Measurement requirements for a Near-Earth Asteroid impact mitigation demonstration mission

A concept for an Impact Mitigation Preparation Mission, called Don Quijote, is to send two spacecrafts to a Near-Earth Asteroid (NEA): an Orbiter and an Impactor. The Impactor collides with the asteroid while the Orbiter measures the resulting change in the asteroid's orbit, by means of a Radio Science Experiment (RSE) carried out before and after the impact. Three parallel Phase A studies on Don Quijote were carried out for the European Space Agency: the research presented here reflects the outcomes of the study by QinetiQ. We discuss the mission objectives with regard to the prioritisation of payload instruments, with emphasis on the interpretation of the impact. The Radio Science Experiment is described and it is examined how solar radiation pressure may increase the uncertainty in measuring the orbit of the target asteroid. It is determined that to measure the change in orbit accurately a thermal IR spectrometer is mandatory, to measure the Yarkovsky effect. The advantages of having a laser altimeter are discussed. The advantages of a dedicated wide-angle impact camera are discussed and the field-of-view is initially sized through a simple model of the impact.     

Ensemble properties of comets in the Sloan Digital Sky Survey

We present the ensemble properties of 31 comets (27 resolved and 4 unresolved) observed by the Sloan Digital Sky Survey (SDSS). This sample of comets represents about 1 comet per 10 million SDSS photometric objects. Five-band (u, g, r, i, z) photometry is used to determine the comets’ colors, sizes, surface brightness profiles, and rates of dust production in terms of the Afρ formalism. We find that the cumulative luminosity function for the Jupiter Family Comets in our sample is well fit by a power law of the form N(<H) ∝ 10^{(0.49±0.05)H} for H < 18, with evidence of a much shallower fit N(<H) ∝ 10^{(0.19±0.03)H} for the faint (14.5 < H < 18) comets. The resolved comets show an extremely narrow distribution of colors (0.57 ± 0.05 in g ? r for example), which are statistically indistinguishable from that of the Jupiter Trojans. Further, there is no evidence of correlation between color and physical, dynamical, or observational parameters for the observed comets.     

Advected fields in maps: II. Dynamo action in the stretch–fold–shear map

The growth of magnetic field is considered in the stretch–fold–shear map in the limit of weak diffusion. Numerical results are given for insulating, perfectly conducting and periodic boundary conditions. The resulting eigenvalue branches and magnetic fields are related to eigenvalue branches for perfect dynamo action, obtained for zero diffusion using a complex variable formulation.

The effect of diffusion on these perfect dynamo modes depends on their structure, growth rate and the diffusive boundary conditions employed. In some cases, the effect of diffusion is a small perturbation, giving a correction going to zero in the limit of weak diffusion, with a scaling exponent given analytically. In other cases weak diffusion can entirely destroy a perfect dynamo branch. Diffusive boundary layers can also generate entirely new branches.

These different cases are elucidated, and within the framework of the asymptotic approximations used (which do not constitute a rigorous proof), it is seen that for all three boundary conditions employed, the stretch–fold–shear map is a fast dynamo.     

Magnetic Helicity in fast dynamos

The behaviour of magnetic helicity in kinematic dynamos at large magnetic Reynolds number is considered. Hughes, et al . [ Phys. Lett. A 223 , 167-172 (1996)] observe that the relative helicity tends to zero in the limit of large magnetic Reynolds number. This paper gives upper bounds on the helicity, by relating the helicity spectrum to the energy spectrum. These bounds are confirmed by numerical simulation and the distribution of helicity over scales is considered. Although it is found that the total helicity becomes small in the limit of high conductivity, there can remain significant, but cancelling, helicity at large and small scales of the field. This is illustrated by considering the evolution of helicity in the stretch-twist-fold dynamo picture.     

Comparison of dew point temperature estimation methods in Southwestern Georgia

Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model.     

TEMPORAL AND SPATIAL ASPECTS OF BLOWING DUST IN THE MOJAVE AND COLORADO DESERTS OF SOUTHERN CALIFORNIA, 1973–1994

Although dust storms rarely occur in southern California's deserts, blowing dust often reduces visibility, and large spatial and temporal variability in the frequency of blowing dust occurs throughout the region. On average only 1.3 dust storms occur in the study area each year. The annual average number of dust events (visibility <11 km) is 18.0, with the Coachella Valley being dustiest region, averaging 37.8 dust events each year. Mean annual frequencies of dust events for 1973–1994 are mapped, showing a core of activity centered over the Imperial/Coachella Valley region, with fewer dust events around the periphery of the study area. Most stations show a coherent temporal pattern of dust frequency during the period 1973–1994, with the mid-1970s experiencing the most dust. Blowing dust generally was absent from all stations during 1979–1983, 1987–1989, and 1992–1994. The mid-1980s were moderately dusty and 1990–1991 saw a return to very dusty conditions, possibly resulting from below-normal precipitation and increased anthropogenic disturbances. Dust events in the Mojave Desert characteristically occur during the winter to spring months (February-May), associated with dry frontal activity, and are largely absent during the dry summer months. The Colorado Desert experiences a similar seasonal distribution of dust events, but has more summer events, usually associated with convective thunderstorms. Frequencies of blowing dust have weak, but statistically significant, correlations with mean annual and antecedent precipitation, suggesting that complex processes control dust emission. [Key words: blowing dust, dust storms, Mojave Desert, Colorado Desert, wind erosion.]     

An application-driven approach to terrain model construction

Terrain is a surface phenomenon that is measured, modelled, and mapped. However, it is continuously variable and must be simulated by points or mathematical equations that are inherently approximations. The error induced by digitally represented terrain can propagate to surface derivatives and geographical information science (GIS) applications where topography is considered. This can lead to uncertainty in model predictions and the use of data that are unfit for the application to which they are intended. This article outlines the problem of uncertainty in terrain representation and demonstrates the consequences for volcanic mudflow modelling. The response of a simple least-cost single flow algorithm to input parameters was investigated in order to assess output variation from the different sources of input variation. Elevation error was modelled with a probability density function (PDF) and propagated through stochastic simulation (Monte Carlo). Such combined uncertainty and sensitivity analyses enabled a qualitative judgement of the relative significance of elevation error on the flow model prediction. Different methods for terrain model construction were considered and show that supplementing global positioning system (GPS) measurements with information from field notes and reconnaissance photographs greatly improved the model performance and reduced the uncertainty. It is concluded that in terms of validity of model results, there is no substitute for constructing an elevation model that is informed by the terrain.     

Geosocial gauge: a system prototype for knowledge discovery from social media

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.