scholarly journals Determination of zenith hydrostatic delays and the development of new global long-term GNSS-derived precipitable water vapor

2016 ◽  
Author(s):  
Xiaoming Wang ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Changyong He ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Pressure ◽  
Meteorological Data ◽  
Accurate Determination ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Average Value ◽  
Rms Errors

Abstract. Surface pressure is a vital meteorological variable for the accurate determination of precipitable water vapor (PWV) using Global Navigation Satellite Systems (GNSS). The lack of pressure observations is a big issue for the study of climate using historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either an empirical model (e.g. Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g. ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band of −30 to 30° and the average value of Root-Mean-Square (RMS) errors across all the stations in this region is 2.4 mb. Correspondingly, an error of 5.6 mm and 1.0 mm in its resultant zenith hydrostatic delay (ZHD) and PWV is expected. In addition, GPT2w-derived pressure usually has a larger error in the cold season due to large diurnal ranges, which is not considered in the GPT2w model. The average value of the RMS errors of the ERA-Interim-derived pressure across all the 108 stations is 1.1 mb, which will lead to an equivalent error of 2.5 mm and 0.4 mm in its resultant ZHD and PWV respectively. Our research also indicates that the ERA-Interim-derived pressure has the potential to be used as a useful meteorological data source to obtain high accuracy PWV on a global scale for climate studies and the GPT2w-derived pressure can be potentially used for climatology as well although it may be only suitable for the tropical regions.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5566 ◽  
Author(s):  
Qingzhi Zhao ◽  
Xiongwei Ma ◽  
Wanqiang Yao ◽  
Yang Liu ◽  
Zheng Du ◽  
...  
Keyword(s):  
Water Vapor ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Precipitable Water ◽  
Satellite System ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Drought Monitoring ◽  
The Loess Plateau ◽  
Monitoring Index

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

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

2021 ◽  
Vol 13 (7) ◽  
pp. 1390
Author(s):  
Haobo Li ◽  
Xiaoming Wang ◽  
Suqin Wu ◽  
Kefei Zhang ◽  
Erjiang Fu ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
New Method ◽  
New Model ◽  
Precipitation Records ◽  
Optimal Set

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.

scholarly journals A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

2021 ◽  
Vol 24 (2) ◽  
pp. 744
Author(s):  
Wayan Suparta ◽  
Aris Warsita ◽  
Ircham Ircham
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Low Cost ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Weather System ◽  
Spatial And Temporal Scales

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I<sup>2</sup>C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

scholarly journals Evaluation and Calibration of MODIS Near-Infrared Precipitable Water Vapor over China Using GNSS Observations and ERA-5 Reanalysis Dataset

2021 ◽  
Vol 13 (14) ◽  
pp. 2761
Author(s):  
Dantong Zhu ◽  
Kefei Zhang ◽  
Liu Yang ◽  
Suqin Wu ◽  
Longjiang Li
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Grid Point ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Calibration Model ◽  
Systematic Bias ◽  
Reanalysis Dataset ◽  
The Mean

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China.

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