An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm’s development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm³/cm³. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

Two years of observational data from the 532 nm Rayleigh lidar were used to study the vertical profile characteristics of atmospheric gravity wave potential energy density (GWPED) between 40–80 km above Jiuquan (40° N, 95° E) for the first time. The atmospheric gravity waves (AGWs) characteristics are presented in terms of the atmospheric relative temperature perturbation, along with the estimated annual and seasonal GWPED with high spatial and temporal resolution (0.5 km and 1 h). The annual potential energy mass density ${\mathrm{E}}_{\mathrm{pm}}$ and volume density ${\mathrm{E}}_{\mathrm{pv}}$ vertical profiles show that the GWPED in the upper mesosphere is close to the adiabatic growth rate. The seasonal vertical profiles result shows that ${\mathrm{E}}_{\mathrm{pm}}$ is higher in autumn–winter than in spring–summer in all the observed altitudes. The GWPED approaches adiabatic growth above 61 and 65 km in spring–summer and autumn–winter, respectively. The AGWs severely dissipate below the turning altitudes and transfer energy into the background atmosphere. The GWPED scale heights show that the AGWs dissipation rate of spring–summer is close to that of autumn–winter. Furthermore, based on the wind data from SD–WACCM, the influence of critical level filtering on AGWs is discussed. It plays an important role in affecting the seasonal variation in GWPED.

Meteor radar is one of the key tools for studying the atmospheric dynamics in the mesosphere and lower thermosphere. The physical parameters obtained by meteor radar inversion can provide important statistical information for research. The daily and annual variations in meteor azimuth distribution detected by meteor radars contain information about meteor source regions and patterns related to the rotation and revolution of the Earth. Using the meteor parameters from two meteor radars located in Mohe (53.5° N, 122.3° E) and Wuhan (30.6° N, 114.4° E), this study calculates the variation patterns in the meteor azimuth distribution over the two sites over 1 year. Additionally, this study introduces the variable, Max_Azi, to describe the position of the peak of azimuth distribution. The peak value of azimuth distribution is calculated by Gaussian fitting to quantify the variation patterns in azimuth distribution. This study provides complementary information on the azimuth distribution in high and middle latitudes. The results indicated that the azimuth distribution variation for the Mohe meteor radar is consistent with the Earth’s revolution model.

为了弥补中高层大气风场探测数据的不足，对2007年COSMIC全球掩星在20～60km高度内的温度数据进行网格化插值，利用梯度风计算方法，计算得到20～60km高度内的月平均纬向风场，分别与ECMWF再分析数据和HWM07模式进行对比分析.结果表明，利用COSMIC温度数据计算得出的纬向风与ECMWF的纬向风十分接近，而与HWM07结果有一定差异，但总体变化趋势一致.与ECMWF再分析数据相比，利用COSMIC温度数据计算的月平均纬向风场平均偏差为-1.50～-0.08m·^-1，标准差为1.50～11.95m·^-1.与HWM07模式风场相比，利用COSMIC温度计算的月平均纬向风场平均偏差为-0.83～1.21m·^-1，标准差为3.69～11.14m·^-1.