High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.     

Potential of IRNSS/NavIC L5 signals for ionospheric studies

Indian Space Research Organization (ISRO) has developed an indigenous system named Indian Regional Navigation Satellite System (IRNSS) or NavIC (Navigation with Indian Constellation), that consists of 7 satellites and transmits navigation signal in L and S bands. ISRO, for validation of the system, has installed many IGS (IRNSS/GPS/SBAS) receivers scattered over the Indian region. Using preliminary data from two geographically widely separated stations over India, this paper presents the results on studies on parameters of IRNSS signal quality and discusses how these parameters may be used to study the ionospheric behavior over the Indian region. The results show the importance and advantages of using IRNSS data for such studies.     

雄安新区人才队伍建设思考

人才是实现民族振兴、赢得国际竞争优势的战略资源,雄安新区建设发展离不开人才的重要支撑。本文以雄安新区人才队伍建设为研究对象,剖析这一研究的意义和现状,探究雄安新区人才队伍建设存在的问题,最后提出解决这一问题的建议。     

Erratum to: Use of the DBM Model to the Predict of Arrival of Coronal Mass Ejections to the Earth

Cosmic Research - An Erratum to this paper has been published: https://doi.org/10.1134/S0010952521120017     

A regional tropospheric model for airborne GPS applications

A regional tropospheric model can be constructed using surveys from GPS ground networks. Using this model the tropospheric delays of a kinematic station within the region can be interpolated. However, such a model is generally not suitable for an airborne platform high above the ground networks. In this paper, a method of constructing a regional tropospheric model for airborne GPS applications is described. First, the kinematic station in the air is projected onto the ground. Then the tropospheric delays at projected point are interpolated from those of the ground networks. Finally, the tropospheric delays at projected point are extended upward to the airborne platform using pressure and temperature gradients and humidity exponential function. For validation of this method, the data of airborne campaign carried out by BKG in cooperation with GFZ and BGR 2008 in the northern Alps are used. The results show that GPS kinematic positioning precision in height component can be improved using this method.     

Influence of the interplanetary magnetic field on ULF oscillations of the ionospheric resonator

A weak but statistically reliable dependence of the diurnal activity of oscillations in the ionospheric Alfvén resonator on orientation of the interplanetary magnetic field ahead of the magnetospheric front has been detected based on observations of ULF oscillations at Sayan solar observatory Mondy of the Institute of Solar–Terrestrial Physics. The interpretation of the result has been proposed. The essence is that the electromagnetic fluctuations penetrate into the magnetosphere from the interplanetary environment and influence the ionospheric resonator. The formulation of the problem and the method of solving it are part of the broad program of the experimental and theoretical study of the influence of the interplanetary magnetic field on the oscillation regime of ULF oscillations of the magnetosphere.     

A data-driven approach to modeling high-density terminal areas: A scenario analysis of the new Beijing,China airspace

Airports are being developed and expanded rapidly in China to accommodate and pro-mote a growing aviation market. The future Beijing Daxing International Airport (DAX) will serve as the central airport of the JingJinJi megaregion, knitting the Beijing, Tianjin, and Hebei regions together. DAX will be a busy airport from its inception, relieving congestion and accommodating growth from Beijing Capital International Airport (PEK), currently the second busiest airport in the world in passengers moved. We aim to model terminal airspace designs and possible conflicts in the future Beijing Multi-Airport System (MAS). We investigate standard arrival procedures and mathematically model current and future arrival trajectories into PEK and DAX by collecting large quantities of publicly available track data from historical arrivals operating within the Beijing terminal airspace. We find that (1) trajectory models constructed from real data capture aberrations and deviations from standard arrival procedures, validating the need to incorporate data on histor-ical trajectories with standard procedures when evaluating the airspace and (2) given all existing constraints, DAX may be restricted to using north and east arrival flows, constraining the capacity required to handle the increases in air traffic demand to Beijing. The results indicate that the termi-nal airspace above Beijing, and the future JingJinJi region, requires careful consideration if the full capacity benefits of the two major airports are to be realized.     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

Grinding temperature and energy ratio coefficient in MQL grinding of high-temperature nickel-base alloy by using different vegetable oils as base oil

Vegetable oil can be used as a base oil in minimal quantity of lubrication(MQL). This study compared the performances of MQL grinding by using castor oil, soybean oil, rapeseed oil, corn oil, sunflower oil, peanut oil, and palm oil as base oils. A K-P36 numerical-control precision surface grinder was used to perform plain grinding on a workpiece material with a high-temperature nickel base alloy. A YDM–III 99 three-dimensional dynamometer was used to measure grinding force,and a clip-type thermocouple was used to determine grinding temperature. The grinding force, grinding temperature, and energy ratio coefficient of MQL grinding were compared among the seven vegetable oil types. Results revealed that(1) castor oil-based MQL grinding yields the lowest grinding force but exhibits the highest grinding temperature and energy ratio coefficient;(2) palm oil-based MQL grinding generates the second lowest grinding force but shows the lowest grinding temperature and energy ratio coefficient;(3) MQL grinding based on the five other vegetable oils produces similar grinding forces, grinding temperatures, and energy ratio coefficients, with values ranging between those of castor oil and palm oil;(4) viscosity significantly influences grinding force and grinding temperature to a greater extent than fatty acid varieties and contents in vegetable oils;(5) although more viscous vegetable oil exhibits greater lubrication and significantly lower grinding force than less viscous vegetable oil, high viscosity reduces the heat exchange capability of vegetable oil and thus yields a high grinding temperature;(6) saturated fatty acid is a more efficient lubricant than unsaturated fatty acid; and(7) a short carbon chain transfers heat more effectively than a long carbon chain. Palm oil is the optimum base oil of MQL grinding, and this base oil yields 26.98 N tangential grinding force,87.10 N normal grinding force, 119.6 °C grinding temperature, and 42.7% energy ratio coefficient.