At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications.

https://www.mdpi.com/2072-4292/12/11/1817/pdf

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

Constellations of GNSS-R satellites improve the coverage of regions of interest by repeating measurements in a shorter period of time than with a single spacecraft. However, the temporal and spatial resolution of the samples are dependent on each other. Detecting short time scale changes is generally done with coarser spatial resolution. Likewise, detailed observations of a region with small-scale features require longer intervals of time between observations. This study demonstrates the relationship between temporal and spatial resolution and its dependence on key mission design parameters such as the number of satellites, the number of orbit planes, and their inclination.

Relationship Between Temporal and Spatial Resolution for a Constellation of GNSS-R Satellites

Reanalysis products have played an important role in space geodetic tropospheric delay retrieval and modeling in the past two decades. As the release of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), with improved temporal-spatial resolutions compared to its predecessor, ECMWF Re-Analysis Interim (ERAI), the performance of ERA5 in tropospheric delay retrieval was comprehensively investigated in this study. Hourly tropospheric delays in zenith and 5° elevation directions at 312 International GNSS Service (IGS) stations covering the year of 2018 from ERA5 and ERAI were ray-traced. Taking IGS Zenith Total Delay (ZTD) as reference, the reanalysis-derived ZTDs were evaluated in annual, seasonal and diurnal scales, and superior performances of ERA5 ZTD to ERAI ZTD in all scales were revealed, with bias, Root Mean Square (RMS) and standard deviation of 1.6, 11.0 and 10.1 mm for ERA5, and of 3.1, 13.8 and 12.6 mm for ERAI, respectively. Due to the absence of reliable Slant Path Delay (SPD) references, the SPDs as well as the mapping factors derived from ERA5 and ERAI were directly compared and converted to equivalent station height errors at these stations. Obvious differences were also found for SPDs and mapping factors, especially for the wet component, with slant wet delay and wet mapping factor difference RMSs of 51.9 mm and 146.1 × 10−3, respectively, corresponding to equivalent station height RMSs of about 10.4 mm. Tropospheric delays and models (e.g., mapping functions) derived from ERA5, with improved performance and temporal resolution (e.g., to support potential tropospheric parameter diurnal variation study), therefore can be expected for space geodetic applications.

Improved performance of ERA5 in global tropospheric delay retrieval

High-precision GNSS PWV retrieval using dense GNSS sites and in-situ meteorological observations for the evaluation of MERRA-2 and ERA5 reanalysis products over China

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

/pdf/spatiotemporal-characteristics-of-gnss-derived-precipitable-3z07ju0tkt.pdf

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer.

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth’s surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna’s available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO’s altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth’s surface is related to the LEO inclination and constellation construction.

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Earth Rotation Parameters (ERP) are indispensable in the transformation between the Celestial Reference Frame and the Terrestrial Reference Frame, and significant for high-precision space navigation and positioning. As a key parameter in ERP, Polar Motion (PM) is of great importance in analyzing and understanding the dynamic interaction between solid Earth, atmosphere, ocean and other geophysical fluids. The diverse excitations, as well as complex motion mechanisms of PM, make it more difficult for its high-precision prediction. In this study, the characteristics of PM from 1962 to 2018 are firstly analyzed. The main period term of the PM is extracted and reconstructed by the Fourier Transform Band-Pass Filter, which indicates Chandler’s amplitude has decayed to its lowest state in 2016 and then enters into the next growth stage. More importantly, a Retrograde Semi-annual Wobble (RSAW) is detected and confirmed for the first time. Secondly, the contributions of Retrograde Annual Wobble (RAW) and RSAW terms to PM are analyzed and compared. Results demonstrate that the magnitudes of RAW and RSAW terms to PM from 1962 to 2018 are about 3–8 mas. Finally, in view of the existence of RAW and RSAW in PM, an improved PM prediction algorithm with considering the influence of RAW and RSAW based on least squares and autoregressive model (LS + AR) is developed. The results show that the inclusion of RAW term can effectively improve the accuracy of the LS + AR model in the prediction span of 1–360 days for both components of PM. Besides, considering the RSAW term, the prediction accuracy can be further improved in the prediction spans of 50–310 days for x component of PM, and in the prediction spans of 50–180 days for y component of PM.

An improved prediction algorithm for Earth’s polar motion with considering the retrograde annual and semi-annual wobbles based on least squares and autoregressive model

A modified algorithm combining equivalence theory with MPI technology to improve Global Navigation Satellite System data processing efficiency is presented. The reservation of covariance matrix, el...

Chunhua Jiang

Papers

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

An improved prediction algorithm for Earth’s polar motion with considering the retrograde annual and semi-annual wobbles based on least squares and autoregressive model

A parallel equivalence algorithm based on MPI for GNSS data processing