Improved performance of ERA5 in global tropospheric delay retrieval

Zhou, Yaozong; Lou, Yidong; Zhang, Weixing; Kuang, Cuilin; Liu, Wenxun; Bai, Jingna

doi:10.1007/s00190-020-01422-3

Cited by 32 publications

(19 citation statements)

References 38 publications

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“…ERA5 is also among the reanalysis dataset that has recently received great attention. Zhou investigated the performance of this dataset compared to ERA-Interim dataset and found that ZTD and SPD values obtained from ERA5 dataset had better results than ERA-Interim (Zhou et al 2020 ). The ERA5 data used in the present study had a spatial resolution of 0.125° and a 6-h temporal resolution.…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…The first step in determining this parameter is to use meteorological data. Radiosonde data as well as reanalysis datasets are considered important data sources that have been widely used for modeling and estimating tropospheric errors over the past decade (Böhm et al 2006 ; Böhm et al 2015 ; Nafisi et al 2012 ; Zhou et al 2020 ). Reanalysis datasets have recently received more attention due to their arrangement in a regular grid and having a global scale.…”

Section: Introductionmentioning

confidence: 99%

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Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

2022

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Weighted mean temperature ( ) is used to determine water vapor content, precipitable water vapor, and integrated water vapor (IWV) in GNSS. This parameter is highly correlated with climate conditions as well as the type of the region. The case study is performed in Iran which has diverse climate. ERA5 reanalysis datasets were used at a compact grid of 0.125 × 0.125 between 2007 and the end of 2019 to model the . The data obtained from 12 radiosonde stations along with an IGS station located in Tehran were employed in this research. Five models were examined for . Bevis model, linear grouping model (LGM), and linear nearest grid point model (LNGPM) were considered as linear models, and harmonic model (HM) and GPT2w model were used as nonlinear models. In LGM method the study region was divided into smaller areas with different linear model coefficients using spatial grouping method. The local model in each radiosonde station was considered as a reference. According to the results, the accuracy of linear models (Bevis and LGM model) was between 3 and 8 K (radiosonde data as reference); also 7 out of 12 stations in the LGM had higher accuracy than the Bevis model (based on RMSE). The accuracy of the two GPT2w models and the harmonic model was higher than the previous two models, and it was between 2 and 4 K. The IWV values were obtained using zenith total delay observations of IGS station located in Tehran using 5 models and were compared with the IWV values of the radiosonde station. The accuracy of the values in three linear models, Bevis, LGM, and LNGPM, was, respectively, 0.2, 0.17, and 0.14 kg m −2 , and in the two nonlinear models, GPT2w and HM, was 0.13 kg m −2 .

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Section: Datasets and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

2022

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“…It has notable improvements in data assimilation technology and resolution compared to the previous generation product, ERA-Interim [16]. Previous studies have pointed out that the global IGS stations' tropospheric delay retrieved from ERA5 reanalysis data has higher accuracy [17]. In addition, the substantial improvement in the temporal resolution is of great significance to the study of small-scale tropospheric delay series periodic changes [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Huang

et al. 2022

Remote Sensing

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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.

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“…Many researchers have used the tropospheric delay derived from reanalysis data to validate ZTD measured by GNSS and concluded that they basically agreed with each other (Haase et al., 2001; Liou & Huang, 2000; Liou et al., 2000, 2001; Song et al., 2004, 2005; Vedel et al., 2001; Walpersdorf et al., 2007). Conversely, researchers are beginning to use the high‐precision GNSS ZTD to validate ZTD derived from the reanalysis data acquired by a number of meteorological observation systems (Andrei & Chen, 2008; Guerova et al., 2003; Zhang et al., 2019; Zhou et al., 2020). This supports the feasibility and reliability of the tropospheric delay derived from GNSS and reanalysis data, which can be used as references for the comparisons in this paper.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Troposphere Products Derived From VMF Data Server With ERA5 and IGS Data Over China

Yang

Guo

et al. 2021

Earth and Space Science

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Improved performance of ERA5 in global tropospheric delay retrieval

Cited by 32 publications

References 38 publications

Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

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

Assessment of the Troposphere Products Derived From VMF Data Server With ERA5 and IGS Data Over China

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