Tropospheric Delay in the Neapolitan and Vesuvius Areas (Italy) by Means of a Dense GPS Array: A Contribution for Weather Forecasting and Climate Monitoring

Studying the spatiotemporal distribution and motion of water vapour (WV), the most variable greenhouse gas in the troposphere, is pivotal, not only for meteorology and climatology, but for geodesy, too. In fact, WV variability degrades, in an unpredictable way, almost all geodetic observation based on the propagation of electromagnetic signal through the atmosphere. We use data collected on a dense GPS network, designed for the purposes of monitoring the active Neapolitan (Italy) volcanoes, to retrieve the tropospheric delay parameters and precipitable water vapour (PWV). This study has two main targets: (a) the analysis of long datasets (11 years) to extract trends of climatological meaning for the region; (b) studying the main features of the time evolution of the PWV during heavy raining events to gain knowledge on the preparatory stages of highly impacting thunderstorms. For the latter target, both differential and precise point positioning (PPP) techniques are used, and the results are compared and critically discussed. An increasing trend, amounting to about 2 mm/decades, has been recognized in the PWV time series, which is in agreement with the results achieved in previous studies for the Mediterranean area. A clear topographic effect is detected for the Vesuvius volcano sector of the network and a linear relationship between PWV and altitude is quantitatively assessed. This signature must be taken into account in any modelling for the atmospheric correction of geodetic and remote-sensing data (e.g., InSAR). Characteristic temporal evolutions were recognized in the PWV in the targeted thunderstorms (which occurred in 2019 and 2020), i.e., a sharp increase a few hours before the main rain event, followed by a rapid decrease when the thunderstorm vanished. Accounting for such a peculiar trend in the PWV could be useful for setting up possible early warning systems for those areas prone to flash flooding, thus potentially providing a tool for disaster risk reduction

Analysis of Precipitable Water Vapour in Angola Using GNSS Observations

For accurate weather predictions and analysis of extreme events, a good estimate of the amount of water content in the atmosphere is essential. This information is provided by several techniques like radiosondes that measure this parameter at various heights. However, most of them are very limited spatially and temporarily or suffer from measurement specific constraints. To complement these techniques, Precipitable Water Vapor (PWV) can be measured with GNSS (Global Navigation Satellite System) at CORS (Continuously Operating Reference Stations) networks. when the temperature and pressure are also known at the station location. PWV can be derived from the delay in the GNSS signal when it passes through the troposphere. In the framework of SUGGEST-AFRICA, it is being implemented a system to use the national GNSS stations for the automatic computation of PWV in Angola. Thus, this dissertation intends to describe the necessary steps to develop a system to be used for supporting meteorological and climate applications in Angola. SUGGEST-AFRICA also funded the installation of 5 weather stations, collocated with GNSS stations in Angola namely: Benguela, Cabinda, Cuito, Luanda and Namibe, in order to obtain pressure and temperature which is necessary to obtain the PWV estimates. When there are no nearby meteorological stations, the potential alternative is to use values from global/regional models. Methodologies have been optimized to passive and actively access the GNSS data; the PWV estimations are computed using PPP (Precise Point Positioning), which permits the estimation of each station separately; solutions have been validated using internal values. In addition, analyses are presented to evaluate the reliability of the network. This work presents preliminary results for the variation of the ZTD data available all around the territory in Angola and how they relate to the seasonal variations in water vapour. Also, presents preliminary results for the time-series variation of PWV in the Luanda station (collocated by the SEGAL group). This study is supported by SUGGEST-AFRICA, funded by Fundação Aga Khan and FCT. It uses computational resources provided by C4G – Collaboratory for Geosciences (PINFRA/22151/2016). It is also supported by project FCT/UIDB/50019/2020 – IDL funded by FCT.Para precisão da previsão do tempo e análise de eventos extremos é fundamental uma boa estimativa do vapor da água na atmosfera. O vapor da água na atmosfera é fornecido por várias técnicas como radio sondagem que mede este parâmetro em várias alturas. No entanto, muito dessas técnicas são limitadas devido a resolução espacial e temporal ou sofrem restrições específicas de medição. Para completar estas limitações encontrado nas demais técnicas, o vapor da água precipitável (PWV) pode ser medido pelo GNSS (Sistemas de navegação global por satélite) CORS (Rede nacional de estações de referência de operação continua). PWV pode ser obtido a partir do atraso do sinal de GNSS através da troposfera, quando a temperatura e a pressão também são conhecidas derivado da localização duma estação meteorológica. No âmbito da SUGGEST-ÁFRICA, esta ser implementado um sistema de modo a calcular o PWV de uma maneira automática em Angola. Assim, nesta dissertação pretende descrever os passos necessários para desenvolver tal sistema a ser utilizado para apoiar aplicações meteorológicas e climáticas em Angola. SUGGEST-ÁFRICA também financiou a instalação de 5 estações meteorológicas, colocada com estações GNSS em Angola, nomeadamente: Benguela, Cabinda, Cuito, Luanda e Namibe, a fim de obter a pressão e a temperatura necessárias para obter as estimativas PWV. Aconselha-se o uso dos modelos globais/regionais para aquisição de valores de pressão e temperatura quando não existe dados nas estações meteorológicas adjacentes. As metodologias foram otimizadas para o acesso passivo e ativo dos dados GNSS; a estimação do vapor de água precipitável é calculada usando a técnica PPP (Posicionamento do ponto preciso), que permite a determinação de cada estação individualmente e separadamente; as soluções foram validadas usando valor interno. Além disso, são apresentadas análises para avaliar a fiabilidade da rede. Este trabalho, também apresenta resultados preliminares para a variação de todo dados do ZTD disponível em Angola e a forma como se relacionam com as variações sazonais do vapor de água. Também, apresenta variação da série temporal do PWV na estação meteorológica de Luanda (instalado pela SEGAL). Este estudo é suportado pela SUGGEST-ÁFRICA, financiado pela fundação Aga Khan e FCT. Utiliza recurso computacional fornecido pela C4G – Colaboração de Geociências (PINFRA/ 22151/2016). Também é apoiado pelo projecto FCT/UIDB/50019/2020 – IDL financiado pela FCT

Analysis of Precipitable Water Vapour in Nigeria using GNSS Observations

Water Vapour estimation using ground-based Global Navigation Satellite System (GNSS) observations is a well-established technology that contributes to weather forecast, research, and climate monitoring. Water vapour in the atmosphere is directly related with precipitation that may lead to extreme event (e.g., floods). The application of GNSS to sense the total amount of water vapour integrated along the signal path in the troposphere is what is referred to as GNSS meteorology. GNSS has the advantage of all-weather condition, low cost with high temporal and spatial resolution when compared to other classical methods of water vapour measuring that are expensive and/or with low spatial and temporal coverage. When GNSS signals are transmitted from GNSS satellites in space to ground-based GNSS receivers, they experience a tropospheric delay (an error source in GNSS positioning) often represented in GNSS meteorology as the Zenith Total Delay (ZTD). The ZTD is the sum of the Zenith Hydrostatic Delay and the Zenith Wet Delay and it is one of the products of GNSS data processing. The ZTD can be converted to Precipitable Water Vapour (PWV) when surface temperature and pressure values are known at the GNSS site using a conversion factor (?) that is dependent on the weighted mean temperature (Tm) and pressure. This dissertation focuses on the estimation and analysis of water vapour in Nigeria using GNSS observations. The Nigerian Permanent GNSS Network (NIGNET) stations observations and products were retrieved from the infrastructure implemented by Office of the Surveyor General of the Federation (OSGoF). Processing of the data was carried out using online software (GipsyX) for the estimation of ZTD. Fifteen GNSS stations were used in this research and the period 2009 to 2021 was considered. The characteristics of the ZTD over the territory of Nigeria was investigated. The range of ZTD variation in Nigeria for the period used in this research was found to be approximately between 1900mm to 2700mm in the NIGNET stations. The two main seasons in Nigeria were significantly noticed as low peaks were found to be occurring during the dry (winter) season while high peaks were remarkably seen during the rainy (summer) season. The amplitude of the seasonal variation within the period under investigation is between a minimum of 36mm to a maximum of 124mm with the Northern region having higher values than the Southern part. It was discovered ultimately by the results obtained from the analyses, that ZTD variation in both the Northern and Southern regions are influenced by the 4 distinct climates and other local weather conditions including temperature and the trade wind from Sahara Desert and the Atlantic Ocean.A estimativa de vapor de água usando observações do Sistema Global de Navegação por Satélite (GNSS) é uma tecnologia bem estabelecida que tem dado um contributo importante para a realização de previsões meteorológicas, investigação e monitorização climática. O vapor de água na atmosfera está diretamente relacionado com a precipitação que pode levar a eventos extremos (por exemplo, inundações). A área de estudo do uso de dados GNSS para detetar a quantidade total de vapor de água integrado ao longo do caminho do sinal na troposfera é designado de meteorologia GNSS. O GNSS tem como vantagem de poder ser utilizado em todas as condições climáticas, apresentar baixo custo e alta resolução temporal e espacial quando comparado a outros métodos clássicos de medição de vapor de água, normalmente mais caros e/ou com baixa cobertura espacial e temporal. Quando os sinais GNSS são transmitidos dose satélites para recetores terrestres, existe um atraso troposférico (uma fonte de erro no posicionamento GNSS) frequentemente representado na meteorologia GNSS como o Atraso Zenital Total (ZTD em Inglês ). O ZTD é a soma do Atraso Zenital e do Atraso Zenital Húmido e é um dos produtos do processamento de dados GNSS. O ZTD pode ser convertido em PWV quando os valores de temperatura e pressão da superfície são conhecidos no local através de um fator de conversão (?) que depende da temperatura média ponderada (Tm) e da pressão. Esta dissertação tem como objetivo a estimativa e análise de vapor de água na Nigéria usando observações GNSS. As observações e produtos das estações da Rede Permanente GNSS da Nigéria (NIGNET) foram obtidos através da infraestrutura implementada pelo OSGoF. O processamento dos dados foi realizado por meio de software online (GipsyX) para a estimativa do ZTD. Dados de quinze estações GNSS foram utilizadas na análise correspondendo ao período entre 2009 a 2021, para avaliar as características da ZTD sobre o território da Nigéria. A faixa de variação de ZTD na Nigéria para o período considerado foi de aproximadamente 1900mm a 2700mm nas estações NIGNET. As duas principais estações climáticas na Nigéria destacaram-se, com picos baixos que ocorreram durante a estação seca (inverno), e picos altos observados durante a estação chuvosa (verão). A amplitude da variação sazonal no período sob investigação é entre um mínimo de 36mm e um máximo de 124mm com a região norte tendo valores mais elevados que a região sul. Pelos resultados obtidos das análises foi ainda possível verificar que a variação da ZTD nas regiões Norte e Sul são influenciadas pelos 4 climas distintos e outras condições climáticas locais, incluindo temperatura e ventos alísios do deserto do Saara e do Oceano Atlântico

Applications of GNSS Slant Path Delay Data on Meteorology at Storm Scales

This chapter focuses on applications of Global Navigation Satellite Systems (GNSS) slant path delay data (SPD) to obtain signals from thunderstorms or rainbands. Current operational numerical weather prediction systems (NWPs) use water vapor distributions derived by GNSS technology as vital information for predicting convective rainfall. Mostly, zenith total delay or integrated water vapor data are used at horizontal scales of several tens of kilometers for this purpose. Beyond such operational use, SPD can be used to obtain information on storms (cumulonimbus) at horizontal scales of less than 10 km. For instance, found that SPD represents very small-scale phenomena of less than 10 km and can be used to estimate water vapor distribution around a thunderstorm with a strong tornado, and succeeded in improving the forecast skill of a rainband at 10 km scale. This chapter reviews SPD, which is invaluable for predicting thunderstorms and/or rainbands

Estimation of Atmospheric Precipitable Water Using the Global Positioning System

This research focuses on using the Global Positioning System (GPS) for atmospheric precipitable water (PW) estimation. Water vapor, measured in terms of PW, plays a crucial role in atmospheric processes and short-term weather forecasting. Traditional methodologies for measuring atmospheric water vapor distributions have known inadequacies, resulting in the motivation to gain good water vapor characterization via GPS. The ability to accurately forecast cloud formation and other weather phenomenon is critical, especially in the case of military operations. Using a network of GPS receivers, it is possible to estimate precipitable water throughout the network region with better accuracy than traditional methods and on a more consistent near real-time basis. First, an investigation into the effects of introducing less accurate, near real-time GPS ephemerides was accomplished. Secondly, the network geometry and data availability were degraded to simulate potential military operational constraints. Finally, several interpolation methods were applied to quantify the ability to estimate the water vapor distribution over the entire network region with limited data availability and network geometry constraints. Results showed that International GPS Service (IGS) ultra-rapid orbits introduced minimal PW estimation error (~1-2mm) while maintaining near real-time capability. The degraded perimeter network also introduced minimal PW estimation error (~1-2 mm) at the included stations, indicating potential application in constrained data environments. However, the interpolation investigation showed an overall inability to determine PW distribution over the entire network region

Precipitable water vapour retrieval from GPS precise point positioning and NCEP CFSv2 dataset during typhoon events

Radiosonde is extensively used for understanding meteorological parameters in the vertical direction. Four typhoon events, including three landfalls (MERANTI, NEPARTAK, and MEGI) and one non-landfall (MALAKAS), were chosen in analysing the precipitable water vapour (PWV) characteristics in this study. The spatial distribution of the three radiosonde stations in Zhejiang province does not meet the requirement in analysing changes in PWV during typhoon event. Global position system (GPS) observations are an alternative method for deriving the PWV. This enables improvements in the temporal⁻spatial resolution of PWV computed by the radiosonde measurements. The National Centers for Environmental Prediction (NCEP) re-analysed data were employed for interpolating temperature and atmosphere pressure at the GPS antennas height. The PWV computed from GPS observations and NCEP re-analysed data were then compared with the true PWV. The maximum difference of radiosonde and GPS PWV was not more than 30 mm at Taiz station. The Root-Mean-Square (RMS) of PWV differences between radiosonde and GPS was not more than 5 mm in January, February, March, November, and December. It was slightly greater than 5 mm in April. High RMS in May, June, July, August, September, and October implies that differences in GPS and radiosonde PWVs are evident in these months. Correlation coefficients of GPS and radiosonde PWVs were more than 0.9, indicating that the changes in GPS and radiosonde PWVs are similar. Radiosonde calculated PWVs were used for GPS PWV calibration for understanding the PWV changes during the period of a typhoon event. The results from three landfall typhoons show that the average PWV over Zhejiang province is increasing and approaching China mainland. In contrast, MALAKAS did not make landfall and shows a decreasing PWV trend, although it was heading to China mainland. Generally, the PWV change can be used to predict whether the typhoon will make landfall in these cases. PWV spatial distribution of MERANTI shows that PWV peaks change along the typhoon epicenter over Zhejiang province

Comprehensive study on the tropospheric Wet Delay and horizontal gradients during a severe weather event

GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events.Peer ReviewedPostprint (published version

The estimation of precipitable water vapour from GPS measurements in South Africa

Includes bibliographical references (leaves 110-115).The propagation of the Global Positioning System (GPS) signal from the satellite to the receiver is affected by, among other factors, the atmosphere through which it passes and, whereas the affects of the ionosphere can be eliminated by the differencing of two transmitted frequencies, the affects of the troposphere remain one of the major sources of noise in traditional geodetic and positioning applications of GPS. This noise can, however, be turned into a signal for the meteorologist and, by applying suitable constraints and processing strategies, it is possible to estimate the amount of precipitable water vapour (PWV) in the atmosphere. The application of the GPS data for the estimation of PWV in the atmosphere is not a new concept and has been described in numerous publications and reports since the early 1990's (Bevis et al., 1992, Rocken et al., 1993). This project is, however, an attempt to test the technique using the South African network of permanent GPS base stations. This thesis sets out to answer four fundamental questions: i. In theory, can GPS observations be used to estimate the amount of precipitable water vapour (PWV) in the atmosphere? ii. What permanent GPS networks are being used in other countries around the world for similar applications and how successful are these applications? iii. Can data derived from the South African network of permanent GPS base stations, TrigNet, be used to estimate PWV with sufficient accuracy to be able to supplement the radiosonde upper air measurements of the South African Weather Service (SAWS)? iv. Is the estimation of PWV as derived from the GPS observations a true reflection of reality using the radiosonde ascent measurements and numerical weather model (NWM) data as a method of independent verification? The primary data sets used to estimate atmospheric PWV at hourly intervals for March 2004 were; i. GPS data derived from the South African network of permanent GPS base stations provided by the Chief Directorate of Surveys and Mapping (CDSM); and ii. Surface meteorological measurements supplied by the South African Weather Service (SAWS). The two independent data sets used to verify and test the technique were; i. Upper air measurements derived from radiosonde ascents provided by the SAWS. These measurements were used to compute Integrated Water Vapour (IWV) and then converted to PWV; and ii. PWV estimates derived from a Numerical Weather Model provided by the Department of Environmental and Geographical Sciences of UCT. By the comparing the estimates of PWV from the three techniques, viz. GPS, radiosonde and NWM, it was found that GPS will meet the accuracy requirements of the meteorologist and could be used to supplement radiosonde measurements for use in numerical weather models

Monitoring and Prediction of Severe Weather Phenomena through GNSS Meteorology

While primary use of Global Navigation Satellite System (GNSS) is positioning, navigation, and timing (PNT), various GNSS applications have emerged over the past decades that includes GNSS meteorology. GNSS meteorology is the remote sensing of the atmospheric constituents in the neutral atmosphere – mostly in the troposphere - using GNSS to deliver information about the state of atmosphere. Precipitable water vapor (PWV) is the total amount of water vapor in a column of air above the earth surface that varies rapidly with short temporal and spatial-scale during severe meteorological phenomena. The amount of PWV contained in the neutral atmosphere can be retrieved from GNSS signals received by ground-based GNSS observations. GNSS is an excellent tool where it is not affected by weather conditions (e.g., presence of clouds, which derive a challenge to traditional weather monitoring technologies). Another benefit of GNSS is the data availability and accessibility. This dissertation focuses on developing a PWV prediction model using GNSS observations to monitor and forecast the path of severe precipitations induced by hurricanes. By using the GNSS-derived PWV and meteorological variables, the trend of the water vapor distribution is determined for the time frames of before, during, and after the severe precipitation. For each time frame a unique prediction model is developed suing a principle component regression (PCR). The developed model can forecast the severe precipitation track induced by a hurricane up to 24 hours in advance. In this dissertation the prediction models are examined using a proposed statistical model for different types of hurricanes. The case studies are: 1) Hurricane Mathew in 2016, 2) Hurricane Harvey in 2017, 3) Hurricane Irma in 2017, and 4) Hurricane Florence in 2018. In each hurricane case study the patterns of the GNSS-derived PWV fluctuations are analyzed. In particular, a sudden and sharp increment in the PWV followed by sharp descending trends was observed a few hours prior to the onset of precipitation. Also, the predicted PWV rate of change is dramatically increased prior to a severe precipitation. Moreover, in each case study, the probability of precipitation rapidly increased when the PWV reached a threshold in the range of 50 mm to 55 mm. The threshold is determined by analyzing the correlation between PWV fluctuations and occurrence of rainfall during the hurricane lifetime. The threshold is applied for classification of prediction models into the “right before”, “during” and “right after” models based on the hurricane development stage. It should be emphasized that this study specially focuses on “right before” model, which is the most useful model to analyze the movement of hurricane. The proposed method was validated by analyzing the distribution pattern of the predicted PWV residual, its magnitude, and the actual observed PWV in the test site. For a robust analysis considering the uncertainty from the measurement noise and other error sources in the GNSS-derived PWV, the prediction residual at multiple sites in a local area are evaluated within the grids in the test area. The grid size is determined with the consideration of the test site and the geometric distribution of available CORS. The high probably location of heavy precipitation location by the grid-based prediction well agreed with the observed rain pattern that can be used for predicting the hurricane path. In addition, the negative correlation between the residuals of PWV measurements to the prediction model and the magnitude of precipitation was revealed. It shows the magnitude of the predicted model residuals can be used for hurricane tracking and potentially applies to evaluate the storm intensity. This study demonstrates the feasibility of GNSS for monitoring severe precipitations and proves the effectiveness of the statistical model for forecasting the precipitation path during the hurricane that is potentially applied to a hazard early warning system