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On the scaling and limits of predictability, using a physics-based distributed hydrologic model and assimilated quantitative precipitation estimates from radar
Baxter E. Vieux, University of Oklahoma, Norman, OK; and E. Mazroi and V. Chandrasekar
Development of an engineered system to provide site-specific flood forecasts is the subject of a testbed under development by the Collaborative Adapative Sensing of the Atmosphere (CASA) Engineering Research Center. Flood hazards can be mitigated given timely information to take actions that reduce losses. Intense and prolonged tropical storms combined with urbanization produce significant flood hazards in coastal areas. Quantitative precipitation estimates (QPE) derived from radar are assimilated into a physics-based distributed runoff model for an urban drainage basin that is well characterized in terms of hydraulics and infiltration. Understanding the error structure and dependence on scale will help design the radar system deployed for site-specific forecasting. Simulation experiments using radar data sampled at various resolutions identify the limits to predictability for various basin sizes. Spatial resolution effects on the prediction error structure are expected to be scale-dependent on drainage area. Tests conducted for Brays Bayou in Houston Texas reveal that the resolution of the assimilated QPE affects runoff prediction and is scale dependent. Radar data used in this analysis is derived from both S-band (NEXRAD) and X-band radars. The drainage area that can be accurately forecast with a given resolution poses a limit to the predictability of runoff forecasts. Information derived from this study will be used in the design of the CASA radar system called NetRAD.
The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a National Science Foundation Engineering Research Center created in fall 2003. Led by the University of Massachusetts at Amherst with several partners including the University of Oklahoma, CASA is establishing a revolutionary new paradigm in which systems of distributed, collaborative, and adaptive sensor (DCAS) networks are being developed to overcome fundamental limitations in current approaches particularly the inability to sample the lower parts of the atmosphere.
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Session 1, Probabilistic Hydrometeorological forecasting part II
Monday, 10 January 2005, 4:00 PM-5:30 PM
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