A Comprehensive Evaluation of Key Tropospheric Parameters from ERA5 and MERRA-2 Reanalysis Products Using Radiosonde Data and GNSS Measurements

In order to study and forecast extreme weather, a comprehensive and systematic analysis of the spatial and temporal relationship between Precipitable Water Vapor (PWV), predicted by Numerical Weather Predication (NWP) data, and precipitation, is necessary. The goal of this paper was to study the temporal and spatial relationship between PWV and precipitation during the so-called ‘July 20’ (18–21 July 2021) heavy rainstorm in Zhengzhou. Firstly, the PWV data provided by 120 radiosonde stations uniformly distributed throughout the world, and two IGS stations in China, in 2020, was used to evaluate the accuracy of PWV estimation by ERA5 and MERRA-2 data, and the factors affecting the accuracy of NWP PWV were explored. Secondly, ERA5 PWV and the precipitation data of six meteorological stations were used to qualitatively analyze the relationship between PWV and precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. Finally, a quantitative study was conducted by an eigenvalue matching method. The main experimental results were as follows. Compared with MERRA-2 PWV, the accuracy of ERA5 PWV was slightly higher. Latitude, altitude and season were the influencing factors of the NWP PWV estimation accuracy. The change trend of ERA5 PWV was consistent with both 24 h cumulative precipitation and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. The average optimal matching degree and optimal matching time between NWP PWV and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou was 56.6% and 3.68 h, respectively. The maximum optimal matching degree was 80.3%. The spatial–temporal relationship between NWP PWV and surface precipitation was strong.

Section: Nwp Pwv Estimation Methodsmentioning

confidence: 99%

Spatial–Temporal Relationship Study between NWP PWV and Precipitation: A Case Study of ‘July 20’ Heavy Rainstorm in Zhengzhou

Chen

Liu

et al. 2022

“…However, this also underlines the deficiency of accuracy in inversion ZTDs at low latitudes. Research has shown that drastic changes in temperature and water vapor could affect the accuracy of ZTD inversions using tropospheric delay models at low latitudes [45]. Temperature, precipitation, and light are important indicators of the climatic conditions of a region, indicating that different climate types may affect the inversion accuracy of tropospheric delay.…”

Section: Climate Correlation Analysis Of Errorsmentioning

confidence: 99%

“…ing tropospheric delay models at low latitudes [45]. Temperature, precipitation, and light are important indicators of the climatic conditions of a region, indicating that different climate types may affect the inversion accuracy of tropospheric delay.…”

Section: Climate Correlation Analysis Of Errorsmentioning

confidence: 99%

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

Liu

Huang

et al. 2022

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type.

“…The International GNSS Service (IGS) center is operating a worldwide GNSS network with more than 400 GNSS reference stations and provides regularly a 5 min sampling ZTD (zenith total delay) tropospheric product. The accuracy of IGS ZTD estimates has been proven to be better than 5 mm [37][38][39]. To ensure the reliability of the IGS ZTD estimates, the ZTD data are screened using a quality control scheme described by Bock et al [40], and the abnormal observation data are removed.…”

Section: Pwv Retrieval From Gnssmentioning

confidence: 99%

Investigation of Antarctic Precipitable Water Vapor Variability and Trend from 18 Year (2001 to 2018) Data of Four Reanalyses Based on Radiosonde and GNSS Observations

Zeng

Huang

et al. 2021

Self Cite

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade−1) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.