Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Consistent thermosphere density and wind data from satellite observations: A study of satellite aerodynamics and thermospheric products

The German CHAMP, US/German GRACE, and European Space Agency (ESA) GOCE and Swarm Earth Explorer satellites have provided a data set of accelerometer observations allowing the derivation of thermospheric density and wind products for a period spanning more than 15 years. With the advent of highly accurate satellite accelerometer measurements, the neutral density and wind characterization has been significantly improved. These observations provided detailed information on the thermospheric forcing by Solar Extreme Ultraviolet radiation and charged particles, and revealed for the first time the extent of forcing by processes in lower layers of the atmosphere. Because the focus of most of previous research was on relative changes in density, the scale differences between the CHAMP, GRACE, GOCE and Swarm data sets, so far, have been largely ignored. These scale differences originate from errors in the aerodynamic modelling, specifically in the modelling of the gas-surface interactions (GSI) of the satellite. Once detailed 3D geometry models of these satellites are available, the key parameters to describe the satellite aerodynamics can be estimated by cleverly making use of variations in satellite orientation and simultaneous observations by multiple satellites. The first step for obtaining more consistent density and wind data sets consisted of meticulously modelling the satellite outer surface. For this dissertation work, this was done by collecting information from technical drawings and pre-launch pictures, and generating a CAD model of the selected satellites. In the following phase, these geometries were given as input to a rarefied gas-dynamics simulator. The Direct Simulation Monte Carlo approach was used with the SPARTA software to compute the force coefficients under different conditions of satellite speed, atmospheric temperature and local chemical composition. Once all the mission scenarios had been simulated, an aerodynamic data set was generated and applied in the processing of satellite accelerations into thermospheric density and wind data products. To this aim, the Near Real-Time Density Model (NRTDM) software, developed at TU Delft, was used. The data were generated from accelerometer observations and, when necessary, with the help of GPS-based accelerations estimated by a Precise Orbit Determination (POD) technique. Multiple comparisons were performed with empirical and physics-based models. This helped in determining for which conditions the models are performing better, and also which models’ features would need further development. In the second step, the interaction between atmospheric particles and satellite surfaces was investigated. The way in which atmospheric particles collide with the satellite surfaces have a large influence on the satellite aerodynamic forces and, if proper assumptions are not implemented, can produce large discrepancies in the final thermospheric products. Initially, the GSI assumptions were selected in agreement with the fully diffusive reflection mode. This assumption was adopted to exclusively investigate the geometry modelling influence on thermospheric products. Later, to cover also this research area, multiple simulations described different reflection modes. A wide range of GSI parameters was investigated, and more optimal values were found allowing the derivation of new consistent thermospheric products. Within this study, the energy accommodation coefficient, which describes the energy exchange between particles and satellite surfaces, played a crucial role. Although the value of 0.93 is used commonly in the literature, in this study lower values were identified as optimal. Indeed, a value of 0.82 for the GOCE satellite, and a value of 0.85 for the Swarm and CHAMP satellites have been found to provide more consistent thermospheric data. This resulted in new improved thermospheric density and wind data sets, which have been made available to the scientific community. Among the possible applications, these data can be used for data assimilation for improving current atmospheric models. Resolving the problem of deriving the true absolute thermosphere density scale from satellite dynamics measurements improves orbit predictions for the space debris population and its long-term evolution. Moreover, the new capabilities for computing more consistent drag, density and wind, can also be exploited for future missions that are currently in the design phase.Astrodynamics & Space Mission

Improving the consistency of aerodynamic models and thermospheric density and wind data (PP)

Astrodynamics & Space Mission

CHAMP, GRACE, GOCE and Swarm Thermosphere Density Data with Improved Aerodynamic and Geometry Modelling

Since 2000, accelerometers on board of the CHAMP, GRACE, GOCE and Swarm satellites have provided highresolution thermosphere density data, improving knowledge on atmospheric dynamics and coupling processes in the thermosphere-ionosphere layer. Most of the research has focused on relative changes in density. Scale differences between datasets and models have been largely neglected or removed using ad hoc scale factors. The origin of these variations arises from errors in the aerodynamic modelling, specifically in the modelling of the satellite outer surface geometry and of the gas-surface interactions. Therefore, in order to further improve density datasets and models that rely on these datasets, and in order to make them align with each other in terms of the absolute scale of the density, it is first required to enhance the geometry modelling. Once accurate geometric models of the satellites are available, it will be possible to enhance the characterization of the gassurface interactions, and to enhance the satellite aerodynamic modelling. This presentation offers an accurate approach for determining aerodynamic forces and torques and improved density data for CHAMP, GRACE, GOCE and Swarm. Through detailed high fidelity 3-D CAD models and Direct Simulation Monte Carlo computations, flow shadowing and complex concave geometries can be investigated. This was not possible with previous closed-form solutions, especially because of the low fidelity geometries and the incapability to introduce shadowing effects. This inaccurate geometry and aerodynamic modelling turned out to have relevant influence on derived densities, particularly for satellites with complex elongated shapes and protruding instruments, beams and antennae. Once the geometry and aerodynamic modelling have been enhanced with the proposed approach, the accelerometer data can be reprocessed leading to 81 higher fidelity density estimates. An overview of achieved improvements and dataset comparisons will be provided together with an introduction to the next gas-surface interactions research phase.Astrodynamics & Space Mission

CHAMP, GRACE, GOCE and Swarm density and wind characterization with improved gas-surface interactions modelling (PPT)

PPTAstrodynamics & Space Mission

Next Generation Gravity Mission Elements of the Mass Change and Geoscience International Constellation: From Orbit Selection to Instrument and Mission Design

ESA’s Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA–NASA cooperation in the frame of the Mass Change and Geosciences International Constellation (MAGIC). The mission aims at enabling long-term monitoring of the temporal variations of Earth’s gravity field at relatively high temporal (down to 3 days) and increased spatial resolutions (up to 100 km) at longer time intervals. This implies also that time series of GRACE and GRACE-FO can be extended towards a climate series. Such variations carry information about mass change induced by the water cycle and the related mass exchange among atmosphere, oceans, cryosphere, land and solid Earth and will complete our picture of global and climate change. The main observable is the variation of the distance between two satellites measured by a ranging instrument. This is complemented by accelerometers that measure the nongravitational accelerations, which need to be reduced from ranging measurements to obtain the gravity signal. The preferred satellite constellation comprises one satellite pair in a near-polar and another in an inclined circular orbit. The paper focuses on the orbit selection methods for optimizing the spatial sampling for multiple temporal resolutions and then on the methodology for deriving the engineering requirements for the space segment, together with a discussion on the main mission parameters.Astrodynamics & Space Mission

Characterization of Thermospheric Vertical Wind Activity at 225- to 295-km Altitude Using GOCE Data and Validation Against Explorer Missions

Recently, the horizontal and vertical cross wind at 225- to 295-km altitude were derived from linear acceleration measurements of the Gravity field and steady-state Ocean Circulation Explorer satellite. The vertical component of these wind data is compared to wind data derived from the mass spectrometers of the Atmosphere Explorer C and E and Dynamics Explorer 2 satellites. From a statistical analysis of the 120-s moving-window standard deviation of the vertical wind (σ(Vz)), no consistent discrepancy is found between the accelerometer-derived and the mass spectrometer-derived data. The validated Gravity field and steady-state Ocean Circulation Explorer data are then used to investigate the influence of several parameters and indices on the vertical wind activity. To this end, the probability distribution of σ(Vz) is plotted after distributing the data over bins of the parameter under investigation. The vertical wind is found to respond strongly to geomagnetic activity at high latitudes, although the response settles around a maximum standard deviation of 50 m/s at an Auroral Electrojet index of 800. The dependence on magnetic local time changes with magnetic latitude, peaking around 4:30 over the polar cap and around 01:30 and 13:30 in the auroral oval. Seasonal effects only become visible at low to middle latitudes, revealing a peak wind variability in both local summer and winter. The vertical wind is not affected by the solar activity level.Astrodynamics & Space MissionsControl & Simulatio

Horizontal and vertical thermospheric cross-wind from GOCE linear and angular accelerations

Thermospheric wind measurements obtained from linear non-gravitational accelerations of the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite show discrepancies when compared to ground-based measurements. In this paper the cross-wind is derived from both the linear and the angular accelerations using a newly developed iterative algorithm. The two resulting data sets are compared to test the validity of wind derived from angular accelerations and quantify the uncertainty in accelerometer-derived wind data. In general the difference is found to be less than 50 m/s vertically after high-pass filtering, and 100 m/s horizontally. A sensitivity analysis reveals that continuous thrusting is a major source of uncertainty in the torque-derived wind, as are the magnetic properties of the satellite. The energy accommodation coefficient is identified as a particularly promising parameter for improving the consistency of thermospheric cross-wind data sets in the future. The algorithm may be applied to obtain density and cross-wind from other satellite missions that lack accelerometer data, provided the attitude and orbit are known with sufficient accuracy.Astrodynamics & Space MissionsControl & Simulatio

Update on thermospheric density products from satellite observations

Astrodynamics & Space Mission

Thermosphere densities derived from Swarm GPS observations

After the detection of many anomalies in the Swarm accelerometer data, an alternative method has been developed to determine thermospheric densities for the three-satellite mission. Using a precise orbit determination approach, non-gravitational and aerodynamic-only accelerations are estimated from the high-quality Swarm GPS data. The GPS-derived non-gravitational accelerations serve as a baseline for the correction of the Swarm-C along-track accelerometer data. The aerodynamic accelerations are converted directly into thermospheric densities for all Swarm satellites, albeit at a much lower temporal resolution than the accelerometers would have been able to deliver. The resulting density and acceleration data sets are part of the European Space Agency Level 2 Swarm products. To improve the Swarm densities, two modifications have recently been added to our original processing scheme. They consist of a more refined handling of radiation pressure accelerations and the use of a high-fidelity satellite geometry and improved aerodynamic model. These modifications lead to a better agreement between estimated Swarm densities and NRLMSISE-00 model densities. The GPS-derived Swarm densities show variations due to solar and geomagnetic activity, as well as seasonal, latitudinal and diurnal variations. For low solar activity, however, the aerodynamic signal experienced by the Swarm satellites is very small, and therefore it is more difficult to accurately resolve latitudinal density variability using GPS data, especially for the higher-flying Swarm-B satellite. Therefore, mean orbit densities are also included in the Swarm density product.Astrodynamics & Space Mission