Zhenyi Zhang

Last Name

Zhang

First Name

Zhenyi

ORCID

0000-0002-8792-4497

Organisational unit

09707 - Soja, Benedikt / Soja, Benedikt

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Publications 1 - 8 of 8

GNSS meteorological ensemble tools (GMET): a free-access online service for GNSS meteorological applications
Zhang, Weixing; Lou, Yidong; Zhou, Yaozong; et al. (2024)
GPS Solutions
The signal delay caused by the troposphere layer plays a key role in many applications. It is not only a significant error source in space geodetic techniques such as GNSS and InSAR, but also a valuable data product in meteorological applications, such as climate research and weather forecasting. Hence, the community needs key troposphere-related parameters and models to support data processing, for correcting tropospheric delays or retrieving water vapor content. For this purpose, the GNSS Meteorological Ensemble Tools (GMET) online service (http://gmet.users.sgg.whu.edu.cn) was developed, constituting three modules and using state-of-art methods and data sources. The module for custom products is defined by a specific user input and generates various parameters, including meteorological parameters, mapping function coefficients, and tropospheric corrections for specific regions or sites using numerical weather models (NWMs) or radiosonde data. The module for model products is designed to distribute gridded products via File Transfer Protocol (FTP). Finally, the tropospheric parameter monitoring module provides visualizations of routinely processed zenith tropospheric delay (ZTD) products from about 500 GNSS stations. To facilitate online services, we describe here algorithms and strategies used for the product generation and data processing. All the services are free and open to all users, and feedback from users is welcome for continuing service improvements.
Assimilating UAV-based GNSS ZTDs for Numerical Weather Predictions
Zhang, Zhenyi; Liu, Mengjie; Huber, Valeria; et al. (2024)
EGUsphere
In recent decades, various studies have demonstrated that assimilating tropospheric parameters from ground-based GNSS receivers benefits numerical weather predictions (NWPs). However, the achieved performance is limited by the spatial resolution of GNSS, especially in the vertical direction. With the rapidly developing and growing market of unmanned aerial vehicles (UAVs) and the facilitates of integrating low-cost GNSS hardware into various autonomous systems over the last years, there is a potential to address this problem by utilizing UAVs to collect airborne GNSS data and generate zenith total delays (ZTDs). The airborne GNSS ZTDs can act as a potential complementary source to radiosonde data for obtaining vertical profiles of the troposphere, making it promising to investigate the impact of assimilating GNSS ZTDs of high spatio-temporal resolution in NWPs. In this study, we explored the use of GNSS data collected by a vertically ascending UAV, with ZTDs processed using the software CamaliotGNSS. Based on the airborne GNSS ZTDs, we conducted not only data assimilation but also weather predictions using the Weather Research and Forecasting Model (WRF). With the onboard meteorology observations as references, we found that assimilating airborne GNSS ZTDs positively impacted humidity and temperature forecasts, with their forecasting root-mean-square errors decreasing by about 19% and 29%, respectively. Moreover, by selecting and comparing different subsets of data, we found that better forecasts can be obtained with airborne GNSS ZTDs of higher spatio-temporal resolution. The positive results invite further exploration of applications of airborne platforms such as UAVs in the field of GNSS meteorology.
A Deep Learning-Based Precipitation Nowcasting Model Fusing GNSS-PWV and Radar Echo Observations
Liu, Mengjie; Zhang, Weixing; Lou, Yidong; et al. (2025)
IEEE Transactions on Geoscience and Remote Sensing
Nowcasting plays a critical role in disaster warning systems, and recent advancements in deep learning have shown great potential in improving the accuracy and timeliness of such predictions. This study proposes a novel deep learning-based model for precipitation nowcasting, which integrates global navigation satellite system (GNSS)-derived precipitable water vapor (PWV) data with radar observations. The model introduces two key innovations: multi-source data fusion and time-dimension attention mechanism. These advancements enhance the model's capability to accurately forecast precipitation events, particularly under challenging conditions with high rainfall intensity. In comparative experiments conducted using radar and GNSS data from Hong Kong, the model, incorporating both data fusion and the attention mechanism, demonstrated the best overall performance, with critical success index (CSI) scores increasing by 26% and Heidke skill score (HSS) scores by 23% at the 30 mm/h threshold. Moreover, it effectively simulates rainfall regions and their changing trends, demonstrating the complementary value of GNSS PWV data to radar observations.
Assessment of Drone-based GNSS ZTDs for Data Assimilation and Weather Forecasting
Zhang, Zhenyi; Mengjie, Liu; Pan, Yuanxin; et al. (2024)
Performance assessment of multi-source GNSS radio occultation from COSMIC-2, MetOp-B/C, FY-3D/E, Spire and PlanetiQ over China
Mo, Zhixiang; Lou, Yidong; Zhang, Weixing; et al. (2024)
Atmospheric Research
Global Navigation Satellite System (GNSS) radio occultation (RO) is one of the most crucial observations in atmospheric and climate science. GNSS RO globally produces accurate and long-term stable vertical profiles for essential climate variables with high vertical resolution in all weather conditions. RO measurements offer global coverage but may be limited for specific regions. Currently, various RO satellite constellation programs have been developed by nations and companies, and the growing quantity of RO observations can contribute not only globally but also has the potential to benefit specific regions, such as China. To investigate the potential of RO observation in China, the performance of five operational RO measurements from COSMIC-2, MetOp-B/C, FY-3D/E, Spire and PlanetiQ on data coverage capabilities and quality are assessed by comparing with ERA5 and radiosonde over China. The results of data coverage showed that all RO missions can acquire extensive coverage over China with effective low-altitude penetration capability, whereas MetOp-B/C exhibits some gaps in local time coverage. The results of data quality confirmed that commercial Spire and PlanetiQ are comparable to those of national-led COSMIC-2, MetOp-B/C and FY3D/E, even though Spire exhibited a lower signal-to-noise ratio (SNR). The mean bending angle and refractivity relative differences of all RO measurements are within ±0.53/1.30 % and ± 0.54/0.28 % (with respect to ERA5/radiosonde) in the altitude range of 5 to 35 km, respectively, and the corresponding relative standard deviations (SD) are less than 2.20/6.99 % and 1.35/1.56 %, respectively. Mean temperature and specific humidity differences of all RO measurements are within ±0.18/0.22 K and ± 0.08/0.22 g/kg, respectively, from the near-surface to 12 km, with SD of less than 1.26/1.67 K and 0.84/0.91 g/kg. Among the five RO missions, FY-3D/E exhibits larger errors in refractivity, temperature and specific humidity. The RO profiles derived from GPS, GLONASS, BeiDou and Galileo show comparable quality at the altitudes below 35 km. These results can help users further understand the capabilities and performance of these RO observations and indicate the application potential of numerous RO profiles from multi-source RO measurements, which is anticipated to enhance numerical weather predictions for China.
Improving forecast of “21.7” Henan extreme heavy rain by assimilating high spatial resolution GNSS ZTDs
Liu, Mengjie; Lou, Yidong; Zhang, Weixing; et al. (2025)
Atmospheric Research
Short-term forecasting of extreme weather is crucial for disaster warning and prevention. Many extreme weather events are often accompanied by significant water vapor changes, therefore, assimilating high-precision, high-resolution water vapor observations into numerical models is essential. This study explores the impact of GNSS ZTD assimilation on short-term forecasting of extreme weather using the WRF model on the case of “21.7” Henan extreme heavy rain. The impacts of GNSS ZTD assimilation on model fields and forecast results are analyzed, compared with scenarios where no data or only conventional observational data are assimilated. The results indicate that GNSS products outperform radiosonde data in temporal and spatial resolution, significantly affecting humidity fields in assimilation and providing more detailed water vapor distribution. In terms of precipitation forecasting, the analysis of POD, FAR, and ETS scores shows that GNSS data assimilation primarily impacts moderate to heavy rainfall for this case. During most simulation periods, the scores are higher when GNSS products are assimilated, with the most notable improvements observed at the threshold of 30 mm for 3-h accumulated precipitation, where ETS scores increase by an average of 21 %. However, despite the general improvement in precipitation forecast accuracy, limitations remain in forecasting peak rainfall periods.
Slant tropospheric models based on machine learning
Zhang, Zhenyi; Soja, Benedikt (2025)
IAG Scientific Assembly 2025: Abstract Book
Tropospheric errors pose a major challenge for high-precision applications of space geodetic techniques such as GNSS, InSAR, and VLBI. To estimate tropospheric delays and support real-time geodetic applications, empirical tropospheric models are needed. For example, GPT3, a state-of-the-art model developed by TU Wien, provides meteorological parameters, mapping functions and tropospheric gradients at any time and location and has been widely used by the community. However, traditional models rely on simplistic representations of tropospheric parameters, such as mean values combined with annual and semi-annual amplitudes. They also assume symmetry and use gradient compensation to account for asymmetries, which may limit their accuracy. Given the powerful modeling capabilities of machine learning, we explore its potential to enhance empirical models by directly predicting mapping functions in any direction. In this study, we processed over five years of ray-tracing results from ERA5 to create a training dataset and developed machine-learning-based models for mapping functions. We demonstrate that these models can predict mapping factors in a single step for arbitrary elevation and azimuth angles. Our new model is significantly faster in computation and more compact in size than the traditional GPT3 model, while maintaining comparable accuracy regarding global performance.
Assimilating Ground-Based and High-Dynamic Airborne GNSS Zenith Total Delays Into Numerical Weather Predictions
Zhang, Zhenyi; Liu, Mengjie; Zhang, Weixing; et al. (2025)
IEEE Transactions on Geoscience and Remote Sensing
The assimilation of ground-based global navigation satellite system (GNSS) zenith total delays (ZTDs) has been demonstrated to benefit meteorological applications such as weather forecasting and monitoring. However, their effects are limited by the restricted 3-D spatial resolution, especially in the vertical direction. Fortunately, the fast-developing uncrewed aerial vehicle (UAV) market offers an opportunity to obtain airborne GNSS ZTDs that contain atmospheric information at different locations. Given their unprecedented vertical coverage and spatial resolution, it is promising to assimilate them and further improve numerical weather predictions (NWPs), which, however, has not been investigated and is thus still unclear to the community. In this article, we obtained UAV-based GNSS ZTDs and assimilated them using the WRFDA package, with ERA5 and radiosonde as references to evaluate ZTD and relative humidity (RH) accuracy. Our results show that assimilating airborne GNSS ZTDs improved the humidity field, with root-mean-square error (RMSE) and bias decreasing by up to 22% and 57%, respectively, compared to merely assimilating ground-based GNSS ZTDs. The improvements are more significant with a higher spatial-temporal resolution of the airborne observations. This study contributes to further expanding the application of GNSS meteorology and offers initial ideas for using airborne GNSS to benefit weather forecasts.

Publications 1 - 8 of 8

Zhenyi Zhang

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