Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan

Precipitable Water Vapor (PWV) plays an important role for weather forecasting. It is helpful in evaluating the changes of the weather system via observing the distribution of water vapor. The ability of calculating PWV from Global Positioning System (GPS) signals is useful to understand the special weather phenomenon. In this study, 95 ground-based GPS and rainfall stations in Taiwan were utilized from 2006 to 2012 to analyze the relationship between PWV and rainfall. The PWV data were classified into four classes (no, light, moderate and heavy rainfall), and the vertical gradients of the PWV were obtained and the variations of the PWV were analyzed. The results indicated that as the GPS elevation increased every 100?m, the PWV values decreased by 9.5?mm, 11.0?mm, 12.2?mm and 12.3?mm during the no, light, moderate and heavy rainfall conditions, respectively. After applying correction using the vertical gradients mentioned above, the average PWV thresholds were 41.8?mm, 52.9?mm, 62.5?mm and 64.4?mm under the no, light, moderate and heavy rainfall conditions, respectively. This study offers another type of empirical threshold to assist the rainfall prediction and can be used to distinguish the rainfall features between different areas in Taiwan.     

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.     

GPS sensing of precipitable water vapour during the March 2010 Melbourne storm

The March 2010 Melbourne storm is used as a case study to examine the potential of using Global Positioning System (GPS) observations for studying the precipitable water vapour (PWV) field. The Victorian statewide GPS infrastructure network, i.e. GPSnet, was used in this study. GPSnet is currently the only statewide and densest GPS infrastructure network in Australia, which provides an excellent opportunity to examine the distribution of water vapour as the severe weather system passed over the state. Data from 15 GPSnet stations were processed over a one-week period, i.e. a few days prior to and after the storm passage, during which the course of the storm extended from the west to the southeast corner of the state. In addition, data from two radiosonde sites of the Australian Bureau of Meteorology Upper Air Network were used to compare and validate the GPS derived PWV measurements. The findings demonstrate that there is strong spatial and temporal correlation between variations of the ground-based GPS-PWV estimates and the passage of the storm over the state. This is encouraging as the ground-based GPS water vapour sensing technique can be considered as a supplemental meteorological sensor in studying severe weather events. The advantage of using ground-based GPS-PWV technique is that it is capable of providing continuous observation of the storm passage with high temporal resolution. The spatial resolution of the distribution of water vapour is dependent on the geographical location and density of the GPS stations.     

Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn

The rainfall process of Chengdu region in autumn has obvious regional features. Especially, the night-time rain rate of this region in this season is very high in China. Studying the spatial distribution and temporal variation of regional atmospheric precipitable water vapor (PWV) is important for our understanding of water vapor related processes, such as rainfall, evaporation, convective activity, among others in this area. Since GPS detection technology has the unique characteristics, such as all-weather, high accuracy, high spatial and temporal resolution as well as low cost, tracking and monitoring techniques on water vapor has achieved rapid developments in recent years. With GPS–PWV data at 30-min interval gathered from six GPS observational stations in Chengdu region in two autumns (September 2007–December 2007 and September 2008–December 2008), it is revealed that negative correlations exist between seasonally averaged value of GPS–PWV as well as its variation amplitude and local terrain altitude. The variation of PWV in the upper atmosphere of this region results from the water vapor variation from surface to 850 hPa. With the help of Fast Fourier Transform (FFT), it is found that the autumn PWV in Chengdu region has a multi-scale feature, which includes a seasonal cycle, 22.5 days period (quasi-tri-weekly oscillation). The variation of the GPS–PWV is related to periodical change in the transmitting of the water vapor caused by zonal and meridional wind strengths’ change and to the East Asian monsoon system. According to seasonal variation characteristics, we concluded that the middle October is the critical turning point in PWV content. On a shorter time scale, the relationship between autumn PWV and ground meteorological elements was obtained using the composite analysis approach.     

Comparison of satellite orbit ephemerides for use in GPS meteorology

This paper discusses GPS (Global Position System) meteorology. The research presented is based on a comparison of values of precipitable water vapour PWV, based on GPS measurements using final and predicted ephemerides of satellite orbits. We analysed recent year’s improvement in predicting ephemerides. We compared the data outputs from a radiosonde using GPS receiver measurements directly from the meteorological station from which the radiosondes were launched. The results indicate a high quality of the predicted ephemerides. This finding makes predicted ephemerides highly usable for near real-time estimations of PWV. To use PWV in meteorological forecast applications, this high speed of PWV values supply is necessary.     

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

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.     

Tomographic determination of the spatial distribution of water vapor using GPS observations

With the advent of the GPS navigation system, a promising ground based technique has been introduced which makes it possible to estimate the amount of water vapor in the troposphere from operational GPS networks at relatively low additional costs. While the estimation of the integrated amount is currently well established, the determination of the spatial water vapor distribution and its temporal variation are still a major challenge. To account for the vertical resolution, several tomographic approaches were pursued. We developed the software package AWATOS (atmospheric water vapor tomography software) which is based on the assimilation of double differenced GPS observations. Applying a least-squares inversion, the inhomogeneous spatial distribution of water vapor is determined. An extensive investigation has been carried out in Switzerland. GPS measurements are performed by the dense permanent Swiss national GPS network AGNES of the Swiss Federal Office of Topography (swisstopo). A total of 40 equally distributed water vapor profiles have been estimated on an hourly basis. For the purpose of validation, 22 radiosonde profiles were used at the GPS and meteorological station Payerne. Furthermore, data of the numerical weather model aLMo (alpine model in Switzerland, MeteoSwiss) were compared with the tomographic results. An overall good agreement of the three methods with an rms of better than 1.6 g/m³ absolute humidity was achieved. The results show that AGNES can be used as a dedicated network for the purpose of GPS-tomography, using a horizontal resolution of approximately 50 km and height layers of 300–500 m thickness in the lower troposphere.     

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.     

Global monitoring of tropospheric water vapor with GPS radio occultation aboard CHAMP

The global positioning system radio occultation (GPS RO) technique provides a powerful tool for atmospheric sounding which requires no calibration, is not affected by clouds, aerosols or precipitation, and provides an almost uniform global coverage. The paper deals with application of GPS RO measurements from CHAllenging Minisatellite Payload (CHAMP) for the retrieval of tropospheric water vapor profiles. CHAMP RO data are available since 2001 with up to 200 high resolution atmospheric profiles per day. We introduce a new direct method for water vapor retrieval from GPS RO data. Additionally, a 1Dvar algorithm is used for this purpose. The so derived CHAMP water vapor profiles are validated with radiosonde data on a global scale. Here, both methods come to statistically comparable results revealing a negative bias of less than 0.1 g/kg and a standard deviation of less than 1 g/kg specific humidity in the mid troposphere. Potentials of CHAMP RO retrievals for monitoring the mean tropospheric water vapor distribution on a global scale are presented.     

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.     

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.     

A comparative and numerical study of effects of gravity waves in small miss-distance and miss-time GPS radio occultation temperature profiles

The Global Positioning System (GPS) Radio Occultation (RO) technique has global coverage and is capable of generating high vertical resolution temperature profiles of the upper troposphere and lower stratosphere with sub-Kelvin accuracy and long-term stability, regardless of weather conditions. In this work, we take advantage of the anomalously high density of occultation events at the eastern side of the highest Andes Mountains during the initial mission months of COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate). This region is well-known for its high wave activity. We choose to study two pairs of GPS RO, both containing two occultations that occurred close in time and space. One pair shows significant differences between both temperature profiles. Numerical simulations with a mesoscale model were performed, in order to understand this discrepancy. It is attributed to the presence of a horizontal inhomogeneous structure caused by gravity waves.     

FY-3 Meteorological Satellites and the Applications

FY-3 is the second generation polar-orbiting meteorological satellite of China. The first satellite named FY-3A of this series was launched on 27 May 2008. The first operational satellite named FY-3C of this series was launched on 23 September, 2013. The new generation satellites are to provide three-dimensional, quantitative, multi-spectral global remote sensing data under all weather conditions, which will greatly help the operational numerical weather prediction, global climate change research, climate diagnostics and prediction, and natural disaster monitoring. They will also provide help for many other fields such as agriculture, forestry, oceanography and hydrology. With the abovementioned capability, the FY-3 satellites can make valuable contributions to improving weather forecasts, global natural-disaster and environmental monitoring.      

基于GMF方法的捕风一号风速反演分析

全球尺度高时空分辨率海面风场探测是当前全球气象研究及预报预测领域的关注热点之一,传统海面风场探测技术存在测量区域有限,且受天气环境限制明显等问题.基于全球导航卫星系统-反射(GNSS-R)测量技术风速反演原理,以捕风一号1级数据产品为输入,欧洲中期天气预报中心再分析风速数据为参考风速,采用地球物理模型函数(GMF)方法...     

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.     

Evaluation and improvement of coastal GNSS reflectometry sea level variations from existing GNSS stations in Taiwan

Global sea level rise due to an increasingly warmer climate has begun to induce hazards, adversely affecting the lives and properties of people residing in low-lying coastal regions and islands. Therefore, it is important to monitor and understand variations in coastal sea level covering offshore regions. Signal-to-noise ratio (SNR) data of Global Navigation Satellite System (GNSS) have been successfully used to robustly derive sea level heights (SLHs). In Taiwan, there are a number of continuously operating GNSS stations, not originally installed for sea level monitoring. They were established in harbors or near coastal regions for monitoring land motion. This study utilizes existing SNR data from three GNSS stations (Kaohsiung, Suao, and TaiCOAST) in Taiwan to compute SLHs with two methods, namely, Lomb–Scargle Periodogram (LSP)-only, and LSP aided with tidal harmonic analysis developed in this study. The results of both methods are compared with co-located or nearby tide gauge records. Due to the poor quality of SNR data, the worst accuracy of SLHs derived from traditional LSP-only method exceeds 1?m at the TaiCOAST station. With our procedure, the standard deviations (STDs) of difference between GNSS-derived SLHs and tide gauge records in Kaohsiung and Suao stations decreased to 10?cm and the results show excellent agreement with tide gauge derived relative sea level records, with STD of differences of 7?cm and correlation coefficient of 0.96. In addition, the absolute GNSS-R sea level trend in Kaohsiung during 2006–2011 agrees well with that derived from satellite altimetry. We conclude that the coastal GNSS stations in Taiwan have the potential of monitoring absolute coastal sea level change accurately when our proposed methodology is used.     

Time distribution of the precipitable water vapor in central Saudi Arabia and its relationship to solar activity

Water vapor is the most important greenhouse gas. It plays a major role in the dynamics of atmospheric circulation, radiation exchange within the atmosphere, and climate variability. Knowledge of the distribution of water vapor is important for understanding climate change and global warming.     

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.     

Impact of local and global factors and meteorological parameters in temporal variation of atmospheric potential gradient

In this research work, we have performed comparative diurnal variations of atmospheric Potential Gradient (PG) of fair-weather days by using the data of three stations installed in Northern, Pakistan for the year 2018. We investigated the impact of both local and global factors and meteorological parameters in the diurnal variation of atmospheric Potential Gradient on the annual and seasonal time scale. We observed two peaks, primary and secondary. This is because of the land-based measurements of annual and seasonal variations. The annual average curve of Potential Gradient of all three stations: Islamabad (CES), Muzaffarabad (MZF), and Balakot (BKT) demonstrated a notable deviation from the standard oceanic Carnegie curve. The atmospheric Potential Gradient variations are due to numerous meteorological factors e.g., air pollution, humidity, aerosol particles, fog, and temperature. Among three stations, the MZF station is located in highland (mountainous) and it demonstrated a higher atmospheric Potential Gradient. We further differentiate the results of our three stations with global results for authenticity and observed coherence between them. In addition, a positive correlation of fair-weather Potential Gradient is observed with temperature and a notable correlation between relative humidity and atmospheric Potential Gradient for all the three observatories.