A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM_2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM_2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM_2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM_2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM_2.5 changes. PM_2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM_2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM_2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m⁻³ m⁻¹ s. This study highlighted the impact of meteorological conditions on PM_2.5 growth, although it was much smaller than the anthropogenic emissions impact.

Accurate terrestrial water storage (TWS) estimation is important to evaluate the situation of the water resources over the Yangtze River Basin (YRB). This study exploits the TWS observation from the new temporal gravity field model, HUST-Grace2016 (Huazhong University of Science and Technology), which is developed by a new low-frequency noise processing strategy. A novel GRACE (Gravity Recovery and Climate Experiment) post-processing approach is proposed to enhance the quality of the TWS estimate, and the improved TWS is used to characterize the drought and flood events over the YRB. The HUST-Grace2016-derived TWS presents good agreement with the CSR (Center for Space Research) mascon solution as well as the PCR-GLOBWB (PCRaster Global Water Balance) hydrological model. Particularly, our solution provides remarkable performance in identifying the extreme climate events e.g., flood and drought over the YRB and its sub-basins. The comparison between GRACE-derived TWS variations and the MODIS-derived (Moderate Resolution Imaging Spectroradiometer) inundated area variations is then conducted. The analysis demonstrates that the terrestrial reflectance data can provide an alternative way of cross-comparing and validating TWS information in Poyang Lake and Dongting Lake, with a correlation coefficient of 0.77 and 0.70, respectively. In contrast, the correlation is only 0.10 for Tai Lake, indicating the limitation of cross-comparison between MODIS and GRACE data. In addition, for the first time, the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) vertical velocity data is incorporated with GRACE TWS in the exploration of the climate-induced hydrological activities. The good agreement between non-seasonal NCEP/NCAR vertical velocities and non-seasonal GRACE TWSs is found in flood years (2005, 2010, 2012 and 2016) and drought years (2006, 2011 and 2013). The evidence shown in this study may contribute to the analysis of the mechanism of climate impacts on the YRB.

The way to reach the prelaunch baseline accuracy is still one of crucial issues for Gravity Recovery and Climate Experiment (GRACE) temporal gravity field model determination. In this work, two hybrid processing strategies are developed to improve the accuracy of GRACE temporal solution. One is for kinematic empirical parameters, the other is for arc‐specific parameters. These two hybrid processing strategies are compared with two solely processing strategies including filter predetermined strategy (FPS) and pure predetermined strategy (PPS). Assessing with a closed‐loop simulation study, the results demonstrate the outperformance of our hybrid processing strategies. Using GRACE Level 1B data, the GRACE monthly gravity field models HUST‐FPS‐PPS and HUST‐Grace2019 are, respectively, determined with the hybrid processing strategies, and they are compared with the solely solutions HUST‐FPS and HUST‐PPS. The comparison results demonstrate that (1) as for the kinematic empirical parameter, the hybrid processing strategy can retain the temporal signal but also reduce the noise level. For instance, the annual amplitudes over Amazon River Basin are 16.0 cm for HUST‐PPS, 19.1 cm for HUST‐FPS, and 19.4 cm for HUST‐FPS‐PPS. In contrast, the cumulative geoid difference of HUST‐FPS‐PPS reduces by 11% and 26%, when compared to HUST‐PPS and HUST‐FPS. (2) As for the arc‐specific parameters, the hybrid processing strategy also significantly improves the recovered monthly solution. Compare with Center for Space Research RL05 and HUST‐FPS‐PPS, the cumulative geoid difference of HUST‐Grace2019 reduces by 19% and 12%. The comparison results in spatial domain also support our conclusions. The hybrid processing strategy of this work is likely applicable for GRACE Follow‐On data.

During temporal gravity field model determination, the kinematic empirical parameters are mainly designed to remove the strong bias, drift, and 1‐cycle per revolution variations in range‐rates. In practice, two different strategies are commonly used to process these empirical parameters. One is to determine the empirical parameters before solving spherical harmonic coefficients, called Pure Predetermined Strategy (PPS). The other is to simultaneously determine the empirical parameters and spherical harmonic coefficients, called Pure Simultaneous Strategy (PSS). In this study, apart from these two strategies, a novel processing strategy called Filter Predetermined Strategy (FPS) is also discussed. These different processing strategies may result in different solutions. With the Gravity Recovery and Climate Experiment Level 1B data spanning 2005 to 2010, the impacts of different kinematic empirical parameters processing strategies were assessed in detail. The numerical results indicate that (1) using three different processing strategies and their hybrids can determine the temporal gravity field model, while (2) the solutions via PPS present apparent temporal signal attenuation, which is approximately 15% lower in annual amplitude in Amazon River Basin, and 15% lower in yearly trend in Greenland, and (3) the signal‐to‐noise ratios of the solutions via PPS are generally smaller than those of the solutions via FPS and PSS, and (4) the performance of FPS is superior in terms of postfit range‐rates, but compatible with PSS in terms of other cross comparisons. According to comprehensive comparison results in terms of temporal signals and noise, the performance of our Huazhong University of Science and Technology models determined via FPS is in excellent accordance with other representative temporal gravity field models, such as CSR RL05, GFZ RL05a, and JPL RL05.