Evaluation of precipitable water vapor variation for east mediterranean using GNSS

Darrag, Mohamed; AbouAly, Nadia; Mohamed, A.M.A.; Becker, Matthias; Saleh, Mohamed

doi:10.1007/s40328-020-00292-7

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…It has become more attractive than traditional techniques as a result of its advantages [4][5][6]. The precipitable water vapor (PWV) value derived from GNSS, which refers to the depth of water in a column of the atmosphere if all the water vapor in the column condenses into liquid water, has been in widespread use in many fields, such as in research on climate change, weather forecasting and the monitoring of extreme weather events [3,[7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Long

Dong

et al. 2021

Atmosphere

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The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Long

Dong

et al. 2021

Atmosphere

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“…where the h t and h g are the heights of the target location and the grid point for interpolation, respectively, and T c m is the corrected T m of the grid point. The δ of each grid node is also considered to be a variable with characteristics of inter-annual, annual and semiannual variations, which is also fitted by an equation which is the same as Equation (5). However, T m is assumed to decrease approximately linearly with the height in the development of the GTrop-Tm model, which may be inconsistent with reality.…”

Section: Gtrop-tm Model Predictionsmentioning

confidence: 99%

“…Detecting atmospheric water vapor with Global Navigation Satellite System (GNSS) has been paid more attention than traditional techniques (such as water vapor radiometer, radiosonde etc. ), because it has the advantages of low-cost, all-weather, high-precision and high-resolution in space and time [1][2][3][4][5]. The key to realize real-time conversion from zenith wet delay (ZWD) to precipitable water vapor (PWV) when using GNSS for remote sense water vapor is to obtain accurate weighted mean temperature (T m ) in time.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

et al. 2021

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Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

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“…Water vapor is the most important greenhouse gas in nature and plays a vital role in cloud formation, precipitation, and the atmospheric radiation budget [1]. Changes in atmospheric water vapor, especially tropospheric precipitable water (PW), directly affect the frequency of precipitation and provide resources for the study of climate change [2,3]. Water cycles also play an important role in global warming [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

Ren

Xie

et al. 2021

Remote Sensing

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Water vapor transport affects regional precipitation and climate change. The measurement of precipitable water (PW) and water vapor flux (WVF) is of great importance for the study of precipitation and water vapor transport. This study presented a new method of computing PW and estimating WVF using the water vapor vertical column density (VCD) and profile retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS), combined with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 wind profiles. We applied our method to MAX-DOAS observations in the coastal (Qingdao) and inland (Xi’an) cities of China from June 2019 to May 2020 and compared the results to the ERA5 reanalysis datasets. Good agreement with ERA5 datasets was found; the correlation coefficient (r) of the PW and the zonal and meridional WVFs were r ≥ 0.92, r = 0.77, and r ≥ 0.89, respectively. The comparison results showed the feasibility and reliability of estimating PW and WVF using MAX-DOAS. Then, we analyzed the seasonal and diurnal climatology of the PW and WVFs in Qingdao and Xi’an. The results indicated that the seasonal and diurnal variations of the PW in the two cities were similar. The zonal water vapor transport of the two cities mainly involved eastward transport, Qingdao’s meridional water vapor mainly involved southward transport, and that of Xi’an mainly involved northward transport. The WVFs of the two cities were higher in the afternoon than in the morning, which may be related to wind speed. The results also indicated that the WVF transmitting belts appeared at around 2 and 1.4 km above the surface in Qingdao and around 2.8, 2.6, 1.6, and 1.0 km above the surface in Xi’an. Before precipitation, the WVF transmitting belt moved from near the ground to a high level, reaching its maximum at about 2 km, and the PW and meridional vertically integrated WVF increased. Finally, the sources and transports of water vapor during continuous precipitation and torrential rain were analyzed according to a 24 h backward trajectory. The air mass from the southeast accounted for more than 84% during continuous precipitation in Xi’an, while the air mass from the ocean accounted for more than 75% during torrential rain in Qingdao and was accompanied by a high-level ocean jet stream. As an optical remote sensing instrument, MAX-DOAS has the advantages of high spatiotemporal resolution, low cost, and easy maintenance. The application of MAX-DOAS to meteorological remote sensing provides a better method for evaluating the PW and WVF.

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Evaluation of precipitable water vapor variation for east mediterranean using GNSS

Cited by 4 publications

References 19 publications

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

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