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Simple Summary: Recently, Monochamus alternatus have broken through their original distribution areas, showing a tendency to spread to higher latitudes and successfully colonizing the newly invaded areas. Hence, it is important to analyze the potential global suitable area of M. alternatus with the latest occurrence coordinates for the monitoring and scientific prevention and control of M. alternatus. The optimized MaxEnt model was first used to predict and analyze the potentially suitable areas for M. alternatus on a global scale. The results showed that the main temperature factors influencing the potential distribution of M. alternatus are monthly mean temperature difference (Bio2), minimum temperature of the coldest month (Bio6), and mean temperature of the warmest quarter (Bio10) and that the main precipitation factors are annual precipitation (Bio12) and precipitation of the driest month (Bio14). Our results indicated that the current and future potential suitable areas of M. alternatus might be distributed worldwide. M. alternatus is considered to be an important and effective insect vector for the spread of the important international forest quarantine pest, Bursaphelenchus xylophilus. The precise determination of potential suitable areas of M. alternatus is essential to monitor, prevent, and control M. alternatus worldwide. According to the distribution points and climatic variables, the optimized MaxEnt model and ArcGIS were used to predict the current and future potentially suitable areas of M. alternatus worldwide. The optimized MaxEnt model parameters were set as feature combination (FC) = LQHP and β = 1.5, which were determined by the values of AUCdiff, OR10, and ΔAICc. Bio2, Bio6, Bio10, Bio12, and Bio14 were the dominant bioclimatic variables affecting the distribution of M. alternatus. Under the current climate conditions, the potentially suitable habitats of M. alternatus were distributed across all continents except Antarctica, accounting for 4.17% of the Earth's total land area. Under future climate scenarios, the potentially suitable habitats of M. alternatus increased significantly, spreading to a global scale. The results of this study could provide a theoretical basis for the risk analysis of the global distribution and dispersal of M. alternatus as well as the precise monitoring and prevention of this beetle. [ABSTRACT FROM AUTHOR]

The third Antarctic Survey Telescope array instrument at Dome A in Antarctica, the AST3-3 telescope, has been in commissioning from March 2021. We deployed AST3-3 at the Yaoan astronomical station in Yunnan Province for an automatic time-domain survey and follow-up observations with an optimised observation and protection system. The telescope system of AST3-3 is similar to that of AST3-1 and AST3-2, except that it is equipped with a 14 K × 10 K QHY411 CMOS camera. AST3-3 has a field of view of 1. 65 ∘ × 1. 23 ∘ and is currently using the g band filter. During commissioning at Yaoan, AST3-3 aims to conduct an extragalactic transient survey, coupled with prompt follow-ups of opportunity targets. In this paper, we present the architecture of the AST3-3 automatic observation system. We demonstrate the data processing of observations by representatives SN 2022eyw and GRB 210420B. [ABSTRACT FROM AUTHOR]

Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are 'true'. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the International Reference Ionosphere (IRI)-2016 model for application in the Antarctic region, total electron content (TEC) data from the Global Navigation Satellite Systems (GNSS) observation data in 2018 are assimilated into the IRI-2016 model by updating the effective ionospheric parameter, IG12 index on a daily basis. The functional relationship between the IG12 index and the longitude, latitude, and the day of year (DOY) is fitted by using the spherical crown harmonic function and the polynomial, and finally establish an updated experiential model of IG12 indices over the Antarctic region. Conclusions that were reached were: 1) the updated IG12 index varies greatly over different geographical locations and 2) it is also apparent that the accuracy of the IRI-2016 model is worse in the perpetual night than that in the perpetual day. In order to verify our method, the TEC calculated by the IRI-2016 model driven by the updated IG12 index and that calculated by the original IRI-2016 model are compared with the GNSS-TEC, and the results show that the updated IRI-2016 model has improved the accuracy of the BIAS and root mean square (RMS) of the TEC calculation by 97% and 87%, respectively, on the fitting moments, while 75% and 54% on the predicting moments. In addition, compared with the original IRI-2016 model, it is found that the updated IRI-2016 model improves the accuracy of the NmF2 calculation by approximately 23% on average for the fitting time and 8% for the predicting time. [ABSTRACT FROM AUTHOR]