Multi-observation meteorological and GNSS data comparison with Numerical Weather Prediction model

Wilgan, Karina; Rohm, Witold; Bosy, Jarosław

doi:10.1016/j.atmosres.2014.12.011

Cited by 56 publications

(39 citation statements)

References 27 publications

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“…The representativeness error arises when the point observations can well represent small spatial scales but the model cannot, and this error may be extreme in complex mountainous terrain, where there is a mismatch between the model and actual terrain (Zhang et al, 2013). Although a previous study (Wilgan et al, 2015) indicated that the one-point method may have a better performance than the four-point method in mountainous areas, this phenomenon was not confirmed in our study. To investigate the performance of the aforementioned methods in the determination of pressure and its impact on the resultant ZHD and IWV, the pressure, ZHD and IWV at 108 stations for the period 2000-2013 resulting from both GPT2w and ERAInterim using the aforementioned two methods are compared against surface pressure measurements and their resultant ZHD and IWV.…”

Section: Comparison and Analysiscontrasting

confidence: 54%

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

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Abstract. Surface pressure is a necessary meteorological variable for the accurate determination of integrated water vapor (IWV) using Global Navigation Satellite System (GNSS). The lack of pressure observations is a big issue for the conversion of historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either a blind model (e.g., Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g., ERAInterim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations at four epochs (00:00, 06:00, 12:00 and 18:00 UTC) each day for the period 2000-2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band between −30 and 30 • and the average value of 6 h root-mean-square errors (RMSEs) across all the stations in this region is 2.5 hPa. Correspondingly, an error of 5.8 mm and 0.9 kg m −2 in its resultant zenith hydrostatic delay (ZHD) and IWV is expected. However, for the stations located in the mid-latitude bands between −30 and −60 • and between 30 and 60 • , the mean value of the RMSEs is 7.3 hPa, and for the stations located in the high-latitude bands from −60 to −90 • and from 60 to 90 • , the mean value of the RMSEs is 9.9 hPa. The mean of the RMSEs of the ERAInterim-derived pressure across at the selected 100 stations is 0.9 hPa, which will lead to an equivalent error of 2.1 mm and 0.3 kg m −2 in the ZHD and IWV, respectively, determined from this ERA-Interim-derived pressure. Results also show that the monthly IWV determined using pressure from ERAInterim has a good accuracy − with a relative error of better than 3 % on a global scale; thus, the monthly IWV resulting from ERA-Interim-derived pressure has the potential to be used for climate studies, whilst the monthly IWV resulting from GPT2w-derived pressure has a relative error of 6.7 % in the mid-latitude regions and even reaches 20.8 % in the highlatitude regions. The comparison between GPT2w and seasonal models of pressure-ZHD derived from ERA-Interim and pressure observations indicates that GPT2w captures the seasonal variations in pressure-ZHD very well.

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Section: Comparison and Analysiscontrasting

confidence: 54%

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Wang

Zhang

et al. 2017

Atmos. Meas. Tech.

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“…The reason for such behavior is that the humidity values provided by the WRF model after the rainfall are too high. We experienced similar problems in the past with another NWP model (COAMPS) producing too wet conditions (Wilgan et al 2015). Moreover, for many stations, the WRF data are experiencing problems also during the 'moderate rainfall' period, but without a clear trend as in the 'after rainfall' period.…”

Section: Interpolation Of Ztdmentioning

confidence: 93%

“…The standard deviation of the differences between the two reference data sources RS and GNSS is 12.8 mm. In our previous investigations (Wilgan et al 2015), the standard deviation of residuals ZTD RS − ZTD GNSS for station WROC was 9.9 mm, but the study was conducted during winter months, when the water vapor content is the smallest. The distance between RS WROCLAW and the closest GNSS station is ∼6 km; for RS LEGIONOWO, it is ∼9 km and for RS LEBA ∼40 km.…”

Section: Interpolation Of Ztdmentioning

confidence: 99%

Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

Wilgan

Hurter

Geiger

et al. 2016

J Geod

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Precise positioning requires an accurate a priori troposphere model to enhance the solution quality. Several empirical models are available, but they may not properly characterize the state of troposphere, especially in severe weather conditions. Another possible solution is to use regional troposphere models based on real-time or nearreal time measurements. In this study, we present the total refractivity and zenith total delay (ZTD) models based on a numerical weather prediction (NWP) model, Global Navigation Satellite System (GNSS) data and ground-based meteorological observations. We reconstruct the total refractivity profiles over the western part of Switzerland and the total refractivity profiles as well as ZTDs over Poland using the least-squares collocation software COMEDIE (Collocation of Meteorological Data for Interpretation and Estimation of Tropospheric Pathdelays) developed at ETH Zürich. In these two case studies, profiles of the total refractivity and ZTDs are calculated from different data sets. For Switzerland, the data set with the best agreement with the reference radiosonde (RS) measurements is the combination of ground-based meteorological observations and GNSS ZTDs. Introducing the horizontal gradients does not improve the vertical interpolation, and results in slightly larger biases and standard deviations. For Poland, the data set based on meteorological parameters from the NWP Weather Research and Forecasting (WRF) model and from a combination of the NWP model and GNSS ZTDs shows the best agreement with the reference RS data. In terms of ZTD, the combined NWP-GNSS observations and GNSS-only data set exhibit the best accuracy with an average bias (from all stations) of 3.7 mm and average standard deviations of 17.0 mm w.r.t. the reference GNSS stations.

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“…5 WRF ZTD is 10 mm and the standard deviation between radiosonde ZTD and WRF ZTD is 14 mm. In the inter-comparison study using multiple techniques (Wilgan et al, 2015), the discrepancy between GNSS observations and radiosonde was found to be 10 mm. According to the EGVAP requirements (Met Office, 2012), this accuracy of the GNSS data is sufficient for the assimilation in NWP models.…”

Section: Gnss Datamentioning

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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Multi-observation meteorological and GNSS data comparison with Numerical Weather Prediction model

Cited by 56 publications

References 27 publications

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

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

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