To compensate radar reflectivity for attenuation effect, a new method for attenuation correction of the radar reflectivity using arbitrary oriented microwave link (referred henceforth to as ACML) is developed and evaluated. Referring to the measurement of arbitrary oriented microwave link, the ACML method optimizes the ratio of specific attenuation to specific differential phase which is a key parameter in attenuation correction schemes. The proposed method was evaluated using real data of a dual-polarization X-band radar and measurements of two microwave links during two rainstorm events. The results showed that the variation range of the optimized ratio was overall consistent with the results of the previous studies. After attenuation correction with the optimal ratios, the radar reflectivity was significantly compensated, especially at long distances. The corrected reflectivity was more intense than the reflectivity corrected by the “self-consistent” (SC) method and closer to the reflectivity of a nearby S-band radar. The effectiveness of the method was also verified by comparing the rain rates estimated by the X-band radar with those derived by rain gauges. It is demonstrated that arbitrary oriented microwave link can be adopted to optimize the attenuation correction of radar reflectivity.

For decades, the magnetic direction finder (MDF) technique has been used to give the azimuth of return stroke (RS) in cloud‐to‐ground lightning (CG). Due to the possible polarization error produced by inclined lightning channels, cloud flashes are excluded by most MDF waveform discrimination procedures. In this study, the capability of the orthogonal magnetic coils in determining the azimuth of the initial breakdown pulses (IBPs) and leaders in CG is analyzed and discussed. First, a lightning orthogonal magnetic field sensor with high sensitivity (about 33 nT/V) as well as broad bandwidth (3 kHz to 2 MHz) has been developed. According to the transmission line model, RSs with current 10 kA occurred within 500 km could be detected by this system theoretically. Thereafter, a theoretical analysis was derived in order to evaluate its polarization error with arbitrarily oriented lightning channels. In order to overcome the effect of random nonvertical channels on orientation results, a statistical MDF method is proposed. A field lightning observation was conducted in the year 2017, in which RSs, leaders, and IBPs in CG or cloud flash from two thunderstorms are oriented by the proposed MDF technique. The average azimuth difference between the mathematic expectation of leaders and corresponding RSs is 2.4°. Most of the azimuth in IBPs lies within the region of radar composite reflectivity above 45 dBZ, which indicates that the initiation location of lightning flashes in this study mostly located in these strong echo regions.

Automatically classifying a huge amount of ground‐based auroral images is essential to facilitate aurora morphology statistical research and aid in comprehending the magnetospheric dynamics. However, facing the challenge of insufficient labeled images, deep learning methods perform suboptimally on small auroral image data sets, and traditional machine learning methods based on handcrafted features heavily rely on expert knowledge. In this paper, we propose a novel method that leverages the merits of both traditional machine learning and deep learning methods by extracting deep second‐order tensor features to train a support vector machine (SVM). To improve compactness and discriminative ability of the features, we comply the intrinsic data geometry on Riemannian manifold to employ dimensionality reduction and map the dimensionality‐reduced features from Riemannian space to Euclidean space for the SVM classifier. Experimental results on small aurora data sets conclusively demonstrate the effectiveness of our method, exhibiting competitive performance with recent aurora classification methods.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.