The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model

We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5{\deg}, covering the time frame 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the Ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the global average over more than 1000 stations, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands. While some differences exist, overall GLWS2.0 agrees quite well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.Comment: Preprin

Research on general theory and methodology in geodesy in Poland in 2019–2022

We present a summary of research carried out in 2019–2022 in Poland in the area of general theory and methodology in geodesy. The study contains a description of original contributions by authors affiliated with Polish scientific institutions. It forms part of the national report presented at the 28th General Assembly of the International Union of Geodesy and Geophysics (IUGG) taking place on 11-20 July 2023 in Berlin, Germany. The Polish authors developed their research in the following thematic areas: robust estimation and its applications, prediction problems, cartographic projections, datum transformation problems and geometric geodesy algorithms, optimization and design of geodetic networks, geodetic time series analysis, relativistic effects in GNSS (Global Navigation Satellite System) and precise orbit determination of GNSS satellites. Much has been done on the subject of estimating the reliability of existing algorithms, but also improving them or studying relativistic effects. These studies are a continuation of work carried out over the years, but also they point to new developments in both surveying and geodesy.We hope that the general theory and methodology will continue to be so enthusiastically developed by Polish authors because although it is not an official pillar of geodesy, it is widely applicable to all three pillars of geodesy

Tracking hurricanes Harvey and Irma using GPS tropospheric products

The 2017 Hurricanes season was one of the most powerful severe weather events producing catastrophic socio-economic and environmental effects on the east coast of the United States. Therefore, tracking their path accurately is extremely useful. Today Global Navigation Satellite Systems (GNSS) tropospheric products, such as Zenith Wet Delays (ZWD), and Integrated Water Vapor (IWV) are used as complementary data sets in Numerical Weather Prediction (NWP) models. In this study, we employed GPS-derived IWV and horizontal tropospheric gradient information to monitor and investigate the complicated characteristics of hurricane events in their spatial and temporal distribution using a dense ground network of GPS stations. Our results show that a surge in GPS-derived IWV occurred several hours prior to the manifestation of the major hurricanes Harvey and Irma. We used the derived GPS-derived IWV information as input to spaghetti lines weather models, allowing us to predict the paths of Harvey and Irma hurricanes. As such, a parameter directly estimated from GPS can provide an additional resource for improving the monitoring of hurricane path

On the noise characteristics of time series recorded with nearby located GPS receivers and superconducting gravity meters

International audienc

Total Impact of Periodic Terms and Coloured Noise on Velocity Estimates

The uncertainties of velocity estimates for position time series of Global Navigation Satellite System (GNSS) stations are mainly affected by a misfit of the deterministic model applied to this data. Insufficiently modelled seasonal signals will propagate into the stochastic model and falsify the results of the noise analysis besides the velocity estimates and their uncertainties. In this presentation we derived the General Dilution of Precision (GDP) of velocity uncertainties. We define this dilution as the ratio between the uncertainties of velocities determined when different deterministic and stochastic models are applied. In this way we discuss, referring to previously published results, how insufficiently modelled seasonal signals influence station velocity uncertainties with white and coloured noise. Using simulated and real data from selected (115) IGS (International GNSS Service) stations we show that the noise character affects GNSS data more than seasonals for time series longer than 9 years

On the combined effect of periodic signals and colored noise on velocity uncertainties

The velocity estimates and their uncertainties derived from position time series of Global Navigation Satellite System stations are affected by seasonal signals and their harmonics, and the statistical properties, i.e., the stochastic noise, contained in the series. If the deterministic model in the form of linear trend and periodic terms is not accurate enough to describe the time series, it will alter the stochastic model, and the resulting effect on the velocity uncertainties can be perceived as a result of a misfit of the deterministic model. The effects of insufficiently modeled seasonal signals will propagate into the stochastic model and falsify the results of the noise analysis, in addition to velocity estimates and their uncertainties. We provide the general dilution of precision (GDP) of velocity uncertainties as the ratio of uncertainties of velocities determined from to two different deterministic models while accounting for stochastic noise at the same time. In this newly defined GDP, the first deterministic model includes a linear trend, while the second one includes a linear trend and seasonal signals. These two are tested with the assumption of white noise only as well as the combinations of power-law and white noise in the data. The more seasonal terms are added to the series, the more biased the velocity uncertainties become. With increasing time span of observations, the assumption of seasonal signals becomes less important, and the power-law character of the residuals starts to play a crucial role in the determined velocity uncertainties. With reference frame and sea level applications in mind, we argue that 7 and 9 years of continuous observations is the threshold for white and flicker noise, respectively, while 17 years are required for random-walk to decrease GDP below 5% and to omit periodic oscillations in the GNSS-derived time series taking only the noise model into consideration

Alendronate 70 therapy in elderly women with post-menopausal osteoporosis: the problem of compliance

Introduction: More than half of those with chronic diseases, including osteoporosis, discontinue treatment during the first year of its administration. This problem increases over the course of continued follow-up. Additionally, it has been observed that 13% of women, prescribed oral daily alendronate, do not even start the treatment, while 20% of patients discontinue the therapy during the first four months. On the other hand, those patients who are compliant achieve increased bone mass density with a simultaneous decrease of fracture risk. The aim of our study was to assess the adherence to the recommended alendronate 70 administration protocol over the course of 12 months by women with post-menopausal osteoporosis. Material and methods: Adherence (compliance plus persistence) to alendronate 70 therapy was assessed in a prospective study of 153 post-menopausal women, followed up for one year with monitoring every two months. Results: Adherence to therapy of all the study participants was high during the entire study period, the patients remaining compliant after a year in 95.08 ± 1.39% (mean ± SEM) of cases, and the mean persistence with medication was 347.05 ± 5.07 days. In the group of patients who interrupted treatment, the mean persistence was 212.44 days. One of the study participants did not start the treatment, and another two discontinued the therapy within 30-60 days of the study onset (between the first two visits). Facilitated contacts with the doctor, continuous access to prescribed treatment and frequent visits significantly improved patient compliance. The common reason for discontinuation was side effects, while age (but not education) affected the rate of compliance with therapy. The worst results were obtained in the group of patients with osteoporosis diagnosed more than five years before the study, particularly in the subgroup where alendronate was being used for the first time or where treatment resumed after a substantial break. Conclusions: The obtained results indicate that better adherence to alendronate 70 therapy, administered once a week, depends on more frequent monitoring of treated patients. (Pol J Endocrinol 2011; 62 (1): 24-29)Wstęp: Wykazano, że ponad 50% pacjentów leczonych z powodu chorób przewlekłych, w tym osteoporozy, przerywa terapię w ciągu pierwszego roku jej stosowania. Problem ten narasta z czasem trwania obserwacji. Stwierdzono, że 13% pacjentów z osteoporozą w ogóle nie rozpoczyna leczenia, a ponad 20% przerywa terapię w ciągu pierwszych 4 miesięcy trwania choroby. Wykazano, że przestrzeganie przez pacjentów wprowadzonej terapii osteoporozy poprawia gęstość mineralną kości i zmniejsza ryzyko złamań. Celem pracy była ocena, w rocznym badaniu prospektywnym, stopnia przestrzegania zaleceń długotrwałej terapii preparatem alendronian 70 przez pacjentki leczone z powodu osteoporozy. Materiał i metody: Ocenie poddano 153 pacjentki w wieku 48-89 lat z rozpoznaną osteoporozą leczone alendronianem 70 mg, jeden raz w tygodniu, przez okres roku. Pacjentki monitorowano co 2 miesiące. Wzięto pod uwagę czas trwania choroby, ciągłość i systematyczność przyjmowania leków oraz przyczynę przerwania stosowanego leczenia. Wyniki: Podczas trwania badania stopień przestrzegania zasad terapii u wszystkich uczestniczek badania był wysoki i łącznie systematyczność po roku utrzymało 95,08 ± 1,39% (średnia ± SEM), a średnia długość przyjmowania leków wyniosła 347,05 ± 5,07 dni. W grupie pacjentek, które przerwały leczenie średni okres stosowania się do zaleceń wyniósł 212,44 dnia. Z całej grupy jedna pacjentka w ogóle nie podjęła leczenia, 2 przerwały w okresie 30-60 dni stosowania, czyli między 2 kolejnymi wizytami. Ułatwienie pacjentowi kontaktu z lekarzem, stały dostęp do leku oraz częste wizyty w znaczny sposób poprawiają stosowanie się pacjentów do zaleceń. Najczęstszą przyczyną przerwania leczenia były działania niepożądane stosowanego preparatu. Wiek, lecz nie wykształcenie, miały wpływ na przestrzeganie terapii. Najgorsze wyniki uzyskano w grupie pacjentek z rozpoznaniem osteoporozy dłuższym niż 5 lat, szczególnie w podgrupie, u której włączono alendronian 70 pierwszy raz lub podjęto leczenie po przerwie. Uzyskane wyniki wskazują na fakt lepszego przestrzegania zaleceń lekarskich przez pacjentów leczonych z powodu osteoporozy w trakcie dobrego monitorowania terapii preparatem cotygodniowym. Krytycznymi momentami decydującymi o przerwaniu leczenia były objawy niepożądane związane z terapią. Wnioski: W przeprowadzonym badaniu wykazano, że częstsze monitorowanie w znaczący sposób poprawia przestrzeganie zasad terapii alendronianem stosowanym w dawkach cotygodniowych. (Endokrynol Pol 2011; 62 (1): 24-29

The Combined Effect of Periodic Signals and Noise on the Dilution of Precision of GNSS Station Velocity Uncertainties

Station velocity uncertainties determined from a series of Global Navigation Satellite System (GNSS) position estimates depend on both the deterministic and stochastic models applied to the time series. While the deterministic model generally includes parameters for a linear and several periodic terms, the stochastic model is a representation of the noise character of the time series in form of a power-law process. For both of these models the optimal model may vary from one time series to another while the models also depend, to some degree, on each other. In the past various power-law processes have been shown to fit the time series and the sources for the apparent temporally-correlated noise were attributed to, for example, mismodelling of satellites orbits, antenna phase centre variations, troposphere, Earth Orientation Parameters, mass loading effects and monument instabilities

Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions

Long series of Zenith Wet Delay (ZWD) obtained as part of a homogeneous re-processing of Global Positioning System solutions constitute a reliable set of data to be assimilated into climate models. The correct stochastic properties, i.e. the noise model of these data, have to be identified to assess the real value of ZWD trend uncertainties since assuming an inappropriate noise model may lead to over- or underestimated error bounds leading to statistically insignificant trends. We present the ZWD time series for 1995–2017 for 120 selected globally distributed stations. The deterministic model in the form of a trend and significant seasonal signals were removed prior to the noise analysis. We examined different stochastic models and compared them to widely assumed white noise (WN). A combination of the autoregressive process of first-order plus WN (AR(1) + WN) was proven to be the preferred stochastic representation of the ZWD time series over the generally assumed white-noise-only approach. We found that for 103 out of 120 considered stations, the AR(1) process contributed to the AR(1) + WN model in more than 50% with noise amplitudes between 9 and 68 mm. As soon as the AR(1) + WN model was employed, 43 trend estimates became statistically insignificant, compared to 5 insignificant trend estimates for a white-noise-only model. We also found that the ZWD trend uncertainty may be underestimated by 5–14 times with median value of 8 using the white-noise-only assumption. Therefore, we recommend that AR(1) + WN model is employed before tropospheric trends are to be determined with the greatest reliability