Towards Reliable Velocities of Permanent GNSS Stations

In the modern geodesy the role of the permanent station is growing constantly. The proper treatment of the time series from such station lead to the determination of the reliable velocities. In this paper we focused on some pre-analysis as well as analysis issues, which have to be performed upon the time series of the North, East and Up components and showed the best, in our opinion, methods of determination of periodicities (by means of Singular Spectrum Analysis) and spatio-temporal correlations (Principal Component Analysis), that still exist in the time series despite modelling. Finally, the velocities of the selected European permanent stations with the associated errors determined following power-law assumption in the stochastic part is presented

Spatio-temporal filtering for determination ofcommon mode error in regional GNSS networks

The spatial correlation between different stationsfor individual components in the regional GNSS networksseems to be significant. The mismodelling in satelliteorbits, the Earth orientation parameters (EOP), largescaleatmospheric effects or satellite antenna phase centrecorrections can all cause the regionally correlated errors.This kind of GPS time series errors are referred to ascommon mode errors (CMEs). They are usually estimatedwith the regional spatial filtering, such as the "stacking".In this paper, we show the stacking approach for the setof ASG-EUPOS permanent stations, assuming that spatialdistribution of the CME is uniform over the whole regionof Poland (more than 600 km extent). The ASG-EUPOSis a multifunctional precise positioning system based onthe reference network designed for Poland. We used a 5-year span time series (2008-2012) of daily solutions in theITRF2008 from Bernese 5.0 processed by the Military Universityof Technology EPN Local Analysis Centre (MUTLAC). At the beginning of our analyses concerning spatialdependencies, the correlation coefficients between eachpair of the stations in the GNSS network were calculated.This analysis shows that spatio-temporal behaviour of theGPS-derived time series is not purely random, but there isthe evident uniform spatial response. In order to quantifythe influence of filtering using CME, the norms L1 and L2were determined. The values of these norms were calculatedfor the North, East and Up components twice: beforeperforming the filtration and after stacking. The observedreduction of the L1 and L2 norms was up to 30% dependingon the dimension of the network. However, the questionhow to define an optimal size of CME-analysed subnetworkremains unanswered in this research, due to thefact that our network is not extended enough

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

International audienc

Impact of Meteorological Conditions on the Dynamics of the COVID-19 Pandemic in Poland

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel coronavirus. The role of environmental factors in COVID-19 transmission is unclear. This study aimed to analyze the correlation between meteorological conditions (temperature, relative humidity, sunshine duration, wind speed) and dynamics of the COVID-19 pandemic in Poland. Data on a daily number of laboratory-confirmed COVID-19 cases and the number of COVID-19-related deaths were gatheredfrom the official governmental website. Meteorological observations from 55 synoptic stations in Poland were used. Moreover, reports on the movement of people across different categories of places were collected. A cross-correlation function, principal component analysis and random forest were applied. Maximum temperature, sunshine duration, relative humidity and variability of mean daily temperature affected the dynamics of the COVID-19 pandemic. An increase intemperature and sunshine hours decreased the number of confirmed COVID-19 cases. The occurrence of high humidity caused an increase in the number of COVID-19 cases 14 days later. Decreased sunshine duration and increased air humidity had a negative impact on the number of COVID-19-related deaths. Our study provides information that may be used by policymakers to support the decision-making process in nonpharmaceutical interventions against COVID-19