出方差分量抗差估计[101]、基于齐性检验的验后方差估计[102]、基于拉普拉斯算子的自适应平滑[103]等方法确定层析模型的权比信息。但是，文献[104]指出直接使用上述方法确定各类观测方程的权值时会放大噪声，且仅保证层析模型中各类方程验后单位权方差在数学或统计意义上相等有时需要多次迭代，耗时且不够严密。因此，文献[105]提出了同时顾及同类型不同观测值和不同类型观测值权比信息的水汽层析方法，并进一步在multi-GNSS(BDS、GPS、GLONSAA)水汽层析中进行验证[86]，取得了较好的层析效果。
在层析模型解算方面，主要包括迭代和非迭代两种解算方法。非迭代方法忽视小奇异值的变化会导致层析结果波动很大[104]，主要包括奇异值分解(singular value decomposition，SVD)[79]、Kalman滤波方法[106]及最小二乘法[107]等。针对上述方法的缺陷，文献[108]基于最小偏差原则提出了一种改进岭估计的层析模型解算方法，解算效果优于SVD。迭代方法以代数重构算法(algebraic reconstruction technique，ART)[109]为主，但其层析结果非常依赖初值的精度且迭代终止条件难以确定[110]，导致层析成像的准确性受到严重限制。为解决该问题，众多学者提出约束代数重建算法[111]、组合重构算法[112]、自适应代数重构算法[113]、自适应联合代数重构算法[114]等改进方法，以获取高精度的三维水汽反演结果。
此外，水汽反演结果受多种因素的影响，除上述介绍的水平网格划分、层析高度选取、垂直约束确定因素外，水汽层析还受单/多系统观测数据、测站密度等的影响。随着multi-GNSS的发展及测站布设数目的增多，可望进一步提高水汽反演的精度和可靠性。在多系统数据选择方面，主要分为单系统和多系统水汽层析研究。相关学者利用实测GPS和仿真GPS数据[115]、仿真Galileo数据[116]和四系统仿真数据[117]验证了多系统组合方案较单系统方案拥有分布更均匀的观测信号和更优化的格网空格率，在一定程度上改善了三维水汽层析结果。此外，实测GPS与GLONASS数据组合[118-119]也同样证实了组合系统拥有更高的精度和可靠性。文献[120]利用GPS、GLONASS、BDS及Galileo实测数据证明了相对于单系统，多系统组合中射线穿过的网格覆盖率明显增加，多系统层析结果明显优于单系统层析结果，其层析水汽廓线的RMS平均改善率为10%。在测站密度影响方面，文献[121]发现，测站密度较高时射线穿过的网格覆盖率明显增加，较多系统层析，测站密度疏密变化对层析结果有更大影响。截至目前，三维水汽层析反演水汽密度的监测精度平均能够达到1.2 g/cm3。
3 GNSS水汽应用 3.1 改善GNSS定位结果方面ZTD作为GNSS定位的重要误差之一，对于改善GNSS定位精度和收敛速度具有重要影响[122-124]。将对流层延迟模型应用于GNSS定位，主要包括建立经验对流层延迟模型和引入数值天气模式的对流层延迟产品，从而进行GNSS定位中的对流层延迟修正。
在利用经验对流层延迟模型改善PPP收敛速度方面，相关学者较早地提出了附加虚拟对流层延迟观测值约束PPP的方法，包括直接改正型[125]和虚拟观测值约束型[126]，其在高程方向上的平均收敛时间提高约14%。文献[127]提出了一种附加先验对流层信息约束的PPP模型，明显提升了PPP在N方向的收敛速度。文献[128]首次利用基于B样条函数的对流层层析成像方法改善PPP的收敛时间，与Saastamoinen模型相比，该方法总体上可将静态和动态PPP的收敛速度均提高7%~9%。在利用数值预报模式的对流层延迟产品改善PPP收敛速度方面，文献[124]将天气研究和预报模型(weather research and forecasting model, WRF)同化的GNSS ZTD结果应用于PPP中，发现静态PPP在水平和高程方向上的收敛时间分别缩短了13%和20%。文献[129]将WRF输出的ZWD用于重建对流层延迟，发现静态PPP在E方向的收敛速度提高了9.3%。
在利用对流层延迟模型或产品改善PPP定位精度方面，文献[130]提出了基于多面函数的对流层拟合模型，将PPP的定位精度优化至分米级。文献[125]提出附加虚拟对流层延迟观测值约束PPP的新算法，在卫星数量较少的情况下明显优于传统PPP。在此基础上，相关学者分别通过自适应参数法[131]、将外部对流层延迟作为虚拟观测值[132]、使用ZWD估计对流层延迟[133]、反距离加权内插[134]等方法约束对流层延迟，进一步提高了PPP的定位精度。文献[128]研究了对流层层析成像方法对PPP定位精度的影响，表明基于对流层层析成像技术可将静态和动态PPP的定位精度分别提高至5.9、6.2 mm。在利用数值预报模式产生的对流层延迟产品改善PPP定位精度方面，相关学者利用WRF模型同化的ZTD数据[124]和ZWD数据[129]结合最小二乘估计重建对流层延迟，发现PPP在水平和高程分量上的精度均有明显提高。
3.2 短临降雨预警方面GNSS水汽监测具有高精度、高时空分辨率的优势，能够反映水汽的快速时空变化，可用于短临降雨监测和预警，主要包括基于最小二乘拟合对流层参数和基于机器学习等智能算法的降雨预警两个方面。
在利用最小二乘拟合对流层参数的降雨预警方面，该方法基于最小二乘算法拟合长时序GNSS PWV或ZTD，并构建相关预警因子，对降雨事件进行预测[135-136]。文献[137]证实了ZTD及其增量变化可作为短临强降雨事件的指示信号。随后相关学者依托大量历史数据构建了适用于地中海气候[135]及亚热带季风气候的中国浙江省地区[29, 136, 138]，中国香港地区[139]，以及热带雨林气候的新加坡[5]的短临降雨预警模型，但上述模型错报率介于60%~70%之间。因此，文献[140]构建了融合PWV值，以及PWV与ZTD的变化量和变化率5种预测因子的降雨预警模型，显著改善了降雨预报模型错报率(低于30%)，预警效果优于现有模型。
在利用机器学习等智能算法的降雨预警方面，当前主要是利用智能算法构建多种气象参数与降雨间的非线性映射关系，从而实现短临降雨事件预警[141]。相关学者尝试利用机器学习[142]、非线性自回归外生神经网络[143]和反向传播神经网络[141, 144]联合多种气象参数构建短临降雨预警模型。试验结果表明，机器学习的引入明显提升了传统降雨预警模型的精度。此外，通过引入机器学习等算法建立GNSS PWV或ZTD与多种气象参数和降雨量间的高维非线性关系，也可实现不同时间尺度的降雨量预警。如相关学者利用后向反馈传播神经网络实现月尺度[145]; 利用前馈反向传播神经网络和非线性自回归神经网络实现天尺度[146-148]; 利用监督学习方法实现小时尺度[149]的降雨量预警，取得了较好的效果。
3.3 GNSS辅助数值预报方面数值预报模式同化GNSS水汽信息类型主要包括同化PWV和ZTD两种。
在同化GNSS PWV数据方面，文献[150]首次证实了同化GNSS PWV数据能够提高数值预报模式对强降雨事件的预报精度，改善率为32%。在改善模式初始场精度应用中，文献[151]发现同化GNSS PWV数据能够改善模式初始场湿度信息，6 h累积降雨量预报效果平均提高了5%，且降雨量越大，改善越明显。文献[152]证实了同化PWV数据后，模式输出的6 h累积降雨量和降雨地点与实际降雨均符合较好。此外，文献[153]发现同化PWV能够改善大气低层湿度分布。GNSS PWV对模式温度场[154-155]和风场[156]的调整也表明了同化GNSS PWV为数值预报模式提供了丰富合理的大气水汽、热力与动力场信息，有效减少了模式对降雨预报的误差。在降雨强度和降雨位置的预报应用中，同化高频次的GNSS PWV数据对提高大雨、暴雨量级以上的预报能力非常重要[157]，对提高模式预报暴雨的时段、强度[158]、落区[159]及精度[160]均有显著效果。
在同化GNSS ZTD数据方面，相较于同化PWV，其优势在于能够避免由ZTD转换为PWV时引起的转换误差[161]，且ZTD数据包含对流层气压和温度等更多信息[162]，因此数值同化GNSS观测数据逐渐集中在同化ZTD数据上。文献[163]首次同化ZTD进行四维变分试验，发现6 h和12 h的累积降雨量预报精度分别提高了33.15%和25.08%。在改善模式初始场精度应用中，文献[164]发现同化ZTD能够改善模式中对流层中低层湿度精度。文献[165-166]发现同化ZTD增加了初始场中垂直方向的湿度信息。文献[167]证明了同化GNSS ZTD能有效改善数值预报模式的初始湿度场。此外，同化ZTD使得数值预报结果在低层水汽符合更强，台风主体降水预报位置的精度得到改善[168]。在降雨强度和位置的预报中，文献[169-170]验证了同化ZTD数据能够提高短期降雨预报的可靠性。随后相关研究发现，同化GNSS ZTD数据能够提高强降水预报命中率并改善模式降雨的强度和位置预报[171]。
3.4 长期气候监测方面水汽是导致全球变暖的重要温室气体[172]，影响着天气和全球气候系统的动态变化[173]。GNSS技术经过30多年发展积累了大量的水汽资料[174]，可用于异常气候和旱涝监测等研究。
在异常气候监测方面，异常气候事件主要包括厄尔尼诺-南方涛动(El Niño-southern oscillation，ENSO)和热带气旋(tropical cyclones，TC)。文献[175]较早证实了大气水汽可用于描述ENSO的演变过程; 随后相关学者分析了ENSO期间水汽的异常变化[176]、GNSS PWV和海表温度异常之间的关系[177]，并利用PWV构建ENSO指数[178]开展了水汽在ENSO事件监测方面的研究。在TC监测方面，文献[179]较早分析了台风经过期间对水汽的影响; 随后学者围绕TC发生期间PWV的时空变化[180]、TC的运动轨迹[181]等进行分析。此外，相关学者围绕北大西洋涛动[182-183]、大气层河流[184-185]及热浪[186-187]等异常气候事件也进行了相关研究。
在旱涝监测方面，主要包括利用PWV改善现有干旱监测指数和构建新的干旱指数两个方面。在利用PWV改善干旱监测指数方面，文献[188]首次利用PWV和温度构建潜在蒸散发的残差模型，并提出了改进的Thornthwaite(RTH)模型; 文献[189]进一步提出适用于中国区域的RTH(C-RTH)模型; 随后文献[190]针对C-RTH模型中PWV计算时存在精度损失的问题，提出一种利用ZTD建立高精度干旱监测指数模型的方法。此外，文献[191]在顾及站点位置的基础上加入高程信息，利用多项式方法建立了高精度潜在蒸散发模型。在利用PWV进行干旱监测方面，文献[192]首次发现基于PWV与降水计算的降水效率(precipitation efficiency，PE)可用于干旱、湿润气候预报，但由于存在数量级差异，PE的等级划分未有明确的标准; 随后，文献[193]利用PWV的非线性趋势分析了旱涝事件的演变过程。此外，文献[194]针对PE指数缺陷，在表达式、多时间尺度和标准化3个方面进行了改进，提出了标准化的降水转化指数(standardized precipitation conversion index，SPCI)，结果表明在全球范围内SPCI和SPEI之间旱、涝监测的平均百分比偏差仅分别为2.77%、3.75%。
3.5 其他方面应用大气水汽的增减与大气运动状态密切相关，而台风[138]、ENSO[178]、雷暴气象气候[195-196]、地震[197]等事件的发生会伴随强烈的大气运动和水汽变化。
在台风监测方面，相关学者基于GNSS技术探测台风过境前后水汽的变化情况，发现台风过境期间水汽呈现出先增后减的趋势[179, 198]，可应用于捕捉恶劣天气环境下高时空分辨率水汽分布及其复杂的变化特征[199]，以及判断台风是否登陆[200]等。随后，开展了利用GNSS PWV监测飓风事件路径[201]、预测台风位置[202]、推算台风速度和加速度[181]及台风移动方向[203]等研究，同时构建了台风背景下的短临降雨预警模型，并取得了良好的预测效果[135, 138]。
在空气质量监测方面，文献[204]发现GNSS信号传播过程中受气溶胶、沙尘、灰尘等气体凝结物的影响，导致对流层延迟发生变化，证实了GNSS技术可用于反演大气中PMx/AOD等空气质量参数含量的可行性[204]。部分学者分别在区域和全球尺度研究了GNSS ZTD/PWV与PM2.5[205-207]、PM10[208-209]、AOD[210]等空气质量参数之间的关系，发现ZTD/PWV与PMx/AOD等参数间具有较强的相关性。随后，相关学者基于GNSS PWV预测PM10[209]，并结合温度、气压等气象参数预测PM2.5[206, 211]和AOD[210, 212]等空气质量参数，基于GNSS ZTD分别建立了PM2.5[213-214]和AOD[215]的预测模型，实现了对雾霾天气的有效监测和预警[213]，提高了雾霾天气监测和预报的准确性[209]。
4 总结与展望 4.1 GNSS水汽监测研究总结与展望当前基于GNSS技术的实时、高精度二维水汽反演已经较成熟，但在如何进一步获取高时空分辨率水汽信息方面仍有待研究，仅依靠提高测站空间密度难以完全实现任意区域高空间分辨率水汽获取的现实需求。因此，在水汽探测技术多样化的条件下，多源水汽融合是解决高时空分辨率水汽获取的有效途径。目前，以GNSS技术为主导的多源水汽融合研究刚刚起步，GNSS联合多技术应用的研究难题主要是空间扩展和实时获取。有限站点输入的GNSS数据无法表示区域所有地表环境，站点与像元的空间和高程匹配、数据驱动方式确定、技术联合方案设计均对高时空二维水汽监测精度有重要影响。此外，加强过程驱动与数据驱动模式的水汽融合研究是提高水汽反演精度的重要途径。若能联合多种水汽探测技术实现与GNSS同水平精度的高时空分辨率水汽监测，对于环境和气候方面的监测和预警将具有重要意义。
在地基GNSS三维水汽反演方面，优势在于可以获取水汽的三维廓线信息，且与二维水汽具有相当的时间分辨率和精度。但水汽层析难度较二维水汽探测大，在水汽廓线应用方面研究较少。目前，三维水汽层析在网格划分选区、垂直高度确定、测站密度选择、层析模型构建、模型权比确定、层析结果解算等影响水汽层析结果的关键环节有众多方法，但在任意层析区域如何确定普适化层析策略方面还有待进一步研究。层析区域位置、气候、测站密度、水汽含量等方面的差异均会直接影响水汽层析关键环节的处理策略，因此，应进一步扩展特定区域水汽层析算法的普适性。GNSS反演高精度三维水汽的关键是针对层析建模的各个环节在现有海量算法中总结出普适性的水汽层析模型构建流程，并提出针对不同层析区域特点的自适应调整策略，减少人为干预对层析结果的影响，在保证水汽层析精度的条件下，最大限制降低特定环境因素对水汽层析结果的影响。普适性的高精度三维水汽层析算法对于评估水汽层析性能、扩展应用场景等具有重要价值。
4.2 GNSS水汽应用总结与展望基于GNSS的多维水汽探测技术较成熟，且在相关领域进行了系统研究，但GNSS水汽在现实中的实际应用依然偏少，缺乏GNSS水汽在相关行业大规模应用的现实场景。众多学者利用GNSS水汽在改善定位结果、短临降雨预警、长期旱涝监测、空气质量监测等方面进行了大量研究，但对于如何将上述研究成果进一步转化为业务系统并验证其可靠性和有效性还有待进一步探讨。另外，相对于二维水汽，三维水汽应用更少，因此，如何进一步拓展GNSS三维水汽应用场景至关重要; 可单独利用三维水汽信息构建相关指标因子或将其产品与数值预报模式同化，扩展三维水汽信息的利用价值。
随着全球和区域卫星导航定位技术的不断发展，GNSS水汽监测技术在气象学等领域势必会有更多应用，进一步将现有研究成果应用到国民经济发展的现实需求中，对于拓展GNSS的应用前景具有重要意义。
| [1] |
BEVIS M, BUSINGER S, HERRING T A, et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system[J]. Journal of Geophysical Research: Atmospheres, 1992, 97(D14): 15787-15801. DOI:10.1029/92JD01517 |
| [2] |
ASKNE J, NORDIUS H. Estimation of tropospheric delay for microwaves from surface weather data[J]. Radio Science, 1987, 22(3): 379-386. DOI:10.1029/RS022i003p00379 |
| [3] |
陈世范. GPS气象观测应用的研究进展与展望[J]. 气象学报, 1999, 57(2): 242-252. CHEN Shifan. Advance and prospect on research of GPS at mospheric sounding and its application[J]. Acta Meteorologica Sinica, 1999, 57(2): 242-252. |
| [4] |
GENDT G, DICK G, REIGBER C, et al. Near real time GPS water vapor monitoring for numerical weather prediction in Germany[J]. Journal of the Meteorological Society of Japan, 2004, 82(1B): 361-370. |
| [5] |
MANANDHAR S, LEE Y H, MENG Y S, et al. GPS-derived PWV for rainfall nowcasting in tropical region[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4835-4844. DOI:10.1109/TGRS.2018.2839899 |
| [6] |
GUNTI S, NARENDRAN J, MURALIKRISHNAN S. PWV estimation using GPS and its comparison with INSAT-3D rainfall data[J]. Journal of the Indian Society of Remote Sensing, 2021, 49(6): 1453-1460. DOI:10.1007/s12524-021-01324-7 |
| [7] |
李国平, 黄丁发. GPS气象学研究及应用的进展与前景[J]. 气象科学, 2005, 25(6): 651-661. LI Guoping, HUANG Dingfa. Advances and prospects in the study of GPS meteorology[J]. Scientia Meteorologica Sinica, 2005, 25(6): 651-661. DOI:10.3969/j.issn.1009-0827.2005.06.013 |
| [8] |
徐晓华, 李征航. GPS气象学研究的最新进展[J]. 黑龙江工程学院学报, 2002, 16(1): 14-18. XU Xiaohua, LI Zhenghang. The advanced development on the research of GPS meteorology[J]. Journal of Heilongjiang Institute of Technology, 2002, 16(1): 14-18. DOI:10.3969/j.issn.1671-4679.2002.01.003 |
| [9] |
WELLS D, BECK N, DELIKARAOGLOU D, et al. Guide to GPS positioning[C]//Proceedings of 1987 Canadian GPS Associates. [S. l.]: University of New Brunswick. 1987.
|
| [10] |
LEICK A. GLONASS satellite surveying[J]. Journal of Surveying Engineering, 1998, 124(2): 91-99. DOI:10.1061/(ASCE)0733-9453(1998)124:2(91) |
| [11] |
BENEDICTO J, DINWIDDY S E, GATTI G, et al. Galileo: satellite system design and technology developments[M]//European Space Agency. [S. l.]: Int. Business, 2000.
|
| [12] |
鄂盛龙, 周刚, 龙海, 等. BDS全球定位服务能力及天顶对流层延迟估计性能评估[J]. 大地测量与地球动力学, 2021, 41(8): 789-794. E Shenglong, ZHOU Gang, LONG Hai, et al. Performance evaluation of BDS global positioning service and zenith tropospheric delay estimation[J]. Journal of Geodesy and Geodynamics, 2021, 41(8): 789-794. |
| [13] |
罗佳, 陈志平, 徐晓华. 利用COSMIC掩星资料研究对流层/下平流层大气比湿对ONI指数的响应[J]. 地球物理学报, 2018, 61(2): 466-476. LUO Jia, CHEN Zhipeng, XU Xiaohua. Specific humidity response in the troposphere and lower stratosphere to ONI revealed by COSMIC observations[J]. Chinese Journal of Geophysics, 2018, 61(2): 466-476. |
| [14] |
ROCKEN C, ANTHES R, EXNER M, et al. Analysis and validation of GPS/MET data in the neutral atmosphere[J]. Journal of Geophysical Research: Atmospheres, 1997, 102(D25): 29849-29866. DOI:10.1029/97JD02400 |
| [15] |
毛节泰. GPS的气象应用[J]. 气象科技, 1993(4): 45-49. MAO Jietai. GPS for meteorological applications[J]. Meteorological Science and Technology, 1993(4): 45-49. |
| [16] |
王小亚, 朱文耀, 严豪健, 等. 地面GPS探测大气可降水量的初步结果[J]. 大气科学, 1999, 23(5): 605-612. WANG Xiaoya, ZHU Wenyao, YAN Haojian, et al. Preliminary results of precipitable water vaopr monitored by ground-based GPS[J]. Chinese Journal of Atmospheric Sciences, 1999, 23(5): 605-612. DOI:10.3878/j.issn.1006-9895.1999.05.11 |
| [17] |
TAKIGUCHI H, KATO T, KOBAYASHI H, et al. GPS observations in Thailand for hydrological applications[J]. Earth, Planets and Space, 2000, 52(11): 913-919. DOI:10.1186/BF03352305 |
| [18] |
陈永奇, 刘焱雄, 王晓亚, 等. 香港实时GPS水汽监测系统的若干关键技术[J]. 测绘学报, 2007, 36(1): 9-12, 25. CHEN Yongqi, LIU Yanxiong, WANG Xiaoya, et al. GPS Real-time estimation of precipitable water vapor-Hong Kong experiences[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1): 9-12, 25. DOI:10.3321/j.issn:1001-1595.2007.01.002 |
| [19] |
曹寿凯, 魏加华, 乔禛, 等. 地基GPS的大气可降水量反演精度验证[J]. 南水北调与水利科技, 2021, 19(3): 520-527. CAO Shoukai, WEI Jiahua, QIAO Zhen, et al. Verification of retrieval accuracy of PWV based on ground-based GPS signal[J]. South-to-North Water Transfers and Water Science & Technology, 2021, 19(3): 520-527. |
| [20] |
高志钰, 李建章, 刘彦军, 等. 利用BDS数据反演大气可降水量及其精度分析[J]. 测绘通报, 2019(5): 35-38, 47. GAO Zhiyu, LI Jianzhang, LIU Yanjun, et al. Research on the accuracy of atmospheric precipitable water vapor with BDS[J]. Bulletin of Surveying and Mapping, 2019(5): 35-38, 47. DOI:10.13474/j.cnki.11-2246.2019.0145 |
| [21] |
PAN Lin, GUO Fei. Real-time tropospheric delay retrieval with GPS, GLONASS, Galileo and BDS data[J]. Scientific Reports, 2018, 8(1): 17067. DOI:10.1038/s41598-018-35155-3 |
| [22] |
SOHN D H, PARK K D, KIM Y H. Determination of precipitable water vapor from combined GPS/GLONASS measurements and its accuracy validation[J]. Journal of Korean Society for Geospatial Information Science, 2013, 21(4): 95-100. DOI:10.7319/kogsis.2013.21.4.095 |
| [23] |
段晓梅, 曹云昌. 北斗和GPS反演大气可降水量的对比分析[J]. 气象, 2018, 44(12): 1575-1582. DUAN Xiaomei, CAO Yunchang. Comparison of atmospheric precipitable water vapor retrieved by BeiDou and GPS[J]. Meteorological Monthly, 2018, 44(12): 1575-1582. DOI:10.7519/j.issn.10000526.2018.12.007 |
| [24] |
韩阳, 吕志伟, 徐剑, 等. 基于BDS/GPS观测量的大气可降水量反演精度分析[J]. 导航定位学报, 2017, 5(1): 39-45. HAN Yang, LV Zhiwei, XV Jian, et al. Retrieval of precipitable water vapor from BDS and GPS observations[J]. Journal of Navigation and Positioning, 2017, 5(1): 39-45. |
| [25] |
李宏达, 张显云, 廖留峰, 等. 利用GPS/BDS/GLONASS/Galileo组合PPP反演大气可降水量[J]. 测绘通报, 2020(6): 63-66, 98. LI Hongda, ZHANG Xianyun, LIAO Liufeng, et al. Retrieval of precipitable water vapor by using combined GPS/BDS/GLONASS/Galileo PPP method[J]. Bulletin of Surveying and Mapping, 2020(6): 63-66, 98. DOI:10.13474/j.cnki.11-2246.2020.0182 |
| [26] |
徐韶光, 熊永良, 刘宁, 等. 利用地基GPS获取实时可降水量[J]. 武汉大学学报(信息科学版), 2011, 36(4): 407-411. XU Shaoguang, XIONG Yongliang, LIU Ning, et al. Real-time PWV obtained by ground GPS[J]. Geomatics and Information Science of Wuhan University, 2011, 36(4): 407-411. |
| [27] |
FANG Peng, GENDT G, SPRINGER T, et al. IGS near real-time products and their applications[J]. GPS Solutions, 2001, 4(4): 2-8. DOI:10.1007/PL00012861 |
| [28] |
CHOI K K, RAY J, GRIFFITHS J, et al. Evaluation of GPS orbit prediction strategies for the IGS Ultra-rapid products[J]. GPS Solutions, 2013, 17(3): 403-412. DOI:10.1007/s10291-012-0288-2 |
| [29] |
ZHAO Qingzhi, YAO Yibin, YAO Wanqiang, et al. Real-time precise point positioning-based zenith tropospheric delay for precipitation forecasting[J]. Scientific Reports, 2018, 8(1): 7939. DOI:10.1038/s41598-018-26299-3 |
| [30] |
YUAN Yubin, ZHANG Kefei, ROHM W, et al. Real-time retrieval of precipitable water vapor from GPS precise point positioning[J]. Journal of Geophysical Research: Atmospheres, 2014, 119(16): 10044-10057. DOI:10.1002/2014JD021486 |
| [31] |
LU Cuixian, LI Xingxing, GE Maorong, et al. Estimation and evaluation of real-time precipitable water vapor from GLONASS and GPS[J]. GPS Solutions, 2016, 20(4): 703-713. DOI:10.1007/s10291-015-0479-8 |
| [32] |
LI Xingxing, TAN Han, LI Xin, et al. Real-time sensing of precipitable water vapor from BeiDou observations: Hong Kong and CMONOC networks[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(15): 7897-7909. |
| [33] |
LU Cuixian, FENG Guolong, ZHENG Yuxin, et al. Real-time retrieval of precipitable water vapor from Galileo observations by using the MGEX network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4743-4753. DOI:10.1109/TGRS.2020.2966774 |
| [34] |
LU Cuixian, LI Xingxing, NILSSON T, et al. Real-time retrieval of precipitable water vapor from GPS and BeiDou observations[J]. Journal of Geodesy, 2015, 89(9): 843-856. DOI:10.1007/s00190-015-0818-0 |
| [35] |
LI Xingxing, DICK G, LU Cuixian, et al. Multi-GNSS meteorology: real-time retrieving of atmospheric water vapor from BeiDou, Galileo, GLONASS, and GPS observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6385-6393. DOI:10.1109/TGRS.2015.2438395 |
| [36] |
高志钰. 区域大气可降水量反演及应用研究[D]. 兰州: 兰州交通大学, 2019. GAO Zhiyu. Research on inversion and application of regional atmospheric precipitable water vapor[D]. Lanzhou: Lanzhou Jiaotong University, 2019. |
| [37] |
GURBUZ G. On variations of the decadal precipitable water vapor (PWV) over Turkey[J]. Advances in Space Research, 2021, 68(1): 292-300. DOI:10.1016/j.asr.2021.03.010 |
| [38] |
WEI Jiahua, SHI Yang, REN Yan, et al. Application of ground-based microwave radiometer in retrieving meteorological characteristics of Tibet Plateau[J]. Remote Sensing, 2021, 13(13): 2527. DOI:10.3390/rs13132527 |
| [39] |
桂柯. 运用地基探测与卫星遥感方法研究中国地区大气含水量[D]. 成都: 成都信息工程大学, 2017. GUI Ke. Study of precipitable water vapor over china based on ground observations and satellite remote sensing[D]. Chengdu: Chengdu University of Information Technology, 2017. |
| [40] |
WANG Yizhu, LIU Hailei, ZHANG Yong, et al. Validation of FY-4A AGRI layer precipitable water products using radiosonde data[J]. Atmospheric Research, 2021, 253: 105502. DOI:10.1016/j.atmosres.2021.105502 |
| [41] |
邓小花, 翟盘茂, 袁春红. 国外几套再分析资料的对比与分析[J]. 气象科技, 2010, 38(1): 1-8. DENG Xiaohua, ZHAI Panmao, YUAN Chunhong. Comparative analysis of NCEP/NCAR, ECMWF and JMA reanalysis[J]. Meteorological Science and Technology, 2010, 38(1): 1-8. DOI:10.19517/j.1671-6345.2010.01.001 |
| [42] |
HUANG Liangke, MO Zhixiang, LIU Lilong, et al. Evaluation of hourly PWV products derived from ERA5 and MERRA-2 over the Tibetan Plateau using ground-based GNSS observations by two enhanced models[J]. Earth and Space Science, 2021, 8(5): e2020EA001516. |
| [43] |
WANG Junhong, ZHANG Liangying. Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements[J]. Journal of Climate, 2008, 21(10): 2218-2238. DOI:10.1175/2007JCLI1944.1 |
| [44] |
GUI Ke, CHE Huizheng, CHEN Quanliang, et al. Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China[J]. Atmospheric Research, 2017, 197: 461-473. DOI:10.1016/j.atmosres.2017.07.021 |
| [45] |
ZHAO Qingzhi, YANG Pengfei, YAO Wanqiang, et al. Hourly PWV dataset derived from GNSS observations in China[J]. Sensors, 2019, 20(1): 231. DOI:10.3390/s20010231 |
| [46] |
JIN Shuanggen, LUO O F. Variability and climatology of PWV from global 13-year GPS observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(7): 1918-1924. DOI:10.1109/TGRS.2008.2010401 |
| [47] |
CAMPANELLI M, LUPI A, NAKAJIMA T, et al. Summertime columnar content of atmospheric water vapor from ground-based Sun-sky radiometer measurements through a new in situ procedure[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D19): D19304. DOI:10.1029/2009JD013211 |
| [48] |
KAUFMAN Y J, GAO B C. Remote sensing of water vapor in the near IR from EOS/MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(5): 871-884. DOI:10.1109/36.175321 |
| [49] |
GAO Bocai, KAUFMAN Y J. Water vapor retrievals using moderate resolution imaging spectroradiometer (MODIS) near-infrared channels[J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D13): 4389. |
| [50] |
CHANG Liang, GAO Guoping, JIN Shuanggen, et al. Calibration and evaluation of precipitable water vapor from MODIS infrared observations at night[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2612-2620. DOI:10.1109/TGRS.2014.2363089 |
| [51] |
GONG Shaoqi, HAGAN D F, LU Jing, et al. Validation on MERSI/FY-3A precipitable water vapor product[J]. Advances in Space Research, 2018, 61(1): 413-425. DOI:10.1016/j.asr.2017.10.005 |
| [52] |
XU Jiafei, LIU Zhizhao. Radiance-based retrieval of total water vapor content from sentinel-3A OLCI NIR channels using ground-based GPS measurements[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 104: 102586. DOI:10.1016/j.jag.2021.102586 |
| [53] |
时芳琳. 中国区大气可降水量卫星遥感评估及其对气溶胶反演的影响分析[D]. 焦作: 河南理工大学, 2018. SHI Fanglin. The validation of the precipitable water vapor of multisensor satellites and the impact of AOD retrieve in China[J]. Jiaozuo: Henan Polytechnic University, 2018. |
| [54] |
赵静旸, 宋淑丽, 朱文耀. ERA-Interim应用于中国地区地基GPS/PWV计算的精度评估[J]. 武汉大学学报(信息科学版), 2014, 39(8): 935-939, 1008. ZHAO Jingyang, SONG Shuli, ZHU Wenyao. Accuracy assessment of applying ERA-interim reanalysis data to calculate ground-based GPS/PWV over China[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8): 935-939, 1008. |
| [55] |
SSENYUNZI R C, ORURU B, D'UJANGA F M, et al. Performance of ERA5 data in retrieving Precipitable Water Vapour over East African tropical region[J]. Advances in Space Research, 2020, 65(8): 1877-1893. DOI:10.1016/j.asr.2020.02.003 |
| [56] |
MARÍN J C, BARRETT B S. Seasonal and intraseasonal variability of precipitable water vapour in the Chajnantor plateau, Chile[J]. International Journal of Climatology, 2017, 37(S1): 958-971. |
| [57] |
JIANG Jie, ZHOU Tianjun, ZHANG Wenxia. Evaluation of satellite and reanalysis precipitable water vapor data sets against radiosonde observations in central Asia[J]. Earth and Space Science, 2019, 6(7): 1129-1148. DOI:10.1029/2019EA000654 |
| [58] |
WANG Yan, YANG Kun, PAN Zhengyang, et al. Evaluation of precipitable water vapor from four satellite products and four reanalysis datasets against GPS measurements on the Southern Tibetan Plateau[J]. Journal of Climate, 2017, 30(15): 5699-5713. DOI:10.1175/JCLI-D-16-0630.1 |
| [59] |
ISIOYE O A, COMBRINCK L, BOTAI J O. Retrieval and analysis of precipitable water vapour based on GNSS, AIRS, and reanalysis models over Nigeria[J]. International Journal of Remote Sensing, 2017, 38(20): 5710-5735. DOI:10.1080/01431161.2017.1346401 |
| [60] |
FUJITA M, WADA A, IWABUCHI T, et al. GPS-PWV dataset by GPS preciptable water research project (GRASP)[C]//Proceedings of the American Geophysical Union, [S. l.]: AGU, 2012.
|
| [61] |
CHEN Biyan, LIU Zhizhao. Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(19): 11442-11462. |
| [62] |
WANG Xiaoming, ZHANG Kefei, WU Suqin, et al. Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(2): 833-852. DOI:10.1002/2015JD024181 |
| [63] |
ZHAO Qingzhi, DU Zheng, YAO Wanqiang, et al. Hybrid precipitable water vapor fusion model in China[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2020, 208: 105387. DOI:10.1016/j.jastp.2020.105387 |
| [64] |
LI Zhenhong, MULLER J P, CROSS P. Comparison of precipitable water vapor derived from radiosonde, GPS, and moderate-resolution imaging spectroradiometer measurements[J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D20): 4651. DOI:10.1029/2003JD003372 |
| [65] |
ZHU Dantong, ZHANG Kefei, YANG Liu, et al. Evaluation and calibration of MODIS near-infrared precipitable water vapor over china using GNSS observations and ERA-5 reanalysis dataset[J]. Remote Sensing, 2021, 13(14): 2761. DOI:10.3390/rs13142761 |
| [66] |
XIONG Zhaohui, SUN Xiaogong, SANG Jizhang, et al. Modify the accuracy of MODIS PWV in China: a performance comparison using random forest, generalized regression neural network and back-propagation neural network[J]. Remote Sensing, 2021, 13(11): 2215. DOI:10.3390/rs13112215 |
| [67] |
刘备, 王勇, 娄泽生, 等. CMONOC观测约束下的中国大陆地区MODIS PWV校正[J]. 测绘学报, 2019, 48(10): 1207-1215. LIU Bei, WANG Yong, LOU Zesheng, et al. The MODIS PWV correction based on CMONOC in Chinese mainland[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10): 1207-1215. DOI:10.11947/j.AGCS.2019.20180386 |
| [68] |
KHANIANI A S, NIKRAFTAR Z, ZAKERI S. Evaluation of MODIS Near-IR water vapor product over Iran using ground-based GPS measurements[J]. Atmospheric Research, 2020, 231: 104657. DOI:10.1016/j.atmosres.2019.104657 |
| [69] |
HE Jia, LIU Zhizhao. Water vapor retrieval from MODIS NIR channels using ground-based GPS data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5): 3726-3737. DOI:10.1109/TGRS.2019.2962057 |
| [70] |
HE Jia, LIU Zhizhao. Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data[J]. Remote Sensing of Environment, 2021, 258: 112384. DOI:10.1016/j.rse.2021.112384 |
| [71] |
YAO Yibin, XU Xingyu, HU Yufeng. Establishment of a regional precipitable water vapor model based on the combination of GNSS and ECMWF data[J]. Atmospheric Measurement Techniques Discussions, 2018, 1-21. |
| [72] |
ZHAO Qingzhi, DU Zheng, LI Zufeng, et al. Two-step precipitable water vapor fusion method[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5801510. |
| [73] |
LI Xueying, LONG Di. An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach[J]. Remote Sensing of Environment, 2020, 248: 111966. DOI:10.1016/j.rse.2020.111966 |
| [74] |
ZHANG Bao, YAO Yibin, XIN Linyang, et al. Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation[J]. Journal of Geodesy, 2019, 93(12): 2605-2620. DOI:10.1007/s00190-019-01322-1 |
| [75] |
ZHANG Bao, YAO Yibin. Precipitable water vapor fusion based on a generalized regression neural network[J]. Journal of Geodesy, 2021, 95(3): 36. DOI:10.1007/s00190-021-01482-z |
| [76] |
CCOICA-LÓPEZ K L, PASAPERA-GONZALES J J, JIMENEZ J C. Spatio-temporal variability of the precipitable water vapor over Peru through MODIS and ERA-interim time series[J]. Atmosphere, 2019, 10(4): 192. DOI:10.3390/atmos10040192 |
| [77] |
刘萌, 唐荣林, 李召良, 等. 数据驱动的蒸散发遥感反演方法及产品研究进展[J]. 遥感学报, 2021, 25(8): 1517-1537. LIU Meng, TANG Ronglin, LI Zhaoliang, et al. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration[J]. National Remote Sensing Bulletin, 2021, 25(8): 1517-1537. |
| [78] |
MA Xiongwei, YAO Yibin, ZHANG Bao, et al. Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method[J]. Atmospheric Environment, 2022, 269: 118850. DOI:10.1016/j.atmosenv.2021.118850 |
| [79] |
FLORES A, RUFFINI G, RIUS A. 4D tropospheric tomography using GPS slant wet delays[J]. Annales Geophysicae, 2000, 18(2): 223-234. DOI:10.1007/s00585-000-0223-7 |
| [80] |
PERLER D, GEIGER A, HURTER F. 4D GPS water vapor tomography: new parameterized approaches[J]. Journal of Geodesy, 2011, 85(8): 539-550. DOI:10.1007/s00190-011-0454-2 |
| [81] |
DING N, ZHANG S B, WU S Q, et al. Adaptive node parameterization for dynamic determination of boundaries and nodes of GNSS tomographic models[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(4): 1990-2003. DOI:10.1002/2017JD027748 |
| [82] |
ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. Troposphere water vapour tomography: a horizontal parameterised approach[J]. Remote Sensing, 2018, 10(8): 1241. DOI:10.3390/rs10081241 |
| [83] |
TROLLER M, BVRKI B, COCARD M, et al. 3D refractivity field from GPS double difference tomography[J]. Geophysical Research Letters, 2002, 29(24): 2149. |
| [84] |
CHEN Biyan, LIU Zhizhao. Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model[J]. Journal of Geodesy, 2014, 88(7): 691-703. DOI:10.1007/s00190-014-0715-y |
| [85] |
YAO Yibin, ZHAO Qingzhi. A novel, optimized approach of voxel division for water vapor tomography[J]. Meteorology and Atmospheric Physics, 2017, 129(1): 57-70. DOI:10.1007/s00703-016-0450-4 |
| [86] |
ZHAO Qingzhi, YAO Yibin, CAO Xinyun, et al. An optimal tropospheric tomography method based on the multi-GNSS observations[J]. Remote Sensing, 2018, 10(2): 234. DOI:10.3390/rs10020234 |
| [87] |
何秀凤, 詹伟, 施宏凯. 顾及边界信号及垂直约束的GNSS水汽层析方法[J]. 测绘学报, 2021, 50(7): 853-862. HE Xiufeng, ZHAN Wei, SHI Hongkai. A GNSS water vapor tomography method considering boundary signals and vertical constraint[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(7): 853-862. DOI:10.11947/j.AGCS.2021.20200433 |
| [88] |
WANG Xiaoying, WANG Xianliang, DAI Ziqiang, et al. Tropospheric wet refractivity tomography based on the BeiDou satellite system[J]. Advances in Atmospheric Sciences, 2014, 31(2): 355-362. DOI:10.1007/s00376-013-2311-0 |
| [89] |
赵庆志, 姚宜斌, 姚顽强, 等. 利用ECMWF改善射线利用率的三维水汽层析算法[J]. 测绘学报, 2018, 47(9): 1179-1187. ZHAO Qingzhi, YAO Yibin, YAO Wanqiang, et al. A method to improve the utilization rate of satellite rays for three-dimensional water vapor tomography using the ECMWF data[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9): 1179-1187. DOI:10.11947/j.AGCS.2018.20170412 |
| [90] |
CHAMPOLLION C, MASSON F, BOUIN M N, et al. GPS water vapour tomography: preliminary results from the ESCOMPTE field experiment[J]. Atmospheric Research, 2005, 74(1-4): 253-274. DOI:10.1016/j.atmosres.2004.04.003 |
| [91] |
ROHM W, BOSY J. The verification of GNSS tropospheric tomography model in a mountainous area[J]. Advances in Space Research, 2011, 47(10): 1721-1730. DOI:10.1016/j.asr.2010.04.017 |
| [92] |
NOTARPIETRO R, CUCCA M, GABELLA M, et al. Tomographic reconstruction of wet and total refractivity fields from GNSS receiver networks[J]. Advances in Space Research, 2011, 47(5): 898-912. DOI:10.1016/j.asr.2010.12.025 |
| [93] |
VAN BAELEN J, REVERDY M, TRIDON F, et al. On the relationship between water vapour field evolution and the life cycle of precipitation systems[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(S1): 204-223. DOI:10.1002/qj.785 |
| [94] |
BENEVIDES P, CATALÃO J, MIRANDA P. Experimental GNSS tomography study in Lisbon (Portugal)[J]. Física de la Tierra, 2014, 26: 65-79. |
| [95] |
赵庆志, 姚宜斌, 罗亦泳. 附加辅助层析区域提高射线利用率的水汽反演方法[J]. 武汉大学学报(信息科学版), 2017, 42(9): 1203-1208, 1222. ZHAO Qingzhi, YAO Yibin, LUO Yiyong. A method to improve the utilization of observation for water vapor tomography by adding assisted tomographic area[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1203-1208, 1222. DOI:10.13203/j.whugis20150592 |
| [96] |
YAO Y B, ZHAO Q Z, ZHANG B. A method to improve the utilization of GNSS observation for water vapor tomography[J]. Annales Geophysicae, 2016, 34(1): 143-152. DOI:10.5194/angeo-34-143-2016 |
| [97] |
ZHAO Qingzhi, YAO Yibin. An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area[J]. Annales Geophysicae, 2017, 35(1): 87-95. DOI:10.5194/angeo-35-87-2017 |
| [98] |
YAO Yibin, ZHAO Qingzhi. Maximally using GPS observation for water vapor tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7185-7196. DOI:10.1109/TGRS.2016.2597241 |
| [99] |
胡鹏, 黄观文, 张勤, 等. 顾及边界入射信号的多模水汽层析方法[J]. 测绘学报, 2020, 49(5): 557-568. HU Peng, HUANG Guanwen, ZHANG Qin, et al. A multi-GNSS water vapor tomography method considering boundary incident signals[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 557-568. DOI:10.11947/j.AGCS.2020.20190113 |
| [100] |
张豹. 地基GNSS水汽反演技术及其在复杂天气条件下的应用研究[D]. 武汉: 武汉大学, 2016. ZHANG Bao. The study of water vapor inversion using ground-based GNSS and its applications in severe weather conditions[D]. Wuhan: Wuhan University, 2016. |
| [101] |
宋淑丽. 地基GPS网对水汽三维分布的监测及其在气象学中的应用[D]. 上海: 中国科学院研究生院(上海天文台), 2004. SONG Shuli. Sensing three dimensional water vapor structure with ground-based GPS network and the application in meteorology[D]. Shanghai: Shanghai Astronomical Observatory (Chinese Academy of Sciences), 2004. |
| [102] |
GUO Jiming, YANG Fei, SHI Junbo, et al. An optimal weighting method of global positioning system (GPS) troposphere tomography[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12): 5880-5887. DOI:10.1109/JSTARS.2016.2546316 |
| [103] |
ZHANG Bao, FAN Qingbiao, YAO Yibin, et al. An improved tomography approach based on adaptive smoothing and ground meteorological observations[J]. Remote Sensing, 2017, 9(9): 886. DOI:10.3390/rs9090886 |
| [104] |
MÖLLER G. Reconstruction of 3D wet refractivity fields in the lower atmosphere along bended GNSS signal paths[D]. Vienna, Austria: Department for Geodesy and Geoinformation, 2017.
|
| [105] |
ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. A troposphere tomography method considering the weighting of input information[J]. Annales Geophysicae, 2017, 35(6): 1327-1340. DOI:10.5194/angeo-35-1327-2017 |
| [106] |
毕研盟, 杨光林, 聂晶. 基于Kalman滤波的GPS水汽层析方法及其应用[J]. 高原气象, 2011, 30(1): 109-114. BI Yanmeng, YANG Guanglin, NIE Jing. Method of GPS water vapor tomography based on Kalman filter and its application[J]. Plateau Meteorology, 2011, 30(1): 109-114. |
| [107] |
HIRAHARA K. Local GPS tropospheric tomography[J]. Earth, Planets and Space, 2000, 52(11): 935-939. DOI:10.1186/BF03352308 |
| [108] |
ZHAO Qingzhi, LI Zufeng, YAO Wanqiang, et al. An improved ridge estimation (IRE) method for troposphere water vapor tomography[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2020, 207: 105366. DOI:10.1016/j.jastp.2020.105366 |
| [109] |
王维, 王解先. 基于代数重构技术的对流层水汽层析[J]. 计算机应用, 2011, 31(11): 3149-3151, 3156. WANG Wei, WANG Jiexian. Ground-based GPS water vapor tomography based on algebraic reconstruction technique[J]. Journal of Computer Applications, 2011, 31(11): 3149-3151, 3156. |
| [110] |
BENDER M, DICK G, GE Maorong, et al. Development of a GNSS water vapour tomography system using algebraic reconstruction techniques[J]. Advances in Space Research, 2011, 47(10): 1704-1720. DOI:10.1016/j.asr.2010.05.034 |
| [111] |
丁楠, 张书毕. 基于分组排序的水汽层析约束ART算法[J]. 大地测量与地球动力学, 2017, 37(5): 482-486, 491. DING Nan, ZHANG Shubi. Grouping and sorting based water vapor tomography constraint ART algorithm[J]. Journal of Geodesy and Geodynamics, 2017, 37(5): 482-486, 491. DOI:10.14075/j.jgg.2017.05.009 |
| [112] |
夏朋飞, 叶世榕. 一种基于组合重构算法的对流层层析技术[J]. 大地测量与地球动力学, 2017, 37(9): 928-932. XIA Pengfei, YE Shirong. A troposphere tomography technique based on combined reconstruction algorithm[J]. Journal of Geodesy and Geodynamics, 2017, 37(9): 928-932. DOI:10.14075/j.jgg.2017.09.011 |
| [113] |
张文渊, 张书毕, 左都美, 等. GNSS水汽层析的自适应代数重构算法[J]. 武汉大学学报(信息科学版), 2021, 46(9): 1318-1327. ZHANG Wenyuan, ZHANG Shubi, ZUO Dumei, et al. Adaptive algebraic reconstruction algorithms for GNSS water vapor tomography[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1318-1327. DOI:10.13203/j.whugis20190387 |
| [114] |
刘文轩. 基于自适应联合代数重构算法的广域对流层快速层析及应用[D]. 武汉: 武汉大学, 2020. DOI: 10.27379/d.cnki.gwhdu.2020.000382. LIU Wenxuan. Wide-area rapid tropospheric tomography adaptive simultaneous iterative reconstruction technique and its application[J]. Wuhan: Wuhan University, 2020. DOI: 10.27379/d.cnki.gwhdu.2020.000382. |
| [115] |
BENDER M, STOSIUS R, ZUS F, et al. GNSS water vapour tomography-expected improvements by combining GPS, GLONASS and Galileo observations[J]. Advances in Space Research, 2011, 47(5): 886-897. DOI:10.1016/j.asr.2010.09.011 |
| [116] |
BENEVIDES P, NICO G, CATALÃO J, et al. Analysis of Galileo and GPS integration for GNSS tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 1936-1943. DOI:10.1109/TGRS.2016.2631449 |
| [117] |
王维, 宋淑丽, 王解先, 等. 长三角地区多模GNSS斜路径观测分布及水汽仿真层析[J]. 测绘学报, 2016, 45(2): 164-169, 177. WANG Wei, SONG Shuli, WANG Jiexian, et al. Distribution analysis of multi GNSS slant delays and simulated water vapor tomography in Yangtze River delta[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(2): 164-169, 177. DOI:10.11947/j.AGCS.2016.20140648 |
| [118] |
夏朋飞, 叶世榕, 江鹏. GPS/GLONASS组合精密单点定位技术在三维水汽层析中的应用[J]. 大地测量与地球动力学, 2015, 35(1): 72-76. XIA Pengfei, YE Shirong, JIANG Peng. Research on three-dimensional water vapor tomography using GPS/GLONASS PPP method[J]. Journal of Geodesy and Geodynamics, 2015, 35(1): 72-76. |
| [119] |
DONG Zhounan, JIN Shuanggen. 3D water vapor tomography in Wuhan from GPS, BDS and GLONASS observations[J]. Remote Sensing, 2018, 10(1): 62. |
| [120] |
ZHAO Qingzhi, YAO Yibin, CAO Xinyun, et al. Accuracy and reliability of tropospheric wet refractivity tomography with GPS, BDS, and GLONASS observations[J]. Advances in Space Research, 2019, 63(9): 2836-2847. DOI:10.1016/j.asr.2018.01.021 |
| [121] |
ZHAO Qingzhi, ZHANG Kefei, YAO Wanqiang. Influence of station density and multi-constellation GNSS observations on troposphere tomography[J]. Annales Geophysicae, 2019, 37(1): 15-24. DOI:10.5194/angeo-37-15-2019 |
| [122] |
BOCK O, DOERFLINGER E. Atmospheric modeling in GPS data analysis for high accuracy positioning[J]. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 2001, 26(6-8): 373-383. DOI:10.1016/S1464-1895(01)00069-2 |
| [123] |
殷海涛, 黄丁发, 熊永良, 等. GPS信号对流层延迟改正新模型研究[J]. 武汉大学学报(信息科学版), 2007, 32(5): 454-457. YIN Haitao, HUANG Dingfa, XIONG Yongliang, et al. New model for tropospheric delay estimation of GPS signal[J]. Geomatics and Information Science of Wuhan University, 2007, 32(5): 454-457. |
| [124] |
WILGAN K, HADAS T, HORDYNIEC P, et al. Real-time precise point positioning augmented with high-resolution numerical weather prediction model[J]. GPS Solutions, 2017, 21(3): 1341-1353. DOI:10.1007/s10291-017-0617-6 |
| [125] |
YAO Yibin, YU Chen, HU Yufeng. A new method to accelerate PPP convergence time by using a global zenith troposphere delay estimate model[J]. The Journal of Navigation, 2014, 67(5): 899-910. DOI:10.1017/S0373463314000265 |
| [126] |
YAO Yibin, PENG Wenjie, XU Chaoqian, et al. Enhancing real-time precise point positioning with zenith troposphere delay products and the determination of corresponding tropospheric stochastic models[J]. Geophysical Journal International, 2017, 208(2): 1217-1230. DOI:10.1093/gji/ggw451 |
| [127] |
宋超, 郝金明, 张鹤. 利用先验对流层延迟约束加快PPP重新收敛方法[J]. 测绘科学技术学报, 2015, 32(5): 441-444. SONG Chao, HAO Jinming, ZHANG He. A method to accelerate PPP re-convergence with prior troposphere delay constraint[J]. Journal of Geomatics Science and Technology, 2015, 32(5): 441-444. DOI:10.3969/j.issn.1673-6338.2015.05.001 |
| [128] |
HAJI-AGHAJANY S, AMERIAN Y, VERHAGEN S, et al. The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy[J]. Journal of Geodesy, 2021, 95(7): 78. DOI:10.1007/s00190-021-01528-2 |
| [129] |
GONG Yangzhao, LIU Zhizhao, CHAN P W, et al. Augmenting GNSS PPP accuracy in south china using water vapor correction data from WRF assimilation results[M]//YANG Changfeng, XIE Jun. China satellite navigation conference (CSNC 2021) proceedings. Singapore: Springer, 2021: 653-670.
|
| [130] |
姚宜斌, 张瑞, 易文婷, 等. 一种新的区域对流层拟合模型及其在PPP中的应用[J]. 武汉大学学报(信息科学版), 2012, 37(9): 1024-1027. YAO Yibin, ZHANG Rui, YI Wenting, et al. A new regional troposphere fitting model and its application to PPP[J]. Geomatics and Information Science of Wuhan University, 2012, 37(9): 1024-1027. DOI:10.13203/j.whugis2012.09.013 |
| [131] |
SHI Junbo, XU Chaoqian, GUO Jiming, et al. Local troposphere augmentation for real-time precise point positioning[J]. Earth, Planets and Space, 2014, 66(1): 1-13. DOI:10.1186/1880-5981-66-1 |
| [132] |
HAN Houzeng, XU Tianhe, WANG Jian. Tightly coupled integration of GPS ambiguity fixed precise point positioning and MEMS-INS through a troposphere-constrained adaptive Kalman filter[J]. Sensors, 2016, 16(7): 1057. DOI:10.3390/s16071057 |
| [133] |
DE OLIVEIRA P S JR, MOREL L, FUND F, et al. Modeling tropospheric wet delays with dense and sparse network configurations for PPP-RTK[J]. GPS Solutions, 2017, 21(1): 237-250. DOI:10.1007/s10291-016-0518-0 |
| [134] |
宋佳, 李敏, 赵齐乐, 等. 一种区域实时对流层内插模型及其在PPP中的应用[J]. 测绘通报, 2018(4): 1-5, 15. SONG Jia, LI Min, ZHAO Qile, et al. A real time regional zenith troposphere delay model and its application in PPP[J]. Bulletin of Surveying and Mapping, 2018(4): 1-5, 15. DOI:10.13474/j.cnki.11-2246.2018.0100 |
| [135] |
BENEVIDES P, CATALAO J, MIRANDA P M A. On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall[J]. Natural Hazards and Earth System Sciences, 2015, 15(12): 2605-2616. DOI:10.5194/nhess-15-2605-2015 |
| [136] |
YAO Yinbin, SHAN Lulu, ZHAO Qingzhi. Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application[J]. Scientific Reports, 2017, 7(1): 12465. DOI:10.1038/s41598-017-12593-z |
| [137] |
李黎, 匡翠林, 朱建军, 等. 基于实时精密单点定位技术的暴雨短临预报[J]. 地球物理学报, 2012, 55(4): 1129-1136. LI Li, KUANG Cuilin, ZHU Jianjun, et al. Rainstorm nowcasting based on GPS real-time precise point positioning technology[J]. Chinese Journal of Geophysics, 2012, 55(4): 1129-1136. DOI:10.6038/j.issn.0001-5733.2012.04.008 |
| [138] |
ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. GPS-based PWV for precipitation forecasting and its application to a typhoon event[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2018, 167: 124-133. DOI:10.1016/j.jastp.2017.11.013 |
| [139] |
LI Haobo, WANG Xiaoming, WU Suqin, et al. Development of an improved model for prediction of short-term heavy precipitation based on GNSS-derived PWV[J]. Remote Sensing, 2020, 12(24): 4101. DOI:10.3390/rs12244101 |
| [140] |
ZHAO Qingzhi, LIU Yang, MA Xiongwei, et al. An improved rainfall forecasting model based on GNSS observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4891-4900. DOI:10.1109/TGRS.2020.2968124 |
| [141] |
LI Haobo, WANG Xiaoming, ZHANG Kefei, et al. A neural network-based approach for the detection of heavy precipitation using GNSS observations and surface meteorological data[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2021, 225: 105763. DOI:10.1016/j.jastp.2021.105763 |
| [142] |
MANANDHAR S, DEV S, LEE Y H, et al. A data-driven approach to detect precipitation from meteorological sensor data[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE, 2018: 3872-3875.
|
| [143] |
BENEVIDES P, CATALAO J, NICO G. Neural network approach to forecast hourly intense rainfall using GNSS precipitable water vapor and meteorological sensors[J]. Remote Sensing, 2019, 11(8): 966. DOI:10.3390/rs11080966 |
| [144] |
LIU Yang, ZHAO Qingzhi, YAO Wanqiang, et al. Short-term rainfall forecast model based on the improved BP-NN algorithm[J]. Scientific Reports, 2019, 9(1): 19751. DOI:10.1038/s41598-019-56452-5 |
| [145] |
MISHRA N, SONI H K, SHARMA S, et al. Development and analysis of artificial neural network models for rainfall prediction by using time-series data[J]. International Journal of Intelligent Systems and Applications, 2018, 10(1): 16-23. DOI:10.5815/ijisa.2018.01.03 |
| [146] |
PARTAL T, CIGIZOGLU H K, KAHYA E. Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data[J]. Stochastic Environmental Research and Risk Assessment, 2015, 29(5): 1317-1329. DOI:10.1007/s00477-015-1061-1 |
| [147] |
RAHIMI Z, SHAFRI H Z M, NORMAN M. A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX)[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2018, 178: 74-84. DOI:10.1016/j.jastp.2018.06.011 |
| [148] |
LE T T, PHAM B T, LY H B, et al. Development of 48-hour precipitation forecasting model using nonlinear autoregressive neural network[M]//HA-MINH C, VAN DAO D, BENBOUDIEMA F, et al. CIGOS 2019, innovation for sustainable infrastructure. Singapore: Springer, 2020: 1191-1196.
|
| [149] |
ZHAO Qingzhi, LIU Yang, YAO Wanqiang, et al. Hourly rainfall forecast model using supervised learning algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-9. |
| [150] |
KUO Y H, GUO Yongrun, WESTWATER E R. Assimilation of precipitable water measurements into a mesoscale numerical model[J]. Monthly Weather Review, 1993, 121(4): 1215-1238. DOI:10.1175/1520-0493(1993)121<1215:AOPWMI>2.0.CO;2 |
| [151] |
袁招洪, 丁金才, 陈敏. GPS观测资料应用于中尺度数值预报模式的初步研究[J]. 气象学报, 2004, 62(2): 200-212. YUAN Zhaohong, DING Jincai, CHEN Min. Preliminary study on applying GPS observations to mesoscale numerical weather prediction model[J]. Acta Meteorologica Sinica, 2004, 62(2): 200-212. |
| [152] |
宋淑丽, 朱文耀, 丁金才, 等. 上海GPS综合应用网对可降水汽量的实时监测及其改进数值预报初始场的试验[J]. 地球物理学报, 2004, 47(4): 631-638. SONG Shuli, ZHU Wenyao, DING Jincai, et al. Real time monitoring of PWV from SGCAN and its application test in numerical weather forecast[J]. Chinese Journal of Geophysics, 2004, 47(4): 631-638. DOI:10.3321/j.issn:0001-5733.2004.04.013 |
| [153] |
张晶. LAPS同化GPS/PWV资料在中尺度分析与暴雨预报中的应用研究[D]. 南京: 南京信息工程大学, 2013. ZHANG Jing. Assimilation of GPS data in LAPS and its application in mesoscale analysis and precipitation forecasts[D]. Nanjing: Nanjing University of Information Science and Technology, 2013. |
| [154] |
PARSONS D, MACHOL J, GIBSON W P, et al. Preliminary progress on improving the characterization of water vapor[J]. National Center for Atmospheric Research, 1999(5): 89-92. |
| [155] |
张朝林, 陈敏, KUO Y H, 等. "00.7"北京特大暴雨模拟中气象资料同化作用的评估[J]. 气象学报, 2005, 63(6): 922-932. DOI:10.3321/j.issn:0577-6619.2005.06.009 |
| [156] |
刘聪. GPS可降水资料在西安地区天气预报中的应用分析[D]. 兰州: 兰州大学, 2019. LIU Cong. Application analysis on GPS precipitable water wapor in weather forecast at Xi'an region[D]. Lanzhou: Lanzhou University, 2019. |
| [157] |
范水勇, 张朝林, 仲跻芹. MM5三维变分系统在北京地区冷暖季背景场误差的对比分析[J]. 高原气象, 2006, 25(5): 855-861. FAN Shuiyong, ZHANG Chaolin, ZHONG Jiqin. Contrast analysis of background error of MM5 3DVAR system in cold and warm seasons in Beijing[J]. Plateau Meteorology, 2006, 25(5): 855-861. DOI:10.3321/j.issn:1000-0534.2006.05.012 |
| [158] |
贝纯纯, 李昕, 王元, 等. GPS/PWV资料在梅雨锋暴雨个例中的同化试验[J]. 气象科学, 2016, 36(2): 149-157. BEI Chunchun, LI Xin, WANG Yuan, et al. Assimilation experiment of GPS/PWV data in the rainstrom case of Meiyu front[J]. Journal of the Meteorological Sciences, 2016, 36(2): 149-157. |
| [159] |
楚艳丽. 基于GPS资料和多尺度分析的水汽扰动与暴雨关系研究[D]. 南京: 南京信息工程大学, 2014. CHU Yanli. Relationship between moisture disturbance and heavy rainfall based on GPS data and multi-scale analysis[D]. Nanjing: Nanjing University of Information Science and Technology, 2014. |
| [160] |
曾明剑, 张备, 周嘉陵, 等. GPS/PWV资料同化在强降水过程中的定量作用评估[J]. 气象科学, 2014, 34(1): 77-86. ZENG Mingjian, ZHANG Bei, ZHOU Jialing, et al. Quantitative evaluation for GPS/PWV data assimilation in heavy precipitation events[J]. Journal of the Meteorological Sciences, 2014, 34(1): 77-86. |
| [161] |
BRENOT H, DUCROCQ V, WALPERSDORF A, et al. GPS zenith delay sensitivity evaluated from high-resolution numerical weather prediction simulations of the 8-9 September 2002 flash flood over southeastern France[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D15): D15105. DOI:10.1029/2004JD005726 |
| [162] |
姚宜斌, 何畅勇, 张豹, 等. 一种新的全球对流层天顶延迟模型GZTD[J]. 地球物理学报, 2013, 56(7): 2218-2227. YAO Yibin, HE Changyong, ZHANG Bao, et al. A new global zenith tropospheric delay model GZTD[J]. Chinese Journal of Geophysics, 2013, 56(7): 2218-2227. |
| [163] |
DE PONDECA M S F V, ZOU Xiaolei. A case study of the variational assimilation of GPS zenith delay observations into a mesoscale model[J]. Journal of Applied Meteorology, 2001, 40(9): 1559-1576. DOI:10.1175/1520-0450(2001)040<1559:ACSOTV>2.0.CO;2 |
| [164] |
YAN X, DUCROCQ V, POLI P, et al. Impact of GPS zenith delay assimilation on convective-scale prediction of Mediterranean heavy rainfall[J]. Journal of Geophysical Research: Atmospheres, 2009, 114(D3): D03104. |
| [165] |
BENNITT G V, JUPP A. Operational assimilation of GPS zenith total delay observations into the Met Office numerical weather prediction models[J]. Monthly Weather Review, 2012, 140(8): 2706-2719. DOI:10.1175/MWR-D-11-00156.1 |
| [166] |
ARRIOLA J S, LINDSKOG M, THORSTEINSSON S, et al. Variational bias correction of GNSS ZTD in the HARMONIE modeling system[J]. Journal of Applied Meteorology and Climatology, 2016, 55(5): 1259-1276. DOI:10.1175/JAMC-D-15-0137.1 |
| [167] |
MASCITELLI A, FEDERICO S, FORTUNATO M, et al. Data assimilation of GPS-ZTD into the RAMS model through 3D-Var: preliminary results at the regional scale[J]. Measurement Science and Technology, 2019, 30(5): 055801. DOI:10.1088/1361-6501/ab0b87 |
| [168] |
周炳君. 地基GPS ZTD资料质量控制及在华东区域模式中的同化应用[D]. 南京: 南京信息工程大学, 2020. DOI: 10.27248/d.cnki.gnjqc.2020.000043. ZHOU Bingjun. Ground-based GPS ZTD data quality control and assimilation application in east china regional model[D]. Nanjing: Nanjing University of Information Science and Technology, 2020. DOI: 10.27248/d.cnki.gnjqc.2020.000043. |
| [169] |
CUCURULL L, VANDENBERGHE F, BARKER D, et al. Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December 2001 storm event over the western Mediterranean Sea[J]. Monthly Weather Review, 2004, 132(3): 749-763. DOI:10.1175/1520-0493(2004)132<0749:TVDAOG>2.0.CO;2 |
| [170] |
SHOJI Y, KUNⅡ M, SAITO K. Assimilation of nationwide and global GPS PWV data for a heavy rain event on 28 July 2008 in Hokuriku and Kinki, Japan[J]. SOLA, 2009, 5: 45-48. DOI:10.2151/sola.2009-012 |
| [171] |
VEDEL H, HUANG X Y, HAASE J, et al. Impact of GPS zenith tropospheric delay data on precipitation forecasts in Mediterranean France and Spain[J]. Geophysical Research Letters, 2004, 31(2): L02102. |
| [172] |
SHERWOOD S C, ROCA R, WECKWERTH T M, et al. Tropospheric water vapor, convection, and climate[J]. Reviews of Geophysics, 2010, 48(2): RG2001. |
| [173] |
SUPARTA W. The use of GPS meteorology for climate change detection[C]//Proceedings of 2012 International Conference on Green and Ubiquitous Technology. Bandung: IEEE, 2012: 71-73.
|
| [174] |
VAQUERO-MARTÍNEZ J, ANTÓN M. Review on the role of GNSS meteorology in monitoring water vapor for atmospheric physics[J]. Remote Sensing, 2021, 13(12): 2287. DOI:10.3390/rs13122287 |
| [175] |
SEIDEL D J. Water vapor: distribution and trends[C]//Proceedings of the American Geophysical Union Special Report Water Vapor in the Climate System. Washington DC: [s. n.], 2000.
|
| [176] |
SUPARTA W, ISKANDAR A. Monitoring of GPS water vapor variability during ENSO events over the Borneo Region[J]. Asian Journal of Earth Sciences, 2012, 5(3): 88-95. DOI:10.3923/ajes.2012.88.95 |
| [177] |
SUPARTA W, ISKANDAR A, SINGH M S J. A new technique to observe ENSO activity via ground-based GPS receivers[M]//GAOL F L, SHRIVASTAVA K, AKHTAR J. Recent trends in physics of material science and technology. Singapore: Springer, 2015: 173-186.
|
| [178] |
ZHAO Qingzhi, LIU Yang, YAO Wanqiang, et al. A novel ENSO monitoring method using precipitable water vapor and temperature in southeast China[J]. Remote Sensing, 2020, 12(4): 649. DOI:10.3390/rs12040649 |
| [179] |
LIOU Y A, HUANG C Y. GPS observations of PW during the passage of a typhoon[J]. Earth, Planets and Space, 2000, 52(10): 709-712. DOI:10.1186/BF03352269 |
| [180] |
ZHU Mingchen, LIU Zhizhao, HU Wusheng. Observing water vapor variability during three super typhoon events in Hong Kong based on GPS water vapor tomographic modeling technique[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(15): e2019JD032318. |
| [181] |
ZHAO Qingzhi, MA Xiongwei, YAO Wanqiang, et al. A new typhoon-monitoring method using precipitation water vapor[J]. Remote Sensing, 2019, 11(23): 2845. DOI:10.3390/rs11232845 |
| [182] |
STENSETH N C, MYSTERUD A, OTTERSEN G, et al. Ecological effects of climate fluctuations[J]. Science, 2002, 297(5585): 1292-1296. DOI:10.1126/science.1071281 |
| [183] |
GURBUZ G, JIN Shuanggen. Long-time variations of precipitable water vapour estimated from GPS, MODIS and radiosonde observations in Turkey[J]. International Journal of Climatology, 2017, 37(15): 5170-5180. DOI:10.1002/joc.5153 |
| [184] |
NEIMAN P J, HUGHES M, MOORE B J, et al. Sierra barrier jets, atmospheric rivers, and precipitation characteristics in northern California: a composite perspective based on a network of wind profilers[J]. Monthly Weather Review, 2013, 141(12): 4211-4233. DOI:10.1175/MWR-D-13-00112.1 |
| [185] |
KINGSMILL D E, NEIMAN P J, MOORE B J, et al. Kinematic and thermodynamic structures of Sierra barrier jets and overrunning atmospheric rivers during a landfalling winter storm in northern California[J]. Monthly Weather Review, 2013, 141(6): 2015-2036. DOI:10.1175/MWR-D-12-00277.1 |
| [186] |
ClOUTIER-BISBEE S R, RAGHAVENDRA A, MILRAD S M. Heat waves in Florida: climatology, trends, and related precipitation events[J]. Journal of Applied Meteorology and Climatology, 2019, 58(3): 447-466. DOI:10.1175/JAMC-D-18-0165.1 |
| [187] |
RAGHAVENDRA A, MILRAD S M. On the relationship between heat waves and extreme precipitation in a warming climate[M]//CASTILLO F, WEHNER M, STONE D A. Extreme Events and Climate Change: A Multidisciplinary Approach. Hoboken: John Wiley & Sons, Inc., 2021: 183-203.
|
| [188] |
ZHAO Qingzhi, MA Xiongwei, YAO Wanqiang, et al. Improved drought monitoring index using GNSS-derived precipitable water vapor over the loess plateau area[J]. Sensors, 2019, 19(24): 5566. DOI:10.3390/s19245566 |
| [189] |
MA Xiongwei, ZHAO Qingzhi, YAO Yibin, et al. A novel method of retrieving potential ET in China[J]. Journal of Hydrology, 2021, 598: 126271. DOI:10.1016/j.jhydrol.2021.126271 |
| [190] |
ZHAO Qingzhi, MA Yongjie, LI Zufeng, et al. Retrieval of a high-precision drought monitoring index by using GNSS-derived ZTD and temperature[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 8730-8743. DOI:10.1109/JSTARS.2021.3106703 |
| [191] |
ZHAO Qingzhi, SUN Tingting, ZHANG Tengxu, et al. High-precision potential evapotranspiration model using GNSS observation[J]. Remote Sensing, 2021, 13(23): 4848. DOI:10.3390/rs13234848 |
| [192] |
BORDI I, ZHU Xiuhua, FRAEDRICH K. Precipitable water vapor and its relationship with the standardized precipitation index: ground-based GPS measurements and reanalysis data[J]. Theoretical and Applied Climatology, 2016, 123(1-2): 263-275. DOI:10.1007/s00704-014-1355-0 |
| [193] |
WANG Xiaoming, ZHANG Kefei, WU Suqin, et al. The correlation between GNSS-derived precipitable water vapor and sea surface temperature and its responses to El Niño-Southern Oscillation[J]. Remote Sensing of Environment, 2018, 216: 1-12. DOI:10.1016/j.rse.2018.06.029 |
| [194] |
ZHAO Qingzhi, MA Xiongwei, YAO Wanqiang, et al. A drought monitoring method based on precipitable water vapor and precipitation[J]. Journal of Climate, 2020, 33(24): 10727-10741. DOI:10.1175/JCLI-D-19-0971.1 |
| [195] |
GUEROVA G, DIMITROVA T, GEORGIEV S. Thunderstorm classification functions based on instability indices and GNSS IWV for the Sofia Plain[J]. Remote Sensing, 2019, 11(24): 2988. DOI:10.3390/rs11242988 |
| [196] |
SUPARTA W, WARSITA A, IRCHAM I. A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2021, 24(2): 744-753. DOI:10.11591/ijeecs.v24.i2.pp744-753 |
| [197] |
毛冬海, 黎峻宇, 刘立龙. 分析ZTD在地震过程中的变化[C]//第八届中国卫星导航学术年会论文集. 南京: 中国卫星导航学术年会组委会, 2017: 205-208. MAO Donghai, LI Junyun, LIU Lilong. The analysis of ZTD's change in the process of the earthquake[C]//Proceedings of the 8th Organizing Committee of China Satellite Navigation Academic Annual Conference. Nanjing: CSNC, 2017: 205-208. |
| [198] |
TU Manhong, ZHANG Weixing, BAI Jingna, et al. Spatio-temporal variations of precipitable water vapor and horizontal tropospheric gradients from GPS during typhoon Lekima[J]. Remote Sensing, 2021, 13(20): 4082. DOI:10.3390/rs13204082 |
| [199] |
SONG D S, GREJNER-BRZEZINSKA D A. Remote sensing of atmospheric water vapor variation from GPS measurements during a severe weather event[J]. Earth, Planets and Space, 2009, 61(10): 1117-1125. DOI:10.1186/BF03352964 |
| [200] |
TANG Xu, HANCOCK C M, XIANG Zhiyong, et al. Precipitable water vapour retrieval from GPS precise point positioning and NCEP CFSv2 dataset during typhoon events[J]. Sensors, 2018, 18(11): 3831. DOI:10.3390/s18113831 |
| [201] |
EJIGU Y G, TEFERLE F N, KLOS A, et al. Monitoring and prediction of hurricane tracks using GPS tropospheric products[J]. GPS Solutions, 2021, 25(2): 76. DOI:10.1007/s10291-021-01104-3 |
| [202] |
KANG I, PARK J. Use of GNSS-derived PWV for predicting the path of typhoon: case studies of soulik and kongrey in 2018[J]. Journal of Surveying Engineering, 2021, 147(4): 04021018. DOI:10.1061/(ASCE)SU.1943-5428.0000369 |
| [203] |
HE Qimin, ZHANG Kefei, WU Suqin, et al. Real-time GNSS-derived PWV for typhoon characterizations: a case study for super typhoon Mangkhut in Hong Kong[J]. Remote Sensing, 2020, 12(1): 104. |
| [204] |
SOLHEIM F S, VIVEKANANDAN J, WARE R H, et al. Propagation delays induced in GPS signals by dry air, water vapor, hydrometeors, and other particulates[J]. Journal of Geophysical Research: Atmospheres, 1999, 104(D8): 9663-9670. DOI:10.1029/1999JD900095 |
| [205] |
刘强, 陈西宏, 孙际哲, 等. 基于中国部分城市气象条件的对流层延迟分析[J]. 天文学报, 2014, 55(2): 180-188. LIU Qiang, CHEN Xihong, SUN Jizhe, et al. Tropospheric delay analysis based on some Chinese cities' meteorologic conditions[J]. Acta Astronomica Sinica, 2014, 55(2): 180-188. DOI:10.3969/j.issn.0001-5245.2014.02.008 |
| [206] |
WEI Pengzhi, XIE Shaofeng, HUANG Liangke, et al. Ingestion of GNSS-derived ZTD and PWV for spatial interpolation of PM2.5 concentration in central and southern China[J]. International Journal of Environmental Research and Public Health, 2021, 18(15): 7931. DOI:10.3390/ijerph18157931 |
| [207] |
李燕敏, 高雅萍, 魏瑶, 等. GNSS反演数据与PM2.5质量浓度相关性研究[J]. 测绘地理信息, 2019, 44(4): 65-67. LI Yanmin, GAO Yaping, WEI Yao, et al. Correlation between GNSS inversion data and PM2.5 concentration data[J]. Journal of Geomatics, 2019, 44(4): 65-67. |
| [208] |
郭洁, 李国平, 黄文诗. GPS可降水量与大雾天气关系的初步分析[J]. 自然灾害学报, 2011, 20(4): 142-146. GUO Jie, LI Guoping, HUANG Wenshi. Preliminary analysis of relationship between GPS-based precipitable water, vapor and weather with dense fog[J]. Journal of Natural Disasters, 2011, 20(4): 142-146. |
| [209] |
LAU L, HE Jun. Investigation into the effect of atmospheric particulate matter (PM2.5 and PM10) concentrations on GPS signals[J]. Sensors, 2017, 17(3): 508. DOI:10.3390/s17030508 |
| [210] |
赵庆志, 苏静, 杨鹏飞, 等. 利用GNSS PWV的AOD自适应预测方法[J]. 测绘学报, 2021, 50(10): 1279-1289. ZHAO Qingzhi, SU Jing, YANG Pengfei, et al. AOD adaptive prediction method based on GNSS PWV[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1279-1289. DOI:10.11947/j.AGCS.2021.20210052 |
| [211] |
BIJJAHALLI S, SABATINI R, GARDI A. GNSS performance modelling and augmentation for urban air mobility[J]. Sensors, 2019, 19(19): 4209. DOI:10.3390/s19194209 |
| [212] |
ALIYU Y A, BOTAI J O. Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates[J]. Atmospheric Environment, 2018, 193: 24-32. DOI:10.1016/j.atmosenv.2018.09.001 |
| [213] |
GUO Min, ZHANG Hanwei, XIA Pengfei. A method for predicting short-time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay[J]. Meteorological Applications, 2020, 27(1): e1866. |
| [214] |
郭敏, 张捍卫, 张红利. GNSS产品预测小时尺度上PM2.5浓度的不同模型分析研究[J]. 地球物理学进展, 2020, 35(6): 2068-2074. GUO Min, ZHANG Hanwei, ZHANG Hongli. Analysis and research of different model of PM2.5 concentration change prediction on hourly scale based on GNSS product[J]. Progress in Geophysics, 2020, 35(6): 2068-2074. |
| [215] |
梁春丽. 联合地基GNSS与MODIS的雾霾预测[D]. 桂林: 桂林理工大学, 2018. DOI: 10.27050/d.cnki.gglgc.2018.000247. LIANG Chunli. Haze forecast of joint ground-based GNSS and MODIS[D]. Guilin: Guilin University of Technology, 2018. DOI: 10.27050/d.cnki.gglgc.2018.000247. |