Beyond Abstraction: Philosophy as a Practical Qualitative Research Method

In this paper, I take up a discussion of what philosophic method is, and why it should be viewed as an important qualitative research method. After clarifying the nature of philosophic method within the larger framework of social practices, I argue that philosophy is important to both practice and research, and I suggest that philosophers work in concert with other qualitative researchers. I argue that recently (relatively speaking) philosophy has been viewed with some understandable disdain among both practitioners and researchers as an enjoyable but abstract (and therefore useless) social practice. That perception can be fixed but only if philosophical research becomes once again explicitly relevant to practice (particularly educational practice). Finally, I provide a brief example of how philosophy can indeed be relevant to practice from a recent symposium that I participated in on reflection in service-learning education

Designing and Composing for Interdependent Collaborative Performance with Physics-Based Virtual Instruments

Interdependent collaboration is a system of live musical performance in which performers can directly manipulate each other’s musical outcomes. While most collaborative musical systems implement electronic communication channels between players that allow for parameter mappings, remote transmissions of actions and intentions, or exchanges of musical fragments, they interrupt the energy continuum between gesture and sound, breaking our cognitive representation of gesture to sound dynamics. Physics-based virtual instruments allow for acoustically and physically plausible behaviors that are related to (and can be extended beyond) our experience of the physical world. They inherently maintain and respect a representation of the gesture to sound energy continuum. This research explores the design and implementation of custom physics-based virtual instruments for realtime interdependent collaborative performance. It leverages the inherently physically plausible behaviors of physics-based models to create dynamic, nuanced, and expressive interconnections between performers. Design considerations, criteria, and frameworks are distilled from the literature in order to develop three new physics-based virtual instruments and associated compositions intended for dissemination and live performance by the electronic music and instrumental music communities. Conceptual, technical, and artistic details and challenges are described, and reflections and evaluations by the composer-designer and performers are documented

Collab-Hub: A system for creating telematic musical performance environments for Max and web-based instruments

Collab-Hub is a networking tool for sharing data across the internet for multimedia collaboration. Utilizing the Max graphical coding environment and its integrated Node.js functionality, Collab-Hub allows for multiple users to send data easily and remotely so they can collaborate with that data in real-time. In this talk, we discuss the design of the Collab-Hub framework and provide examples of using it to create scalable and reconfigurable interaction layouts between Max patches and web-based instruments/interfaces. An analysis of pertinent historical precedents highlights the advantages Collab-Hub provides to artists who have little to no web development experience or to those who may be new to creating telematic and remote performance environments. A showcase of works created with Collab-Hub demonstrates the wide variety of artistic endeavors made possible through the framework

Collab-Hub: A system for creating collaborative telematic music performances with web-based instruments and creative development platforms

Collab-Hub is a networking tool for sharing data across the internet for multimedia collaboration. Through CollabHub, web audio applications, custom hardware controllers/instruments, and digital instruments created for embedded and desktop computers can all be designed around a single framework, providing a unified approach to creating multi-modal networked experiences. In this talk, we discuss the design of the Collab-Hub system and provide examples of using it to create interaction layouts between web-based instruments/interfaces and digital musical instruments designed in Max and Pure Data. We also showcase custom APIs for developing audiovisual software clients in Unity and hardware clients through Arduino. An analysis of pertinent historical precedents highlights the advantages Collab-Hub provides to artists who may not have experience developing collaborative projects between browsers and desktop software or may be entirely new to designing telematic and remote performance environments. A showcase of works created with Collab-Hub (featuring networked interactivity across the internet, digital instrument design through the aforementioned creative development platforms and the WebAudio API, and multimodal art technologies) demonstrates the wide variety of artistic endeavors made possible through the framework

Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins

Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS)

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting sy

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)