Dunes on Titan observed by Cassini Radar

Thousands of longitudinal dunes have recently been discovered by the Titan Radar Mapper on the surface of Titan. These are found mainly within ±30° of the equator in optically-, near-infrared-, and radar-dark regions, indicating a strong proportion of organics, and cover well over 5% of Titan's surface. Their longitudinal duneform, interactions with topography, and correlation with other aeolian forms indicate a single, dominant wind direction aligned with the dune axis plus lesser, off-axis or seasonally alternating winds. Global compilations of dune orientations reveal the mean wind direction is dominantly eastwards, with regional and local variations where winds are diverted around topographically high features, such as mountain blocks or broad landforms. Global winds may carry sediments from high latitude regions to equatorial regions, where relatively drier conditions prevail, and the particles are reworked into dunes, perhaps on timescales of thousands to tens of thousands of years. On Titan, adequate sediment supply, sufficient wind, and the absence of sediment carriage and trapping by fluids are the dominant factors in the presence of dunes.     

Noble gases in eleven H-chondrites

He, Ne, Ar and Xe were measured in aliquots of 11 H-chondrites, to complement trace element studies on the same meteorites (Laulet al., 1972). Bielokrynitschie, Charsonville, Pultusk and Supuhee have lost radiogenic gases before cosmic-ray exposure and Doroninsk, during exposure.     

Simulating the moderating effect of a lake on downwind temperatures

A steady-state, two-dimensional numerical model is used to simulate air temperatures and humidity downwind of a lake at night. Thermal effects of the lake were modelled for the case of moderate and low surface winds under the cold-air advective conditions that occur following the passage of a cold front. Surface temperatures were found to be in good agreement with observations. A comparison of model results with thermal imagery indicated the model successfully predicts the downwind distance for which thermal effects due to the lake are significant.     

The measurement and extraction of the <nu>6 atmospheric emission band of CFC-12 from interfering emission features

Ground-based thermal emission measurements of the zenith sky have been made at Peterborough, Ontario since January 1993. In this paper, the measurement of the ₆ band of atmospheric CFC-12, an important greenhouse gas, is presented for a cold, clear day in January 1994. A spectrum of the non-CFC-12 emission features has been simulated using the FASCD3P radiation code and measured radiosonde profiles of temperature, pressure and humidity. This has enabled a satisfactory subtraction of the interfering emission features from the CFC-12 emission spectrum. A comparison of the observed and simulated ₆-bands of CFC-12 shows good agreement at all frequencies of emission. From these spectra the total downward greenhouse radiative flux from the CFC-12 ₆ emission based for a very cold day has been estimated to be 0.27 W m⁻²±10%.     

Separation of global time-variable gravity signals into maximally independent components

The Gravity Recovery and Climate Experiment (GRACE) products provide valuable information about total water storage variations over the whole globe. Since GRACE detects mass variations integrated over vertical columns, it is desirable to separate its total water storage anomalies into their original sources. Among the statistical approaches, the principal component analysis (PCA) method and its extensions have been frequently proposed to decompose the GRACE products into space and time components. However, these methods only search for decorrelated components that on the one hand are not always interpretable and on the other hand often contain a superposition of independent source signals. In contrast, independent component analysis (ICA) represents a technique that separates components based on assumed statistical independence using higher-order statistical information. If one assumes that independent physical processes generate statistically independent signal components added up in the GRACE observations, separating them by ICA is a reliable strategy to identify these processes. In this paper, the performance of the conventional PCA, its rotated extension and ICA are investigated when applied to the GRACE-derived total water storage variations. These analyses have been tested on both a synthetic example and on the real GRACE level-2 monthly solutions derived from GeoForschungsZentrum Potsdam (GFZ RL04) and Bonn University (ITG2010). Within the synthetic example, we can show how imposing statistical independence in the framework of ICA improves the extraction of the ‘original’ signals from a GRACE-type super-position. We are therefore confident that also for the real case the ICA algorithm, without making prior assumptions about the long-term behaviour or on the frequencies contained in the signal, improves over the performance of PCA and its rotated extension in the separation of periodical and long-term components.     

Evaluating the impact of data quantity, distribution and algorithm selection on the accuracy of 3D subsurface models using synthetic grid models of varying complexity

Testing the accuracy of 3D modelling algorithms used for geological applications is extremely difficult as model results cannot be easily validated. This paper presents a new approach to evaluate the effectiveness of common interpolation algorithms used in 3D subsurface modelling, utilizing four synthetic grids to represent subsurface environments of varying geological complexity. The four grids are modelled with Inverse Distance Weighting and Ordinary Kriging, using data extracted from the synthetic grids in different spatial distribution patterns (regular, random, clustered and sparse), and with different numbers of data points (100, 256, 676 and 1,600). Utilizing synthetic grids for this evaluation allows quantitative statistical assessment of the accuracy of both interpolation algorithms in a variety of sampling conditions. Data distribution proved to be an important factor; as in many geological situations, relatively small numbers of randomly distributed data points can generate more accurate 3D models than larger amounts of clustered data. This study provides insight for optimizing the quantity and distribution of data required to accurately and cost-effectively interpolate subsurface units of varying complexity.     

Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.     

The design and the development of a hyperspectral and multispectral airborne mapping system

Flexible and cost-effective tools for rapid image acquisition and natural resource mapping are needed by land managers. This paper describes the hardware and software architecture of a low-cost system that can be deployed on a light aircraft for rapid data acquisition. The Hyperspectral and Multispectral Cameras for Airborne Mapping (HAMCAM) was designed and developed in the Geospatial Laboratory for Environmental Dynamics at the University of Idaho as a student-learning tool, and to enhance the existing curriculum currently offered. The system integrates a hyperspectral sensor with four multispectral cameras, an Inertial Navigation System (INS), a Wide Area Augmentation System (WAAS)-capable Global Positioning System (GPS), a data acquisition computer, and custom software for running the sensors in a variety of different modes. The outputs include very high resolution imagery obtained in four adjustable visible and near-infrared bands from the multispectral imager. The hyperspectral sensor acquires 240 spectral bands along 2.7 nm intervals within the 445–900 nm range. The INS provides aircraft pitch, roll and yaw information for rapid geo-registration of the imagery. This paper will discuss the challenges associated with the development of the system and the integration of components and software for implementation of this system for natural resource management applications. In addition, sample imagery acquired by the sensor will be presented.     

Consideration of time-correlated errors in a Kalman filter applicable to GNSS

An algorithm for considering time-correlated errors in a Kalman filter is presented. The algorithm differs from previous implementations in that it does not suffer from numerical problems; does not contain inherent time latency or require reinterpretation of Kalman filter parameters, and gives full consideration to additive white noise that is often still present but ignored in previous implementations. Simulation results indicate that the application of the new algorithm yields more realistic and therefore useful state and covariance information than the standard implementation. Results from a field test of the algorithm applied to the problem of kinematic differential GPS demonstrate that the algorithm provides slightly pessimistic covariance estimates whereas the standard Kalman filter provides optimistic covariance estimates.