The influence of ionospheric thin shell height on TEC retrieval from GPS observation

We investigate the influence of assumed height for the thin shell ionosphere model on the Total Electron Content（TEC） derived from a small scale Global Positioning System（GPS） network. TEC and instrumental bias are determined by applying a grid-based algorithm to the data on several geomagnetically quiet days covering a 10 month period in 2006. Comparisons of TEC and instrumental bias are made among assumed heights from 250 km to 700 km with an interval of 10 km. While the TEC variations with time follow the same trend, TEC tends to increase with the height of the thin shell. The difference in TEC between heights 250 km and 700 km can be as large as～8 TECU in both daytime and nighttime. The times at which the TEC reaches its peak or valley do not vary much with the assumed heights. The instrumental biases, especially bias from the satellite, can vary irregularly with assumed height. Several satellites show a large deviation of～3 ns for heights larger than 550 km. The goodness of fit for different assumed heights is also examined. The data can be generally well-fitted for heights from 350 km to 700 km. A large deviation happens at heights lower than 350 km. Using the grid-based algorithm, there is no consensus on assumed height as related to data fitting. A thin shell height in the range 350-500 km can be a reasonable compromise between data fitting and peak height of the ionosphere.

Detectability of the global weed Hypochaeris radicata is influenced by species, environment and observer characteristics

Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conducted time-limited repeat sur-veys of H.radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east australia.single-species single-season site-occupancy modelling was used to determine if detectability of H.radicata was altered by five covariates,observer,litter height,grazing,maximum plant height and flowering state.Important Findings Detectability for H.radicata varied significantly with observer,litter height,plant maximum height and flowering state,but not with graz-ing.Despite significant observer-specific variation,there was a con-sistent increase in detectability with plant height and when plants are in flower for all observers.Detectability generally decreased as litter height increases.Perfect or constant detection rates cannot be assumed in plant surveys,even for easily recognizable plants in simple survey conditions.understanding how detectability is influ-enced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data,or by determining optimal sur-vey conditions to increase the reliability of collected data.For plants with traits similar to H.radicata,surveying when most plants are at maximum height or in flower,increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors.We speculate that detection rates may be lower,more variable and involve additional covariates when surveying during the peak flow-ering spring season with the presence of more warm season and taller annual species.

IDENTIFICATION ERROR BOUNDS AND ASYMPTOTIC DISTRIBUTIONS FOR SYSTEMS WITH STRUCTURAL UNCERTAINTIES

This work is concerned with identification of systems that are subject to not only measurement noises, but also structural uncertainties such as unmodeled dynamics, sensor nonlinear mismatch, and observation bins. Identification errors are analyzed for their dependence on these structural uncertainties. Asymptotic distributions of scaled sequences of estimation errors are derived.