Seasonal Vertical Crustal Motions (SVCM) in Yunnan are assessed from 27 continuous Global Positioning System (cGPS) stations observations over the period 2010–2015. Results demonstrate significant SVCM in Yunnan with annual cyclic movements as the dominant pattern, the mean annual cyclic amplitude and initial phase is 9.7 mm and 2.9 months, respectively. These annual cyclic movements exhibit a high degree of spatial uniformity as a whole, while some spatial differences exist across regions. GPS seasonal vertical time-series show strong consistency with GRACE (Gravity Recovery and Climate Experiment) data, providing further indication that regional deformation exerts a dominant effect on the GPS vertical time-series. Over the period, the Yunnan area featured substantial precipitation that shows significant annual periodicity and a strong correlation with the GPS vertical time-series. The seasonal precipitation is therefore concluded to be the regional dominant factor that governs vertical cyclic crustal movements in this area. Differences in topography and precipitation between western and eastern Yunnan (i.e. across the Jinshajiang–Red River Fault) result in a greater magnitude of SVCM in western Yunnan compared to that in eastern Yunnan. Vertical linear movement rates of Yunnan derived from GPS also exhibit evident differences between eastern and western Yunnan. Therefore, results suggest that the Red River Fault is an important factor in the spatial variability of vertical crustal movements in Yunnan.

Almost no in situ seismic data are available for Venus; therefore, we can constrain the deep structure of this planet only from the geodetic data obtained by spacecraft. Of particular interest is Venus’s core‐mantle boundary, which holds clues about the origin and evolution of this celestial body. In this study, we build a series of different mantle/core models of Venus based on several mantle composition models and compare their associated Love numbersk₂with the observed values. Due to the large uncertainty in the observed values ofk₂, the state of Venus’s core cannot be reliably constrained. However, the expected precision ofk₂obtained by the EnVision mission will sufficiently reduce the acceptable model space and contribute to estimating the mantle viscosity structure. Based on current geodetic data, we find that the bottom of Venus’s mantle may not feature a phase transition from perovskite to post‐perovskite if the FeO content of the mantle is less than 8.1 wt%; this region may be different from Earth’s D” layer. Furthermore, we find that the combination of observedk₂andQcan be used to distinguish whether the lowermost part of Venus’s mantle is a high‐temperature basalt layer or a thin thermal boundary layer if observations ofQcan be obtained.

Monitoring the hydrological activities of subglacial lakes is critical to understanding the subglacial hydrological system and evaluating the internal mass changes of the Antarctic ice sheet. Drainage or filling events of active lakes lead to elevation changes in the ice surface. These changes can be observed by satellite altimetry, but the monitoring must be conducted continuously since the water movements in active subglacial lakes may occur frequently. We used CryoSat-2 Baseline-D and ICESat-2 data from 2010 to 2020 to obtain the time series of the ice surface elevation changes for 17 active lakes. We also evaluated the uncertainty of the time series derived from the CryoSat-2 data by cross-validation. The mean and RMS of the biases between the CryoSat-2-based and ICESat-2-based time series are generally less than 0.3 m and 1.0 m, respectively. However, the mean and RMS are greater over the lakes with rough ice surfaces, such as Whillans₆, KT1, Mac3, and Slessor₂₃. The drainage and filling events continue exhibiting in the extended period and the hydrological activities of SLW, L12, Whillans₆, L78, and Mac1 occurred periodically. Furthermore, we inferred the hydrological connections between the lakes combining simulated water pathways.

Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41°. The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, >73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error < 3.9 mm/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.