A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity fields in the lower atmosphere using GNSS measurements

Forootan, Ehsan; Dehvari, Masood; Farzaneh, Saeed; Khaniani, Ali Sam

doi:10.1016/j.asr.2021.08.012

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…To apply the RO observations, the study area is discretized into 1 °× 1° patches in both the latitude and longitude directions, i.e., 285 patches used for training ELM. The value of the wet refractivity indices is almost zero at altitudes higher than 10 km [4]; thus, in each patch, the observations above 10.5 km are disregarded. Figure 3 Radiosonde stations provide observations of the temperature, pressure, dew point temperature, and relative humidity at different altitudes, alongside precipitable water at the station [44].…”

Section: Region Of Studymentioning

confidence: 99%

“…These observations are provided at 12 and 24 UTC every day. Due to the high accuracy, this type of observation has been widely used for evaluating the modelling results [4,6,23,45]. To evaluate the radiosonde measurements, Survo et al 2015 [46] compared precipitable water from radiosonde measurements with microwave radiometer observations and GPS, where their results showed an agreement of about 1 mm.…”

Section: Region Of Studymentioning

confidence: 99%

“…The water vapor content or wet refractivity indices information from atmospheric models can be applied to investigate climate change [1], predicting storm hazards or rainfall [2]. This parameter can be obtained from measurements of the in situ meteorological instruments (e.g., radiosonde stations) [3], the atmospheric tomography method using space-based observations [4][5][6], or existing atmospheric models [7,8]. Wet refractivity indices are based on water vapor pressure and temperature and reveal numerous local features due to severe weather variability [4].…”

Section: Introductionmentioning

confidence: 99%

“…This parameter can be obtained from measurements of the in situ meteorological instruments (e.g., radiosonde stations) [3], the atmospheric tomography method using space-based observations [4][5][6], or existing atmospheric models [7,8]. Wet refractivity indices are based on water vapor pressure and temperature and reveal numerous local features due to severe weather variability [4]. Therefore, a dataset with high spatialtemporal resolution is required to construct the atmospheric models to be able to represent the local fluctuations in the weather-related parameters and to improve their accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…They also require a meticulous mathematical framework to account for limitations, such as the need for and regularization to compensate for the ill condition of these models [10]. For example, Forootan et al 2019 [4] applied a functional tomography approach to overcome this problem, showing that the retrieved wet refractivity indices can be estimated with the mean Root-Mean-Square Error (RMSE) of about 1.9 ppm, which was 22% better than that related to the corresponding numerical model values. Other examples of the tomography research can be found in [6,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

et al. 2023

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Constructing accurate models that provide information about water vapor content in the troposphere improves the reliability of numerical weather forecasts and the position accuracy of low-cost Global Navigation Satellite System (GNSS) receivers. However, developing models with high spatial-temporal resolution demands compact observational datasets in the regions of interest. Empirical models, such as the Global Pressure and Temperature 3 (GPT3w), have been constructed based on the monthly averaged outputs of numerical weather models. These models are based on the assimilation of existing measurements to provide estimations of atmospheric parameters. Therefore, their accuracy may be reduced over regions with a low resolution of radiosonde or continuous GNSS stations. By emerging and increasing the Low-Earth-Orbiting (LEO) satellites that measure atmospheric parameter profiles using the Radio Occultation (RO) technique, new opportunities have appeared to acquire high-resolution atmospheric observations at different altitudes. This study aims to apply these RO observations to improve the accuracy of the GPT3w model over Iran, which is sparse in terms of long-term GNSS and radiosonde measurements. The temperature, pressure, and water vapor pressure parameters from the GPT3w model have been used as the input layers of the Extremely Learning Machine (ELM) technique. The wet refractivity indices from the RO technique are considered target parameters in the output layer to train the ELM. The RO observations of 2007–2020 are applied for training, and those of 2020–2022 for evaluating the performance of the developed ELM. Our numerical results indicate that the developed ELM decreases the Root-Mean-Square Error (RMSE) values of the wet refractivity indices by about 17 percent, compared to the original GPT3w RMSE values. Additionally, the wet refractivity indices from ELM have revealed correlation coefficients of about 0.64, which is about 1.9 times those related to the original GPT3w model. The performance of ELM has also been examined by comparison with the data of six located radiosonde stations covering the year 2020. This comparison shows an improvement of about 14 percent in the average RMSE values of the estimated wet refractivity indices.

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Section: Region Of Studymentioning

confidence: 99%

Section: Region Of Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

et al. 2023

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Multi-GNSS Tomography: Case Study of the July 2021 Flood in Germany

Wilgan

Brenot

Biondi

et al. 2023

International Association of Geodesy Symposia

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Due to climate change, intensive storms and severe precipitation will continue to happen, causing destructive flooding. In July 2021, a series of storms with prolonged rain episodes took place in Europe. Several countries were affected by severe floods following that rainfall, causing many deaths and material damage. Thus, a good understanding and forecasting of such events are of uttermost importance. This study highlights the interest of multi-GNSS tomography for the 3D modelling of the neutral atmosphere refractivity. The tropospheric parameters have been retrieved for the July 2021 flood in Germany from two tomographic solutions with different constraining options using either GPS-only or multi-GNSS estimates. Our investigations show that the stand-alone solution (especially the multi-GNSS) is producing more patterns of refractivity, and is temporally more stable. We compare the tomographic results with external observations such as radiosondes and GNSS radio-occultations from Metop-A & -B satellites. The results show that tomography is producing wetter conditions than the reference. However, we can see the precursor information of the initiation of deep convection in the ground-based GNSS technique.

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INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique

Cegla,

Moeller,

Hordyniec

et al. 2024

J Geod

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The current GNSS meteorology literature focuses on ground-based and space-based GNSS observations separately, without exploring potential synergies. In this study, we propose combining the two data sources using GNSS tomography to overcome current limitations in (1) horizontal resolution of GNSS space-based, (2) low vertical resolution of GNSS ground-based tropospheric retrievals when the number of GNSS ground-based observations is limited and (3) instability of the tomography system due to a lack of observations traversing the atmosphere horizontally. Our study on the combination of GNSS ground-based and space-based presents an innovative way for data integration based on uncertainty estimation. The developed integrated tomography operator, based on 3D ray tracing principles, is tested on 30 days of simulated data with 101 ground stations and over 240 radio occultation events, using three different station layouts. The a priori data introduced into the tomography processing is from a deterministic model, while ray tracing uses the ERA5 reanalysis wet refractivity field to obtain input data for individual test cases. The results are verified by comparing tomography output to ERA5 reanalysis. We observed a decrease in tomography RMSE between 2% and 16% in the case of an integrated solution, depending on GNSS station layout and the number and geometry of radio occultation ray paths. We show that a single RO event during one processing epoch can shift the wet refractivity estimates by 2 to 5 ppm closer to the correct solution compared to ground-based-only GNSS tomography.

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A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity fields in the lower atmosphere using GNSS measurements

Cited by 13 publications

References 34 publications

Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

Multi-GNSS Tomography: Case Study of the July 2021 Flood in Germany

INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique

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