A Gibbs sampling disaggregation model for orographic precipitation |
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| Institution: | 1. Institut National de la Recherche Scientifique, Centre Eau, Terre et Environnement, 490 de la Couronne, Québec City, Québec, Canada G1K 9A9;1. Division of Marine Environment & Bioscience, Korea Maritime and Ocean University, Busan 606-791, South Korea;2. Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Japan;3. Geological Survey of Japan, AIST, Japan;1. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi, China;2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China;3. Department of Soil Science, University of Saskatchewan, Saskatoon, Canada;4. Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy;1. Department of Toxicology, University of Zaragoza, Veterinary Faculty, Calle Miguel Servet 177, E50013 Zaragoza, Spain;2. Instituto de Ciencia de Materiales de Aragón, Consejo Superior de Investigaciones Científicas-University of Zaragoza, Calle Pedro Cerbuna, s/n. E50009 Zaragoza, Spain;1. School of Plant Sciences, The University of Arizona, Tucson, AZ 85721, United States;2. USDA-ARS, School of Plant Sciences, The University of Arizona, Tucson, AZ 85721, United States |
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| Abstract: | Hydrological applications in complex topographic areas need high spatial resolution precipitation data. Some daily high-resolution products are now available for recent past data, even in complex terrain. While the spatial resolution of Regional Climate Models (RCMs) and operational meteorological models are becoming increasingly fine, there still exists a mismatch between the spatial resolutions of observed or estimated recent past data and simulated or forecasted precipitation.Statistical disaggregation models can generate precipitation on a high-resolution grid using as input a mesoscale precipitation grid (e.g., RCM or meteorological grid). In this paper, a Gibbs sampling disaggregation model previously developed for flat areas is adapted to account for topography. Only one variable, the topographic anomaly, is added to the original model. The model is applied on a 300 km × 300 km area in the northwestern United States, covering the Olympic Mountains and the Cascade Range. Daily high-resolution precipitation data for the 2002–2005 period are used to estimate the model parameters. Using 750 days taken from the 2006–2008 period, 36, 52-km grid boxes are disaggregated on 4.3-, 8.7-, 13-, 17.3- and 26-km grids; each day being simulated nine times. Thank to the Gibbs sampling algorithm, the original model, which does not account for topography, is able to capture the mesoscale topographic structure of the daily precipitation, while the adapted model accounting for topography is better suited to recreate the local impact of topography on interannual means, interday standard deviation, and maximum values. The model outputs could be used by hydrological modelers who need high-resolution precipitation data in complex topographic area application. |
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| Keywords: | Statistical disaggregation Gibbs sampling Orographic precipitation Olympic Mountains Cascade Range |
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