Revision of hopewellian trading patterns in midwestern North America based on mineralogical sourcing*     

R. E. Hughes  D. M. Moore  T. E. Berres  K. B. Farnsworth 《Geoarchaeology》1998,13(7):709-729

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Single receiver phase ambiguity resolution with GPS data   总被引：26，自引：12，他引：14  

Willy Bertiger  Shailen D. Desai  Bruce Haines  Nate Harvey  Angelyn W. Moore  Susan Owen  Jan P. Weiss 《Journal of Geodesy》2010,84(5):327-337

Global positioning system (GPS) data processing algorithms typically improve positioning solution accuracy by fixing double-differenced phase bias ambiguities to integer values. These “double-difference ambiguity resolution” methods usually invoke linear combinations of GPS carrier phase bias estimates from pairs of transmitters and pairs of receivers, and traditionally require simultaneous measurements from at least two receivers. However, many GPS users point position a single local receiver, based on publicly available solutions for GPS orbits and clocks. These users cannot form double differences. We present an ambiguity resolution algorithm that improves solution accuracy for single receiver point-positioning users. The algorithm processes dual- frequency GPS data from a single receiver together with wide-lane and phase bias estimates from the global network of GPS receivers that were used to generate the orbit and clock solutions for the GPS satellites. We constrain (rather than fix) linear combinations of local phase biases to improve compatibility with global phase bias estimates. For this precise point positioning, no other receiver data are required. When tested, our algorithm significantly improved repeatability of daily estimates of ground receiver positions, most notably in the east component by approximately 30% with respect to the nominal case wherein the carrier biases are estimated as real values. In this “static” test for terrestrial receiver positions, we achieved daily repeatability of 1.9, 2.1 and 6.0 mm in the east, north and vertical (ENV) components, respectively. For kinematic solutions, ENV repeatability is 7.7, 8.4, and 11.7 mm, respectively, representing improvements of 22, 8, and 14% with respect to the nominal. Results from precise orbit determination of the twin GRACE satellites demonstrated that the inter-satellite baseline accuracy improved by a factor of three, from 6 to 2 mm up to a long-term bias. Jason-2/Ocean Surface Topography Mission precise orbit determination tests results implied radial orbit accuracy significantly below the 10 mm level. Stability of time transfer, in low-Earth orbit, improved from 40 to 7 ps. We produced these results by applying this algorithm within the Jet Propulsion Laboratory’s (JPL’s) GIPSY/OASIS software package and using JPL’s orbit and clock products for the GPS constellation. These products now include a record of the wide-lane and phase bias estimates from the underlying global network of GPS stations. This implies that all GIPSY–OASIS positioning users can now benefit from this capability to perform single-receiver ambiguity resolution.  相似文献   

Transport and retention of a conservative tracer in an isolated creek–marsh system     

J.O. Blanton  A.J. Garrett  J.S. Bollinger  D.W. Hayes  L.D. Koffman  J. Amft  T. Moore 《Estuarine, Coastal and Shelf Science》2010

A study of tracer transport and retention in a small tidal creek and marsh system located in the southeastern US was conducted using a three-dimensional hydrodynamic model and data from a dye tracer release. The model simulated tidally driven flow, including inundation and drying out of the marshes and the dispersal of the dye tracer. Flow measurements in the tidal creek showed that the simulations appeared to generally duplicate the tidally driven flow into and out of the tidal creeks and marshes. The dye tracer experiment was conducted to test the hydrodynamic model's ability to simulate dispersal of a point release of pollutants into the creek during an incoming tide. The simulations reproduced much of the experimental measurements, but bathymetric errors and lack of resolution of the smallest arms of the tidal creeks affected the ability to faithfully reproduce the initial peak in measured dye tracer concentrations at a sampling location far from the boundary. The lack of the smallest tidal creeks led to some trapping of water and dye in the marshes. Two independent estimates of flushing time yielded values between 1.6 and 2.4 days. An uncertainty analysis indicates that model simulations are sensitive to variations in parameters such as water depth and marsh grass density. On the other hand, omission of the smallest tidal creeks in simulations may be partially offset by decreasing marsh grass density. Further improvements must rely on more accurate and detailed bathymetric data that resolves the smaller arms of the tidal creeks and includes quantitative information about the density and distribution of marsh grass.  相似文献   

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables     

Andras J. Szeitz  R. Dan Moore 《水文研究》2023,37(1):e14800

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.  相似文献