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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 9072

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Dr. Li Li

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1. Geomatics Engineering, School of Geographical Science and Geomatics Engineering, Shihu Campus, Suzhou University of Science and Technology, Suzhou 215009, China
2. Research Center of BeiDou Navigation and Environmental Remote Sensing, Shihu Campus, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: GNSS meteorology; GNSS precise positioning; rainstorm disaster monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Pengfei Xia

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Guest Editor

GNSS Research Centre, Wuhan University, Wuhan, China
Interests: GNSS data processing and high-precision positioning; GNSS meteorology; space-based GNSS radio occultation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global navigation satellite systems (GNSSs) have become one of the predominant remote sensing systems. GNSS remote sensing can be used to accurately measure precipitable water vapor (PWV), slant water vapor (SWV), zenith total delay (ZTD), slant total delay (STD), horizontal gradients, atmospheric refractivity, snow depth, soil moisture and wave height in the atmosphere and environment. GNSS remote sensing has become a new era of atmospheric sounding as well as severe climate and weather monitoring. GNSS remote sensing not only leads to a better understanding of climate and weather changes, but also helps to monitor and accurately forecast severe weather events, mitigate natural disasters and save human lives.

We present a Special Issue of Atmosphere titled “GNSS Remote Sensing in Atmosphere and Environment”. We invite you to contribute to this Special Issue with original research and review articles on topics including, but not limited to:

Water vapor retrievals based on GNSS, radiosonde, microwave radiometer, and other observation systems;
Multi-sensor data assimilation and model optimization;
Weather, climate, and environment monitoring using GNSS remote sensing;
Short-term rainstorm monitoring and forecasting based on GNSS-derived tropospheric parameters (ZTD, ZWD or PWV);
Machine learning, artificial intelligence and deep learning algorithms and applications in weather prediction and climate analyses using GNSS remote sensing;
New research and applications of GNSS remote sensing in atmosphere and environment.

Dr. Li Li
Dr. Pengfei Xia
Guest Editors

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Keywords

GNSS
remote sensing
atmosphere
troposphere
water vapor
data assimilation
environment
precipitation forecast
nowcasting of severe weather events
GNSS reflectometry

Published Papers (8 papers)

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Research

22 pages, 7378 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

by Ning Huang, Shiyang Fu, Biyan Chen, Liangke Huang and Wenping Jin

Atmosphere 2024, 15(3), 306; https://doi.org/10.3390/atmos15030306 - 29 Feb 2024

Viewed by 678

Abstract

Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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17 pages, 3069 KiB

Open AccessEditor’s ChoiceArticle

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

by Li Li, Yixiang Ma, Kai Li, Jianping Pan and Mingsong Zhang

Atmosphere 2024, 15(3), 255; https://doi.org/10.3390/atmos15030255 - 21 Feb 2024

Viewed by 597

Abstract

The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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23 pages, 8313 KiB

Open AccessArticle

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

by Yin Wu, Lu Huang, Wei Feng and Su Tian

Atmosphere 2024, 15(1), 121; https://doi.org/10.3390/atmos15010121 - 19 Jan 2024

Cited by 1 | Viewed by 971

Abstract

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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17 pages, 9822 KiB

Open AccessArticle

A Comparison of Atmospheric Boundary Layer Height Determination Methods Using GNSS Radio Occultation Data

by Cong Qiu, Xiaoming Wang, Haobo Li, Kai Zhou, Jinglei Zhang, Zhe Li, Dingyi Liu and Hong Yuan

Atmosphere 2023, 14(11), 1654; https://doi.org/10.3390/atmos14111654 - 04 Nov 2023

Viewed by 938

Abstract

The accurate determination of Atmospheric Boundary Layer Height (ABLH) is crucial in various atmospheric studies and practical applications. In this study, we present a comprehensive comparative analysis of five distinct methods for estimating ABLH using Global Navigation Satellite System (GNSS) Radio Occultation (RO) data. These methods encompass the use of bending angle and refractivity profiles, namely Minimum Gradient methods of the Bending Angle (MGBA) and Refractivity (MGR) profiles, breaking point, Wavelet Covariance Transform (WCT), and Double-Parameter Model Function (DPMF). GNSS-RO data from COSMIC-2 and Spire are used. To establish robust validation, radiosonde data are employed as a reference, ensuring the reliability of our findings. The results reveal notable variations in the performances of these ABLH estimation methods. Specifically, the MGBA, MGR, breaking point, and DPMF methods exhibit strong correlations with the reference data. Conversely, the WCT method displays weaker correlations, higher biases, and elevated root-mean-square-errors, suggesting limitations in capturing the true ABLH. Furthermore, we remove outlier screening to facilitate a comparison of the differences among the five methods. The WCT and DPMF methods can detect strong variations in the profiles near the Earth’s surface and consider them as ABLH. However, these variations are caused by errors. The MGBA method emerges as a reliable and stable option, while the WCT and DPMF methods should be used with caution due to the lower quality of the GNSS-RO profiles near the Earth’s surface. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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15 pages, 5153 KiB

Open AccessArticle

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

by Zufeng Li, Yongjie Ma, Jing Liu, Yang Liu, Wei Ren and Qingzhi Zhao

Atmosphere 2023, 14(8), 1309; https://doi.org/10.3390/atmos14081309 - 18 Aug 2023

Cited by 1 | Viewed by 1038

Abstract

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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15 pages, 3724 KiB

Open AccessArticle

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

by Juntao Wu, Mingkun Su, Jun Gong, Lingsa Pan, Jiale Long and Fu Zheng

Atmosphere 2023, 14(6), 934; https://doi.org/10.3390/atmos14060934 - 26 May 2023

Cited by 1 | Viewed by 879

Abstract

With global warming, extreme weather such as floods and waterlogging occurs more frequently and seriously in recent years. During the flood, the surrounding environment of the GNSS (Global Navigation Satellite System) station will change as the volume of water increases. Considering the multipath error is directly relevant to the observation environment, thus, the influence of flood on the L4 combination observation (a geometry-free ionosphere-free linear combination of carrier phase) which is related to the multipath error of GPS (Global Positioning System) and GLONASS satellites is investigated in depth. In addition, the ground track repetition periods of GPS and GLONASS satellites are analyzed in the sky plot to illustrate the rationality of chosen reference day. Based on the results of the satellite sky plot, one and eight days are adopted to demonstrate the influence of flood on L4 combination observation for GPS and GLONASS satellites, respectively. Real data sets collected at the ZHNZ GNSS observation station during the flood from DOY (Day of Year) 193 to DOY 204, 2021 are used. Experimental results show that the flood has a significant impact on the L4 combination observation of GPS and GLONASS satellites, and the fluctuation of L4 under flood performs much larger than that of without flood. For GPS satellites, the maximum RMS (root mean square) increase rate of L4 under flood is approximately 186.67% on the G31 satellite. Even for the minimum RMS increase rate, it can reach approximately 23.52%, which is the G02 satellite. Moreover, the average RMS increase rate of GPS and GLONASS satellites can reach approximately 109.53% and 43.65%, respectively. In addition, the influence of rainfall and hardware device are also investigated, which can further demonstrate that the fluctuation of L4 is mainly caused by the flood but not by the rainfall and hardware device elements. Thus, based on the above results, the influence of flood on L4 observation should be taken into account during the applications of L4 used, such as the retrieval of soil moisture and vegetation water content based on GNSS L4 combination observations Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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14 pages, 13345 KiB

Open AccessArticle

Global Navigation Satellite System-Based Retrieval of Precipitable Water Vapor and Its Relationship with Rainfall and Drought in Qinghai, China

by Shengpeng Zhang, Fenggui Liu, Hongying Li, Qiang Zhou, Qiong Chen, Weidong Ma, Jing Luo and Yongsheng Huang

Atmosphere 2023, 14(3), 517; https://doi.org/10.3390/atmos14030517 - 07 Mar 2023

Cited by 1 | Viewed by 1236

Abstract

Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (T_m) (QH-T_m) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in T_m. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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15 pages, 2821 KiB

Open AccessArticle

Enhancing GNSS-R Soil Moisture Accuracy with Vegetation and Roughness Correction

by Zhounan Dong, Shuanggen Jin, Guodong Chen and Peng Wang

Atmosphere 2023, 14(3), 509; https://doi.org/10.3390/atmos14030509 - 06 Mar 2023

Viewed by 1798

Abstract

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm³cm⁻³, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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