Variability Study of the SX Phoenicis Star BL Camelopardalis

New time-series photometric observations of BL Cam in the V band and white light were made during 2005 to 2007 at the Xinglong Station of China. The frequency analysis confirms two closely separated frequencies, 25.181 d-1 and 25.571 d-1, but the frequency of 31-32 d-1 reported in the literature was not detected in the new data. New times of maximum light were determined from both our light curves and those available on Internet, allowing a more comprehensive study of the O - C diagram, together with the times of maximum light in the literature. A new interpretation, including the period increasing before 1988 and decreasing since 1992 of BL Cam and the light-time effect in a binary system, looks plausible.     

Two strong radio bursts at high and medium Galactic latitude

The Nasu Observatory, which is composed of eight 20 m elements, was constructed for observing radio transients over a wide field at 1400 MHz. We report on two radio transients detected in consecutive drift scanning observations at declination 32° over a period of about two months. One of the two transients, WJN J1039+3200, appeared at =10^h39^m40^s±10^s, δ=32°±0.4° on March 4, 2005, and the other one, WJN J0645+3200, appeared at =06^h45^m25^s±10^s, δ=32°±0.4° on March 24, 2005. Both exhibited flux densities in excess of 1 Jy, and the burst durations were up to two days. Since there are few examples of radio transients outside the Galactic plane, these are very important observations. We have previously reported on four radio transients with features that look like the two transients detected this time. Of these six WJN transients in total, five had a duration of up to two days, and one up to three days. Four of the transients were detected at high Galactic latitude of b > 30°. Counterparts of the six WJN transients included X-ray sources in four events and had a consistency of 66%. The consistency of γ-ray, PGC Galaxy, NVSS, and FIRST sources was concentrated at about 50%. We were not able to find any special features in the counterparts. The distribution was verified by making a log N–log S plot using data for the four previously detected transients and the new ones. As a result, the distribution of the radio transients that we observed might have an isotropic distribution not dependent on Galactic longitude and Galactic latitude. The detection probability was calculated based on the assumption of an isotropic distribution. The 2σ upper probability limit for detection of transients of 1000 mJy or more is 0.0049 [deg⁻² yr⁻¹]. We cannot yet identify these two radio transients, because their features are different from any radio bursts observed in the past.     

Local meteoric water lines describe extratropical precipitation

Stable water isotopes δ¹⁸O and δ²H are used to investigate precipitation trends and storm dynamics to advance knowledge of precipitation patterns in a warming world. Herein, δ¹⁸O and δ²H were used to determine the relationship between extratropical cyclonic precipitation and local meteoric water lines (LMWLs) in the eastern Ohio Valley and the eastern United States. Precipitation volume weighted and unweighted central Ohio LMWLs, created with samples collected during 2012–2018, showed that temperature had the greatest effect on precipitation isotopic composition. HYSPLIT back trajectory modelling showed that precipitation was primarily derived from a mid-continental moisture source. Remnants of major hurricanes were collected as extratropical precipitation during the 2012–2018 sampling period in central Ohio. Extratropical precipitation samples were not significantly different from the samples that created the central Ohio LMWL. Six additional LMWLs were derived from United States Geological Survey (USGS) Atmospheric Integrated Research Monitoring Network (AIRMoN) samples collected in Pennsylvania, Delaware, Tennessee, Vermont, New Hampshire, and Oxford, Ohio. Meteoric water lines describing published samples from Superstorm Sandy, plotted with these AIRMoN LMWLs, showed isotopic composition of Superstorm Sandy precipitation was commonly more depleted than the average isotopic composition at the mid-latitude locations. Meteoric water lines describing the Superstorm Sandy precipitation were not significantly different in slope from LMWLs generated within 300 km of the USGS AIRMoN site. This finding, which was observed across the eastern Ohio Valley and eastern United States, demonstrated a consistent precipitation δ²H–δ¹⁸O relationship for extratropical cyclonic and non-cyclonic events. This work also facilitates the analysis of storm development based on the relationship between extratropical event signature and the LMWL. Analysis of extratropical precipitation in relation to LMWLs along storm tracks allows for stronger development of precipitation models and understanding of which climatic and atmospheric factors determine the isotopic composition of precipitation.     

扩展水凝物控制变量的雷达资料同化对台风“天鸽”数值模拟的影响

利用WRF(Weather Research and Forecast)模式及WRFDA(WRF model data assimilation system)系统,针对2017年台风“天鸽”个例通过同化雷达径向速度(Vr)和反射率因子(RF),研究水凝物控制变量的雷达资料同化对台风分析预报的影响。研究表明：雷达径向速度的直接同化有效地改进了模式初始场中台风涡旋区的中小尺度信息,分析场中产生了气旋性的风场增量,对模式背景场中的台风有显著增强作用。通过在传统控制变量中扩展针对水凝物的控制变量可有效地同化雷达反射率因子资料,对初始场的水物质进行调整,并对随后确定性预报的台风路径和强度都有一定的正效果。此外,相比没有水凝物控制变量的雷达同化试验,加入了水凝物控制变量的雷达资料同化试验降水预报效果更好。这为将我国近海的地基多普勒天气雷达用于台风初始化分析和预报提供了一定的技术支撑和保障。     

MODIS和OMI数据评估阅兵期间北京市大气减排成效

卫星观测不仅能反映区域宏观大气污染状况,也能从城市尺度上监测大气污染物的变化。基于以上优势,本文利用MODIS气溶胶光学厚度(AOD)和OMI对流层NO_2垂直柱浓度数据,比较2015年与2012年—2014年以及2015年3个时期(减排前、减排中、减排后)AOD和NO_2柱浓度的变化,定性分析了阅兵期间华北平原地区污染物减排效果,重点定量评估北京市联控减排措施的效果。研究发现2015年减排中华北平原重污染地区AOD和NO_2柱浓度相比于前3年同期有明显降低。定量分析北京市的减排效果得到:2015年减排中较前3年同期而言,AOD降低59%,NO_2柱浓度降低41%;较2015年减排前而言,AOD降低73%,NO_2柱浓度降低30%,去除气象条件影响后,AOD下降43%,NO_2柱浓度下降21%,说明严格的联控减排措施有效地改善了空气质量,气象条件也起到积极的作用。减排措施结束后,AOD和NO_2柱浓度比减排中分别增加159%和71%。研究结果表明,卫星遥感与地基监测评估效果相当,能反映北京地面污染物排放能力;它既能观测区域尺度大气污染变化,又可评估城市尺度大气污染减排。随着卫星技术水平的提高,期望未来卫星遥感可作为一种独立手段来定量评估区域及城市尺度空气质量减排措施的效果。     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.