With the launch of altimetry satellites with different observation frequencies and different survey missions, it is necessary to integrate multi-satellites altimeter data to establish a new global marine gravity anomaly model. Based on Ka-band sea surface heights (SSHs) from SARAL/AltiKA and Ku-band SSHs from other satellites (including HY-2A) in geodetic missions and exact repeat missions, the global marine gravity anomaly model of SDUST2021GRA on a 1^′ × 1^′ grid is derived. Gridded deflections of vertical (DOV) are determined from along-track geoid gradients by the least squares collocation method, in which the noise variances of along-track geoid gradients are obtained by the iteration method for Ka-band geodetic mission and by the SSH crossover discrepancies for other altimetry missions. SDUST2021GRA is recovered from the gridded DOVs by the inverse Vening Meinesz formula, and analyzed by comparing with the recognized marine gravity anomaly models of DTU17 and SIO V30.1. Finally, the accuracy of SDUST2021GRA, DTU17, and SIO V30.1 is assessed by preprocessed shipborne gravity anomalies. In conclusion, the differences between SDUST2021GRA and recognized models are small, indicating the reliability of SDUST2021GRA. The differences are mainly concentrated between −5 and 5 mGal, which accounts for more than 95 % of the total number. Assessed by shipborne gravity, the accuracy of SDUST2021GRA is 2.37 mGal globally, which is higher than that of DTU17 (2.74 mGal) and SIO V30.1 (2.69 mGal). The precision advantage of SDUST2021GRA is mainly concentrated in offshore areas. HY-2A-measured altimeter data have an important role on gravity anomaly recovery in areas with complex coastlines and many islands. SDUST2021GRA is concluded to reach an international advanced level for the altimeter-derived marine gravity model, especially in the offshore area. The SDUST2021GRA model data are freely available at https://doi.org/10.5281/zenodo.6668159 (Zhu et al., 2022).

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

To investigate the temporal-spatial distribution and evolutions of global Total Electron Content (TEC), we estimate the global TEC data from 1999 to 2013 by processing the GPS data collected by the International Global Navigation Satellite System (GNSS) Service (IGS) stations, and robustly constructed the TEC time series at each of the global 5°×2.5° grids. We found that the spatial distribution of the global TEC has a pattern where the number of TECs diminishes gradually from a low-latitude region to high-latitude region, and anomalies appear in the equatorial crest and Greenland. Temporal variations show that the peak TEC appears in equinoctial months, and this corresponds to the semiannual variation of TEC. Furthermore, the winter anomaly is also observed in the equatorial area of the northern hemisphere and high latitudes of the southern hemisphere. Morlet wavelet analysis is used to determine periods of TEC variations and results indicate that the 1-day, 26.5-day, semi-annual and annual cycles are the major significant periods. The fitting results of a quadratic polynomial show that the effect of solar activity on TEC is stronger in low latitudes than in mid-high latitudes, and stronger in the southern hemisphere than in the northern hemisphere. But the effect in low latitudes in the northern hemisphere is stronger than that in low latitudes in the southern hemisphere. The effect of solar activity on TECs was analyzed with the cross wavelet analysis and the wavelet coherence transformation, and we found that there appears to be a strong coherence in the period of about 27 days. So the sunspot as one index of solar activity seriously affects the TEC variations with the sun’s rotation. We fit the TEC data with the least squares spectral analysis to study the periodic variations of TEC. The changing trend of TEC is generally -0.08 TECu per year from 1999 to 2013. So TECs decrease over most areas year by year, but TECs over the Arctic around Greenland maintained a rising trend during these 15 years.