92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Thursday, 26 January 2012: 4:45 PM
The Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program: An Overview
Room 339 (New Orleans Convention Center )
H.J.S. Fernando, Univ. of Notre Dame, Notre Dame, IN

The prediction of weather in mountainous (complex) terrain remains a formidable challenge in physical meteorology. Flows in complex terrain consist of those due to diurnal thermal forcing (valley and slope flows) and to large-scale synoptic influence, which, when perturbed by terrain and land-cover variability lead to notable high-frequency phenomena such as trapped and propagating waves, flow instabilities, turbulence, wind gusts, flow pulsations, gap and separated flows, secondary circulation, intermittency, wakes and eddy shedding. The bulk of these represent (unresolved) sub-grid scale processes of meso-scale models, and hitherto no sound methodologies exist to parameterize the aggregate effect of such processes. The MATERHORN program was conceived in response to the Multidisciplinary University Research Initiative (MURI) of the Department of Defense (DoD) to address scientific issues that stymie the prediction of mountain weather. It aims to achieve rapid progress in predictability of mountain weather. The participants include the University of Notre Dame, Naval Research Laboratory, Naval Post Graduate School, University of California at Berkeley, University of Utah, University of Virginia, US Army Dugway Proving Ground and the Army Research Laboratory. The international partners include the University College (London, UK), University of Salento (Italy), University of Cambridge (UK) and Tel Aviv University (Israel).

The MATERHORN program includes four major components: (i) A modeling component (MATERHORN-M) that delves into mesoscale predictability aspects such as model errors, sensitivities to initial and boundary conditions, error growth, predictability limits, limitations of parameterizations and data assimilation methods. This research will utilize WRF and COAMPS as modeling platforms; (ii) A comprehensive field experimental component (MATERHORN-X) in the Granite Mountain Atmospheric Test Bed (GMAST) of the US Army Dugway Proving Ground; (iii) A technology development component (MATERHORN-T) that develops new instrumentation, thus enabling the collection of flow, turbulence and moisture data over scales and footprints that are currently unachievable; and (iv) A parameterization component (MATERHORN-P) that helps improve subgrid parameterizations of mesoscale models, based on etiology and dynamics of processes delineated via high resolution simulations, laboratory experiments and field data.

The MATERHORN-M will have four focus areas, each supported by a mesoscale data assimilation framework. These include: (a) Quantifying spatial and temporal scales of error growth internal to a mesoscale model, and relating them to initial-condition uncertainty; (b) Determining whether the errors can be reduced by improving initial conditions or whether the models are already near the limits of predictability imposed by chaos; (c) Proposing and testing strategies that will reduce the important initial-condition errors while bringing the models closer to predictability limits; (d) Quantifying and characterizing the importance of model inadequacy in maintaining prediction errors that are not reduced as much as expected.

The MATERHORN-X will be centered on experiments at GMAST, which is conceivably the most heavily instrumented and comprehensive test bed for studying atmospheric processes within complex-terrain. In addition to the vast array of permanent monitoring devices located at GMAST, several pieces of sophisticated remote and in-situ atmospheric measurement devices will be installed in the vicinity of the Granite Mountain. The first of the two field campaigns in 2012 will take place during the spring, focusing on moist soil conditions and frequent synoptic disturbances. The second field campaign will take place during the fall under drier and more quiescent conditions. A third experiment is planned for winter 2015 to investigate fog dynamics. These field programs aim to characterize surface conditions related to mountain winds, investigate the planetary boundary layer in complex-terrain, study surface exchange processes, and to observe the interaction of flows across a wide range of space-time scales. The data collected will be used for model evaluation and data assimilation. In addition, supporting laboratory studies are planned to elicit fundamental physical processes active in complex terrain, which are difficult to untangle from the mix of physical processes present in nature.

MATERHORN-T will develop cutting edge technologies to help probe aspects of mountain flows that are currently untenable due to technology limitations. These include the development of an instrumented unmanned aerial vehicle (UAV), remote sensors, samplers for moisture measurements and a novel turbulence measurement system for variable atmosphere.

MATERHORN-P will employ theoretical analysis, high-resolution simulations with novel modeling and terrain-representation methodologies, laboratory studies and analysis of field observations to develop conceptual models and sub-grid parameterizations with improved physics that help quantification of complex terrain flows. The new parameterizations will be implemented in mesoscale models, and their efficacy will be evaluated using new and archival data taken under diverse meteorological conditions. The WRF and COAMPS modeling platforms will be used.

On the pragmatic front, the proposed research will address the DoD weather support needs for warfighting in complex terrain, where winds, moisture, turbulence and their variability are critical for aerial combat and for mapping dispersion of toxic agents and obscurant pathways. In another vein, most of the cities in the world are located in mountainous terrain, wherein air quality is sensitively related to local weather conditions. As such, the accuracy of weather prediction is indispensible for reliable air quality prediction and planning.

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