Research in AOSS

• Atmospheric Science
• Space Science
• Space Physics Research Laboratory
• Partners

• Space Science Research

  • Aeronomy
  • High Energy Density Physics / Laboratory Astrophysics
  • Interdisciplinary Research
  • Magnetospheric & Ionosphere / Thermosphere Physics
  • Numerical Methods & Scientific Computing
  • Planetary Atmospheres
  • Planetary Magnetospheres
  • Radiative Transfer, Remote Sensing & Instrumentation
  • Solar & Heliospheric Physics
  • Space Weather
  • Statistical Methods & Data Assimilation

• Related activities, departments & centers

  • Space blogs, educational activities & forecasts
  • Related U-M departments & research Laboratories
  • National Research Centers for Atmospheric & Space Sciences
  • AOSS in Space

Interdisciplinary Research

Projects

Title: Integration of physical and social sciences for development of a sustainable water resource policy in Bolivia, South America

PI: Christopher Poulson (UM-Geological Sciences)

Co-Is: Todd Ehlers (UM-Geological Sciences), Maria Carmen Lemos (UM-SNRE), Allison Steiner (UM-AOSS)

Funding Source: U-M Graham Environmental Sustainability Institute

Abstract: Future climate change and population growth are expected to stress existing water resources in many regions around the world. In Bolivia and other parts of South America, water availability and quality are major problems and the additional stress on this resource is likely to exacerbate existing water-related issues and create new water related crises and confrontations. To mitigate and adapt to future water-related issues that will arise from climate change, policymakers must develop and implement sustainable water resource strategies that address future global change on this resource. There are many challenges to developing a sustainable water resource policy, including: (i) accurately predicting regional scale climate changes, (ii) estimating how these climate changes will affect watershed hydrology, and (iii) using these predictions to create effective water policy given local social, economic, political and cultural conditions.

To overcome these scientific challenges, we have organized a multidisciplinary research team consisting of physical and social scientists, with expertise in climate change, climate modeling, hydrology, and water resources policy making and management. Through this collaboration, we aim to develop a long-term water resource management strategy that can be implemented by local policymakers in Bolivia. This proposal addresses our initial goal to develop a methodology for translating regional climate predictions into water resource predictions that can be effectively utilized in policy making. To this end, we have designed a three-component research plan that integrates regional climate modeling over South America, catchment-scale hydrological modeling, and assessment of policymaking processes and cultures in Bolivia. These activities will lead to the following deliverables: (1) regional-scale predictions of climate and hydrological variables (rainfall, evaporation, surface flow) for future climate change scenarios over tropical South America using a state-of-the-science climate-land surface model, (2) catchment-scale predictions of the impact of climate change on river discharge (water availability) and drainage basin hydrology, and (3) policy guidelines, tailored to Bolivia, for mitigating and/or adapting to changes in water resources due to future climate change.

Title: CMG Research: Adaptive Mesh Refinement for Vortices in Climate and Weather- Forecasting: Comparing and Blending Finite Volume Methods with Vortex/Radial Basis Function Algorithms

PI: John Boyd (UM-AOSS)

Co-PIs: Christiane Jablonowski (UM-AOSS), Robert Krasny (UM-Mathematics)

Funding Source: National Science Foundation

Research Summary: Nested models cover a hurricane or a nation-state with high resolution while the rest of the globe is spanned by a coarser grid. Our finite volume and vortex models do something more elaborate (“microadaptation”): changing resolution so as to overlay much smaller features with fine grid cells. We propose to test the perils and possibilities of such feature-based adaptation on geophysical vortices. This is anything but a trivial task. Turbulent flows are a mixture of coherent vortices with complex, filamentary internal structure, interacting with a sea of spiral, stretching vortex filaments. Waves on all scales propagate through and modify the coherent structures and are in turn reshaped; fronts are a ubiquitious and challenging part of tropospheric dynamics. Does it do any good to have high resolution only over thin filaments of high vorticity gradient? Does high resolution just over the core of a vortex improve predictability and climate?
Simple, idealized tests are a first step, but not enough. Our goal is ultimately to investigate the mathematics of adaptive schemes on tough problems on parallel computers.

Title: Center for Radiative Shock Hydrodynamics (CRASH)

PI: R. Paul Drake (UM-AOSS)

Co-PIs: James Holloway (UM-NERS), Ken Powell (UM-AERO), Quentin Stout (UM-AOSS and EECS)

Funding Source: NNSA Office of Advanced Simulation and Computing

Research Summary: The overarching goal of CRASH is to accurately simulate RH and quantify the accuracy with which we can predict a given measured output from a given experiment. The CRASH research team aims for a quantitative assessment of how closely their simulations reflect reality.
To substantially improve the ability to do predictive simulations of high - energy - density and astrophysical flows, Center researchers are:

• Developing a software framework for RH to serve as a testbed for development, verification and validation of RH modeling elements.
• Developing a system for hierarchically validating the software framework.
• Extending an existing experimental effort, centered on radiative shocks, to obtain data and quantify uncertainties in the experiments.
• Simulating these experiments and quantifying the accuracy of the simulations.
• Establishing a doctoral program for Predictive Science and Engineering.

Title: Center for Space Environment Modeling

PI: Tamas Gombosi (UM-AOSS)

Co-PIs: Ken Powell (UM-AERO), Quentin Stout (UM-AOSS and EECS)

Funding Source: Air Force Office of Scientific Research (AFOSR), NASA, National Science Foundation, US Department of Defense

Research Summary:The Center for Space Environment Modeling (CSEM) is an interdisciplinary research organization of the College of Engineering, the University of Michigan. CSEM is comprised of a tightly integrated group of faculty and students from the Department of Aerospace Engineering, the Department of Atmospheric, Oceanic and Space Sciences, and the Department of Electrical Engineering and Computer Science.
As our nation's technologies deployed in space and on the ground become more sophisticated, they also become more vulnerable to various dynamic phenomena which occur in the near-Earth space environment. As a consequence, a national goal has been set to produce a physics-based model of the space environment, capable of providing accurate predictions of the environment in order to enable the operators of technologies to undertake mitigating practices to protect their assets from 'space weather' storms.
The overall goal of CSEM is to develop high-performance, first-principles based computational models to describe and predict hazardous conditions in the near-earth space environment extending from the sun to the ionosphere, called space weather. In order to achieve predictive capability, the models must run considerably faster than real time on mid-size parallel computers.
In order to achieve its ambitious goal CSEM spans accross discipline and departmental boundaries. Its participants combine expertize in modern numerical algorithms, high-performance computational science, and solar, interplanetary, magnetospheric, and ionospheric physics.
CSEM works in close collaboration with a number of scientists from universities, government laboratories, nonprofit organizations, and industry. These collaborations are coordinated by the Consortium for Space Environment Modeling.