Evaluation of Hydrometeor Occurrence Profiles in the Multiscale Modeling Framework Climate Model Using Atmospheric Classification

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 large-scale 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 (nonprecipitating hydrometeors) above 7 km and too much precipitating hydrometeor coverage below 7 km. It is also found 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.