A hybrid orographic plus statistical model for downscaling daily precipitation in northern California

A hybrid (physical-statistical) scheme is developed to resolve the finescal e distribution of daily precipitation over complex terrain. The scheme gene rates precipitation by combining information from the upper-air conditions and From sparsely distributed station measurements: thus, it proceeds in tw o steps. First, an initial estimate of the precipitation is made using a si mplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea, The model is driv en by the 2.5 degrees x 2.5 degrees gridded National Oceanic and Atmospheri c Administration-National Centers for Environmental Prediction upper-air pr ofiles, and its parameters are tuned using the observed precipitation struc ture of the region, Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight tr ajectory. Second, the precipitation is adjusted using errors between derive d precipitation and observations from nearby sites. The study area covers t he northern half of California, including coastal mountains, central valley , and the Sierra Nevada. The model is run for a 5-km rendition of terrain f or days of January-March over the period of 1988-95. A jackknife analysis d emonstrates the validity of the approach. The spatial and temporal distribu tions of the simulated precipitation field agree well with the observed pre cipitation, Further, a mapping of model performance indices (correlation co efficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfa ctorily in resolving daily precipitation at 5-km resolution.