Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde

Sun, Yilin; Yang, Fei; Liu, Mingjia; Li, Zhicai; Xu, Gong; Wang, Yingying

doi:10.1016/j.atmosres.2023.106664

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…Jiang et al [39] systematically evaluated the effectiveness of ZTD data calculated using ERA5 data. The datasets are used as important resources for research in GNSS meteorology and space geodesy [40,41]. In this paper, ERA5 data were used to analyze the spatiotemporal characteristics of Gaussian coefficients and surface ZTD.…”

Section: Datamentioning

confidence: 99%

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Zhang,

Zuo,

Guo

et al. 2024

Remote Sensing

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Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

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Section: Datamentioning

confidence: 99%

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Zhang,

Zuo,

Guo

et al. 2024

Remote Sensing

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“…Jiang et al (2020) systematically evaluated the effectiveness of ZTD data calculated using ERA5. ERA5 data are used as important resources for research in GNSS meteorology and space geodesy (Chen et al 2021;Sun et al 2023).…”

Section: Datamentioning

confidence: 99%

A New Grid Model for Estimation of Zenith Tropospheric Delay Considering Vertical Adjustment Based on Gaussian Function over China

Zhang,

Zuo,

Guo

et al. 2024

Preprint

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Zenith tropospheric delay (ZTD) play an important role in the troposphere. This study proposed a new ZTD vertical profile grid model considering the height scale factor (NGZTD-H) based on the Gaussian function that adjusts the ZTD from the starting to the target height. To estimate the ZTD directly, a new NGZTD that considers the ZTD delicate diurnal variation was developed. The effectiveness of vertical and spatial interpolation for the NGZTD-H model was validated. Regarding vertical interpolation, the root mean squared error (RMSE) of the NGZTD-H model improved by 58% and 22%, compared to the global pressure and temperature 3 (GPT3) model. For spatial interpolation, the RMSE of the NGZTD-H model improved by 17% compared to that of the GPT3 model. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

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“…The hourly data of the boundary layer height (BLH), surface pressure (SP), 2 m temperature (T2M), 2 m dewpoint temperature (D2M), relative humidity (RH), east wind speed (10 m u-component of wind, U10), and north wind speed (10 m v-component of wind, V10) were used as input factors in this study (Table 1). Notably, the existing research surface ERA5 meteorological data are conducive to PM 2.5 estimation in China [35][36][37]. At night, the boundary layer stabilizes and decreases in height, allowing pollutants to accumulate at the ground level.…”

Section: Meteorological Datamentioning

confidence: 99%

High Spatial Resolution Nighttime PM2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model

Ma,

Zhang,

Chen

et al. 2023

Remote Sensing

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The concentration of particulate matter (PM2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM2.5 datasets, and the application of low-light satellite data to analyze nighttime PM2.5 concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM2.5 in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R2 = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM2.5 distribution over the area. Interannual and seasonal trends in nighttime PM2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM2.5 at night steadily decreased. Finally, the estimation of nighttime PM2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM2.5, enabled by nightly PM2.5, will serve as an invaluable resource for future research.

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Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde

Cited by 15 publications

References 37 publications

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

A New Grid Model for Estimation of Zenith Tropospheric Delay Considering Vertical Adjustment Based on Gaussian Function over China

High Spatial Resolution Nighttime PM2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model

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