Precipitable Water Vapor Time Series Research Articles

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Abstract The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R 2 and root mean square error (RMSE) of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

Correlation Analysis of GNSS-Derived Precipitable Water Vapor (PWV) with Rainfall Data in Indonesia

GNSS (Global Navigation Satellite Systems) have become an important tool for various activities related to positioning, such as navigation, construction projects, and deformation measurement. Additionally, GNSS can also estimate Precipitable Vapor Vapor (PWV) for meteorological purposes. PWV is a measure of atmospheric water vapor, which can eventually precipitate as rain. Since Indonesia’s climate is mainly characterized by the change in rainfall, monitoring precipitation is a crucial step in understanding its pattern. In this study, we aim to analyze the correlation between PWV and rainfall in Indonesia. We used GNSS observations from the InaCORS network across Indonesia, as well as rainfall data from GSMaP at the InaCORS stations in 2019 from 1st January to 31st December. Both of these data are normalized every five days (pentad days) and compared to each other to obtain the Pearson’s correlation coefficients. From these results, we generated a heatmap of the Pearson’s correlation coefficients between pentad PWV and rainfall at InaCORS locations in Indonesia, with the highest correlation of 0.753819 in Garut Regency and the lowest correlation of 0.090146 in Fakfak Regency. Moreover, we also analyzed the timeseries of PWV and rainfall comparison at several sampling stations. From the results we found, PWV and rainfall correlate each other in positive way. Southern regions of Indonesia have higher correlation compared to northern regions of Indonesia.

Homogenization of daily precipitable water vapor time series derived from GNSS observations over China

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008–2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75.

Derived precipitable water vapour from GNSS and radiosonde data using time series and spatial least-square

ABSTRACT Precipitable water vapour (PWV) plays an important role in rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In this paper, radiosonde techniques are used to compute PWV’s spatial and temporal variations and GNSS (Global Navigation Satellite Systems) using in spatial only. GNSS data (GPS and GLONASS) from eight Egyptian stations were processed for the year 2014. Five radiosonde stations for the period from 2005 to 2016 were used. Time series is constructed using the daily surface measurements of radiosonde stations. The linear trend is estimated by straight line fit over 12 years of seasonally adjusted PWV time series. The PWV in Egypt has a positive trend in time series at more than five radiosonde sites with a rate of 0.3 mm/year. The monthly cycle is a near sine curve and the stochastic errors are from 0% to 5.4% over 12 years. The comparison between PWV estimated from GNSS data using the PPP approach and radiosonde data for each station in year 2014 was done in the near station. The nearest two stations, GNSS station “MTRH” and radiosonde station “62,306”, get a bias of 0.66 mm. Three common interpolation techniques (Inverse Distance Weighting, Kriging, and Minimum Curve) are used. The biases of the three used methods were 1.65 mm, 1.96 mm and 0.61 mm, respectively. The statistical methods of Minimum Curve interpolation are found superior to other methods with mean error at Mersa-Matrouh, Aswan and Al-Arish stations reaching 0.1 mm, 1.0 mm and 0.30 mm, respectively. The minimum curve technique is recommended in spatial interpolation for the prediction of PWV amount.Abbreviations: PWV: precipitable water vapour; PPP: precise point positioning; GNSS: global navigation satellite system; ZPD: tropospheric zenith path delay; ZWD: zenith wet delay; IDW: inverse distance weighting; MC: minimum curvature; IGS: International GNSS service.

Assessing the impact of variations in atmospheric water vapour content over Nigeria from GNSS measurements

This study analyses the meteorological impact of the variability of precipitable water vapour (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations over Nigeria from 2013 to 2014; these measurements represent the foremost probe of GNSS PWV distribution and variability over Nigeria. In this study, GNSS PWV daily estimates were grouped into monthly and seasonal averages; the variations in the monthly and seasonal estimates of GNSS PWV were characterized and correlated with different weather events that are regarded as good climate change indicators. The results revealed that the spatiotemporal changes in PWV content are largely subjugated by the effects of latitude, topographical features, the seasons and the continental air masses. Our study shows that there is a very strong seasonal interplay among the GNSS PWV, relative humidity, rainfall and cloud estimates. In addition, GNSS PWV and total electron content (TEC) estimates show an opposite relationship; the semi-diurnal relationship between GNSS PWV and TEC is stronger than the seasonal relationship. The seasonal relation among GNSS PWV, temperature and wind speed appears weak, while very strong interplay exists among the GNSS PWV, sun spot number and total solar radiation estimates. Our results confirm that GNSS PWV is a good pointer for weather forecasting/monitoring and fit for climate monitoring if available on a longer time scale. Finally, we recommend the densification of the GNSS network in Nigeria, as this will enable 3D profiling of PWV, thereby providing more information on GNSS PWV time series, which is needed for long-term climatology.

GNSS-derived PWV and meteorological data for short-term rainfall forecast based on support vector machine

Rainfall is one of the key triggers of severe weather, such as floods and mudslides. It is difficult to forecast accurately and timely. With the extended application of global navigation satellite system (GNSS) into meteorology, existing methods have used GNSS-derived atmospheric parameters to forecast rainfall. However, low success rate and high false alarm rate exist because only a few related predictors have been used. To overcome the disadvantages of existing rainfall forecasting models, this study proposes a short-term rainfall forecast model based on the support vector machine (SVM) algorithm. GNSS-derived precipitable water vapor (PWV), meteorological parameters (such as: pressure (P), temperature (T), relative humidity (RH), and dew point temperature (DPT)); and time-related parameters, such as day of the year (DoY), hour of the day (HoD), and minute of the hour (MoH); are considered as rainfall forecast parameters. The correlation analysis between meteorological parameters/PWV and rainfall is first performed by using a five-minute PWV time series obtained from GNSS observations in Singapore over the period of 2010 to 2012. SVM is then used to establish the rainfall forecast model and forecast the occurrence of rainfall from 10 to 60 min in advance. Experimental results present that (1) various parameters (T, P, RH, DPT, and PWV) show different variation characteristics before the occurrence of rainfall, thereby indicating that good results can be obtained to describe the process of rainfall occurrence by using multiple parameters and (2) the SVM-based rainfall forecast model can forecast rainfall with and average true (TFR) and false (FFR) forecast rates of more than 99% and less than 23%, respectively. In this study, the SVM-based rainfall forecast model with multiple factors performs better than existing methods. TFR and FFR of the SVM-based rainfall forecast model are improved by nearly 10% and reduced by more than 10%, respectively.

Seasonal and annual variations of the GPS-based precipitable water vapor over Sumatra, Indonesia

This study investigates the spatial distribution of seasonal and annual as well as semi-annual variations of the precipitable water vapor (PWV) over Sumatra Island, Indonesia, as revealed from a dense network of the Global Positioning System (GPS) receivers. We have analyzed the GPS-based PWV time series at 57 stations during the period of 2004 to 2020, and we revealed some features that have not yet been reported in detail by previous investigators using rainfall data. A notable feature is the presence of the seasonal PWV gradient in the northwest-southeast direction, which exist all times of the year. This gradient represents the annual migration of the Asian-Australian monsoon system, which migrates back and forth in the northwest-southeast direction. In addition, this gradient can also show the periodic changes between the wet and dry seasons over the region. The other feature is the presence of a clear demarcation line around the latitude of 2oS, separating Sumatra Island into the annual and semi-annual regimes. While the semi-annual variation tends to be concentrated in the northern side of 2oS, the annual one dominates the region in the southern side of 2oS. It is also revealed that the stations near the demarcation line are most likely to receive rainfall for most of the year.

A New Cumulative Anomaly-Based Model for the Detection of Heavy Precipitation Using GNSS-Derived Tropospheric Products

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events.

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.

A New Adaptive Absolute Method for Homogenizing GNSS‐Derived Precipitable Water Vapor Time Series

AbstractThe inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization.

Tropical cyclone-induced periodical positioning disturbances during the 2017 Hato in the Hong Kong region

The tropospheric delay is an important error source in the Global Positioning System (GPS) positioning and navigation applications. Although most of the tropospheric delays can be removed in the double-differencing (DD) positioning mode, their remaining residuals can still contaminate the positioning accuracy and become unpredictable when tropospheric condition encounters severe variations such as during a tropical cyclone (TC) event. We investigated the positioning performance of five baselines with lengths ranging from 7.8 to 49.9 km during the 2017 TC Hato. The results showed that the TC Hato brought a significant disturbance to the GPS baseline positioning results, particularly in the vertical (up) component. The TC Hato started to affect Hong Kong and the root mean squares (RMS) of GPS positioning errors increased dramatically from about 30 to 140 mm, when it was at a distance of 400–600 km from Hong Kong on August 22, 2017. We found that the vertical positioning errors on that day have the major periods: 2.7 h, 3.0 h, 3.4 h, 4.0 h, and 4.8 h. Examining the wet and hydrostatic parts of the tropospheric delays via the continuous wavelet spectral analysis, we found that the periodical variation of the positioning results on August 22 was caused by the periodical variation of the precipitable water vapor (PWV). The variation of differenced PWV between two baseline stations had consistent periods of 2–5 h. Besides, the periods of differenced PWV time series are in good agreement with the spiral rainband in the TC. This finding suggests that the TC Hato induce periodical PWV variations at two GPS stations of baseline, which resulted in GPS positioning errors of the same periods.

A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

High-Precision GNSS PWV and Its Variation Characteristics in China Based on Individual Station Meteorological Data

The Global Navigation Satellite System (GNSS) plays an important role in retrieving high temporal–spatial resolution precipitable water vapor (PWV) and its applications. The weighted mean temperature (Tm) is a key parameter for the GNSS PWV estimation, which acts as the conversion factor from the zenith wet delay (ZWD) to the PWV. The Tm is determined by the air pressure and water vapor pressure, while it is not available nearby most GNSS stations. The empirical formular is often applied for the GNSS station surface temperature (Ts) but has a lower accuracy. In this paper, the temporal and spatial distribution characteristics of the coefficients of the linear Tm-Ts model are analyzed, and then a piecewise-linear Tm-Ts relationship is established for each GPS station using radiosonde data collected from 2011 to 2019. The Tm accuracy was increased by more than 10% and 20% for 86 and 52 radiosonde stations, respectively. The PWV time series at 377 GNSS stations from the infrastructure construction of national geodetic datum modernization and Crustal Movement Observation Network of China (CMONC) were further obtained from the GPS observations and meteorological data from 2011 to 2019. The PWV accuracy was improved when compared with the Bevis model. Furthermore, the daily and monthly average values, long-term trend, and its change characteristics of the PWV were analyzed using the high-precision inversion model. The results showed that the averaged PWV was higher in Central-Eastern China and Southern China and lower in Northwest China, Northeast China, and North China. The PWV is increasing in most parts of China, while the some PWVs in North China show a downward trend.

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time.

Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones

Tropical cyclones (TCs) are intense rotating storm systems, characterized by strong winds, heavy humidity and storm surges, and usually cause an abnormal increase in regional water vapor during the period of their occurrence. Global navigation satellite system (GNSS) is well-established and initially mainly for positioning, navigation and timing. However, due to their global coverage and 24/7 operability under all weather conditions, GNSS has been widely used in meteorology such as the retrieval of precipitable water vapor (PWV). Since PWV time series obtained from a regional ground GNSS tracking stations network has high temporal and spatial resolutions, in this research, the temporal and spatial variation trends of regional PWVs during several TCs’ periods in Hong Kong were investigated, and a new method using high-resolution GNSS-PWV time series to estimate a TC’s movement was proposed. Test data were from 15 GNSS tracking stations equipped with meteorological sensors, and three GNSS stations without meteorological sensors but using meteorological data interpolated from ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset, and six TC events occurred in 2017 were selected for cases studies. Moreover, for a better accuracy of GNSS-PWV, which is converted from the zenith wet delay of the GNSS signal multiplying a conversion factor that uses a weighted mean temperature ( $T_{m}$ ), a seasonal multi-factor $T_{m}$ model was established and tested. Results showed that the new $T_{m}$ model outperformed the previous regional $T_{m}$ model used in Hong Kong. In addition, based on the temporal variation trend and spatial distribution of the regional PWV, one can determine if a TC approaches the region. A geometric approach to estimating a TC’s movement was developed and its test results agreed well with the truth. These results suggest the potential of using GNSS signals to monitor the movements of TCs for short-time TC forecasting due to their high temporal resolution.

Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.

Towards a zero-difference approach for homogenizing GNSS tropospheric products

A data homogenization method based on singular spectrum analysis (SSA) was developed and tested on real and simulated datasets. The method identifies abrupt changes in the atmospheric time series derived from Global Navigation Satellite System (GNSS) observations. For simulation and verification purposes, we used the ERA-Interim reanalysis data. Our method of change detection is independently applied to the precipitable water vapor (PWV) time series from GNSS, ERA-Interim and their difference. Then the detected offsets in the difference time series can be related to inconsistencies in the datasets or to abrupt changes due to climatic effects. The issue of missing data is also discussed and addressed using SSA. We appraised the performance of our method using a Monte Carlo simulation, which suggests a promising success rate of 81.1% for detecting mean shifts with values between 0.5 and 3 mm in PWV time series. A GNSS-derived PWV dataset, consisting of 214 stations in Germany, was investigated for possible inhomogeneities and systematic changes. We homogenized the dataset by identifying and correcting 96 inhomogeneous time series containing 134 detected and verified mean shifts from which 45 changes, accounting for approximately 34% of the offsets, were undocumented. The linear trends from the GNSS and ERA-Interim PWV datasets were estimated and compared, indicating a significant improvement after homogenization. The correlation between the trends was increased by 39% after correcting the mean shifts in the GNSS data. The method can be used to detect possible changes induced by climatic or meteorological effects.

Mapping Precipitable Water Vapor Time Series From Sentinel-1 Interferometric SAR

In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.

Water vapor study using MODIS and GPS data at 64 continuous GPS stations (2002–2017) in indian subcontinent

Precipitable Water Vapor (PWV) is estimated using MODIS (Moderate Resolution Imaging Spectro-radio-meter) Terra level 3 data with daily resolution i. e MOD08_D3 at 64 cGPS (continuous Global Positioning System) stations spatially spread over Indian subcontinent between geodetic latitude 5° to 35° N and geodetic longitude of 70° to 96° E. MODIS-PWV is compared with GPS-PWV estimated at these cGPS stations to check the validity of water vapor retrieved from MODIS data in Indian subcontinent. Correlation coefficient (R2) between daily values of MODIS and GPS water vapor is above 0.9 with RMSE (root mean square error) of 2–5 mm for 22 cGPS in peninsular India, above 0.9 with RMSE of 3–6 mm for 5 cGPS in northeast India and above 0.8 with RMSE of 1–9 mm for 26 cGPS in Himalayas. PWV time series at all the cGPS stations indicated distinct seasonal cycle for both MODIS and GPS PWV with high RMSE (~6 mm) in wet months and low RMSE (~3 mm) during dry months. Taking advantage of broad spatial spread of stations and long span of data, model for spatial variability of GPS-PWV for Indian subcontinent is proposed. Inter-annual and seasonal variability of GPS-PWV is discussed in detail for peninsular India, northeast India and Himalayas.

Variations in Water Vapor From AIRS and MODIS in Response to Arctic Sea Ice Change in December 2002–November 2016

Water vapor plays a vital role in the Arctic sea ice and climate change. The Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are useful tools for studying the water vapor variations over the entire Arctic. In this paper, we discuss precipitable water vapor (PWV) variations from the AIRS and MODIS Level-3 data products in response to Arctic sea ice change during December 2002–November 2016. The results indicate that both AIRS- and MODIS-derived PWV measurements tend to be underestimated, and this underestimation is generally greater for the latter than the former over both land and ocean. Additionally, both the AIRS and MODIS retrievals suggest that the atmospheres are drier in the solid sea ice pack and, to a lesser extent, in the marginal ice zone, than in the open ocean in each season. Moreover, statistically significant correlations between the PWV and sea ice concentration (SIC) anomalies are generally observed in all seasons under different ice conditions except in the open ocean, where variations in the PWV may be attributed to the poleward moisture transport from lower latitudes. The Arctic PWV and SIC variation patterns are also dominated by the atmospheric and ice conditions in the solid sea ice pack, respectively. The monthly mean PWV and SIC time series analysis further reveals that the Arctic PWV variations tend to be affected by a delayed effect of approximately 1.2–1.3 months from SIC changes from a climatological perspective.