scholarly journals Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

2018 ◽  
Author(s):  
Peng Jiang ◽  
Shirong Ye ◽  
Yinhao Lu ◽  
Yanyan Liu ◽  
Dezhong Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Statistical Tests ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Temporal Variations ◽  
Radiosonde Data ◽  
High Temporal Resolution ◽  
Time Varying ◽  
Weighted Mean Temperature ◽  
Significance Levels

Abstract. Water-vapor-weighted mean temperature, Tm, is the key variable to estimate mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). In near real-time GPS-PWV retrieving, estimating Tm from surface air temperature Ts is a widely used method because of its high temporal resolution and a fair degree of accuracy. Based on the Tm estimates and the extracted Ts parameters at each reanalysis grid node, analyses of the relationship between Tm and Ts were performed without smoothing of data which will produce superior results than other similar studies. Analyses demonstrate that Ts–Tm relationship has significant spatial and temporal variations. Then static and time-varying global gridded Ts–Tm equations were established and evaluated by comparisons with radiosonde data at radiosonde 758 stations in the Integrated Global Radiosonde Archive (IGRA). Results show that our global gridded Ts–Tm equations have prominent advantages than other globally applied models. Large biases of Bevis equation or latitude-related linear model at considerable stations are removed in gridded Ts–Tm estimating models. Multiple statistical tests at 5 % significance levels show that time-varying global gridded model is superior to other Ts–Tm models at 83.64 % of all radiosonde stations, while no model is significantly better at 5.54 % of sites and others superior at only 10.82 % of sites. GPS-PWV retrievals using different Tm estimates were compared at a number of IGS stations. By application of time-varying global gridded Ts–Tm equations, the relative differences of GPS-PWVs at most sites are within 1 %. Such results are obviously superior to other Ts–Tm models. The differences between GPS-PWVs and radiosonde PWVs are influenced by other comprehensive factors instead of single Tm parameter. However evident improvements still exist at special site by using more precise Ts–Tm equations. PWV errors could decrease by more than 30 % during wetter seasons.

scholarly journals Development of time-varying global gridded <i>T</i><sub>s</sub>–<i>T</i><sub>m</sub> model for precise GPS–PWV retrieval

2019 ◽  
Vol 12 (2) ◽  
pp. 1233-1249 ◽  
Author(s):  
Peng Jiang ◽  
Shirong Ye ◽  
Yinhao Lu ◽  
Yanyan Liu ◽  
Dezhong Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Pressure ◽  
The Other ◽  
Radiosonde Data ◽  
High Temporal Resolution ◽  
Time Varying ◽  
Total Uncertainty ◽  
Weighted Mean Temperature ◽  
Small Uncertainty ◽  
Gpt2w Model

Abstract. Water-vapor-weighted mean temperature, Tm, is the key variable for estimating the mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). For the near-real-time GPS–PWV retrieval, estimating Tm from surface air temperature Ts is a widely used method because of its high temporal resolution and fair degree of accuracy. Based on the estimations of Tm and Ts at each reanalysis grid node of the ERA-Interim data, we analyzed the relationship between Tm and Ts without data smoothing. The analyses demonstrate that the Ts–Tm relationship has significant spatial and temporal variations. Static and time-varying global gridded Ts–Tm models were established and evaluated by comparisons with the radiosonde data at 723 radiosonde stations in the Integrated Global Radiosonde Archive (IGRA). Results show that our global gridded Ts–Tm equations have prominent advantages over the other globally applied models. At over 17 % of the stations, errors larger than 5 K exist in the Bevis equation (Bevis et al., 1992) and in the latitude-related linear model (Y. B. Yao et al., 2014), while these large errors are removed in our time-varying Ts–Tm models. Multiple statistical tests at the 5 % significance level show that the time-varying global gridded model is superior to the other models at 60.03 % of the radiosonde sites. The second-best model is the 1∘ × 1∘ GPT2w model, which is superior at only 12.86 % of the sites. More accurate Tm can reduce the contribution of the uncertainty associated with Tm to the total uncertainty in GPS–PWV, and the reduction augments with the growth of GPS–PWV. Our theoretical analyses with high PWV and small uncertainty in surface pressure indicate that the uncertainty associated with Tm can contribute more than 50 % of the total GPS–PWV uncertainty when using the Bevis equation, and it can decline to less than 25 % when using our time-varying Ts–Tm model. However, the uncertainty associated with surface pressure dominates the error budget of PWV (more than 75 %) when the surface pressure has an error larger than 5 hPa. GPS–PWV retrievals using different Tm estimates were compared at 74 International GNSS Service (IGS) stations. At 74.32 % of the IGS sites, the relative differences of GPS–PWV are within 1 % by applying the static or the time-varying global gridded Ts–Tm equations, while the Bevis model, the latitude-related model and the GPT2w model perform the same at 37.84 %, 41.89 % and 29.73 % of the sites. Compared with the radiosonde PWV, the error reduction in the GPS–PWV retrieval can be around 1–2 mm when using a more accurate Tm parameterization, which accounts for around 30 % of the total GPS–PWV error.

scholarly journals An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Pedro Mateus ◽  
João Catalão ◽  
Virgílio B. Mendes ◽  
Giovanni Nico
Keyword(s):  
Water Vapor ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Weighted Mean ◽  
Zenith Wet Delay ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Zenith Hydrostatic Delay

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

scholarly journals CONSTRUCTION OF REGIONAL WEIGHTED MEAN TEMPERATURE MODEL BASED ON OPTIMIZATION BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1099-1105
Author(s):  
Z. X. Chen ◽  
L. L. Liu ◽  
L. K. Huang ◽  
Q. T. Wan ◽  
X. Q. Mo
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Bp Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Single Station

Abstract. The tropospheric weighted mean temperature (Tm) is one of the key characteristic parameters in the troposphere, which plays an important role in the conversion of Zenith Wet Delay (ZWD) to atmospheric Precipitable Water Vapor (PWV). The precision of Global Navigation Satellite System (GNSS) inversion of PWV can be significantly improved with the accurate calculation of Tm. Due to the strong nonlinear mapping ability of Back Propagation (BP) neural network, the algorithm can be used to excavate the law with massive data. In term of the nonlinear and non-stationary characteristics of GNSS precipitable water vapor, in this paper, we proposes a forecast method of GNSS precipitable water vapor based on BP neural network, which can modelling the weighted mean temperature of troposphere. The traditional BP neural network has some shortcomings, such as large amount of calculation, long training time and easy to appear “over-fitting” phenomenon and so on. In order to optimize the deficiency and numerical simulation, the three characteristic values include water vapor pressure, surface pressure and surface temperature provided are selected as input parameters, named as BP_Tm. The optimal initialization parameters of the model were obtained from the 2016 radiosonde data of 89 radiosonde stations in China, and the modeling and accuracy verification were conducted with the 2017 radiosonde data，and the accuracy of the new model was compared with the common regional Tm model. The results show the BP_Tm model has good simulation accuracy, the average deviation is −0.186K, and the root mean square error is 3.144K. When simulating the weighted mean temperature of a single station, the accuracy of the four models to simulate Tm is compared and analyzed, which the BP_Tm model can obtain good accuracy and reflect better stability and reliability.

scholarly journals Empirical model for mean temperature for Indian zone and estimation of precipitable water vapor from ground based GPS measurements

2007 ◽  
Vol 25 (9) ◽  
pp. 1935-1948 ◽  
Author(s):  
C. Suresh Raju ◽  
K. Saha ◽  
B. V. Thampi ◽  
K. Parameswaran
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Specific Model ◽  
Radiosonde Data ◽  
Gps Data ◽  
Equatorial Station ◽  
Site Specific ◽  
Temperature Variations ◽  
Mean Temperature ◽  
Weighted Mean Temperature

Abstract. Estimation of precipitable water (PW) in the atmosphere from ground-based Global Positioning System (GPS) essentially involves modeling the zenith hydrostatic delay (ZHD) in terms of surface Pressure (Ps) and subtracting it from the corresponding values of zenith tropospheric delay (ZTD) to estimate the zenith wet (non-hydrostatic) delay (ZWD). This further involves establishing an appropriate model connecting PW and ZWD, which in its simplest case assumed to be similar to that of ZHD. But when the temperature variations are large, for the accurate estimate of PW the variation of the proportionality constant connecting PW and ZWD is to be accounted. For this a water vapor weighted mean temperature (Tm) has been defined by many investigations, which has to be modeled on a regional basis. For estimating PW over the Indian region from GPS data, a region specific model for Tm in terms of surface temperature (Ts) is developed using the radiosonde measurements from eight India Meteorological Department (IMD) stations spread over the sub-continent within a latitude range of 8.5°–32.6° N. Following a similar procedure Tm-based models are also evolved for each of these stations and the features of these site-specific models are compared with those of the region-specific model. Applicability of the region-specific and site-specific Tm-based models in retrieving PW from GPS data recorded at the IGS sites Bangalore and Hyderabad, is tested by comparing the retrieved values of PW with those estimated from the altitude profile of water vapor measured using radiosonde. The values of ZWD estimated at 00:00 UTC and 12:00 UTC are used to test the validity of the models by estimating the PW using the models and comparing it with those obtained from radiosonde data. The region specific Tm-based model is found to be in par with if not better than a similar site-specific Tm-based model for the near equatorial station, Bangalore. A simple site-specific linear relation without accounting for the temperature effect through Tm is also found to be quite adequate for Bangalore. But for Hyderabad, a station located at slightly higher latitude, the deviation for the linear model is found to be larger than that of the Tm-based model. This indicates that even though a simple linear regression model is quite adequate for the near equatorial stations, where the temperature variations are relatively small, for estimating PW from GPS data at higher latitudes this model is inferior to the Tm-based model.

scholarly journals Comparisons of Raman Lidar Measurements of Tropospheric Water Vapor Profiles with Radiosondes, Hygrometers on the Meteorological Observation Tower, and GPS at Tsukuba, Japan

2007 ◽  
Vol 24 (8) ◽  
pp. 1407-1423 ◽  
Author(s):  
Tetsu Sakai ◽  
Tomohiro Nagai ◽  
Masahisa Nakazato ◽  
Takatsugu Matsumura ◽  
Narihiro Orikasa ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Temporal Variations ◽  
Radiosonde Data ◽  
Calibration Coefficient ◽  
Lidar Data ◽  
Raman Lidar ◽  
Meteorological Observation ◽  
Meteorological Research Institute ◽  
Water Vapor Mixing Ratio

Abstract The vertical distribution profiles of the water vapor mixing ratio (w) were measured by Raman lidar at the Meteorological Research Institute, Japan, during the period from 2000 to 2004. The measured values were compared with those obtained with radiosondes, hygrometers on a meteorological observation tower, and global positioning system (GPS) antennas near the lidar site. The values of w obtained with the lidar were lower than those obtained with the corrected Meisei RS2-91 radiosonde by 1.2% on average and higher than those obtained with the corrected Vaisala RS80-A radiosonde by 17% for w ≥ 0.5 g kg−1. The lidar data were higher than those radiosondes’ data by 19% or 33% for w &lt; 0.5 g kg−1. The vertical variations of w obtained with the lidar differed from those obtained with the Meisei RS-01G radiosonde and Meteolabor Snow White radiosonde by 5% on average for w ≥ 0.5 g kg−1. The lidar data were lower than those radiosondes’ data by 37% or 39% for w &lt; 0.5 g kg−1. The temporal variations of w obtained with the lidar and the hygrometers on the meteorological tower agreed to within 0.4% at a height of 213 m, although the absolute values differed systematically by 9%–14% due to the incomplete overlap of the laser beam and the receiver’s field of view at heights between 50 and 150 m. The precipitable water vapor obtained with the lidar indicated a mean positive bias of 2 mm (9%–11%) relative to those obtained with GPS. The lidar water vapor calibration coefficient that was calculated using RS2-91 radiosonde data varied by 11% during an 18-month period. Therefore, it is necessary to develop an accurate, yet convenient, method for determining the calibration coefficient for the use of the lidar.

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

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Liangke Huang ◽  
Zhixiang Mo ◽  
Shaofeng Xie ◽  
Lilong Liu ◽  
Jun Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Spatiotemporal Characteristics ◽  
Weighted Mean Temperature ◽  
Radiosonde Station ◽  
The Mean ◽  
Heavy Rainfalls

AbstractPrecipitable 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.

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 A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 698 ◽  
Author(s):  
Chen Liu ◽  
Nanshan Zheng ◽  
Kefei Zhang ◽  
Junyu Liu
Keyword(s):  
Water Vapor ◽  
Interpolation Method ◽  
Geographical Location ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Virtual Sample ◽  
Weighted Mean Temperature ◽  
Sample Points ◽  
Elevation Model ◽  
A Minor

Abstract: The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.

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