GPS-derived Precipitable Water Vapor Research Articles

Evaluation and Analysis of Remotely Sensed Water Vapor from the NASA VIIRS/SNPP Product in Mainland China Using GPS Data

Precipitable water vapor (PWV) is a vitally important factor in atmospheric circulation. PWV is significant for forecasting extreme weather and understanding the dynamics of climate change. Comprehensively evaluating the performance of newly proposed remotely sensed water vapor products is crucial for guaranteeing their suitability for futural PWV applications. In this study, the accuracy of the recently established remotely sensed water vapor product from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (SNPP) (VIIRS-PWV) platform within various regions of mainland China was evaluated via the PWV from Global Positioning System (GPS) observations. The GPS-derived PWV from 231 stations of the Crustal Movement Observation Network of China (CMONOC) from 2012 to 2018 was obtained through precise point positioning (PPP) techniques. The results showed that the mean value of the correlation coefficient (CC), the mean bias (MB), and the root-mean-square error (RMSE) between the VIIRS-PWV and the GPS-PWV were 0.92, −1.6 mm, and 4.7 mm, respectively. These values were comparable with the results of the PWV data derived from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. This indicates that the VIIRS product could provide PWV data with satisfactory accuracy for large-area scientific applications. Moreover, the MB and RMSE of the differences between the GPS-PWV and VIIRS-PWV showed obvious seasonal variations. The VIIRS-PWV generally performed better in winter (with the MB and RMSE values of 0.1 mm and 2.3 mm) than in summer (with the MB and RMSE values of −4.4 and 7.0 mm). Analysis among different regions revealed that the Central South (CS) region of China attained the largest mean RMSE value of 6.3 mm, and the North West (NW) region attained the smallest mean RMSE value of 3.8 mm. In addition, the southern region of China obtained a mean RMSE value of 5.6 mm, while that for the northern region of China was 3.9 mm. This indicates that the VIIRS-PWV has better accuracy within the northern region of China than within the southern region.

Correlation Analysis between Precipitation and Precipitable Water Vapor over China Based on 1999–2015 Ground-Based GPS Observations

Abstract Correlation analysis between precipitable water vapor (PWV) and precipitation over China was conducted by combining high-quality PWV data based on 1999–2015 ground-based global positioning system (GPS) observations with the measurements at matched meteorological stations in the same period. The mean correlation coefficient at all the stations is approximately 0.73, indicating that there is a significant positive correlation between PWV content and precipitation measurements, and the comparison of correlation among different climate types suggests that the distribution characteristics of the correlation coefficients are distinctively related to different climate types. There is also some positive correlation between PWV and precipitation long-term trends, with the correlation coefficients of monthly anomalies ranging generally from 0.2 to 0.6. Furthermore, the intensity of both PWV and precipitation extremes shows a long-term upward trend overall, with the most-intense events showing more significant increases. The extreme precipitation–temperature scaling rate of changes can reach above Clausius–Clapeyron (CC) scaling, whereas that of the extreme PWV-temperature is sub-CC overall, with regional differences in the specific scaling values. The correlation analysis in this work is of great significance for long-term climate analysis and extreme weather understanding, which provides a valuable reference for better utilizing the advantages of PWV data to carry out the studies above. Significance Statement Atmospheric water vapor is crucial to the climate system, especially in the context of global warming, and accurate knowledge of the correlation between precipitable water vapor (PWV) and precipitation is of great significance for long-term climate analysis and extreme precipitation weather forecasting. We take full advantage of the long-term homogeneity of ground-based GPS to conduct long-term correlation analysis between GPS-derived PWV and precipitation over China. Results show a significant positive correlation between them, and the degree of correlation is related to different climate types. The correlation of monthly anomalies is also positive, and, over the long-term, both water vapor and precipitation extremes have been increasing in intensity, with more significant increases occurring in the most-intense events. Extreme precipitation might increase beyond thermodynamic expectations, whereas PWV increases below expectations.

A Linear Regression of Differential PWV Calibration Model to Improve the Accuracy of MODIS NIR All-Weather PWV Products Based on Ground-Based GPS PWV Data

Precipitable water vapor (PWV) products derived from the near-infrared (NIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, onboard Aqua and Terra satellites, were calibrated using a linear differential PWV (LinearDP) calibration model based on GPS-retrieved PWV observations. All MODIS NIR PWV pixels were classified into two groups according to the cloud mask of each pixel. For each group, MODIS NIR PWV products and ground-based PWV data from 453 GPS sites in Australia from January 2017 to December 2018 were utilized to determine the differential PWV by subtracting GPS PWV from MODIS PWV. Then, empirical regression relationship between the differential PWV data and the MODIS PWV products was developed using a linear regression approach. The LinearDP model coefficients were independently obtained from each month for each group. The period for model validation spans from January to December in 2019. Comparison of calibrated MODIS NIR PWV versus GPS-derived PWV over Australia showed that the root-mean-square error (RMSE) of Aqua has reduced 42.61% for clear group, 41.43% for cloudy group, and 41.45% for both clear and cloudy groups; and has respectively reduced 53.76%, 37.03%, and 39.33% for Terra. By comparing against ERA5 PWV data, the RMSE reduced 37.21%–43.14% for Aqua and 38.73%–53.87% for Terra. The improvement of MODIS NIR PWV products is further validated in China, with an RMSE reduction of 24.53%–31.78% for Aqua and 28.26%–38.69% for Terra against reference PWV from 214 GPS stations. The mean bias was reduced to −0.415–0.752 mm in Australia and to −0.382–2.013 mm in China.

Retrieving Precipitable Water Vapor from Real-Time Precise Point Positioning Using VMF1/VMF3 Forecasting Products

Real-time precise point positioning (RT-PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GPS (global positioning system) or GNSS (global navigation satellite systems) station of interest, and the follow-on high-precision retrieval of precipitable water vapor (PWV). The a priori zenith hydrostatic delay (ZHD) and the mapping function used in the PPP approach are the two factors that could affect the accuracy of the PPP-based ZTD significantly. If the in situ atmospheric pressure is available, the Saastamoinen model can be used to determine ZHD values, and the model-predicted ZHD results are of high accuracy. However, not all GPS/GNSS are equipped with an in situ meteorological sensor. In this research, the daily forecasting ZHD and mapping function values from VMF1 forecasting (VMF1_FC) and VMF3 forecasting (VMF3_FC) products were used for the determination of the GPS-derived PWV. The a priori ZHDs derived from VMF1_FC and VMF3_FC were first evaluated by comparing against the reference ZHDs from globally distributed radiosonde stations. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-ZTD and GPS-PWV. Three sets of ZTDs estimated from RT-PPP solutions using the a priori ZHD and mapping function from the following three VMF products were evaluated: (1) VMF1_FC; (2) VMF3_FC (resolution 5° × 5°); (3) VMF3_FC (resolution 1° × 1°). The results showed that, when the ZHDs from 443 globally distributed radiosonde stations from 1 July 2018 to 30 June 2021 were used as the reference, the mean RMSEs of the ZHDs from the three VMF products were 5.9, 5.4, and 4.3 mm, respectively. The ZTDs estimated from RT-PPP at 41 selected IGS stations were compared with those from IGS, and the results showed that the mean RMSEs of the ZTDs of the 41 stations from the three PPP solutions were 8.6, 9.0, and 8.6 mm, respectively, and the mean RMSEs of the PWV converted from their corresponding ZWDs were 1.9, 2.4, and 1.7 mm, respectively, in comparison with the reference PWV from co-located radiosonde stations. The results suggest that the a priori ZHD and mapping function from VMF1_FC and VMF3_FC can be used for the precise determination of real-time GPS/GNSS-PWV in most regions, especially the VMF3_FC (resolution 1° × 1°) product.

Understanding convective storms in a tropical, high-altitude location with in-situ meteorological observations and GPS-derived water vapor

We investigate convective storms over the Sabana de Bogotá, a high-altitude and densely populated area in the Colombian tropical Andes. Convective events are identified using infrared satellite images and in-situ precipitation data. As expected, convection shows a strong early-afternoon peak during the two rainy seasons. Previous studies hypothesize that early-afternoon westerly winds and their moisture advection from the warmer Magdalena Valley are the main explanatory mechanism for intense storms. We find that early-afternoon westerlies are present in 78% of the rainy season days, but convective events develop in only 26% of the days. Thus, although westerlies seem necessary for convection due to the convergence they generate, they only occasionally generate storms, and are therefore not a good predictor. Furthermore, reanalysis data indicate that precipitable water vapor (PWV) at the Magdalena Valley is anomalously low during convective days, suggesting that moisture converges locally instead of being advected from the west. Based on composites of surface wind speed, air temperature, pressure and GPS-derived PWV, we identify the most prominent signals associated to deep convection: a weaker than average wind speed throughout the morning, higher than normal values of surface air temperature towards noon, followed by an anomalous steep increase of PWV and wind speed. These features indicate that convection results from a strong diurnal forcing facilitated by convergence of westerly winds, combined with sufficient water vapor convergence, with a timescale of about 3 hours. This highlights the relevance of high temporal resolution monitoring of PWV offered by Global Navigational Satellite System stations.

PWV Estimation Using GPS and its Comparison with INSAT-3D Rainfall Data

Precipitable water vapor (PWV) plays an important role in understanding the atmosphere and weather. The advancement in Global Navigation Satellite System (GNSS) technology provides the possibility of computing PWV in near real time. In this study, the PWV was computed using the Zenith Tropospheric Delay (ZTD) from Global Positioning System (GPS) data for two International GNSS Service (IGS) stations (IISC & HYDE) for monsoon period (June–September) of 2014 and 2015. The GPS-derived PWV has been validated with reference to radiosonde data and University NAVSTAR Consortium (UNAVCO) PWV products. The coefficient of determination between GPS-derived PWV and radiosonde PWV is 0.73–0.83 for IISC station and 0.76–0.91 for HYDE station. For IISC station, the coefficient of determination between GPS-derived PWV and UNAVCO-derived PWV is 0.80–0.89. Also, in this study, GPS-derived PWV for IISC station has been compared with the rainfall events of Indian National Satellite System (INSAT-3D). From the analysis, we observed a direct relation between PWV and rainfall events from Hydro Estimator Method (HEM), INSAT Multi spectral Rainfall (IMR) products of INSAT-3D. This study highlights the satellite-based observations from GNSS and INSAT satellite for regional weather forecasting in near real time.

On the Diurnal Cycle of GPS-Derived Precipitable Water Vapor over Sumatra

AbstractConvective processes in the atmosphere over the Maritime Continent and their diurnal cycles have important repercussions for the circulations in the tropics and beyond. In this work, we present a new dataset of precipitable water vapor (PWV) obtained from the Sumatran GPS Array (SuGAr), a dense network of GPS stations principally for examining seismic and tectonic activity along the western coast of Sumatra and several offshore islands. The data provide an opportunity to examine the characteristics of convection over the area in greater detail than before. In particular, our results show that the diurnal cycle of PWV on Sumatra has a single late afternoon peak, while that offshore has both a midday and a nocturnal peak. The SuGAr data are in good agreement with GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, as well as with imaging spectrometer data from the Ozone Measuring Instrument (OMI). A comparison between SuGAr and the NASA Water Vapor Project (NVAP), however, shows significant differences, most likely due to discrepancies in the temporal and spatial resolutions. To further understand the diurnal cycle contained in the SuGAr data, we explore the impact of the Madden–Julian oscillation (MJO) on the diurnal cycle with the aid of the Weather Research and Forecasting (WRF) Model. Results show that the daily mean and the amplitude of the diurnal cycle appear smaller during the suppressed phase relative to the developing/active MJO phase. Furthermore, the evening/nighttime peaks of PWV offshore appear later during the suppressed phase of the MJO compared to the active phase.

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

In 2016, three Nigerian cities were listed amongst the World's most polluted in terms of particulate matter (PM). Acknowledging Nigeria's limited resources for outdoor air pollution monitoring, this study attempts to investigate the effects on atmospheric aerosol optical depth and ground PM on GPS derived-precipitable water vapour estimates. The study utilized available GPS-derived precipitable water vapour (GPSPWV), the moderate resolution imaging spectroradiometer aerosol optical depth (MODISAOD) and the ground level particulate matter of less than 10 μm (GPM) datasets for December 2015–November 2016. All the datasets were tested for normality. To evaluate the atmospheric aerosol properties, the MODISPWV estimates were pre-validated using the GPSPWV measurements. The results revealed GPSPWV-MODISPWV agreement (R = 0.964; RMSE = 3.810 mm). The GPSPWV-MODISAOD analysis showed relationship (R ≤ −0.636; RMSE ≤ 0.563) for the atmospheric aerosol experiment, while the collocating GPSPWV-GPM seasonal analysis also revealed significant correlation (R < −0.660). The correlation of combined seasonal datasets for the GPSPWV-MODISAOD and GPSPWV-GPM relationships showed high negative correlation values of 0.79 and 0.68 respectively. The findings of this study is in agreement with similar related studies, as well as serve as position accuracy for future related studies.

Research on variety characteristics of mainland China troposphere based on CMONOC

In this research, we processed the GPS and meteorological data from about 220 stations of Crustal Movement Observation Network of China (CMONOC) observed in 2014 and derived the Zenith Total Delay (ZTD) map in both spatial and temporal dimension. The results of ZTD have high accurate and reliable as IGS and all sites with varying locations show obvious variety characteristics of Chinese mainland. Meanwhile, the precipitable water vapor (PWV) correlation coefficients between GPS observation and upper air sounding is close to 1, and the comparison of GPS-derived PWV and observed PWV from meteorological sites indicating GPS observation data generated in CMONOC project applied to the weather forecast research is feasible. In addition, based on all stations covered the whole Chinese land area and using interpolation algorithms, we make contour plots of PWV distribution per hour. We observe obvious feature that the precipitable water in north and western area is less than south and east area all over this year. High latitudes area may be dry and low latitudes area is wet.

GPS-Derived PWV for Rainfall Nowcasting in Tropical Region

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.

Convective-Permitting Hindcast Simulations during the North American Monsoon GPS Transect Experiment 2013: Establishing Baseline Model Performance without Data Assimilation

AbstractDuring the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. The greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.

Near-global GPS-derived PWV and its analysis in the El Niño event of 2014–2016

Precipitable water vapour (PWV) is a key factor in activities related to climate monitoring and the global hydrologic cycle. In this paper, the PWV time series with an accuracy of about 1.3 mm is obtained on a global scale using the zenith total delay (ZTD) derived from International GNSS Service (IGS). A theoretical error formula from ZTD to PWV reveals that the PWV error induced by errors in ZTD, surface pressure (Ps) and weighted mean temperature (Tm) is about 1–1.5 mm. Ps and Tm are two key factors during the conversion of ZTD to PWV, which can be derived from the Global Geodetic Observing System (GGOS) Atmosphere. The GPS-derived and radiosonde-derived PWV time series are compared at 97 collocated stations on a global scale, which shows the maximum/minimum/mean root mean square (RMS) errors and Bias of 1.8/0.6/1.3 mm and 2.6/2.9/4.0/5.2 mm, respectively with a data utilisation rate of 96.8%. By analysing the periodograms of GPS-derived PWV time series using the Lomb-Scargle method, preliminary result shows the various oscillations characteristics of PWV time series at different stations. Finally, the diurnal variations of PWV time series during the El Niño event of 2014–2016 are analysed and revealed an interesting climate signal.

Precipitable water vapor characterization in the coastal regions of China based on ground-based GPS

Water vapor plays an important role in climate change; thus, studying the spatial distribution and temporal variation of precipitable water vapor (PWV) in the coastal regions of China would help researchers to understand the climate characteristics of those regions. In this paper, 6-year 1-h interval PWV were derived from 27 Global Positioning System stations observations of Chinese coastal GPS observation network, surface meteorological data and European Center for Medium-Range Weather Forecasts (ERA-Interim) reanalysis products. The present study provides the use of these data to investigate the spatial-temporal variability of water vapor throughout the coastal regions of China. Latitude is the main factor affecting the spatial distribution of GPS-derived PWV; that is, PWV decreased by about 1.5mm for each 1° increase of latitude. For regions at the same latitude, a region that is relatively close to the ocean will have a higher content of PWV. The PWV in the southeastern and southwestern coastal regions of China is significantly higher in summer; this may be influenced by the southeastern and southwestern water vapor inflow corridors. The PWV obviously varies monthly, reaching a minimum in January; however, the timing of the maximum varied but usually appeared in June, July or August and was affected by the monsoons. The PWV varies largely between summer and winter with a larger gradient of change in PWV with latitude in winter than in summer. The positive correlation coefficient between PWV and the surface temperature varied in different seasons; this is related to the changes of temperature and the horizontal motion of water vapor. Use of the Fast Fourier Transform method showed that the PWV time series data have multi-scale characteristics. The amplitude and phase of the PWV time series in annual, semiannual, four month and seasonal cycles were extracted through harmonic wave analysis. The amplitude of four month and seasonal cycles did not pass significance testing. The maximum amplitude of the annual PWV cycle is located in mid-latitudes with 22.09mm, which gradually decreases towards high latitude and equator area. The peak time of annual PWV cycle occurs in July, which does not agree with the timing of the maximum monthly average PWV. The semiannual cycle amplitudes is between 0.42 and 6.32mm, with no significant correlation between their distribution and latitude. The peak time of semiannual PWV cycles is during about January–March and July–September, and the peak time gradually becomes late from north to south.

Annual variations of monsoon and drought detected by GPS: A case study in Yunnan, China

The Global Positioning System (GPS) records monsoonal precipitable water vapor (PWV) and vertical crustal displacement (VCD) due to hydrological loading, and can thus be applied jointly to diagnose meteorological and hydrological droughts. We have analyzed the PWV and VCD observations during 2007.0–2015.0 at 26 continuous GPS stations located in Yunnan province, China. We also obtained equivalent water height (EWH) derived from the Gravity Recovery And Climate Experiment (GRACE) and precipitation at these stations with the same period. Then, we quantified the annual variations of PWV, precipitation, EWH and VCD and provided empirical relationships between them. We found that GPS-derived PWV and VCD (positive means downward movement) are in phase with precipitation and GRACE-derived EWH, respectively. The annual signals of VCD and PWV show linearly correlated amplitudes and a two-month phase lag. Furthermore, the results indicate that PWV and VCD anomalies can also be used to explore drought, such as the heavy drought during winter/spring 2010. Our analysis results verify the capability of GPS to monitor monsoon variations and drought in Yunnan and show that a more comprehensive understanding of the characteristics of regional monsoon and drought can be achieved by integrating GPS-derived PWV and VCD with precipitation and GRACE-derived EWH.

A quasi-linear relationship between GPS-derived precipitable water vapor and surface vapor pressure observed on dry days in spring and autumn: a case study at Saga Plain in northern Kyushu Island, Japan

Surface vapor pressure (SVP) is a significant meteorological variable that expresses the status of atmospheric moisture near the ground surface. Its lower values characterize dry atmospheric conditions, and are strongly correlated with the occurrence of days with lower daily minimum temperatures from autumn to the following spring, which frequently induces frost damage in field crops. It is therefore important to obtain information regarding SVP to assess such frosty events. Recent advances in GPS technology have enabled us to infer the atmospheric column-integrated water vapor or precipitable water vapor (PWV) as GPS-derived PWV (GPS-PWV). However, few studies have applied GPS-PWV to such low-temperature-induced events to estimate SVP. In this study, we analyzed GPS and meteorological data that were routinely collected for about 7 years at a plain in the northern part of Kyushu Island, Japan. A quasi-linear relationship between daily-averaged values of GPS-PWV and SVP was obtained specifically under dry conditions in spring and autumn. We related GPS-PWV to SVP by linear regression and theoretically examined their relationship. A simple formula was developed to estimate SVP from GPS-PWV on a daily basis under such conditions. The estimated vapor pressures agreed acceptably with observed values.

Dynamical features of GPS PWV variation associated with lightning activity

ABSTRACTWater vapour is one component that causes spatial and temporal fluctuations in the lower atmosphere, which, in turn, introduce time delays into the global positioning system (GPS) signal. These delays can be exploited to estimate the precipitable water vapour (PWV), which is beneficial for meteorological applications. Because the vertical transfer of warm air to the troposphere triggers instability and atmospheric charges, lightning phenomena can fundamentally affect the GPS signals through changes in water vapour. From this hypothesis, GPS-derived PWV data have been proposed as a tool for monitoring lightning activity. In this paper, the variation of PWV in days with lightning at four selected stations in Peninsular Malaysia during the past two inter-monsoon events in May and November 2009 was observed. To verify the response, a peak alignment method was proposed with regard to the lightning with more than 100 discharge events and lightning days from 07:00 to 20:00 LT. A total of 66 lightning series were assessed, and PWV was observed to increase by approximately 2.46 mm and decrease by 4.16 mm before and after the peak point, respectively, when compared to fair-weather conditions. Approximately 62% of the lightning start times were concentrated within a period of less than 1 h before or after the PWV reached a maximum peak, 24% were observed between 1–2 h, and 14% started after 2 h. This trend implies that the use of GPS PWV data was more consistent and robust for the detection of lightning activity compared to the use of surface temperature and relative humidity data.

Validation of Atmospheric Water Vapor Derived from Ship-Borne GPS Measurements in the Chinese Bohai Sea

Atmospheric water vapor (AWV) was investigated for the first time in the Chinese Bohai Sea using a GPS receiver aboard a lightweight (300-ton) ship. An experiment was conducted to retrieve the AWV using the state-of-the-art GPS precise point positioning (PPP) technique. The effects of atmospheric weighted mean temperature model and zenith wet delay constraint of random walk process on GPS AWV estimates were discussed in the PPP estimation system. The GPS-derived precipitable water vapor (PWV) and slant-path water vapor (SWV) were assessed by comparing with those derived from the Fifth Generation NCAR/Penn State Mesoscale Model (MM5) model. Results showed the PWV and SWV differences between those derived from both GPS and MM5 are 1.5 mm RMS with a bias of 0.2 mm and 3.9 mm RMS with a bias of -0.7 mm respectively. These good agreements indicate that the GPS-derived AWV in dynamic environments has a comparable accuracy with that of the MM5 model. This suggests that high accuracy and high spatio-temporal resolution humidity fields can be obtained using GPS in the Chinese Bohai Sea, which offers a significant potential for meteorological applications and climate studies in this region.

Real-Time GPS Precise Point Positioning-Based Precipitable Water Vapor Estimation for Rainfall Monitoring and Forecasting

GPS-based precipitable water vapor (PWV) estimation has been proven as a cost-effective approach for numerical weather prediction. Most previous efforts focus on the performance evaluation of post-processed GPS-derived PWV estimates using International GNSS Service (IGS) satellite products with at least 3–9-h latency. However, the suggested timeliness for meteorological nowcasting is 5–30 min. Therefore, the latency has limited the GPS-based PWV estimation in real-time meteorological nowcasting. The limitation has been overcome since April 2013 when IGS released real-time GPS orbit and clock products. This becomes the focus of this paper, which investigates real-time GPS precise point positioning (PPP)-based PWV estimation and its potential for rainfall monitoring and forecasting. This paper first evaluates the accuracy of IGS CLK90 real-time orbit and clock products. Root-mean-square (RMS) errors of < 5 cm and ∼0.6 ns are revealed for real-time orbit and clock products, respectively, during July 4–10, 2013. Second, the real-time GPS PPP-derived PWV values obtained at IGS station WUHN are compared with the post-processed counterparts. The RMS difference of 2.4 mm has been identified with a correlation coefficient of 0.99. Third, two case studies, including a severe rainfall event and a series of moderate rainfall events, have been presented. The agreement between the real-time GPS PPP-derived PWV and ground rainfall records indicates the feasibility of real-time GPS PPP-derived PWV for rainfall monitoring. Moreover, the significantly reduced latency demonstrates a promising perspective of real-time GPS PPP-based PWV estimation as an enhancement to existing forecasting systems for rainfall forecasting.

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $ 70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.

Development of Time-dependent mean Temperature Equations for GPS Meteorology

ABSTRACT The mean temperature is one of the key parameters in computing Precipitable Water Vapor (PWV) from Global Positioning System (GPS) measurements and is usually derived as a function of surface temperature through the use of a mean temperature equation (MTE). In this study, two new types of MTEs were developed as functions solely of the observation time so that the mean temperature can be obtained without surface temperature measurements. To validate the new models, we created one-year time series of GPS-derived PWV using the new MTEs and compared them with the radiosonde-observed PWV. The bias and root-mean-square error were on the other of ~1 mm and ~2 mm, respectively. Keywords: GPS, GPS meteorology, mean temperature equation, precipitable water vapor parameter in translating ZWD into PWV. Bevis et al. (1992) suggested that the mean temperature of the atmosphere can be approximated by the surface temperature. The Bevis model (Bevis et al. 1992) was developed using radiosonde measurements taken at 13 sites in North America. Emardson & Derks (2000) developed a model more suited to the climatic conditions of Europe. Liou et al. (2000) developed a local MTE model in addition to monthly MTEs, based on meteorological observations in Taiwan. Woo (2003) and Ha et al. (2006) developed local MTEs based on radiosonde observations in South Korea. All the MTE models mentioned in this paragraph are given as linear functions of the surface temperature and thus temperature-measuring devices should be co-located at the GPS siteIn this study, we propose two new types of mean temperature models, in which surface temperature measurements are unnecessary. The novel concepts behind the newly developed MTE models are introduced in the following section and validation results through the error analysis of GPS PWV values obtained with new models are described in Section 3.