Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China.

Given the increasing frequency and severity of floods caused by climate change, there is a pressing requirement for creative ways to improve public comprehension and control of hydrological phenomena. Contemporary technology provides unparalleled possibilities to transform this domain. This project investigates the possibilities for merging gaming engines and digital twins to enhance flood education, data visualization, and interactive monitoring. This study proposes the utilization of immersive digital twins to enhance the comprehension of hydrological and hydraulic systems. The suggested method utilizes game engines to generate dynamic and interactive models that connect raw data to practical insights, enabling a more profound understanding of flood dynamics. This study underscores the wide-ranging usefulness of digital twins in various watersheds by focusing on the development of advanced monitoring systems, the benefits of improved data visualization, and educational outreach. The incorporation of real-time data via IoT technology considerably improves the significance and precision of these virtual models. This novel approach seeks to refashion flood management approaches by cultivating well-informed stakeholders and advocating for effective environmental education, ultimately leading to more resilient and prepared communities. An immersive digital twin of the real world can assist decision-makers technically, psychologically, and mentally by making complex phenomena easier to understand and visualize, thanks to real-time data and simulations that keep the information up-to-date, consequently leading to a more precise and intuitive decision-making process.

Spatiotemporally seamless precipitable water vapor (PWV) maps with high accuracy, spatiotemporal resolution, and continuity are of significance for climatical research. The current frameworks for the PWV maps have predominantly concentrated on fusing PWV derived from ERA5 reanalysis (ERA-PWV) and MODIS near-infrared data (MOD-NIR-PWV), falling short in accuracy. In this study, PWV derived from Global Navigation Satellite Systems (GNSS-PWV) is introduced to produce spatiotemporally PWV maps with improved accuracy. The fusion framework involves two main steps: 1) spatial fusion for producing an initial PWV map with spatiotemporal continuity through spherical cap harmonic analysis; 2) temporal fusion for producing a refined PWV map with high accuracy through residual correction. GNSS-PWV over 188 stations, 0.25°×0.25° ERA-PWV, and 0.05°×0.05° MOD-NIR-PWV over China from 2013 to 2018 are used to produce the daily 0.05°×0.05° PWV maps. The performance is evaluated using out-of-sample data, containing 18 GNSS-PWV and 90 ERA-PWV, and independent reference data, containing radiosonde-derived PWV over 72 stations. When compared to the out-of-sample GNSS-PWV and ERA-PWV, the PWV maps exhibit mean biases of -1.04 and -0.55 mm and RMS of 1.75 and 0.97 mm, respectively. These are equivalent to 8.7% and 32.1% reductions in bias and 25.5% and 49.5% reductions in RMSE relative to MOD-NIR-PWV. When radiosonde-derived PWV is used as the reference, the PWV maps have the mean bias and RMSE of -0.53 and 2.21 mm, respectively, which outperforms ERA-PWV (-0.75 and 2.69 mm). These results indicate the effectiveness of the novel fusion framework in producing seamless PWV maps.

As one of the atmosphere propagation delays, the tropospheric delay is a significant error source that should be properly handled in high-precision global navigation satellite system (GNSS) applications. We propose an improved zenith tropospheric delay (ZTD) modeling method whereby the piecewise model of the atmospheric refractivity is introduced. Compared with using the exponential model to fit ZTD in vertical direction, the ZTD piecewise model has a better performance. Based on ERA5 2.5° × 2.5° reanalysis data produced by the European Centre for Medium-Range Weather Forecasting (ECMWF) from 2013 to 2017, we establish the regional gridded ZTD model (RGZTD) using a trigonometric function for China and the surrounding areas, which ranges from 70° E to 135° E in longitude and from 15° N to 55° N in latitude. To verify the performance of RGZTD model, the ERA5 ZTD data in 2017–2018, the radiosonde ZTD data from 86 radiosonde stations over China in 2017–2018, and the tropospheric delay products on 251 GNSS stations from Crustal Movement Observation Network of China (CMONOC) in 2016–2017 are used as external compliance check data. The results show that the overall accuracy of RGZTD model is better than that of exponential model, UNB3m model, and GPT3 model. Moreover, the accuracy can be improved by about 13.4%, 7.1%, and 6.2% when ERA5 reanalysis data, radiosonde data, and CMONOC data are used as reference values, respectively. High-accuracy ZTD data can be provided because the RGZTD model takes into account the vertical variation of ZTD through the new piecewise model.

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m³, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m³, which outperforms the non-rainy period with the RMSE of 1.007 g/m³.