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An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia

Article

An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia

Abstract

A campaign was conducted from 23 July to 5 August 2010 to measure atmospheric precipitable water vapor (PWV) using five Global Positioning System (GPS) receivers, stationed at four different locations in Jakarta and Bogor, western Java, Indonesia. Radiosondes were launched at an interval of 6 h to validate the GPS-derived PWV data. The validation resulted in a root mean square error of 2 to 3 mm in PWV. The influence of atmospheric pressure and temperature on GPS-derived PWV was evaluated. A regular semi-diurnal pressure oscillation was observed, showing an amplitude ranging from 3 to 5 hPa, which corresponds to 1.1 to 1.8 mm in PWV. A nocturnal temperature inversion layer was observed in the radiosonde profiles, which resulted in an error of about 0.5 mm in PWV. From 26 to 29 July, there was a passage of distributed rain clouds over western Java, moving southwestward from the equator toward the Indian Ocean. A second precipitation event, with scattered rain clouds forming locally near Bogor, occurred on 2 August. Both events were observed also by a C-band Doppler Radar operated near Jakarta. The highest peak of GPS-derived PWV (about 67 mm) registered during the campaign occurred on 27 July, coinciding with the distributed rainfall event. Spatial variations in the estimated PWV between the four sites were enhanced before both the analyzed rainfall events, on 27 July and 2 August. Peaks in the temporal variability of PWV were also observed in conjunction with the two events. The results indicated a relation between the space-time inhomogeneity of GPS-PWV and rainfall events in the tropics.
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... Because the PWV fluctuation is the indication of precipitation showing an interrelation with other atmospheric parameters, the PWV rate of change (ROC) over time can be used to track the atmospheric variations. Several studies have shown that the PWV significantly increases a few hours before the most intense rainfall, then sharply decreases when precipitation begins to weaken and finally ceases (Realini et al. 2014;Priego et al. 2017;Tahami et al. 2017;Manandhar et al. 2018; Tahami et al. 2020). The literature found that 1) PWV peaks occur at a few hours before the precipitation onset and 2) the PWV ROC sharply varies before the start of the rain in the most rainfall events. ...
... Because the PWV variation acts as an indication of the precipitation showing the interrelation with other atmospheric parameters, the PWV rate of change (ROC) can be used to observe the atmospheric variation over time. Studies have shown that the PWV greatly increases a few hours prior to intense rainfalls, then sharply decreases when precipitation begins to weaken and finally ceases (Realini et al. 2014;Priego et al. 2017;Tahami et al. 2017;Manandhar et al. 2018). The researchers found that 1) PWV peaks occur a few hours before the precipitation onset and 2) the PWV ROC sharply changes before the start of the rain in most rainfall events. ...
Conference Paper
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The tropospheric products of Global Navigation Satellite Systems (GNSS) can be used to determine the density and distribution of water vapor in the atmosphere, and therefore have been used to monitor precipitation events. This study analyzes GNSS-based precipitable water vapor (PWV) measurements calculated from the NOAA Continuously Operating Reference Stations (CORS) GNSS observations, to 1) track spatiotemporal variability of PWV, 2) identify the abnormal fluctuations in PWV level before the arrival of the hurricane at a ground station, and 3) predict the path and relative intensity of hurricane-induced rainfalls. Firstly, we examined PWV perturbations with the local atmospheric elements including temperature and pressure, and relative humidity, and revealed the relationship between the atmospheric parameters and the formation process of severe precipitation. The CORSs are then classified into a training set and a test dataset. Numerically analyzed meteorological constituents for the training dataset were used to derive a PWV prediction model by applying a multivariate regression approach. The PWV prediction model quantifies the relationship among spatiotemporal hurricane intensification, the PWV rate of change, and meteorological variables. To avoid the correlation effect between these variables, a principal component regression (PCR) was applied. From the PWV prediction model, the PWV at each test station was predicted with a 12 and 24 hours of time scale. The PCR is then applied to test the dataset, and the model’s residuals, which are the discrepancy between the model and measurements, are calculated to verify the model. The residual of the predicted model is a key factor to determine the trajectory of hurricane-induced rainfall and its intensity. By analyzing the distribution pattern of the predicted PWV residuals, their magnitude, and the observed PWV at the test site, the probable locations of intense rainfalls due to the storm front passage can be identified. For a robust analysis considering the uncertainty from the measurement noise and other error sources in the GNSS-derived PWV, we defined a grid in the test site that allows evaluating multiple stations’ PWV prediction/measurements. The grid size was determined with the consideration of the test site and the geometric distribution of available CORSs, which their GNSS observations were used as the data feeds into the prediction model. Because various hurricanes have their own spatial and temporal characteristics, the approach is assessed for two different hurricanes that occurred in the same location and showed different types of rainfall events that are Hurricane Mathew in 2016, and Hurricane Irma in 2017. Because both hurricanes landed in Florida and proceeded to Georgia, and South Carolina, the model’s performance can be evaluated under similar geographic and climate characteristics of the study area. The results were validated by the radar reflectivity map and reported NHC hurricane landfall centers. The results showed that for both hurricanes, the highly probable locations of heavy precipitation by the grid-based prediction coincide with the grids with the minimum residuals of the prediction model. In addition, the negative correlation between the residuals of PWV measurements with the prediction model and the magnitude of precipitation was revealed. The magnitude of the predicted model residuals was used for hurricane tracking and applied to the evaluation of the storm-relative intensity. The study showed that predicted locations (grids) were contained at maximums of less than 25% and 32% of total residuals in the area for 12h and 24h prediction time lags, respectively. This study demonstrates the effectiveness of the statistical model for forecasting the intense precipitation path at least several hours before the arrival of a storm that can be applied to a hazard early warning system.
... Because the PWV fluctuation is the indication of precipitation showing an interrelation with other atmospheric parameters, the PWV rate of change (ROC) over time can be used to track the atmospheric variations. Several studies have shown that the PWV significantly increases a few hours before the most intense rainfall, then sharply decreases when precipitation begins to weaken and finally ceases (Realini et al. 2014;Priego et al. 2017;Tahami et al. 2017;Manandhar et al. 2018; Tahami et al. 2020). The literature found that 1) PWV peaks occur at a few hours before the precipitation onset and 2) the PWV ROC sharply varies before the start of the rain in the most rainfall events. ...
... Because the PWV variation acts as an indication of the precipitation showing the interrelation with other atmospheric parameters, the PWV rate of change (ROC) can be used to observe the atmospheric variation over time. Studies have shown that the PWV greatly increases a few hours prior to intense rainfalls, then sharply decreases when precipitation begins to weaken and finally ceases (Realini et al. 2014;Priego et al. 2017;Tahami et al. 2017;Manandhar et al. 2018). The researchers found that 1) PWV peaks occur a few hours before the precipitation onset and 2) the PWV ROC sharply changes before the start of the rain in most rainfall events. ...
... As such, the monitoring for any long-term changes in the amount of water vapor in the atmosphere is significant as it can help detect and predict changes in the earth's climate as well as improve weather forecasting. GNSS meteorology technique was used to investigate severe weather conditions in a campaign conducted (Realini et al., 2014) in western Java, Indonesia where results indicated a relation between the space-time inhomogeneity of GNSS-PWV and rainfall events in the tropics. The study made (Kanda et al., 2000) on the use of GNSS-PWV to monitor strong rainfall showed that periods of maximum GNSS-PWV tended to precede the onset of heavy rain, and that the precipitation frequency was higher for larger hourly increase in GNSS-PWV. ...
... The characteristic behaviour between the evolution of GNSS-PWV and rainfall is also observed during intense rains (rain rate of 15-30 mm/h) and even for heavy rain events (rain rate of 7.5 -15 mm/h) as shown in figures 6 and 7. Similar to most of the torrential rains, there is a steady rise in the GNSS-PWV value prior to the rain event and it occurred near the maximum GNSS-PWV value with a significant reduction after the rain event. As noted in several studies (Kanda et al., 2000;Realini et al., 2014;Benevides et al., 2015), most severe rainfall events occur in descending trends after a long ascending period and that the most intense events occur after steep ascents in GNSS-PWV. To further characterize the time-varying GNSS-PWV in each of the rainfall events, the time-difference between the rain event and the time of the maximum GNSS-PWV was measured. ...
Article
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A standalone Global Navigation Satellite System (GNSS) receiver was utilized in this study to get a measure of the atmospheric water vapor in Davao City, Philippines. It aims to monitor the variability of GNSS precipitable water vapor (PWV) especially during heavy to torrential rain. The results of the study showed a positive correlation between GNSS-PWV and precipitation especially in these severe (heavy to torrential) rain events which implies that the assimilation of atmospheric water vapor measurements can improve forecasts of such events.
... The resulting ZTD time series were validated by comparison with radiosondes, using the 8 available radiosonde launch sites in Italy, and considering the GNSS station nearest to the radiosonde launch site. This comparison resulted in ZTD differences with RMSE values lower than 2 cm (which agrees with what is expected based on the scientific literature) [46][47][48][49]. The high variability of water vapor both in space and time requires highly dense and homogeneously distributed networks, with inter-distances between the stations shorter than 10 km. ...
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The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.
... Because of this, it is difficult to measure. Atmospheric water vapor content is expressed in precipitable water vapor (PWV) and defined as the integrated amount of water vapor condensed in a vertical column from the surface to the top of the atmosphere [3]. Conventional meteorological instruments, such as radiosonde [4][5][6] and GNSS [7][8][9][10] have been widely used to estimate PWV. ...
Article
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Precipitable water vapor (PWV) is a parameter used to estimate water vapor content in the atmosphere. In this study, estimates of PWV from PIMO, PLEG and PPPC global navigation satellite system (GNSS) stations are evaluated regarding the PWV obtained from its collocated radiosonde (RS) stations. GNSS PWV were highly correlated with RS PWV (R ~ 0.97). Mean bias error (MBE) between −0.18 mm and −13.39 mm, and root mean square error (RMSE) between 1.86 mm and 2.29 mm showed a good agreement between GNSS PWV and RS PWV. The variations of PWV are presented. Daily variations of PWV conformed to the daily data of rainfall which agrees to the climate types of Quezon City (Type I), Legaspi (Type II), and Puerto Princesa (Type III) based on the Coronas climate classification. Moreover, PWV monthly variation at all sites is high from May to October (~62 mm) and low from November to April (~57 mm). The relationship between PWV and rainfall at all stations showed positive correlation coefficients between +0.49 to +0.83. Meanwhile, it is observed that when PWV is high (low), its variability is low (high). This study shows the potential of GNSS to study water vapor and its contribution to weather analysis.
... [74] show that deep moist convection can be monitored by GNSS derived PWV variation. Similar increasing and decreasing patterns during intense rain events are highlighted also by [89], [124], [8], and [98]. The existing correlation between ZTD and PWV lead to several attempts to implement models and procedures capable to fully exploit it. ...
... where ρ w the water vapour density, n is the total number of layers between h 1 and h 2 , an ρ d,i , r i , Δh, are respectively the dry air density, the mixing ratio, and the altitude step for layer. (Realini et al. 2014) Dropping the index i for the sake of simplicity, the dry air density ρ d is expressed as ...
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Remote sensing of atmospheric water vapour using GNSS and Satellite data has become an efficient tool in meteorology and climate research. Many satellite data have been increasingly used to measure the content of water vapour in the atmosphere and to characterize its temporal and spatial variations. In this paper, we have used observations from radiosonde data collected from three stations (Algiers, Bechar and Tamanrasset) in Algeria from January to December 2012 to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water vapour (PWV) products. Results show strong agreement between the total precipitable water contents estimated based on radiosondes observations and the ones measured by the sensor MODIS with the correlation coefficients in the range 0.69 to 0.95 and a mean bias, which does not exceed 1.5.
... 3b), respectively. We 174 can confirm that the difference between GNSS and RS41-derived PWV is smaller than 1 175 mm (e.g., mean difference at 14 LST is -0.92 mm with a standard deviation of 1.1 mm) 176 regardless of the time of the day, while LMS6 shows large dry bias in daytime (the difference is comparable to the previous works (e.g.,Realini et al. 2014), which compared 179 GNSS-PWV with Vaisala RS92-derived PWV, but the data consistency between RS92 and180 RS41 are confirmed by several works (e.g., Jensen et al. 2016; Kawai et al. 2017). Based 181 ...
Article
This short article describes humidity data correction based on intercomparison between the two manufacturers' radiosondes with the assessment using precipitable water vapor (PWV) derived from Global Navigation Satellite System (GNSS) signals. In addition, we propose a method to determine whether the same correction procedure can be applied for the case that such intercomparison cannot be conducted. During the intensive observation called Years of the Maritime Continent - Boreal Summer Monsoon study in 2018 (YMC-BSM 2018), intercomparison of radiosonde between Lockheed Martin LMS6 and Vaisala RS41-SGP was conducted at Laoag, Ilocos Norte, Philippines from late July to early August 2018. While their mean difference of relative humidity (RH) showed better than 5 %, dry bias was confirmed for LMS6 only during clear sky daytime soundings based on the comparison of PWV with that derived from GNSS signals. To use different radiosonde data with the same research-quality, we developed a correction table for LMS6 RH data. While a direct intercomparison between different radiosondes and independently developed observational tools such as GNSS-receiver is ideal to evaluate the data quality, it is not always able to be performed. Indeed, we obtained LMS6 radiosonde data at different site at Yap Island, Federated States of Micronesia from another field campaign YMC-BSM 2020, where any intercomparison could not be conducted. In order to decide whether the same correction procedure obtained from YMC-BSM 2018 can be applied to those data, we assessed their similarity based on a relationship between specific humidity from surface meteorological station data that was obtained independently prior to launch and radiosonde specific humidity averaged over 300 m from the initial radiosonde measurement point. This method allowed us to confirm the same behavior between Laoag data in 2018 and Yap data in 2020, thus we applied our correction method to RH data in YMC-BSM 2020.
... According to literature it is possible also, to calculate the total amount of water vapor between two altitudes by integrating the measurements of the water vapor density between these two altitudes as (Realini et al., 2014): ...
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This paper is an evaluation of the atmospheric water vapor remote sensing by GPS signals. The integrated water vapor (IWV) is calculated based on the measurement of the tropospheric zenith total delay (ZTD) effects on the microwave signals emitted by GPS satellites. The methodology proposed in this work is based on the combination of the global navigation satellite system (GNSS) observations and navigation data from the international GNSS service (IGS) products with meteorological data, measured at the stations level, to calculate the ZTD delay and estimate the integrated water vapor value. This work was carried out using data records from12 IGS stations distributed in seven countries in the four seasons of the year. The obtained results are compared with the values generated by Radiosonde measurement and MODIS satellite images level 2 (Water Vapor data product). In more than 90% of cases, the difference between the GPS and Radiosonde solutions is less than 3 mm with a monthly RMS less than 1.6 and a correlation of about 95%. The comparison between the GPS and MODIS shows that in more than 65 % of the time, the difference between the two solutions is less than 4 mm with a monthly RMS less than 2.3, and the correlation is about 73%
Thesis
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While the primary use of the Global Navigation Satellite System (GNSS) is positioning, navigation, and timing (PNT), various GNSS applications have emerged over the past decades that include 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 the atmosphere. Precipitable water vapor (PWV) is the total amount of water vapor in a column of air above the earth's 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., the 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 using a principal 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 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 the occurrence of rainfall during the hurricane's lifetime. The threshold is applied for the 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 especially focuses on the “right before” model, which is the most useful model to analyze the movement of hurricanes. 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 that 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.
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