A COMPARISON OF DIAGNOSTIC CLOUD COVER SCHEMES

The performance of several schemes for diagnosing cloud cover from for ecast model output was tested using a global numerical weather predict ion model and the operational USAF RTNEPH (real-time nephanalysis) clo ud analysis. In the present study, schemes were developed from cloud c over statistics stratified by synoptic weather regime. The synoptic re gimes were defined in terms of vertical profiles of temperature, winds , and moisture. The meteorological significance of these regimes was i llustrated by relating them to synoptic features. The simplest scheme (AVG) assigned the average cloud cover to each of the regimes; a varia nt of the cloud curve algorithm (CCA) technique was developed in which separate cloud-RH curves were derived for;each regime by a mapping of the cumulative frequency distribution of RH and cloud cover. Their pe rformance was compared against a number of other diagnostic schemes, i ncluding a multiple linear regression method that used global regressi on equations for cloud cover from a large number of atmospheric and ge ographic predictors; a version of the Slingo scheme; and simple persis tence. Results indicate that the schemes with the lowest rms errors (A VG, and the regression scheme) also had highly unrealistic frequency d istributions, with too few points that were close to either clear or o vercast values. Persistence was found to provide competitive or superi or forecasts out to 24-36 h. The applicability of these results to imp roved models and cloud observations is discussed.