Benchmark campaign and case study episode in central Europe for development
and assessment of advanced GNSS tropospheric models and products

Douša, Jan; Dick, Galina; Kačmařík, Michal; Brožková, Radmila; Zus, Florian; Brenot, Hugues; Stoycheva, Anastasiya; Möller, Gregor; Kapłon, Jan

doi:10.5194/amt-9-2989-2016

Cited by 80 publications

(93 citation statements)

References 23 publications

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“…This uncertainty is estimated to be about 8-10 mm close to the zenith, increasing to about 8-10 cm at an elevation angle of 5 • . Similar uncertainty of around 8 mm for the zenith direction was also found for ALADIN-CZ model in Douša et al (2016). To compute STDs from ALADIN-CZ, a simplified strategy has been used to model the curve path followed by GNSS signals through the neutral atmosphere, as suggested by Saastamoinen (1972).…”

Section: Std Retrievals From Gnss Observationsmentioning

confidence: 93%

“…Finally, for the required azimuth and elevation angle the STD is "assembled" using the tropospheric parameters. For a detailed description of the tropospheric parameter determination the reader is referred to Douša et al (2016). The differences between the "assembled" and ray-traced STDs are sufficiently small in particular for elevation angles above 10 • .…”

Section: Std Retrievals From Gnss Observationsmentioning

confidence: 99%

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Inter-technique validation of tropospheric slant total delays

et al. 2017

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Abstract. An extensive validation of line-of-sight tropospheric slant total delays (STD) from Global Navigation Satellite Systems (GNSS), ray tracing in numerical weather prediction model (NWM) fields and microwave water vapour radiometer (WVR) is presented. Ten GNSS reference stations, including collocated sites, and almost 2 months of data from 2013, including severe weather events were used for comparison. Seven institutions delivered their STDs based on GNSS observations processed using 5 software programs and 11 strategies enabling to compare rather different solutions and to assess the impact of several aspects of the processing strategy. STDs from NWM ray tracing came from three institutions using three different NWMs and ray-tracing software. Inter-techniques evaluations demonstrated a good mutual agreement of various GNSS STD solutions compared to NWM and WVR STDs. The mean bias among GNSS solutions not considering post-fit residuals in STDs was −0.6 mm for STDs scaled in the zenith direction and the mean standard deviation was 3.7 mm. Standard deviations of comparisons between GNSS and NWM ray-tracing solutions were typically 10 mm ± 2 mm (scaled in the zenith direction), depending on the NWM model and the GNSS station. Comparing GNSS versus WVR STDs reached standard deviations of 12 mm ± 2 mm also scaled in the zenith direction. Impacts of raw GNSS post-fit residuals and cleaned residuals on optimal reconstructing of GNSS STDs were evaluated at intertechnique comparison and for GNSS at collocated sites. The use of raw post-fit residuals is not generally recommended as they might contain strong systematic effects, as demonstrated in the case of station LDB0. Simplified STDs reconstructed only from estimated GNSS tropospheric parameters, i.e. without applying post-fit residuals, performed the best in all the comparisons; however, it obviously missed part of tropospheric signals due to non-linear temporal and spatial variations in the troposphere. Although the post-fit residuals cleaned of visible systematic errors generally showed a slightly worse performance, they contained significant tropospheric signal on top of the simplified model. They are thus recommended for the reconstruction of STDs, particularly during high variability in the troposphere. Cleaned residuals also showed a stable performance during ordinary days while containing promising information about the troposphere at low-elevation angles.

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Section: Std Retrievals From Gnss Observationsmentioning

confidence: 93%

Section: Std Retrievals From Gnss Observationsmentioning

confidence: 99%

Inter-technique validation of tropospheric slant total delays

et al. 2017

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“…The chosen period is covering May and June 2013, with special focus on May, [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]2013 and three shorter cases: a) May, 29-31, 2013, b) June, 17-19, 2013and c) June, 24-26, 2013. The period is chosen in accordance to the COST Action ES1206 GNSS meteorology benchmark (Douša et al, 2016).…”

Section: Datamentioning

confidence: 99%

“…All cases presented in this study are selected from the period of May -June, 2013 and location (Central Europe) covering the benchmark campaign of COST Action ES1206 (Douša et al, 2016). According to synoptic analysis presented in (Douša et al, 2016), the beginning of May 2013 was characterized with cyclonic field over 500hPa, which in turn resulted in precipitation and convection development, moving from west to east.…”

Section: Case Studiesmentioning

confidence: 99%

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Rohm¹,

Guzikowski²,

Wilgan³

et al. 2018

Preprint

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Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of 10 atmospheric behavior as codified in computer models. With this approach, the 'best' estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June, 2013) show that the assimilation of 25 GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8%, and a 20% improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5-23 May, 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48h forecast) 30 but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather Atmos. Meas. Tech. Discuss., https://doi

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“…With the rapid development of the global navigation satellite system (GNSS), the GNSS troposphere tomographic technique has become a potentially powerful method for obtaining three-dimensional distributions of water vapour with high spatio-temporal resolution (Braun, et al, 1999;Flores et al, 2000;Troller, et al, 2002;Nilsson and Gradinarsky, 2006;Perler et al, 2011;Brenot et al, 2014;. In recent years, the integration of GNSS data and NWP has given better results in the field of GNSS meteorology (Douša and Václavovic, 2014;Douša et al, 2016;Wilgan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area

Zhao¹,

Yao²

2017

Ann. Geophys.

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Abstract. The spatio-temporal distribution of atmospheric water vapour information plays a crucial role in the establishment of modern numerical weather forecast models and description of the different weather variations. A troposphere tomographic method has been proposed considering the signal rays penetrating from the side of the area of interest to solve the problem of the low utilisation rate of global navigation satellite system (GNSS) observations. Given the method above needs the establishment of a unit scale factor model using the radiosonde data at only one location in the research area, an improved approach is proposed by considering the reasonability of modelling data and the diversity of the modelling parameters for building a more accurate unit scale factor model. The new established model is established using grid point data derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) and evenly distributed in the tomographic area, which can enhance the number of calculated initial water vapour density values with high accuracy. We validated the improved method with respect to the previous methods, as well as the result from a radiosonde using data from 12 stations from the Hong Kong Satellite Positioning Reference Station Network. The obtained result shows that the number of initial values estimated by the new model is increased by 6.83 %, while the internal and external accuracies are 0.08 and 0.24 g m −3 , respectively. Integrated water vapour (IWV) and water vapour density profile comparisons show that the improved method is superior to previous studies in terms of RMS, MAE, and bias, which suggests higher accuracy and reliability.

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Benchmark campaign and case study episode in central Europe for development and assessment of advanced GNSS tropospheric models and products

Abstract: Abstract. Initial objectives and design of the Benchmark campaign organized within the European COST Action ES1206

Cited by 80 publications

References 23 publications

Inter-technique validation of tropospheric slant total delays

Inter-technique validation of tropospheric slant total delays

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area

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