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.     

Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data

Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%.     

A priori zenith wet delays in the analysis of VLBI CONT sessions

Tropospheric delay is one of the major sources of error in VLBI (Very Long Baseline Interferometry) analysis. The principal component of this error can be accurately computed through reliable surface pressure data —hydrostatic delay— yet there is also a small but volatile component —wet delay— which is difficult to be modelled a priori. In VLBI analysis, troposphere delay is typically modelled in the theoretical delays using Zenith Hydrostatic Delays (ZHD) and a dry mapping function. Zenith Wet Delay (ZWD) is not modelled but estimated in the analysis process. This work studies inter alia the impact of including external GNSS estimates to model a priori ZWD in VLBI analysis, as well as other models of a priori ZWD.In a first stage, two different sources of GNSS troposphere products are compared to VLBI troposphere estimates in a period of 5 years. The solution with the best agreement to VLBI results is injected in the VLBI analysis as a priori ZWD value and is compared to other options to model a priori ZWD. The dataset used for this empirical analysis consists of the six CONT campaigns.It has been found that modelling a priori ZWD has no significant impact either on baseline length and coordinates repeatabilities. Nevertheless, modelling a priori ZWD can change the magnitude of the estimated coordinates a few millimeters in the up component with respect to the non-modelling approach. In addition, the influence of a priori ZWD on Earth Orientation Parameters (EOP) and troposphere estimates —Zenith Total Delays (ZTD) and gradients—has also been analysed, resulting in a small but significant impact on both geodetic products.     

Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland

As a preliminary step for assessing the impact of global positioning system (GPS) refractive delay data in numerical weather prediction (NWP) models, the GPS zenith tropospheric delays (ZTD) are analyzed from 28 permanent GPS sites in the Chinese mainland. The objectives are to estimate the GPS ZTD and their variability in this area. The differences between radiosonde precipitable water vapor (PWV) and GPS PWV have a standard deviation of 4 mm in delay, a bias of 0.24 mm in delay, and a correlation coefficient of 0.94. The correlation between GPS ZTD and radiosonde PWV amounts to 0.89, indicating that the variety of tropospheric zenith delay can reflect the change of precipitable water vapor. The good agreement also guarantees that the information provided by GPS will benefit the NWP models. The time series of GPS ZTD, which were derived continuously from 2002 to 2004, are used to analyze the change of precipitable water vapor in Chinese mainland. It shows that the general trend of GPS ZTD is diminishing from the south-east coastland to the north-west inland, which is in accordance with the distribution of Chinese annual amount of rainfall. The temporal distribution of GPS ZTD in the Chinese mainland is that the GPS ZTD reaches maximum in summer, and it reaches minimum in winter. The long term differences between the observational data sources require further study before GPS derived data become useful for climate studies.     

Real-time zenith tropospheric delays in support of numerical weather prediction applications

The Geodetic Observatory Pecný (GOP) routinely estimates near real-time zenith total delays (ZTD) from GPS permanent stations for assimilation in numerical weather prediction (NWP) models more than 12 years. Besides European regional, global and GPS and GLONASS solutions, we have recently developed real-time estimates aimed at supporting NWP nowcasting or severe weather event monitoring. While all previous solutions are based on data batch processing in a network mode, the real-time solution exploits real-time global orbits and clocks from the International GNSS Service (IGS) and Precise Point Positioning (PPP) processing strategy. New application G-Nut/Tefnut has been developed and real-time ZTDs have been continuously processed in the nine-month demonstration campaign (February–October, 2013) for selected 36 European and global stations. Resulting ZTDs can be characterized by mean standard deviations of 6–10 mm, but still remaining large biases up to 20 mm due to missing precise models in the software. These results fulfilled threshold requirements for the operational NWP nowcasting (i.e. 30 mm in ZTD). Since remaining ZTD biases can be effectively eliminated using the bias-reduction procedure prior to the assimilation, results are approaching the target requirements in terms of relative accuracy (i.e. 6 mm in ZTD). Real-time strategy and software are under the development and we foresee further improvements in reducing biases and in optimizing the accuracy within required timeliness. The real-time products from the International GNSS Service were found accurate and stable for supporting PPP-based tropospheric estimates for the NWP nowcasting.     

GGTm-Ts: A global grid model of weighted mean temperature (Tm) based on surface temperature (Ts) with two modes

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.     

Annual,seasonal and diurnal variations of integrated water vapor using GPS observations over Hyderabad,a tropical station

This paper presents annual, seasonal and diurnal variations of integrated water vapor (IWV) derived from Global Positioning System (GPS) measurements for a tropical site, Hyderabad (17.4° N, 78.46° E). The zenith wet delay (ZWD) due to the troposphere has been computed using GPS observations and collocated meteorological data. ZWD is converted to IWV with very little added uncertainty. Mean monthly IWV values show maximum in July (~50 kg m⁻²) and minimum in December (~15 kg m⁻²). Fast Fourier Transform (FFT) and Harmonic analyses methods have been adopted to extract amplitudes and phases of diurnal (24 h), semi-diurnal (12 h) and ter-diurnal (8 h) oscillations which yielded comparable results. Amplitude of the 24 h component is observed to be maximum in spring whereas 12 h and 8 h components maximize in summer. A cross-correlation study between available daily IWV values and corresponding surface temperatures over one year produced a good correlation coefficient (0.44). The correlation obtained for different seasons got reduced to 0.25, 0.02, −0.39 and 0.21 for winter, spring, summer and autumn seasons respectively. The correlation between IWV and rainfall is poor. The coefficients obtained for the whole year is 0.05 and −0.13 for the rainy season.     

Analysis of precipitable water vapour characteristics from GNSS measurements during the snow season in Liaoning Province,China

Global Navigation Satellite System (GNSS) remote sensing precipitable water vapour (PWV) data from November 2015 to March 2019 were combined with snowfall observation data and used to analyse PWV characteristics in Liaoning Province during the snow season (from November to March the following year) and their relationship with snowfall. The potential of using GNSS for PWV measurements was demonstrated using sounding data with a correlation coefficient higher than 0.9 and a mean bias error lower than 0.5 mm. According to the GNSS PWV data gathered at 30-min intervals from 68 GNSS stations in Liaoning during the snow season, the monthly PWV average was highest in November and lowest in January. Negative correlations were found between PWV and altitude. Most of the water vapour was concentrated in the low layer of the atmosphere, and the contribution of this vapour to the PWV was higher during the snow season than in summer. A total of 43 snow cases were identified using the snowfall records from 53 GNSS stations, and the characteristics of PWV during these snowfalls were analysed. An increase in PWV was observed before snowfall events. Moreover, the influence of synoptic systems and air mass origins on PWV was analysed based on National Centers for Environmental Prediction (NCEP) reanalysis data and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results show that the water vapour condition was better when the synoptic systems or air masses came from areas south of Liaoning.     

Combination methods of tropospheric time series

In this article we present two methods for combination of different Global Navigation Satellite Systems (GNSS) Zenith Total Delay (ZTD) time-series for the same GNSS site, but from different producers or different processing setups. One method has been setup at ASI/CGS, the other at KNMI. Using Near Real-Time (NRT) ZTD data covering 1 year from the E-GVAP project, the performance of the two methods is inter-compared and validation is made against a combined ZTD solution from EUREF, based on post-processed ZTDs. Further, validation of the ASI combined solutions is made against independent ZTDs derived from radiosonde, Numerical Weather Prediction (NWP) model and Very Long Baseline Interferometry (VLBI) ZTD.     

Effects of physical correlations on long-distance GPS positioning and zenith tropospheric delay estimates

The global positioning system (GPS) has become an essential tool for the high precision navigation and positioning. The quality of GPS positioning results mainly depends on the model’s formulations regarding GPS observations, including both a functional model, which describes the mathematical relationships between the GPS measurements and unknown parameters, and a stochastic model, which reflects the physical properties of the measurements. Over the past two decades, the functional models for GPS measurements have been investigated in considerable detail. However, the stochastic models of GPS observation data are simplified, assuming that all the GPS measurements have the same variance and are statistically independent. Such assumptions are unrealistic. Although a few studies of GPS stochastic models were performed, they are restricted to short baselines and short time session lengths. In this paper, the stochastic modeling for GPS long-baseline and zenith tropospheric delay (ZTD) estimates with a 24-h session is investigated using the residual-based and standard stochastic models. Results show that using the different stochastic modelling methods, the total differences can reach as much as 3–6 mm in the baseline component, especially in the height component, and 10 mm in the ZTD estimation. Any misspecification in the stochastic models will result in unreliable GPS baseline and ZTD estimations. Using the residual-based stochastic model, not only the precision of GPS baseline and ZTD estimation is obviously improved, but also the baseline and ZTD estimations are closer to the reference value.     

Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F₂ layer critical frequency(f_oF₂), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of f_oF₂ with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of f_oF₂ are improved.     

Short-term forecasting regional model to predict M(3000)F2 over the European sector: Comparisons with the IRI model during moderate,disturbed, and very disturbed geomagnetic conditions

The hourly measurements of M(3000)F2 (M(3000)F2_meas) and the hourly quiet-time values of M(3000)F2 (M(3000)F2_QT) relative to the ionospheric observatories of Poitiers, Lannion, Dourbes, Slough, Rome, Juliusruh, Kaliningrad, Uppsala, Lyckesele, Sodankyla, and Kiruna as well as the hourly time-weighted accumulation series derived from the geomagnetic planetary index ap (ap(τ)), were considered during the period January 1957–December 2003 and used for the development of 11 short-term forecasting local models (STFLM) of M(3000)F2.     

GNSS water vapour tomography – Expected improvements by combining GPS,GLONASS and Galileo observations

The German Research Centre for Geosciences (GFZ) operates a GNSS water vapour tomography system using about 350 German GNSS stations. The GNSS data processing at the GFZ works in near real-time and provides zenith total delays, integrated water vapour and slant delay data operationally. This large data set of more than 50,000 slant delays per hour is used to reconstruct spatially resolved humidity fields by means of tomographic techniques. It can be expected that additional observations from the future Galileo system provide more information with improved quality. A simulation study covering 12 h at 14 July 2009 was therefore started to estimate the impact of GPS, Galileo and GLONASS data on the GNSS tomography. It is shown that the spatial coverage of the atmosphere with slant paths is highly improved by combining observations from two or three satellite systems. Equally important for a reliable tomographic reconstruction is the distribution of slant path intersections as they are required to locate the integrated delay information. The number of intersection points can be increased by a factor of 4 or 8 if two or three systems are combined and their distribution will cover larger regions of the atmosphere. The combined data sets can be used to increase the spatiotemporal resolution of the reconstructed humidity fields up to 30 km horizontally, 300 m vertically and 15 min. The reconstruction quality could not be improved considerably using the currently available techniques.     

Estimation of precipitable water vapour from GPS measurements in Argentina: Validation and qualitative analysis of results

This paper presents PWV estimates from GPS data computed at four continuously operated GPS stations in Argentina established at Buenos Aires, Córdoba, Rosario and Salta over a 1 year period (2006–2007). The objective is to analyze the behaviour of the GPS PWV estimation using mean tropospheric temperature (_Tm$T_m$) values from the Bevis model, Sapucci model and obtained by a numerical integration of variables provided by the operational analysis of the National Centre of Environmental Prediction (NCEP). The results are validated using PWV values from nearest radio soundings. Moreover, a comparison between PWV values determined from microwave sensors deployed on the NOAA-18 satellite and PWV from GPS observations is also presented.     

量子电压的发展及应用

本文通过对直流电压国家副基准、可编程约瑟夫森直流电压标准、可编程量子低频电压标准、宽量程量子化电压标准、脉冲驱动型量子电压标准及便携式免液氦量子电压标准的描述，主要介绍了我国量子电压副基准的历史、现状、今后的发展及应用前景。     

BPL长波授时信号传输时延的时间变化分析

由于GNSS系统的脆弱性,Loran-C/BPL系统作为GNSS系统的备份研究,受到国内外的重视,但如何获取高精度的传播时延一直是制约长波系统实现高精度授时的瓶颈。本文从测量角度出发分析路径时延的空间变化和时间变化,着重利用实际测量数据分析路径时延的时间变化规律。结果表明信号传播路径相似、且传播路径上天气变化相似的条件下相距100km内的用户其接收信号的传播时延时间变化规律基本相同,这为长波差分授时奠定基础。     

Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity,India

Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM_2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM_2.5 across the city. The highest percentage reduction was observed in BC_ff (black carbon attributable to fossil fuel combustion), followed by total BC and PM_2.5. No decrease in BC_bb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM_2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BC_ff were reduced, while they remained almost the same for PM_2.5 and BC_bb. Analysis of PCB data reveal the highest reduction in PM_2.5 in an industrial cluster area. The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed.     

Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field

The paper deals with the relation of the southern orientation of the north–south component _Bz$B_z$ of the interplanetary magnetic field to geomagnetic activity (GA) and subsequently a method is suggested of using the found facts to forecast potentially dangerous high GA. We have found that on a day with very high GA hourly averages of _Bz$B_z$ with a negative sign occur at least 16 times in typical cases. Since it is very difficult to estimate the orientation of _Bz$B_z$ in the immediate vicinity of the Earth one day or even a few days in advance, we have suggested using a neural-network model, which assumes the worse of the possibilities to forecast the danger of high GA – the dominant southern orientation of the interplanetary magnetic field. The input quantities of the proposed model were information about X-ray flares, type II and IV radio bursts as well as information about coronal mass ejections (CME). In comparing the GA forecasts with observations, we obtain values of the Hanssen–Kuiper skill score ranging from 0.463 to 0.727, which are usual values for similar forecasts of space weather. The proposed model provides forecasts of potentially dangerous high geomagnetic activity should the interplanetary CME (ICME), the originator of geomagnetic storms, hit the Earth under the most unfavorable configuration of cosmic magnetic fields. We cannot know in advance whether the unfavorable configuration is going to occur or not; we just know that it will occur with the probability of 31%.