[1] From OH airglow imaging observation carried out over one year in Indonesia, 74 events of gravity waves in the MLT (Mesosphere and Lower Thermosphere) region were extracted. Observed period, horizontal wavelength and observed horizontal phase speeds of gravity waves were typically 5 -13 min, 13-45 km and 37-75 m/s, respectively. Propagation directions were mostly southward except for the period between December and February, when eastward propagation was preferential. Spatial distributions of tropospheric clouds, estimated with GMS (Geostationary Meteorological Satellite) were consistent with the propagation direction of gravity waves, i.e., these clouds mainly existed in the opposite direction to the propagation direction of waves. This suggests that horizontal propagation characteristics of the short period gravity waves in the low latitude MLT region are mainly affected by the distribution of the wave sources in the troposphere, and the effect of the background mean wind in the middle atmosphere is smaller, as it is weaker ($20 m/s) in the equatorial region.

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Abstract. Gravity wave signatures were extracted from OH airglow observations using all-sky CCD imagers at four different stations: Cachoeira Paulista (CP) (22.7 • S, 45 • W) and São João do Cariri (7.4 • S, 36.5 • W), Brazil; Tanjungsari (TJS) (6.9 • S, 107.9 • E), Indonesia and Shigaraki (34.9 • N, 136 • E), Japan. The gravity wave parameters are used as an input in a reverse ray tracing model to study the gravity wave vertical propagation trajectory and to estimate the wave source region. Gravity waves observed near the equator showed a shorter period and a larger phase velocity than those waves observed at low-middle latitudes. The waves ray traced down into the troposphere showed the largest horizontal wavelength and phase speed. The ray tracing results also showed that at CP, Cariri and Shigaraki the majority of the ray paths stopped in the mesosphere due to the condition of m 2 <0, while at TJS most of the waves are traced back into the troposphere. In summer time, most of the back traced waves have their final position stopped in the mesosphere due to m 2 <0 or critical level interactions (|m| →∞), which suggests the presence of ducting waves and/or waves generated in-situ. In the troposphere, the possible gravity wave sources are related to meteorological front activities and cloud convections at CP, while at Cariri and TJS tropical cloud convections near the equator are the most probable gravity wave sources. The tropospheric jet stream and the orography are thought to be the major responsible sources for the waves observed at Shigaraki.

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Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec2, while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent.

We observed night sky quality in several LAPAN stations (Agam, Bandung, Pontianak, Sumedang, Garut, Pasuruan, and Biak) which were conducted from April until July 2018 using Unihedron Sky Quality Meter LU-DL type. Observational data from all of the observational points were then sent regularly to a centralized database for further use. Although most of the measurements were done in overcast conditions, we were able to determine the representative clear sky brightness statistically. The results showed that the light pollution level of the most of the stations are moderate (the values at Biak, Agam, Sumedang, and Pontianak are 20.0, 19.5, 19.6, and 17.7 mpsas respectively) and the stations which are located near or in cities are high (Bandung and Pasuruan with 17.1 and 18.0 mpsas, respectively). In a particular station (Garut) the light pollution is low (20.6 mpsas), so it is good to make this spot to be a location of astrotourism.

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Night sky brightness (NSB) research related to the artificial light pollution issues has been increasing all over the world, using various measurement techniques and tools. The research produces tens of thousands of data for each month such that proper handling, processing and analysing the data become challenging. In this article, we demonstrate an alternative method for processing the NSB data by utilising pattern recognition techniques: Canny edge detection and Hough transform. These techniques were applied to identify data and extract important parameter from the NSB density plot semi-automatically. Datasets collected from Bandung, Garut, Subang and Sumedang used as the test cases. Three time-segments (dusk, night and dawn) became the main focus of the analyses and our method successfully extracted following parameters: the rate of sky brightness change at dusk and dawn, the average NSB at night and the intersections which indicate transition time. This method, along with its many possible improvements, enables us to process data more effectively and encourage more observation campaigns to be conducted in the future.