Inertial gravity waves observed by a Doppler wind LiDAR and their possible sources

In this paper,we use wind observations by a Doppler wind LiDAR near Delingha(37.4°N,97.4°E),Qinghai,Northwestern China to study the characteristics of inertial gravity waves in the stratosphere.We focus on 10–12 December 2013,a particularly interesting case study.Most of the time,the inertial gravity waves extracted from the LiDAR measurements were stationary with vertical wavelengths of about 9–11 km and horizontal wavelengths of about 800–1000 km.However,for parts of the observational period in this case study,a hodograph analysis indicates that different inertial gravity wave propagation features were present at lower and upper altitudes.In the middle and upper stratosphere(~30–50 km),the waves propagated downward,especially during a period of stronger winds,and to the northwest–southeast.In the lower stratosphere and upper troposphere(~10–20 km),however,waves with upward propagation and northeast–southwest orientation were dominant.By taking into account reanalysis data and satellite observations,we have confirmed the presence of different wave patterns in the lower and upper stratosphere during this part of the observational period.The combined data sets suggest that the different wave patterns at lower and upper height levels are likely to have been associated with the presence of lower and upper stratospheric jet streams.

Generating a radioheliograph image from SDO/AIA data with the machine learning method

Radioheliograph images are essential for the study of solar short term activities and long term variations, while the continuity and granularity of radioheliograph data are not so ideal, due to the short visible time of the Sun and the complex electron-magnetic environment near the ground-based radio telescope. In this work, we develop a multi-channel input single-channel output neural network, which can generate radioheliograph image in microwave band from the Extreme Ultra-violet(EUV) observation of the Atmospheric Imaging Assembly(AIA) on board the Solar Dynamic Observatory(SDO). The neural network is trained with nearly 8 years of data of Nobeyama Radioheliograph(No RH) at 17 GHz and SDO/AIA from January 2011 to September 2018. The generated radioheliograph image is in good consistency with the well-calibrated No RH observation. SDO/AIA provides solar atmosphere images in multiple EUV wavelengths every 12 seconds from space, so the present model can fill the vacancy of limited observation time of microwave radioheliograph, and support further study of the relationship between the microwave and EUV emission.