PNNL

Abstract

Vertical profiles of hydrometeor occurrence from the Multiscale Modeling Framework (MMF) climate model are compared with profiles observed by a vertically pointing millimeter wavelength cloud-radar (located in the U.S. Southern Great Plains) as a function of the largescale atmospheric state. The atmospheric state is determined by classifying (or clustering) the large-scale (synoptic) fields produced by the MMF and a numerical weather prediction model using a neural network approach. The comparison shows that for cold frontal and post-cold frontal conditions the MMF produces profiles of hydrometeor occurrence that compare favorably with radar observations, while for warm frontal conditions the model tends to produce hydrometeor fractions that are too large with too much cloud (non-precipitating hydrometeors) above 7 km and too much precipitating hydrometeor coverage below 7 km. We also find that the MMF has difficulty capturing the formation of low clouds and that for all atmospheric states that occur during June, July, and August, the MMF produces too much high and thin cloud, especially above 10 km.