Our Research

The Planetary Boundary Layer (PBL) is the atmospheric region closest to the ground which directly influences human activity. Improved PBL observation by satellites has the potential to advance wide-ranging scientific problems including weather and air quality forecasting, climate modeling, and pollution emissions and transport.

The objective of this project is to establish an observing system simulation experiment (OSSE) platform that can analyze the performance of future PBL observing missions. Deploying an OSSE helps identify and refine preferred designs within a large architectural trade space.

This project adapts the Tradespace Analysis Tool for Constellations (TAT-C) to ingest environmental data sets such as the GEOS 5 Nature Run (G5NR) and produce observable regions for each mission architecture for further analysis using the Parallel OSSE (ParOSSE) platform developed at the NASA Jet Propulsion Laboratory.

The National Oceanic and Atmospheric Administration (NOAA) operates a constellation of satellites that collect information for short- and long-term weather forecasts. The objective of this project is to develop automated assessment methods and tools to explore future NOAA mission concepts that may rely on distributed or decentralized architectures.

This project works with the Advanced Systems Performance Evaluation for NOAA (ASPEN) tool which uses a Sensor/Constellation Performance (SCP) table to structure front-end inputs for mission evaluation. The SCP table contains observation performance attributes for a range of geophysical variables relevant to NOAA. This project uses the Tradespace Analysis Tool for Constellations (TAT-C) to automatically conduct coverage/sampling analysis (e.g., temporal refresh and data latency) for new mission concepts. Additionally, this project interfaces with the Parallel Observing System Simulation Experiment (ParOSSE) tool developed at NASA’s Jet Propulsion Lab to estimate resolution and accuracy of future space-based observations.

Snow is a seasonal environmental process that deposits a reflective, insulating cover over the Earth’s land mass that supports numerous ecosystems and provides water supply to one in four people globally. While numerous existing or expected satellite sensors are sensitive to snow and provide information on snow properties, none provide global snow water equivalent (SWE) data at the frequency, resolution, and accuracy to address key hydrological science questions.

The National Academies identified SWE or snow depth as a critical observation in the 2017-2027 Decadal Survey and recommend it as a measurement priority for Earth System Explorer (ESE) class missions. The evolution of snow cover throughout the year is highly dependent on geographical regions, latitudes, and elevations. For example, peak SWE and SWE uncertainty in North America shifts from lower latitudes and elevations in January-April to northern latitudes and elevations in May-June. Seasonal snow is an ideal candidate for an optimized observational strategy that leverages existing sensors and focuses future mission concepts on the most critical areas to provide cost-effective and robust information to improve scientific knowledge.

The objective of this project is to develop a Snow Observing System (SOS) to estimate global SWE and snow melt throughout the season, targeting observations with the greatest impact to spatially- and temporally-varying hydrological metrics. The observing strategy behind SOS targets high-resolution observations in critical areas, taking advantage of new commercial small-satellite platforms which are becoming increasingly available to science applications with innovative operational tasking requests.

This project designs and evaluates collaborative mission concepts to observe convective storm systems. Remote sensing of convective storm systems benefits from responsive operations to identify and monitor cloud dynamics and convective processes to better understand the formation and evolution of storm systems and the effects of climate change at regional and global scales.

Future observing systems with inter-agency, international, and commercial partners constitute a system-of-systems because each partner pursues their own objectives on different development timelines. System-of-systems architecting follows more indirect principles such as identifying stable intermediate forms and exerting leverage at the interfaces.

Co-simulation is an information technology for design, evaluation, and maturation of system-of-systems concepts throughout the system lifecycle. It provides distributed control over constituent systems and allows partners to integrate constituent simulators despite differences in modeling frameworks and languages or other information sharing barriers due to legal or proprietary issues. Applied to convective storm observing systems, co-simulation demonstrates and validates new operational concepts such as alerting, scheduling, and dynamic pointing across organizational boundaries.

A co-simulation platform integrates simulation models of mission and partner nodes through a loosely coupled service-oriented architecture. Front-end mission design activities propose new mission concepts by varying the number of satellites, orbital geometry, and instrument characteristics. Co-simulation executes a joint scenario by coordinating member simulator execution across an information infrastructure. Finally, a back-end science evaluation engine maps collected scenario observations to a measure of scientific utility to evaluate effectiveness.

This Faculty Early Career Development Program (CAREER) grant diagnoses and provides understanding of strategic dynamics among a set of interactive and autonomous design actors through the combined use of game theory and simulations to inform architecture and design decisions. Design activities for engineering systems across infrastructure, aerospace, and defense domains more closely resemble a collective decision-making process than centralized authority in traditional systems engineering practice. This topic is of interest because U.S. agencies and firms are actively architecting systems with decentralized decision authority, described variously as Internet 4.0, cyber-physical systems, Internet-of-things, or systems-of-systems across domains, including energy, transportation, manufacturing, and space systems. The design of these engineering systems differs from that of other systems because their large scale, long lifetime, and proximity to social systems evoke complex features such as adaptation, self-organization, and emergence, which take place over strategic timescales. A deeper understanding of how strategic dynamics impact designer interactions across theoretical and empirical perspectives can help to avoid costly overruns and cancellations by identifying and mitigating undesirable dynamics in conceptual design phases. Advances in systems engineering must develop theory, methods, and tools to coordinate and facilitate collective design activities.

This project builds on a line of economic methods applied to engineering design including utility theory, decision theory, social choice, and game theory. Collective systems design is modeled as a bi-level problem, where lower-level decisions correspond to an optimization problem and upper-level strategy decisions correspond to a coordination game. Normative models of agent behavior are based on classical and Bayesian game theory with utility functions incorporating behavioral factors such as risk attitudes. Multi-agent simulation studies evolution of strategies under repeated interaction among agents. Behavioral experiments collect empirical data about human decision-making for validation. The research will contribute new knowledge about how to characterize, study, and modify the strategic dynamics of engineering systems during conceptual design phases. The educational plan develops and delivers simulation activities to model systems problems by combining technical modeling and social interaction. The simulation activities behave as a highly abstracted model system to elicit rich strategic behaviors through face-to-face interaction, engage students with challenges of socio-technical problems, and retain computational tractability to teach analytical methods in educational contexts. Development and broad public dissemination of simulations in the context of Earth science space missions will expose a wide audience to strategic issues of government-commercial interdependency in space systems.

The New Observing Strategies (NOS) initiative within the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) program envisions future Earth science missions with distributed sensors (nodes) interconnected by a communications fabric that enables dynamic and intelligent operations. Some NOS concepts resemble systems-of-systems or collaborative systems where operational authority is distributed among multiple systems, necessitating new methods for systems engineering and design to cope with more decentralized control over constituent systems. 

The New Observing Strategies Testbed (NOS-T) is a computational environment to develop, test, mature, and socialize NOS operating concepts and technology. NOS-T provides infrastructure to integrate and orchestrate user-contributed applications for system-of-systems test cases with true distributed control over constituent elements. The overall concept, illustrated below, interconnects individual user applications and a NOS-T manager application via common information system infrastructure to coordinate the execution of virtual Earth science missions. NOS-T enables principal investigators to conduct test case executions in the same environment, systematically changing variables to assess the overall efficacy of the proposed new observing strategies. Recorded data and outcomes provide evidence to advance technology readiness level and improve upon or innovate existing Earth science measurement techniques. 

The project git repository containing tools, examples, and further documentation can be found here:  https://github.com/code-lab-org/nost-tools

Coming soon!