Thursday, 13 February 2003: 11:45 AM
Water cycle variability over a small watershed: a one-month comparison of measured and modeled precipitation over the Southern Great Plains
The processes that modulate the water cycle variability in small watersheds operate on horizontal scales that are small relative to the resolution of current operational weather forecast models, which is approximately 30-km. As a consequence, coupled models of the water cycle over these small watersheds must use mesoscale atmospheric models capable of resolving precipitation gradients across the watershed. There have been relatively few systematic attempts to evaluate the precipitation forecasts of mesoscale models on scales of only a few kilometers, or to evaluate the scale dependence of these forecasts. A principal goal of the Department of Energy’s (DOE) Water Cycle Pilot Study (WCPS) is to balance the water budget in a small watershed in the Southern Great Plains using observations of as many water cycle components as possible. Another goal is to evaluate various model components, both atmospheric and hydrologic, that could be joined to form an analysis and forecast system of the water cycle and its variability in this watershed.
As part of the WCPS, we are performing a systematic evaluation of the precipitation fields in two well-documented mesoscale models: MM5 and RAMS. The two models were run at different resolutions (48-km, 12-km, 4-km) for the month of March 2000 over a domain covering DOE’s Atmospheric Radiation Measurement (ARM) Southern Great Plains Cloud and Radiation Testbed (CART) Site. The precipitation produced by the models at different resolutions is being compared to rain gauge-adjusted radar precipitation estimates over the entire domain. High-resolution precipitation forecasts over a small watershed are also being compared with high spatial resolution rain gauge and rain gauge-adjusted data over a small watershed within the larger domain.
Initial results suggest a dry bias in the MM5 model over the one-month test period and analysis of the RAMS data is ongoing. The MM5 model appears to have excellent skill in forecasting precipitation occurrence and location, though some discrepancies are evident in the timing of specific events. We are currently examining the sensitivity of the results to the model resolution.