Characteristic analysis of lightning activities on the Yungui Plateau using ground-based remote sensing

The spatiotemporal distribution of cloud-to-ground (CG) lightning activities on the Yungui Plateau is investigated in this study by using a 5-year dataset (2016–2020) from the ground-based National Lightning Detection Network (CNLDN). The correlations between the lightning activities and different meteorological factors in the region are also analyzed. The results show that there is an obvious difference in the spatial distribution of lightning activities on the Yungui Plateau during the 5 years, with high lightning density in the east and low lightning density in the west. The lightning activities shift and gather more towards the eastern plateau especially after 2019. Affected by the quasi-stationary front in Kunming, the spatial distributions of lightning flashes in cold and warm seasons are different. On the other hand, the frequency of the lightning activities varies from year to year, such as the surge in 2019. But in general, 62% of the lightning activities are produced in summer and the lightning flashes occur more often in the afternoon and evening on the Yungui Plateau. Additionally, it is found that lightning activities in the 5 years are closely related to precipitation and temperature, while there is a weak correlation with relative humidity and almost no correlation with sensible heat flux. The analysis also indicates that the CAPE×P (convective available potential energy times precipitation rate) proxy can be effectively used to describe and predict lightning activities on the Yungui Plateau as the lightning flashes corresponds well to CAPE×P, especially of the spatial distribution

Impact of open crop residual burning on air quality over Central Eastern China during the Mount Tai Experiment 2006 (MTX2006)

The impact of open crop residual burning (OCRB) on O<sub>3</sub>, CO, black carbon (BC) and organic carbon (OC) concentrations over Central Eastern China (CEC; 30–40° N, 111–120° E), during the Mount Tai Experiment in 2006 (MTX2006) was evaluated using a regional chemical transport model, the Models–3 Community Multiscale Air Quality Modeling System (CMAQ). To investigate these pollutants during MTX2006 in June 2006, daily gridded OCRB emissions were developed based on a bottom-up methodology using land cover and hotspot information from satellites. This model system involving daily emissions captured monthly–averages of observed concentrations and day-to-day variations in the patterns of O<sub>3</sub>, CO, BC and OC at the summit of Mount Tai (36° N, 117° E, 1534 m a.s.l., Shandong Province of the People's Republic of China) with high correlation coefficients between the model and observations ranging from 0.55 to 0.69. These results were significantly improved from those using annual biomass burning emissions. For monthly-averaged O<sub>3</sub>, the simulated concentration of 80.8 ppbv was close to the observed concentration (81.3 ppbv). The MTX2006 period was roughly divided into two parts: 1) polluted days with heavy OCRB in the first half of June; and 2) cleaner days with negligible field burning in the latter half of June. Additionally, the first half of June was characterized by two high-pollution episodes during 5–7 and 12–13 June, separated by a relatively cleaner intermediate period during 8–10 June. In the first high-pollution episode, the model captured the high O<sub>3</sub>, CO, BC and OC concentrations at the summit of Mount Tai, which were associated with OCRB over southern CEC and subsequent northward transport. For this episode, the impacts of OCRB emissions on pollutant concentrations were 26% (O<sub>3</sub>), 62% (CO), 79% (BC) and 80% (OC) at the summit of Mount Tai. The daily OCRB emissions were an essential factor in the evaluation of these pollutants during MTX2006. These emissions have a large impact not only on primary pollutants but also on secondary pollutants, such as O<sub>3</sub>, in the first half of June over northeastern Asia. The model reproduced reasonably well the variation of these pollutants in MTX2006, but underestimated daily averages of both CO and BC by a factor of 2, when using emission data from almost solely anthropogenic fuel sources in the latter half of the observation period when field burning can be neglected

Radio Frequency Interference Mitigation

Radio astronomy observational facilities are under constant upgradation and development to achieve better capabilities including increasing the time and frequency resolutions of the recorded data, and increasing the receiving and recording bandwidth. As only a limited spectrum resource has been allocated to radio astronomy by the International Telecommunication Union, this results in the radio observational instrumentation being inevitably exposed to undesirable radio frequency interference (RFI) signals which originate mainly from terrestrial human activity and are becoming stronger with time. RFIs degrade the quality of astronomical data and even lead to data loss. The impact of RFIs on scientific outcome is becoming progressively difficult to manage. In this article, we motivate the requirement for RFI mitigation, and review the RFI characteristics, mitigation techniques and strategies. Mitigation strategies adopted at some representative observatories, telescopes and arrays are also introduced. We also discuss and present advantages and shortcomings of the four classes of RFI mitigation strategies, applicable at the connected causal stages: preventive, pre-detection, pre-correlation and post-correlation. The proper identification and flagging of RFI is key to the reduction of data loss and improvement in data quality, and is also the ultimate goal of developing RFI mitigation techniques. This can be achieved through a strategy involving a combination of the discussed techniques in stages. Recent advances in high speed digital signal processing and high performance computing allow for performing RFI excision of large data volumes generated from large telescopes or arrays in both real time and offline modes, aiding the proposed strategy.Comment: 26 pages, 10 figures, Chinese version accepted for publication in Acta Astronomica Sinica; English version to appear in Chinese Astronomy and Astrophysic

Spatio-temporal characteristics of hazardous weather affecting Chinese airports based on the ERA5/ERA5-land reanalysis dataset

The growing demand for air transportation has led to increased air traffic and airline operations at airports worldwide, while hazardous weather conditions have a considerable impact on the efficiency and safety of air traffic. The long-term and high-resolution state-of-art fifth-generation reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) (ERA5) and ERA5-land provide us a perspective on the climatological characteristics of hazardous weather conditions affecting Chinese airports. These hazardous weather conditions include low-level wind shear (LLWS), limited visibility (LIMV), thunderstorms (TSTMs), and snowfall (SNOW). The LLWS is frequent in winter but rare in summer. At the airports in the north of eastern China, managers should take more precautions about LLWS from February to March and October to November. LIMV is major hazardous weather in the south of eastern China that should be carefully monitored in winter, especially from December to January. In northwestern China, LIMV is rare, especially at Yinchuan and Lanzhou airports. TSTMs frequently occur in South China and Southwest China, especially in Yunnan, Guangxi, Guangdong, and eastern Tibet. The corresponding active period is summer. SNOW frequently occurs over the Tibet Plateau and parts of Xinjiang province. SNOW is generally active in early winter (December–January) at Urumchi airport but at Lhasa airport in late winter (February–March). There is no SNOW throughout the year in the south of eastern China. The observations also verify such characteristics of the annual cycles of the four hazardous weather conditions. The trend analysis does not express many warnings on hazardous weather conditions except the SNOW. The SNOW at Lhasa airport shows an increasing trend. Considering the frequent SNOW in Lhasa, more attention should be put to monitoring this weather here

Icing thickness prediction model for overhead transmission lines

Failures in a large electric power system are often inevitable. Severe weather conditions are one of the main causes of transmission line failures. Using the fault data of transmission lines of Shaanxi Power Grid from 2006 to 2016, in conjunction with meteorological information, this paper analyses the relationship between the temporal-spatial distribution characteristics of meteorological disasters and the fault of transmission lines in Shaanxi Province, China. In order to analyze the influence of micro-meteorology on ice coating, a grey correlation analysis method is proposed. This thesis calculates the grey relational between ice thickness and micro-meteorological parameters such as ambient temperature, relative humidity, wind speed and precipitation. The results show that the correlation between ambient temperature, wind speed and ice thickness is bigger than others. Based on the results of grey correlation analysis, a Multivariate Grey Model (MGM) and a Back Propagation (BP) neural network prediction model are built based on ice thickness, ambient temperature and wind speed. The prediction results of these two models are verified by the case of ice-coating of Shaanxi power grid. The results show that the prediction errors of the two models are small and satisfy the engineering requirement. Then a realistic case is carried out by using these two models. An icing risk map is drawn according to the results

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial\u2013temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of \u201chigh in winter and low in summer\u201d, while the spatial distribution forms a \u201chigh in East and low in west\u201d pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a \u201cdelayed effect\u201d of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

CODED SOCIAL CONTROL: CHINA’S NORMALIZATION OF BIOMETRIC SURVEILLANCE IN THE POST COVID-19 ERA

This article investigates the longevity of health QR codes, a digital instrument of pandemic surveillance, in post-COVID China. From 2020 to 2022, China widely used this tri-color tool to combat the COVID-19 pandemic. A commonly held assumption is that health QR codes have become obsolete in post-pandemic China. This study challenges such an assumption. It reveals their persistence and integration - through mobile apps and online platforms - beyond the COVID-19 public health emergency. A prolonged, expanded and normalized use of tools which were originally intended for contact tracing and pandemic surveillance raises critical legal and ethical concerns. Moreover, their functional transformation from epidemiological risk assessment tools to instruments of behavior modification and social governance heralds the emergence of a Data Leviathan. This transformation is underpinned by a duality of underlying political and commercial forces. These include 1) a structural enabler: a powerful alliance between political authorities and tech giants and 2) an ideological legitimizer: a commitment to collective security over individual autonomy. In contrast to the rights-centric approach embraced by Western democracies to regulate AI-driven biometric surveillance, China adopts a state-industry dominance model of governance