GNSS World of China

Volume 49 Issue 1
Feb.  2024
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SUN Minghan, PANG Zhiguo, LYU Juan, ZHANG Pengjie, CUI Xiangrui. Research progress and prospects of ground-based BDS/GNSS water vapor monitoring in the field of water conservancy[J]. GNSS World of China, 2024, 49(1): 19-33. doi: 10.12265/j.gnss.2023179
Citation: SUN Minghan, PANG Zhiguo, LYU Juan, ZHANG Pengjie, CUI Xiangrui. Research progress and prospects of ground-based BDS/GNSS water vapor monitoring in the field of water conservancy[J]. GNSS World of China, 2024, 49(1): 19-33. doi: 10.12265/j.gnss.2023179
shu

Research progress and prospects of ground-based BDS/GNSS water vapor monitoring in the field of water conservancy

doi: 10.12265/j.gnss.2023179
  • 1.

    China Institute of Water Resources and Hydropower Research, Beijing 100038, China

  • 2.

    Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China

  • Received Date: 2023-09-13
  • Accepted Date: 2023-09-13
    • Available Online: 2024-01-08
    Abstract
  • Almost all water vapor on Earth is concentrated in the troposphere, the content of it has a significant impact on global temperature, precipitation, and other meteorological factors, which can to some extent affect Earth’s climate change and regulate heat balance on a global scale. Tropospheric water vapor monitoring plays a crucial role in water resource management, extreme weather warning, and climate change research. In the process of continuous development and improvement of BDS/GNSS global satellite navigation technology, the retrieval of precipitable water vapor by BDS/GNSS has gradually become a new type of water vapor detection technology. Compared with traditional water vapor detection technology, BDS/GNSS can achieve high-precision and near real-time monitoring of water vapor. To deepen researchers’ understanding of BDS/GNSS precipitable water vapor (PWV), the paper provides a systematic review of the development process and research status of the retrieval of BDS/GNSS PWV, elucidating its inversion principles and methods. It mainly analyzes the application and development direction of ground-based BDS/GNSS water vapor monitoring in the field of water conservancy from high-precision water vapor monitoring, short-term and imminent rainfall forecasting, climate change, and drought & flood monitoring.

     

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  • [1]
    马雄伟, 赵庆志, 姚顽强, 等. PWV对全球气候变化的响应研究[J]. 测绘通报, 2019(S1): 54-59.
    [2]
    朱德军, 李浩博, 王晓明. GNSS遥感技术在智慧水利建设中的应用展望[J]. 水利水电技术(中英文), 2022, 53(10): 33-57.
    [3]
    姚宜斌, 赵庆志. GNSS对流层水汽监测研究进展与展望[J]. 测绘学报, 2022, 51(6): 935-952. DOI: 10.11947/j.issn.1001-1595.2022.6.chxb202206016
    [4]
    张克非, 李浩博, 王晓明, 等. 地基GNSS大气水汽探测遥感研究进展和展望[J]. 测绘学报, 2022, 51(7): 1172-1191.
    [5]
    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
    [6]
    BEVIS M, BUSINGERS, 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
    [7]
    BASILI P, BONAFONI S, FERRARA R, et al. Atmospheric water vapor retrieval by means of both a GPS network and a microwave radiometer during an experimental campaign in Cagliari, Italy, in 1999[J]. IEEE transactions geoscience and remote sensing, 2001, 39(11): 2436-2443. DOI: 10.1109/36.964980
    [8]
    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.
    [9]
    HANG S, TAO Y, KAN W, et al. Evaluation of precipitable water vapor retrieval from homogeneously reprocessed long-term GNSS tropospheric zenith wet delay, and multi-technique[J]. Remote sensing, 2021, 13(21): 4490. DOI: 10.3390/rs13214490
    [10]
    LU C X, FENG G L, ZHENG Y X, 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
    [11]
    施闯, 王海深, 曹云昌, 等. 基于北斗卫星的水汽探测性能分析[J]. 武汉大学学报(信息科学版), 2016, 41(3): 285-289.
    [12]
    郭秋英, 侯建辉, 刘传友, 等. 基于北斗三号的大气水汽探测性能初步分析[J]. 全球定位系统, 2021, 46(1): 89-97,111.
    [13]
    史航, 范士杰, 王丽欣, 等. 基于沿海业务观测的BDS大气可降水量反演[J]. 测绘工程, 2022, 31(4): 30-34. DOI: 10.19349/j.cnki.issn1006-7949.2022.04.005
    [14]
    郑志卿, 张克非, 李龙江, 等. 基于MGEX站多系统GNSS反演大气可降水量精度评估[J]. 全球定位系统, 2022, 47(5): 100-110.
    [15]
    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.
    [16]
    LU C, LI X, GE M, 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
    [17]
    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.
    [18]
    安豪, 严卫, 杜晓勇, 等. GNSS大气海洋遥感技术研究进展[J]. 全球定位系统, 2021, 46(6): 1-10.
    [19]
    张瑞, 宋伟伟, 朱爽. 地基GPS遥感天顶水汽含量方法研究[J]. 武汉大学学报(信息科学版), 2010, 35(6): 691-693.
    [20]
    潘卫华, 余永江, 罗艳艳, 等. 基于地基GPS大气可降水量的福建水汽资源时空分布特征分析[J]. 干旱气象, 2021, 39(4): 577-584.
    [21]
    丁凯文, 张晨晰, 陈宇. 基于随机森林算法的香港地区大气水汽反演[J]. 现代测绘, 2022, 45(6): 7-11.
    [22]
    韩永清, 宋晓蛟, 赵峰. 基于GNSS数据反演云南及周边地区PWV及其精度分析[J]. 测绘与空间地理信息, 2022, 45(9): 89-91.
    [23]
    李秀龙, 丁建勋, 马德富. 基于珠海北斗CORS实时探测大气可降水量[J]. 城市勘测, 2017(2): 118-121. DOI: 10.3969/j.issn.1672-8262.2017.02.027
    [24]
    施闯, 周凌昊, 范磊, 等. 利用北斗/GNSS观测数据分析“21·7”河南极端暴雨过程[J]. 地球物理学报, 2022, 65(1): 186-196. DOI: 10.6038/cjg2022P0706
    [25]
    SAASTAMOINEN J. Contributions to the theory of atmospheric refraction[J]. Bulletin géodésique, 1973, 107(1): 13-34. DOI: 10.1007/BF02522083
    [26]
    HOPFIELD H S. Two-quartic tropospheric refractivity profile for correcting satellite data[J]. Journal of geophysical research, 1969, 74(18): 4487-4499. DOI: 10.1029/JC074I018P04487
    [27]
    BLACK H D, EISNER A. Correcting satellite doppler data for tropospheric effects[J]. Journal of geophysical research: Atmospheres, 1984, 89(D2): 2616-2626. DOI: 10.1029/JD089ID02P02616
    [28]
    何力维, 陈义, 周亚军. 水汽辐射计与PPP估计天顶湿延迟对比研究分析[J]. 全球定位系统, 2015, 40(6): 68-71.
    [29]
    杨晶, 吴亮, 郝海森, 等. 基于静力学延迟模型的河北地区可降水量反演分析[J]. 河北工程技术高等专科学校学报, 2016, 94(3): 7-12.
    [30]
    章迪. GNSS对流层天顶延迟模型及映射函数研究[J]. 测绘学报, 2022, 51(9): 1984. DOI: 10.11947/j.issn.1001-1595.2022.9.chxb202209022
    [31]
    黄东桂, 刘立龙, 黄良珂, 等. 桂林地区暴雨天气下两种对流层模型的适用性分析[J]. 桂林理工大学学报, 2022, 42(3): 672-678.
    [32]
    朱明晨, 赵爱国. UNB 3 m模型的区域精度分析[J]. 测绘与空间地理信息, 2017, 40(6): 26-29.
    [33]
    周润杨, 薛玫娇. 高纬度BDS/GPS PPP中的对流层延迟改正模型优选[J]. 测绘工程, 2018, 27(2): 20-25,31.
    [34]
    李媛, 章浙涛, 何秀凤, 等. 雨雪天气对流层延迟改正模型适用性分析[J]. 导航定位学报, 2022, 10(2): 119-125.
    [35]
    李媛, 李黎, 张振, 等. 长三角地区分季节多因子本地化T_m模型研究[J]. 大地测量与地球动力学, 2020, 40(2): 140-145.
    [36]
    郑磊. 顾及季节变化的西南地区大气加权平均温度模型[J]. 导航定位学报, 2021, 9(4): 98-103.
    [37]
    邹玉学, 岳迎春, 叶涛, 等. 吉林地区非线性大气加权平均温度模型[J]. 导航定位学报, 2020, 8(4): 74-79. DOI: 10.3969/j.issn.2095-4999.2020.04.013
    [38]
    莫智翔, 黎杏, 黄良珂, 等. 顾及多因子影响的中国西部地区大气加权平均温度模型精化研究[J]. 大地测量与地球动力学, 2021, 41(2): 145-151.
    [39]
    韦海福, 陈天伟, 陈明. 顾及区域相对高程的中国区域加权平均温度模型[J]. 大地测量与地球动力学, 2021, 41(10): 1057-1062.
    [40]
    杨林, 郑南山, 高淑照, 等. 基于BP神经网络算法构建香港地区加权平均温度模型[J]. 导航定位学报, 2021, 9(4): 92-97. DOI: 10.3969/j.issn.2095-4999.2021.04.014
    [41]
    万庆同. 一种基于随机森林的区域T_m模型预测方法[J]. 城市勘测, 2022, 191(3): 84-86.
    [42]
    蔡猛, 刘立龙, 黄良珂, 等. GPT3模型反演GNSS大气可降水量精度评定[J]. 大地测量与地球动力学, 2022, 42(5): 483-488.
    [43]
    LI X Y, LONG D. 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: 1-16. DOI: 10.5194/egusphere-egu2020-6282
    [44]
    ZHANG B, YAO Y B, XIN L Y, 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
    [45]
    XIONG Z H, SANG J Z, SUN X G, et al. Comparisons of performance using data assimilation and data fusion approaches in acquiring precipitable water vapor: a case study of a western United States of America area[J]. Water, 2020, 12(10): 1-13. DOI: 10.3390/w12102943
    [46]
    XIONG Z H, ZHANG B, SANG J Z, et al. Fusing precipitable water vapor data in China at different timescales using an artificial neural network[J]. Remote sensing, 2021, 13(9): 1-20. DOI: 10.3390/rs13091720
    [47]
    ZHANG B, YAO Y B. Precipitable water vapor fusion based on a generalized regression neural network[J]. Journal of geodesy, 2021, 95(3): 1-14. DOI: 10.1007/s00190-021-01482-z
    [48]
    LU C X, ZHANG Y S, ZHENG Y X, et al. Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network[J]. GPS solutions, 2022, 27(1): 1-13. DOI: 10.1007/s10291-022-01357-6
    [49]
    方圣辉, 毕创, 乐源, 等. 利用GPS可降水量校正MODIS近红外水汽数据[J]. 测绘科学, 2016, 41(9): 38-41.
    [50]
    王佳, 李琼, 魏加华. 青海MODIS可降水量数据校正研究[J]. 青海大学学报, 2021, 39(2): 77-84.
    [51]
    杨娇, 史岚, 王茜雯, 等. 基于GPS数据的MODIS近红外水汽线性回归模型[J]. 热带地理, 2020, 40(1): 137-144.
    [52]
    段茜茜, 曲建光, 高伟, 等. 基于GPS的MODIS近红外可降水量季节性模型建立[J]. 测绘工程, 2017, 26(12): 21-26.
    [53]
    马赛, 岳迎春, 上官明, 等. 基于GNSS的MODIS大气可降水量校正模型[J]. 南京信息工程大学学报(自然科学版), 2021, 13(2): 154-160.
    [54]
    BAI J N, LOU Y D, ZHANG W X, et al. Assessment and calibration of MODIS precipitable water vapor products based on GPS network over China[J]. Atmospheric research, 2021(254): 105504. DOI: 10.1016/J.ATMOSRES.2021.105504
    [55]
    王勇, 董思思, 刘严萍, 等. 区域MODIS水汽季节修正模型[J]. 遥感信息, 2020, 35(1): 9-14. DOI: 10.3969/j.issn.1000-3177.2020.01.002
    [56]
    LI L, YUAN Z M, LUO P, et al. A system developed for monitoring and analyzing dynamic changes of GNSS precipitable water vapor and its application[C]//China satellite navigation conference (CSNC), 2015: 196. DOI: 10.1007/978-3-662-46638-4_10
    [57]
    刘邢巍, 许超钤, 吴寒, 等. 基于重庆CORS的水汽电离层实时监测方法研究及平台构建[J]. 全球定位系统, 2019, 44(4): 89-95.
    [58]
    ZHAO Q Z, ZHANG X Y, WU K, et al. Comprehensive precipitable water vapor retrieval and application platform based on various water vapor detection techniques[J]. Remote sensing, 2022, 14(10): 2507. DOI: 10.3390/rs14102507
    [59]
    GUTMAN I S, BENJAMIN G S. The role of ground-based GPS meteorological observations in numerical weather prediction[J]. GPS solutions, 2001, 4(4): 16-24. DOI: 10.1007/PL00012860
    [60]
    张利红, 何光碧, 屠妮妮, 等. 不同观测资料在西南地区数值预报中的应用[J]. 高原山地气象研究, 2013, 33(3): 23-30.
    [61]
    MOHAMMAD A S, MAJID A, ALI S K. Numerical simulation of rainfall with assimilation of conventional and GPS observations over north of Iran[J]. Annals of geophysics, 2016, 59(3): 0322. DOI: 10.4401/AG-6919
    [62]
    GONG Y Z, LIU Z Z, CHAN P W, et al. Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the weather research and forecasting (WRF) model over the south China[J]. Atmospheric research, 2023(286): 1-11. DOI: 10.1016/j.atmosres.2023.106677
    [63]
    仲跻芹, GUO Y R, 张京江. 华北地区地基GPS天顶总延迟观测的质量控制和同化应用研究[J]. 气象学报, 2017, 75(1): 147-164. DOI: 10.11676/qxxb2017.010
    [64]
    周炳君, 李昕, 陈耀登, 等. GPS ZTD资料同化对台风“利奇马”模拟的影响研究[J]. 气象科学, 2020, 40(1): 11-21.
    [65]
    SINGH R, OJHA P S, PUVIARASAN N, et al. Impact of GNSS signal delay assimilation on short range weather forecasts over the Indian region[J]. Journal of geophysical research:atmospheres, 2019, 124(17-18): 9855-9873. DOI: 10.1029/2019JD030866
    [66]
    HAAN D S. Assimilation of GNSS ZTD and radar radial velocity for the benefit of very-short-range regional weather forecasts[J]. Quarterly journal of the royal meteorological society, 2013, 139(677): 2097-2107. DOI: 10.1002/QJ.2087
    [67]
    SHI J B, XU C Q, GUO J M, et al. Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting[J]. IEEE transactions geoscience and remote sensing, 2015, 53(6): 3452-3459. DOI: 10.1109/TGRS.2014.2377041
    [68]
    罗宇, 高文娟, 罗林艳, 等. 怀化地区强降水过程中的GPS可降水量特征分析[J]. 中国农学通报, 2019, 35(36): 97-103.
    [69]
    徐爽, 胡鹏宇, 贾越, 等. 2020—2021年沈阳地区4次短时强降水过程的大气可降水量变化对比分析[J]. 气象与环境学报, 2023, 39(2): 28-34.
    [70]
    郭秋英, 赵耀, 黄守凯, 等. 基于北斗PWV的暴雨时空变化特征分析[J]. 全球定位系统, 2022, 47(5): 111-117.
    [71]
    YAO Y B, SHAN L L, ZHAO Q Z. 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
    [72]
    郭敏, 张捍卫, 夏朋飞. GNSS天顶对流层延迟的短时天气预报分析[J]. 测绘科学, 2021, 46(4): 28-36.
    [73]
    郑菲菲, 尹海燕, 张恩红, 等. GNSS数据在河源降水短临预报中的应用[J]. 广东气象, 2023, 45(2): 5-8.
    [74]
    王式太, 张定红, 殷敏, 等. 基于LSTM模型的降雨短临预报[J]. 无线电工程, 2021, 51(11): 1278-1283.
    [75]
    池钦, 赵兴旺, 陈健. 几种典型机器学习算法在短临降雨预报分析研究[J]. 全球定位系统, 2022, 47(4): 122-128.
    [76]
    PEDRO B, JOAO C. F, GIOVANNI N. 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
    [77]
    SONG D S, DOROTA A G B. 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
    [78]
    周苏娅, 闻德保, 梅登奎. 连续台风时期香港区域的水汽变化分析[J]. 大地测量与地球动力学, 2020, 40(1): 82-86.
    [79]
    王钦, 姚宜斌, 刘晨, 等. 台风期间GNSS-PWV与降雨量的相关性探究——以上海市为例[J/OL]. (2022-02-22)[2023-11-16]. 测绘地理信息, 2022. https://kns.cnki.net/kcms/detail/42.1840.P.20220221.1451.001.htm
    [80]
    WANG H S, LIU Y B, LIU Y W, et al. Assimilation of GNSS PWV with NCAR-RTFDDA to improve prediction of a landfall typhoon[J]. Remote sensing, 2022, 14(1): 178. DOI: 10.3390/rs14010178
    [81]
    WON J, KIM D. Analysis of temporal and spatial variation of precipitable water vapor according to path of typhoon EWINIAR using GPS permanent stations[J]. Journal of positioning, navigation, and timing, 2015, 4(2): 87-95. DOI: 10.11003/JPNT.2015.4.2.087
    [82]
    HE Q, ZHANG K, WU S, et al. Real-time GNSS-derived PWV for typhoon characterizations: A case study for super typhoon Mangkhut in Hong Kong[J]. Remote sensing, 2019, 12(1): 104. DOI: 10.3390/rs12010104
    [83]
    朱玉香, 陈永贵, 安春华. 台风“利奇马”在山东期间的GPS-PWV动态特征[J]. 导航定位学报, 2020, 8(6): 103-108.
    [84]
    SUPARTA W, ISKANDAR A, SINGH M S J, et al. A study of El Niño-Southern oscillation impacts to the South China sea region using ground-based GPS receiver[J]. Journal of physics:conference series, 2013, 423(1): 012043. DOI: 10.1088/1742-6596/423/1/012043
    [85]
    SUPARTA W. Observations of precipitable water vapor along the maritime continent associated with El Niño-Southern oscillation activity[J]. Annals of geophysics, 2018, 61(5): 556. DOI: 10.4401/AG-7600
    [86]
    WANG X M, ZHANG K F, WU S Q, 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
    [87]
    ZOFIA B, GRZEGORZ N, BEATA L, et al. Interannual variability of the GNSS precipitable water vapor in the global tropics[J]. Atmosphere, 2021, 12(12): 1698. DOI: 10.3390/atmos12121698
    [88]
    GHASEMIFAR, ELHAM, IRANNEZHAD, et al. The role of ENSO in atmospheric water vapor variability during cold months over Iran[J]. Theoretical and applied climatology, 2022, 148(1-2): 795-817. DOI: 10.1007/s00704-022-03969-x
    [89]
    ZHAO Q, LIU Y, YAO W, 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
    [90]
    ZHAO Q Z, MA X W, YAO W Q, et al. Improved drought monitoring index using GNSS-derived precipitable water vapor over the Loess Plateau area[J]. Sensors (basel, switzerland), 2019, 19(24). DOI: 10.3390/s19245566
    [91]
    ZHAO Q Z, SUN T T, ZHANG T X, et al. High-precision potential evapotranspiration model using GNSS observation[J]. Remote sensing, 2021, 13(23): 4848. DOI: 10.3390/rs13234848
    [92]
    MA X W, ZHAO Q Z, YAO Y B, et al. A novel method of retrieving potential ET in China[J]. Journal of hydrology, 2021, 598(1): 126271. DOI: 10.1016/J.JHYDROL.2021.126271
    [93]
    UANG-AREE P, KINGPAIBOON S, KHUANMAR K. The development of atmospheric crop moisture index for irrigated agriculture[J]. Russian meteorology and hydrology, 2017, 42(11): 731-739. DOI: 10.3103/S1068373917110073
    [94]
    ZHAO Q Z, MA X W, YAO W Q, 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
    [95]
    徐成志. 基于灰色灾变理论的辽阳市洪涝灾害预测[J]. 山西水利科技, 2021, 222(4): 16-18.
    [96]
    HAMID D, ALI H T, OMID R, et al. A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation[J]. Journal of hydrology, 2021, 603(PA). DOI: 10.1016/j.jhydrol.2021.126854
    [97]
    SUPARTA W, ADNAN J, ALI A M. Monitoring of GPS precipitable water vapor during the severe flood in Kelantan[J]. American journal of applied sciences, 2012, 9(6): 825-831. DOI: 10.3844/AJASSP.2012.825.831
    [98]
    SUPARTA, WAYAN, RAHMAN, et al. Investigation of flash flood over the west Peninsular Malaysia by global positing system network[J]. Advanced science letters, 2015, 21(2): 153-157. DOI: 10.1166/ASL.2015.5845
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