International GPS (Global Positioning System) Service for Geodynamics

The International GPS (Global Positioning System) Service for Geodynamics (IGS) began formal operation on January 1, 1994. This first annual report is divided into sections, which mirror different aspects of the service. Section (1) contains general information, including the history of the IGS, its organization, and the global network of GPS tracking sites; (2) contains information on the Central Bureau Information System; (3) describes the International Earth Rotation Service (IERS); (4) details collecting and distributing IGS data in Data Center reports; (6) describes how the IGS Analysis Centers generate their products; (7) contains miscellaneous contributions from other organizations that share common interests with the IGS

IGS 1996 Analysis Center Workshop

Components of the IGS[International GPS (Global Positioning System) Service for geodynamics], have operated a GPS tracking system for several years. The network now contains more than 100 stations and has produced a combined GPS ephemeris that has become the standard for geodesists and geophysicists worldwide. IGS data and products are freely available to all thanks to the cooperation and participation of all the IGS members. The IGS has initiated development of several new products, and technical issues permitting greater accuracy of IGS products have been identified. The IGS convened a workshop on March 1996 in Silver Spring, Maryland, USA, to coordinate these developments and to examine technical problems and solutions. The following topics were addressed: orbit/clock combination; Earth orientation; antenna calibration; SINEX and densification of the International Terrestrial Reference Frame (ITRF) using the GPS; receiver standards and performance; and atmospheric topics

International GPS Service for Geodynamics

This 1995 annual report of the IGS International GPS (Global Positioning System) Service for Geodynamics - describes the second operational year of the service. It provides the many IGS contributing agencies and the rapidly growing user community with essential information on current organizational and technical matters promoting the IGS standards and products (including organizational framework, data processing strategies, and statistics showing the remarkable expansion of the GPS monitoring network, the improvement of IGS performance, and product quality). It also introduces important practical concepts for network densification by integration of regional stations and the combination of station coordinate solutions. There are groups of articles describing general aspects of the IGS, the Associate Analysis Centers (AACs), Data Centers, and IGS stations

JPL IGS Analysis Center Report, 2001-2003

Three GPS orbit and clock products are currently provided by JPL for consideration by the IGS. Each differs in its latency and quality, with later results being more accurate. Results are typically available in both IGS and GIPSY formats via anonymous ftp. Current performance based on comparisons with the IGS final products is summarized. Orbit performance was determined by computing the 3D RMS difference between each JPL product and the IGS final orbits based on 15 minute estimates from the sp3 files. Clock performance was computed as the RMS difference after subtracting a linear trend based on 15 minute estimates from the sp3 files

Bridging the gap between research, policy, and practice: Lessons learned from academic-public partnerships in the CTSA network.

A primary barrier to translation of clinical research discoveries into care delivery and population health is the lack of sustainable infrastructure bringing researchers, policymakers, practitioners, and communities together to reduce silos in knowledge and action. As National Institutes of Health's (NIH) mechanism to advance translational research, Clinical and Translational Science Award (CTSA) awardees are uniquely positioned to bridge this gap. Delivering on this promise requires sustained collaboration and alignment between research institutions and public health and healthcare programs and services. We describe the collaboration of seven CTSA hubs with city, county, and state healthcare and public health organizations striving to realize this vision together. Partnership representatives convened monthly to identify key components, common and unique themes, and barriers in academic-public collaborations. All partnerships aligned the activities of the CTSA programs with the needs of the city/county/state partners, by sharing resources, responding to real-time policy questions and training needs, promoting best practices, and advancing community-engaged research, and dissemination and implementation science to narrow the knowledge-to-practice gap. Barriers included competing priorities, differing timelines, bureaucratic hurdles, and unstable funding. Academic-public health/health system partnerships represent a unique and underutilized model with potential to enhance community and population health

Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix