Augmenting GPS IWV estimations using spatio-temporal cloud distribution extracted from satellite data

Leontiev, Anton; Reuveni, Yuval

doi:10.1038/s41598-018-33163-x

Cited by 11 publications

(13 citation statements)

References 28 publications

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“…For the present study, we used the data obtained from the Israeli GPS network (SOI-APN) that includes more than 20 stations, covering the entire Israeli area [31,32]. These geodetic GPS stations record raw data in RINEX format.…”

Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

“…The ZWD values were calculated for the wet winter season and relatively dry spring season. For a more comprehensive description of this procedure, along with the precise values for the tropospheric parameters, see Leontiev and Reuveni [31,32].…”

Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

“…After the ZWD parameters are estimated from the entire SOI-APN GPS geodetic network, a 2D IWV distribution grid can be extracted while applying different interpolation techniques. In the current study, we applied the Kriging interpolation (rather than the Delaunay interpolation) with the GPS-IWV estimations, which yield the most precise results compare with in-situ radiosonde measurements [32]. The interpolation was performed within the area of 29-35 • N and 34-36 • E, covering the entire Israel area along with some parts of Lebanon, Syria, Jordan, and Egypt (Figure 2a).…”

Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

“…Estimation of WV amount in the troposphere using remote sensing measurements from satellites such as the METEOSAT series is useful for producing proper 2D IWV distribution maps [28][29][30]. Recently, Leontiev and Reuveni [31,32] developed a technique for augmenting IWV estimations using both remote sensing measurements from satellites such as METEOSAT and GNSS tropospheric path delays. The suggested strategy is based first on estimating METEOSAT 7.3 µm WV pixel values by extracting the mathematical dependency between the IWV amount extracted from GPS ZWD and the METEOSAT-10 data.…”

Section: Introductionmentioning

confidence: 99%

“…This method increases the accuracy of the IWV regional map estimations being verified against in situ radiosonde measurements, and assists to acquire the absolute amount of water in the atmosphere, both in the form of clouds and vapor. The application of this method reduced the mean error (ME) and root mean square (RMSE) differences between the GPS-IWV estimations and the radiosonde measurements from 1.77 and 2.81 kg/m 2 to 0.74 and 2.04 kg/m 2 , respectively [31,32]. Here, we investigate the potential of improving WRF model forecasts by assimilation of high-resolution 2D IWV distribution maps derived from GPS tropospheric path delay augmented by METEOSAT-11 WV imagery data, based on the survey of Israel-active per manent network (SOI-APN) with more than 20 GPS geodetic stations over the entire coun try (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

2020

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Improving the accuracy of numerical weather predictions remains a challenging task. The absence of sufficiently detailed temporal and spatial real-time in-situ measurements poses a critical gap regarding the proper representation of atmospheric moisture fields, such as water vapor distribution, which are highly imperative for improving weather predictions accuracy. The estimated amount of the total vertically integrated water vapor (IWV), which can be derived from the attenuation of global positioning systems (GPS) signals, can support various atmospheric models at global, regional, and local scales. Currently, several existing atmospheric numerical models can estimate the IWV amount. However, they do not provide accurate results compared with in-situ measurements such as radiosondes. Here, we present a new strategy for assimilating 2D IWV regional maps estimations, derived from combined GPS and METEOSAT satellite imagery data, to improve Weather Research and Forecast (WRF) model predictions accuracy in Israel and surrounding areas. As opposed to previous studies, which used point measurements of IWV in the assimilation procedure, in the current study, we assimilate quasi-continuous 2D GPS IWV maps, combined with METEOSAT-11 data. Using the suggested methodology, our results indicate an improvement of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative standalone WRF, when both are compared to the radiosonde measured data near the Mediterranean coast. Moreover, significant improvements along the Jordan Rift Valley and Dead Sea Valley areas are obtained when compared to 2D IWV regional maps estimations. Improvements in these areas suggest the impact of the assimilated high resolution IWV maps, with initialization times which coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations, as the distance to the Mediterranean Sea shore, along with other features, dictates its arrival times.

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Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

Section: D Iwv Distribution Maps Derived From Gps Tropospheric Path mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

2020

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Long‐term variability and trends of precipitable water vapour derived from GPS tropospheric path delays over the Eastern Mediterranean

Ziv

Alpert

Reuveni

2021

Intl Journal of Climatology

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The precipitable water vapour (PWV) of 21 Global Navigation Satellite System (GNSS) ground receivers from the Survey Of Israel Active Permanent Network (SOI-APN) in the Eastern Mediterranean area are processed and analysed. We focus on the annual cycles, inter-annual variations and long-term trends. The PWV monthly mean values peak during September-October where October has the highest variability for all stations. PWV values reach minimum level during February-March where Apr is the least variable month for most stations. Generally, the PWV variability decreases with station height. Additionally, harmonic analysis indicates that the annual mode's amplitude decreases with station height. In contrast, the semi-annual mode's accounting for the sub-annual variance increases with station height. The PWV monthly mean anomalies are consistent among all the stations suggesting a common regional driver. Furthermore, a comparison between the PWV station average anomalies and the regional mean anomalies from the ERA5 reanalysis shows a strong correlation with r = 0.95. In addition, a correlation coefficient of r = 0.72 was found with the regional mean moisture flux anomalies at 750 hPa taken from ERA5 and the station average PWV anomalies, thus implying that horizontal moisture flow accounts for most of the inter-annual variability. However, the significance of the 750 hPa pressure level requires future investigation. For the long-term analysis, we find an increasing regional mean trend of 0.5 mm Á decade −1 for the entire data period (1997-2019) as compared to 1 mm Á decade −1 for the last decade (2010-2019) suggesting a recent accelerated moistening of the Eastern Mediterranean region.

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4D modeling of precipitable water vapor to assess flood forecasting by using GPS signals

Sorkhabi,

Djamour

2023

Nat Hazards

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Every year the government and people suffer from the cost of damages caused by natural disasters such as hurricanes and oods. Thanks to Global Positioning System (GPS) permanent station data, today it is possible to determine the amount of Water Vapor (WV) very accurately and reliably. GPS signals pass through different layers of the atmosphere, such as the ionosphere and troposphere, and therefore they are delayed. A signi cant part of the delay is due to the troposphere, which is one of the serious sources of error in GPS. In this research, by using 5 GPS permanent stations located in the Arasbaran region, in northwest of Iran, the Precipitable Water Vapor (PWV) is evaluated in order to show the capacity of this method in forecasting the October 2012 ood over the region. The results showed that the amount of PWV in the atmosphere had increased by 2 mm a few hours before the ood, which could be used as a forecast indicator. The Tikhonov regularization for tropospheric tomography covering the GPS stations region was also performed for latitude and altitude components for 3 consecutive days: the day before the ood, the ood day, and the day after the ood. A high amount of PWV was observed in the troposphere on the day before the ood, and especially on the ood day. During the study period, the results from tomography and the radiosonde were in good agreement, with a signi cance correlation of around 0.95. Also, the values of PWV obtained from GPS and MODIS-NIR do not have an absolute error more than 4.4 mm over the 8 days from 8 October 2012 to

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Augmenting GPS IWV estimations using spatio-temporal cloud distribution extracted from satellite data

Cited by 11 publications

References 28 publications

On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

Long‐term variability and trends of precipitable water vapour derived from GPS tropospheric path delays over the Eastern Mediterranean

4D modeling of precipitable water vapor to assess flood forecasting by using GPS signals

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