Assimilation of GNSS tomography products into WRF using radio occultation data assimilation operator

Hanna, Natalia; Trzcina, Estera; Möller, Gregor; Rohm, Witold; Weber, Robert L.

doi:10.5194/amt-2018-419

Cited by 4 publications

(10 citation statements)

References 49 publications

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“…Several methods can be applied to invert this system of equations depending on the GNSS tomographic model settings. In this study, the wet refractivity fields have been estimated by two GNSS tomography models, that is, the ATom (Atmospheric TOMography) GNSS software from the Vienna University of Technology (TUW) (Hanna et al, 2019; Möller, 2017), and the TOMO2 software developed at the Wroclaw University of Environmental and Life Sciences (WUELS) (Hanna et al, 2019; Rohm, 2013; Rohm & Bosy, 2009, 2011; Rohm et al, 2014, Trzcina & Rohm, 2019). In the TUW model, a least squares adjustment is used for the inversion, whereas in the WUELS model, a Kalman filter is applied.…”

Section: Gnss Tomographymentioning

confidence: 99%

“…The quality of the wet refractivity field estimated by the TUW and WUELS models varies depending on the altitude. The verification was conducted against the RS observations by Hanna et al (2019). The rms error of the wet refractivity is ∼4.0–12.0 ppm in the lower troposphere (0–2 km), 2.0–4.0 ppm in the middle troposphere (2–6 km), and 0.1–2.0 ppm in the upper parts of the troposphere (above 6 km).…”

Section: Tomoref Operatormentioning

confidence: 99%

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TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System

et al. 2020

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Global Navigation Satellite System (GNSS) tomography is a technique that aims to obtain a 3‐D field of humidity in the troposphere. It is based on observations of GNSS signal delays between satellites and ground‐based receivers. The technique has been developed in recent years, showing positive results in the monitoring of severe weather events. The previous studies on assimilation into the numerical weather prediction models are based on available observation operators which are not adjusted to the GNSS tomography data. In this study, we demonstrate an observation operator TOMOREF dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a heavy precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) has been noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, the validation of the precipitation forecasts reveals the positive impact of the GNSS data assimilation within 1 hr after assimilation (the mean bias values are reduced up to 0.1 mm). Additionally, it was observed that assimilation of GNSS tomography data has a greater influence on the WRF model than the Zenith Total Delay (ZTD) observations, which proves the potential of the GNSS tomography data for weather forecasting.

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Section: Gnss Tomographymentioning

confidence: 99%

Section: Tomoref Operatormentioning

confidence: 99%

TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System

et al. 2020

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“…The second type of runs included GPS-IWV data assimilation only (defined here as AssimGPS runs). In contrast to other similar studies [49,50], where only single GPS derived IWV or ZWD/ZTD values were assimilated, in this study, we assimilate the quasi-continuous map of IWV consisted of 120,000 points, as described in Section 2.1. Lastly, the third type of runs assimilated into WRF the GPS-IWV derived maps augmented by the METEOSAT data, as described in Section 2.2 (defined here as AssimGPS-METEOSAT).…”

Section: Wrf Data Assimilation Technique and Implementationmentioning

confidence: 99%

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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“…[38] In addition, Table. 1 shows an applied data set in the area of interest for tomography modelling. According to previous research [18], the horizontal resolution of the tomographic model is 50 km with an exponential model in the vertical direction [20,56,57]. Moreover, a time resolution of 1 hour was applied for this research.…”

Section: Study Areamentioning

confidence: 99%

“…Instead, determination of water vapour using the Global Navigation Satellite Systems (GNSS) is a low cost and effective technique with reasonable precision and more spatio-temporal resolution than was provided by the previous techniques. Over past two decades, the potential of using GNSS to determine the 4D wet refractivity and water vapour fields using tomography has been evaluated in various studies [14][15][16][17][18][19][20][21][22][23][24][25][26]. GNSS tomography is an all-weather condition remote sensing technique that can be used to reconstruct the water vapour or the wet refractivity in the Earth's atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset

Adavi

Rohm

Weber

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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GNSS tomography is an emerging remote sensing technique in the field of meteorology that is gaining increased attention in recent years. This method is used as a tool for atmospheric (particularly tropospheric) sensing and then applied in nowcasting and forecasting research. The tomographic approach can be used to determine the distribution of water vapour (WV), the most active component of the atmosphere. WV is one of the most important drivers of convection and precipitation. In this method, numerous line-of-sight integral observations at different locations and directions are used to derive a 3D distribution of a water vapour structure. One of the challenges in GNSS tomography is that different parameterisation methods are used for computing the design matrix. Here, the effect of the straight-line method versus the raytracing method is investigated for computing the length of a ray which passes through the model element. In addition, the effect of considering the topography of the area in the tomography model is analysed. The accuracy of the developed model is verified using radiosonde measurements in the COST benchmark dataset. Results show that the Eikonal ray-tracing method is superior to other schemes whether used with topography or not. The mean values of RMSE of estimated wet refractivity with respect to the radiosonde profiles for these schemes are about 1.313 ppm and 1.766 ppm, respectively. This work is conducted within COST Action ES1206 on 'Advanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC) (2013-2017)' and IAG Working Group 'GNSS tomography'.

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Assimilation of GNSS tomography products into WRF using radio occultation data assimilation operator

Cited by 4 publications

References 49 publications

TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System

TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System

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

Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset

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