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In this study, the effect of the large height difference on commercially available GNSS baseline solutions is tested. In addition, comparisons to academic software results are provided. Previously the effect of the large height difference was tested on GIPSY relative-PPP results. A sub-network of the IGS consisting of 26 baselines was chosen and the height difference between baseline points was gradually incremented up to about 1650 m. Varying observation sessions of 1-24 hours were generated and processed using the continuous data of the IGS stations. The baseline length between ground stations was around 10 km. Atmosphere related errors over this scale cancel out with relative positioning when there is no height difference between baseline points. Within the frame of this study, we almost adopted the similar experiment design. Differently, we processed the GNSS data using the commercially available Topcon Magnet, v. 4.0.1 software. The results reveal that in terms of horizontal positioning, both the research and commercial software produce more or less similar results. In terms of vertical positioning, as expected, the results of commercially available software are far poorer than those of the research software. Obviously not estimating the troposphere, i.e. using only a standard troposphere model, adversely affected the results of vertical positioning. The biggest impact was on the finding that the accuracy of vertical positioning ranged between 20-90 millimetre over similar scales even if the observation session was extended up to 24 h. This would probably break the routine of monitoring high structures over local scales with only a couple of hours of observation sessions and commercial software with a high expectation of precision results. [ABSTRACT FROM AUTHOR]

Tools from complex network theory can effectively extract spatio-temporal variability patterns from climate data. Such climate networks are often set up utilizing non-linear synchronicity metrics, i.e. event synchronization. In previous studies, event synchronization based precipitation networks did not only reveal the driving mechanisms of extreme precipitation events, but also contributed to predicting these (Boers et al., 2014). Accordingly, we aim here to investigate the annual migration of the Baiu front, as a part of the Asian Monsoon System, between May and July using precipitation networks based on event synchronization and the recently introduced event coincidence analysis.The Baiu front forms around 30°N in May as the Okinawa Baiu, a latitudinally extending rain band, and gradually moves northward during June and July until its withdrawal due to an abrupt northward shift of the subtropical jet and a northward movement of the North Pacific subtropical high. We employ the three-hourly Tropical Rainfall Measuring Mission (TRMM) precipitation estimates (1998-2015) with a spatial resolution of 0.25° to study the migration of the Baiu system. We observe substantial quasi-stationary precipitation during the Baiu season, which are caused by mesoscale convective systems providing up to 50% of the annual precipitation in East Asia. We construct temporally distinct (sliding window approach) network representations of extreme precipitation based on the spatial synchronicity to access the temporal evolution of the Baiu front. Furthermore, we explore different topological and spatial network characteristics to identify key regions and driving factors of the propagation mechanism of the front. By conducting this study, we contribute to a better understanding and prediction of extreme precipitation events during the Baiu season. [ABSTRACT FROM AUTHOR]

There are many error sources affecting the GPS precision. In this study, we focus on howGPS measurements (i.e. baseline components n, e, up that form GPS time series) are affectedby the disturbances in the ionosphere. The solar activity is one such error source. Toquantify the effect of solar activity, sunspot numbers and the solar cycle are taken intoaccount. For this aim, we generated GPS campaign measurements one day per each monthbetween 2002 and 2018. Thus the period of solar cycle extending about 11 years was takeninto account. We designed an experiment in which 6 GPS stations from different latituderegions of the International GNSS Service were used, and the data was sampled to be 8-14hours GPS campaigns. PPP module of the NASA/JPL’s GIPSY/OASIS II software was usedfor the processing. We examined the correlation between the number of sunspots and thestandard error of the coordinate components constituting the campaign GPS time series. Theeffect of the solar activity was investigated for 3 different periods (solar minimum,solar moderate and solar maximum) with respect to the intensity of the number ofsunspots. Along the solar cycle, positive and negative correlations between sun spotnumbers and standard errors were found at some of the stations. This deterioratesthe positioning accuracy level which was previously found using the traditionalways. Keywords: Solar Activity, Accuracy, GNSS, PPP [ABSTRACT FROM AUTHOR]

GPS techniques have been widely used to determine geophysical phenomena which isestimated from time series using linear regression. Mis- or non-modeled error sources (i.e.atmospheric propagation etc.) and monument types lead to noise in the time series. The noisein the series is described as white plus flicker noise combinations. Estimation of unbiasedtrend and its realistic uncertainties are the primary duty for a reliable conclusions. Should thenoise in the series from GPS campaign measurements be introduced, while it is possible toanalyze for a continuous stations’ data? To find an answer to the question, we simulated time series using Hector v1.6. The lengthof the series is 10 years, its standard deviations of driving noise are between the range of 1.3and 4.0 mm with the increments of 0.1 mm; moreover, white and flicker noise fractions havebeen selected as 0.50. Firstly, the noise amplitudes accepted as the truth were calculated fromsimulated series including no gaps via variance component estimation. 3, 7, and 10 dayscampaign set-ups were created. For instance, first 3 consecutive days (January 1st through3rd) from each year in the series were selected and analyzed. Then, the second group(January 4th through the 6th) were analyzed by shifting the previous 3 consecutive days, andso on. This set-up were applied for 7, and 10 days campaigns. After that noiseamplitudes were estimated from 121, 52, and 36 time series from set-up campaigns for 3,7, and 10 days campaigns, respectively. It is hard to estimate noise amplitude forthe series from campaigns due to the small number of measurements. When wecompare the results of GPS campaigns with those of the truth, we conclude thatthe correlations between campaigns need to be taken into account even thoughthere is a bias between the true values and the amplitudes of GPS campaign timeseries. Keywords: Time series, noise analysis, GPS campaign [ABSTRACT FROM AUTHOR]

Installing GNSS permanent stations requires high costs. Besides, the installation is not possible in all types of deformation monitoring. On the other hand, the continuous data is the standard for reliable accuracy and velocity estimation. How can one achieve comparable accuracies with less cost and similar accuracy in studies such as deformation monitoring? One way to reduce the cost is to perform data collection in episodic campaign measurements. In fact GNSS campaigns are a necessity for studies such as landslide monitoring in which the deformation would harm permanent installations. The repetition interval (i.e. the frequency) and observation duration of campaign observations need to be studied for reliable velocity estimation. Previously, GNSS continuous time series were decimated into monthly sampled campaigns (i.e. one campaign per month), and velocity estimation was performed from those campaigns. The data archives of the International GNSS service (IGS) and the time series of the Jet Propulsion Laboratory (JPL) were used for the purpose. Although the spectral character (i.e. amplitude and phase of common periodicities) slightly changed, the velocity estimated from those campaigns produced comparable accuracies to those of the GNSS continuous data. In this study, we reduced the data of the time series forming the GNSS campaigns 3 campaigns per year. This would be a trade-off between the traditional 1 campaign per year procedure in which horizontal velocities are estimated only with about 80% confidence level and the recent procedure 12 campaigns per year producing comparable velocity estimation to the continuous GNSS. Decimating GNSS campaigns as sparse as 4 months more or less eliminates the time correlation between the successive measurements. On the other hand the procedure produces almost equivalent results to the continuous data. [ABSTRACT FROM AUTHOR]

In the complex tectonic frame of the Eastern Mediterranean, the Anatolian Block displays a westward extrusion with relative to Eurasia. This motion is mainly accommodated along its northern and eastern boundaries, the North and East Anatolian Faults. Although there is the increasing number of studies for the Northern member, relatively there is the limited number of studies for the East Anatolian Fault, especially in terms of its spatio-temporal behaviour within and between the earthquake cycles.In our previous studies, we have documented aseismic creep for the 100 km-long Palu Segment in the Elazıg-Bingöl seismic gap by using InSAR, GNSS, and creepmeters where the bedrock is mainly composed of accretionary complexes of the Tethyan oceans. The surface creep rate varies along the fault locally attaining the far field plate velocity (~ 10 mm/yr), which simply suggests that significant portion of the elastic strain is being released aseismically and the fault may be creeping from the surface to the entire seismogenic crust. We started multi-disciplinary studies in order not only to document the spatial distribution of this creep motion but also to have a better understanding of its temporal behaviour. Thus, we aim to study important parameters such as the mechanical properties of fault rocks, the paleoseismic behaviour of the Hazar-Palu and the adjacent segments in addition to ongoing continuous deformation monitoring. We will also implement deformation analysis by using the optical correlation of historical aerial photography for the same section of the fault. Our results will not only contribute to increasing our knowledge on the creeping process but also will provide information on the earthquake behaviour of the EAF, which has been poorly constrained. Keywords: Hazar-Palu, Creep, East Anatolian Fault, Earthquake, InSAR, GNSS [ABSTRACT FROM AUTHOR]