Combining METEOSAT-10 satellite image data with GPS tropospheric path delays 
to estimate regional Integrated Water Vapor (IWV) distribution

Leontiev, Anton; Reuveni, Yuval

doi:10.5194/amt-2016-217

Cited by 3 publications

(10 citation statements)

References 30 publications

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“…In such cases, pressure values derived from empirical models like GPT (Global Pressure Temperature) or GPT2 can be used [20]. Alternatively, Vienna Mapping Function I (VMFI) model can be used, which provides ZHD values for different stations [21]. The VMF1 model derived hydrostatic delays are provided in the Global Geodetic Observing System (GGOS) website [22] and are based on meteorological data from Numerical Weather Models (NWMs).…”

Section: A Gps-derived Water Vapor Contentmentioning

confidence: 99%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

112

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In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

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Section: A Gps-derived Water Vapor Contentmentioning

confidence: 99%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

112

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“…The most common ones use radiosondes 6 – 8 , different techniques of GPS tropospheric path delays 2 , 9 – 16 , or remote sensing measurements from satellites such as the METEOSAT series 17 – 19 . Recently, Leontiev and Reuveni 20 developed a technique for combining GPS tropospheric zenith path delays with near-real time METEOSAT-10 pixel intensity values (using 7.3 and 10.8 WV and surface temperature channels, respectively), in order to obtain absolute IWV (kg/m 2 ) distribution maps. The results showed good agreement between the estimated IWV values obtained from their triangulation strategy, based solely on GPS Zenith Total Delays (ZTD) and METEOSAT-10 surface temperature data (10.8 channel), compared with in-situ IWV measurements from radiosondes.…”

Section: Introductionmentioning

confidence: 99%

“…The suggested technique is also capable of determine the mathematical dependency between METEOSAT-10 7.3 µm WV pixel values and the estimated GPS-IWV absolute amount 20 . The main advantage of using the converted METEOSAT-10 7.3 WV pixel values is that it can potentially be used for producing WV distribution maps using the METEOSAT-10 data and a small number of GPS station data.…”

Section: Introductionmentioning

confidence: 99%

“…The two different approaches for deriving regional WV distribution maps; triangulating IWV estimations using GPS ZWD combined with METEOSAT-10 surface temperature data (10.8 IR channel), or alternatively converting METEOSAT-10 WV intensity pixel values (7.3 WV channel) using a mathematical dependency to a known estimated GPS WV value, were both compared with radiosonde IWV absolute values. It was shown that triangulating IWV data obtained from a network of GPS stations combined with surface temperature estimations is more accurate and detailed than the IWV data obtained from Meteosat-10 7.3 calibrated observations 20 .…”

Section: Introductionmentioning

confidence: 99%

“…Weather Research and Forecasting (WRF) model, http://www.wrf-model.org/index.php ) but do not take into accounts clouds and other hydrometeors 23 . The WRF model, for example, allows to assimilate WV distribution maps similar to the ones obtained using interpolated IWV values derived from GPS ZWD 20 . However, as mentioned above, when interpolating the absolute IWV values from a network of GPS stations, clouds also contribute to the total atmospheric IWV content, and consequently should be taken into account while interpolating between the network points.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Leontiev

Reuveni

2018

Sci Rep

Self Cite

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Water vapor (WV) is the most variable greenhouse gas in the troposphere, therefore investigation of its spatio-temporal distribution and motion is of great importance in meteorology and climatology studies. Here, we suggest a new strategy for augmenting integrated water vapor (IWV) estimations using both remote sensing satellites and global positioning system (GPS) tropospheric path delays. The strategy is based first on the ability to estimate METEOSAT-10 7.3 µm WV pixel values by extracting the mathematical dependency between the IWV amount calculated from GPS zenith wet delays (ZWD) and the METEOSAT-10 data. We then use the surface temperature differences between ground station measurements and METEOSAT-10 10.8 µm infra-red (IR) channel to identify spatio-temporal cloud distribution structures. As a last stage, the identified cloud features are mapped into the GPS-IWV distribution map when preforming the interpolation between adjusted GPS station inside the network. The suggested approach improves the accuracy of estimated regional IWV maps, in comparison with radiosonde data, thus enables to obtain the total water amount at the atmosphere, both in the form of clouds and vapor. Mean and root mean square (RMS) difference between the GPS-IWV estimations, using the spatio-temporal clouds distribution, and radiosonde data are reduced from 1.77 and 2.81 kg/m2 to 0.74 and 2.04 kg/m2, respectively. Furthermore, by improving the accuracy of the estimated regional IWV maps distribution it is possible to increase the accuracy of regional Numerical Weather Prediction (NWP) platforms.

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Flash Floods Prediction Using Precipitable Water Vapor Derived From GPS Tropospheric Path Delays Over the Eastern Mediterranean

Ziv

Reuveni

2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

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A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semi arid parts of the Eastern Mediterranean (EM) region, the spatiotemporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground based Global Navigation Satellite Systems (GNSS) stations. Here, we use the Precipitable Water Vapor (PWV) derived from 9 GNSS ground based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of Machine Learning (ML) models in a binary classification task which predicts whether a flash flood will occur given 24 hours of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 hours prior a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground based data driven approach as demonstrated in this work.

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Combining METEOSAT-10 satellite image data with GPS tropospheric path delays to estimate regional Integrated Water Vapor (IWV) distribution

Cited by 3 publications

References 30 publications

A Data-Driven Approach for Accurate Rainfall Prediction

A Data-Driven Approach for Accurate Rainfall Prediction

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

Flash Floods Prediction Using Precipitable Water Vapor Derived From GPS Tropospheric Path Delays Over the Eastern Mediterranean

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