GNSS-derived Precipitable Water Vapor Research Articles

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

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.

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas.

Determination and assessment of GNSS-derived precipitable water vapor in Indonesia using Ina-CORS

This work is the first attempt to investigate the accuracy and reliability of the Ina-CORS (Indonesian Continuously Operating Reference Stations) for meteorological applications. We used the standard procedures to determine and assess the precipitable water vapor (PWV) obtained from the Global Navigation Satellite System (GNSS) measurements. The GNSS-derived zenith tropospheric delay (ZTD) was estimated using the GAMIT software, and it was then converted to PWV using surface pressure measurements and the water vapor weighted mean temperature. The results were compared against those derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) data.Comparisons against the ECMWF ERA5 data reveal the following findings. Values for the mean bias and root mean square deviation (RMSD) of the GNSS-derived ZTD over the period 2011–2020 are −6.6 mm and 14.1 mm, respectively, representing the total percentage error of about 0.4%. The total errors from the surface pressure measurements and the calculations of water vapor weighted mean temperature introduce errors to the PWV values with the magnitude less than 1 mm. The GNSS-derived PWV values over Indonesia are comparable with those derived from the ECMWF ERA5 data. The mean bias and RMSD of PWV values are −0.60 mm and 2.59 mm, respectively. In addition, spatio-temporal variations of the GNSS-derived PWV can capture the main atmospheric features that are consistent with the other results deduced from long-term rainfall data. One of the features is the annual and semiannual cycles of PWV that largely correspond to the annual migration of the Asian-Australian monsoon system. Based on these results, we can conclude that the Ina-CORS is capable enough for accurate monitoring of the Indonesian weather and climate variations.

Improving the capability of water vapor retrieval from Landsat 8 using ensemble machine learning

Precipitable water vapor (PWV) facilitates the exchange of water and energy between the Earth's surface and the surrounding atmosphere. PWV can be retrieved by employing the water vapor absorption channels in Landsat 8. Unfortunately, the conventional split-window covariance-variance ratio (SWCVR) method is susceptible to various factors, resulting in low retrieval accuracy and availability. Therefore, we improve the traditional SWCVR model and propose a new PWV retrieval method using ensemble machine learning to address these limitations. The new method uses Gradient Boosting Decision Tree (GBDT) to establish the model between brightness temperature, GNSS-derived PWV, and related surface parameters. The results of the test set show that the improved SWCVR model has an RMSE, Bias, and availability of 0.4947 g/cm2, 0.0276 g/cm2, and 29.6%, respectively. By contrast, the GBDT model's corresponding values are 0.2870 g/cm2, −0.0094 g/cm2, and 67.9%, respectively. Compared with SWCVR, the GBDT improves the RMSE and availability by 41.99% and 38.3%, respectively. The GBDT algorithm is significantly better than the SWCVR model in low altitude and complex surface coverage areas. From a temporal perspective, the advantages of the GBDT method are more apparent in the summer. Finally, the SWCVR and GBDT models are externally validated using measured PWV data from the sun photometer and radiosonde, and the RMSE is 0.5148 g/cm2 and 0.3775 g/cm2, respectively. Based on the findings, we know that the accuracy of GBDT has significantly improved against the SWCVR.

Linear and nonlinear GNSS PWV features for heavy rainfall forecasting

Heavy rainfall events usually require sufficient water vapor and intense upward movement of airflow. However, current heavy rainfall prediction models using Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) usually use linear features of PWV (PWV increase and decrease, PWV derivative, etc.), which ignore the process of airflow motion. In this study, an hourly heavy rainfall prediction model using the linear and nonlinear features of GNSS-derived PWV is proposed. It uses five linear and three nonlinear features of PWV, together with the corresponding meteorological data, and uses the Random Forest algorithm to predict heavy rainfall or not. The dataset uses the HKSC station observations in Shamshuipo, Hong Kong and the co-located meteorological station data each summer between 2013 and 2020. The results show that the true detection rate and false alarm rate of the proposed model are 97.4% and 19.6% respectively, which are significant improvements compared with other models.

Global Navigation Satellite System-Based Retrieval of Precipitable Water Vapor and Its Relationship with Rainfall and Drought in Qinghai, China

Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (Tm) (QH-Tm) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in Tm. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai.

Spatial–Temporal Variability of Global GNSS-Derived Precipitable Water Vapor (1994–2020) and Climate Implications

Precipitable water vapor (PWV) is an important component in the climate system and plays a pivotal role in the global water and energy cycles. Over the years, many approaches have been devised to accurately estimate the PWV. Among them, global navigation satellite systems (GNSS) have become one of the most promising and fastest-growing PWV acquisition methods because of its high accuracy, high temporal and spatial resolution, and ability to acquire PWV in all weather and in near real time. We compared GNSS-derived PWV with a 5 min resolution globally distributed over 14,000 stations from the Nevada Geodetic Laboratory (NGL) from 1994 to 2020 with global radiosonde (RS) data, temperature anomalies, and sea height variations. Then, we examined the temporal and spatial variability of the global PWV and analyzed its climate implications. On a global scale, the average bias and root mean square error (RMSE) between GNSS PWV and RS PWV were ~0.72 ± 1.29 mm and ~2.56 ± 1.13 mm, respectively. PWV decreased with increasing latitude, and the rate of this decrease slowed down at latitudes greater than 35°, with standard deviation (STD) values reaching a maximum at latitudes less than 35°. The global average linear trend was ~0.64 ± 0.81 mm/decade and strongly correlated with temperature and sea height variations. For each 1 °C and 1 mm change, PWV increased by ~2.075 ± 0.765 mm and ~0.015 ± 0.005 mm, respectively. For the time scale, the PWV content peaked ~40 days after the maximum solar radiation of the year (the summer solstice), and the delay was ~40 days relative to the summer solstice.

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.

Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data

The determination of the amount of precipitable water vapor (PWV) from global navigation satellite system (GNSS) is restricted to a limited number of ground-based stations with low spatial resolution. The PWV obtained by the multi-source data fusion method has a low spatial resolution because of the low spatial resolution of the data used. Therefore, it is difficult to retrieve PWV maps with sub-km resolution. Therefore, retrieving PWV with high spatial resolution becomes the focus of this study. A dual-scale method for retrieving PWV maps, based on heterogeneous earth data, is proposed, which can obtain sub-kilometer resolution PWV, while maintaining accuracy. First, an approach for analyzing the spatial correlation between land cover types (LCT) and ground-based PWV was proposed, and the meteorological, ecological, topographic, and LCT variables affecting changes in PWV in the near-earth atmosphere were determine, Subsequently, three different regression models, including multiple linear regression (MLR), random forest (RF), and a generalized regression neural network (GRNN), were used to construct the functional model linking heterogeneous earth observation data and ground-based GNSS-derived PWV. Finally, the PWV map with sub-kilometer resolution was generated based on the dual-scale method. Unlike the PWV obtained by data fusion and remote sensing technology, the proposed dual-scale retrieval method can generate PWV maps at 300 m spatial resolution. Statistical analyses demonstrated that the RMS of the PWV derived from the proposed method was less than 2.2 mm, and the bias was close to 0, thereby filling the gap in the sub-kilometer high-precision PWV products.

Comprehensive Precipitable Water Vapor Retrieval and Application Platform Based on Various Water Vapor Detection Techniques

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies.

Precipitable Water Vapor Retrieval Over Land From GCOM-W/AMSR2 Based on a New Integrated Method

Precipitable water vapor (PWV) is an important parameter reflecting atmospheric water vapor, which plays an important role in the global hydrological cycle. At present, brightness temperatures (Tbs) at frequencies of 18 and 23 GHz from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are commonly applied in PWV data retrieval. Two tools are usually employed to perform the retrieval: physical models and neural networks. In this article, we creatively deduced a more universal physical equation and verified the consistency between theoretical and experimental equations both theoretically and numerically. Then, we fully considered the defects of these two tools and proposed a new integrated method that combines the physical model with a neural network. The Tbs in horizontal and vertical polarizations from AMSR2 and GNSS-derived PWV (GNSS-PWV) data from the SuomiNet global network were used to build the physical model. In the construction of the neural network, we also introduced the related surface parameters from GNSS stations as inputs. Validation results obtained with GNSS-PWV data showed that the accuracy of the test set can reach 2.38 and 2.37 mm in the ascending and descending orbit cases, respectively. Compared with the traditional physical model and neural network model, the improvements of the test set were 24.4% and 17.4% in the ascending orbit and 26.4% and 19.4% in the descending orbit. Radiosonde observation (RAOB) data were applied to carry out another external independent verification, and the accuracy of the test set reached 2.70 and 3.54 mm based on the RAOB data.

Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters

Aerosol optical depth (AOD) is one of the basic parameters for determining the total aerosol content, and it exerts an important impact on regional environment pollution. To investigate the spatiotemporal variations of AOD, this study analyzes the relationship of AOD with precipitable water vapor (PWV) derived from a global navigation satellite system (GNSS) and meteorological parameters and proposes an adaptive AOD forecasting (AAF) model. In this model, the initial AOD value is determined using an empirical AOD model that considers annual periodicity, and the AOD difference is fitted using PWV, temperature (T), and surface pressure (P). In addition, this model also considers the time autocorrelation of the AOD difference; the model coefficients can be adaptively updated with training data. AOD data at 550 nm derived from the aerosol robotic network (AERONET), second modern-era retrospective analysis for research and applications (MERRA-2), and Copernicus atmosphere monitoring service (CAMS) for the Beijing-Tianjin-Hebei area are utilized to validate the proposed AAF model. Numerical results show that: 1) the accuracy of AOD derived from MERRA-2 is superior to that obtained from CAMS; 2) AOD is negatively correlated with P, is positively correlated with PWV and T, and has a high time autocorrelation with the AOD difference at consecutive times; and 3) the proposed AAF model demonstrates better performance than the traditional multiple linear regression (MLR) model. The average root mean square error (RMSE), mean absolute error (MAE), and bias of the AAF model are 0.17, 0.14, and -0.04, respectively, and those of the MLR model are 0.31, 0.25, and 0.06, respectively. These results reveal that the proposed AAF model can estimate AOD with high precision and has considerable potential for application in AAF research.

Evaluation of Shipborne GNSS Precipitable Water Vapor Over Global Oceans From 2014 to 2018

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

Use of GNSS-Derived PWV for Predicting the Path of Typhoon: Case Studies of Soulik and Kongrey in 2018

AbstractThe progress of a typhoon can be investigated by analyzing the significant changes in precipitable water vapor (PWV) in the area of interest. In this study, PWV variations of two typhoons i...

FY-3A/MERSI precipitable water vapor reconstruction and calibration using multi-source observation data based on a generalized regression neural network

Precipitable water vapor (PWV) is a key element in the water and energy transfer between the surface and the atmosphere, especially in the Three-River Headwaters (TRH), which exhibits sensitive climate and hydrology. To overcome the limitations of missing data and poor accuracy of the moderate resolution spectral imager (MERSI) on-board the second-generation polar-orbiting satellite FengYun-3A (FY-3A) in the TRH region, a model for FY-3A/MERSI PWV reconstruction, based on generalized regression neural network (GRNN) using meteorological factors, normalized differential vegetation index (NDVI) and digital elevation model (DEM), is established, and then, a method for daily systematic error of MERSI PWV calibrating based on global navigation satellite system (GNSS)-derived PWV is proposed. The root mean square (RMS), standard deviation (Std), and bias values of the reconstructed PWV are 0.57, 0.52, and 0.02 mm compared with the original MERSI PWV. The RMS, Std and bias of the calibrated MERSI PWV are 2.42, 1.92 and 0.14 mm compared with GNSS-derived PWV, and the correlation coefficient between MERSI and GNSS-derived PWV is improved from 0.78 to 0.95, which is satisfactory. Compared with the original MERSI PWV, the proposed reconstruction and calibration model can fill the missing data and effectively improve the accuracy of the MERSI PWV products. The proposed method can provide a basic PWV data ensuring for hydrological and ecological research in the TRH region.

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China.

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM2.5 concentrations. In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

A novel method of retrieving potential ET in China

Terrestrial evaporation is the central link of land surface energy balance, which is very important for climate change, water cycle research and drought monitoring. Potential evapotranspiration (ETP) is an important form of ET, which play a significance for calculating standardized precipitation evapotranspiration index (SPEI). This paper proposed a novel method of retrieving ETP using precipitable water vapor (PWV) and temperature to obtain the high precision and resolution ETP dataset, and the ETP and SPEI are obtained with the temporal-spatial resolutions of monthly and 0.125° × 0.125°, respectively, in China. The PWV derived from the European Center for Medium-range Weather Forecasts (ECMWF) is first validated and calibrated using the Global Navigation Satellite System (GNSS) technique. ETP and SPEI at specific stations are then calculated using the site-based revised Thornthwaith (S-RTH) model with high precision. Finally, spherical harmonic function is applied to fit the coefficients of the S-RTH model in China, and a RTH model over China (C-RTH) with high spatial resolution (0.125° × 0.125°) is established. Statistical result reveals that (1) the average root mean square (RMS) and mean absolute error (MAE) of the calibrated ECMWF-derived PWV have been improved from 2.0/1.7 mm to 1.7/1.4 mm, respectively using the GNSS-derived PWV. (2) The improvement rate of ETP derived from the S-RTH model is approximately 68% compared with that from the TH model, and the average RMS and MAE of the ETP difference between S-RTH and Penman–Monteith (PM) are 10.7 mm and 8.5 mm, respectively. (3) The average RMS and MAE of potential difference between C-RTH and PM are 17.6 mm and 13.7 mm, respectively. Although the accuracy of the C-RTH model is slightly lower than that of the S-RTH model, it is significantly improved compared with the TH model, and the calculated ETP data have been converted from point to surface. The proposed method expands the application of GNSS technique to obtain ETP data set, which provides the basic data guarantee for drought disaster prevention and control in China.

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Abstract. Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modeled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1∘ × 1∘ , named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5∘ × 5∘; (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's Tm values over all pressure levels in the height range from the surface to 10 km altitude were −0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.