000193426 001__ 193426
000193426 005__ 20210531104309.0
000193426 041__ $$aeng
000193426 1001_ $$aKačmařík, Michal
000193426 24510 $$aInter-technique validation of tropospheric slant total delays
000193426 300__ $$a26 stran
000193426 5203_ $$9eng$$aAn 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.
000193426 655_4 $$aanotace
000193426 653_0 $$awater vapor
000193426 653_0 $$aslant delays
000193426 653_0 $$aGNSS
000193426 653_0 $$ameteorology
000193426 653_0 $$aNWM
000193426 653_0 $$aWVR
000193426 7001_ $$aDouša, Jan
000193426 7001_ $$aDick, Galina
000193426 7001_ $$aVáclavovic, Pavel
000193426 7730_ $$92017$$g10(6), str.2183-2208$$tAtmospheric Measurement Techniques$$x1867-1381
000193426 85642 $$uhttps://www.rvvi.cz/riv?s=jednoduche-vyhledavani&ss=detail&h=RIV%2F00025615%3A_____%2F17%3AN0000021%21RIV18-MSM-00025615
000193426 856__ $$uurn:doi:10.5194/amt-10-2183-2017
000193426 85640 $$uhttps://www.atmos-meas-tech.net/10/2183/2017/
000193426 943__ $$aRIV:J
000193426 980__ $$aclanky_vugtk
000193426 985__ $$aanotace
000193426 985__ $$ariv
000193426 985__ $$adousa