Mathematics – Logic
Scientific paper
Jul 1982
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1982jats...39.1545f&link_type=abstract
Journal of Atmospheric Sciences, vol. 39, Issue 7, pp.1545-1554
Mathematics
Logic
2
Scientific paper
Multiple linear regression is used to relate monthly means and year-to-year changes of the monthly mean planetary albedo and infrared flux leaving the atmosphere, as measured by NOAA satellites, to certain meteorological quantities. Physical predictors are selected which are likely to influence cloudiness, such as temperature, relative humidity and wind. Such predictors can be readily obtained from numerical models.Forty-two months of polar orbiter measurements of radiation fluxes and objective analyses from NMC's operational model were related. Continental and oceanic samples were evaluated separately. Checks on the model consisted of independent tests and comparison with estimation of radiation fluxes in which predictors were functions of latitude, longitude and time of year only. Physical predictors are consistently superior, with the single exception of oceanic albedo, where there was little difference.In the case of the infrared flux, 93 and 84% of the variance in the monthly means is explained over land and ocean, respectively. The Planck function computed from a humidity (cloud) sensitive radiating temperature is the dominant predictor, with other humidity predictors also useful. Between 60 and 72% of the variance of the albedo is explained; results over land again are superior. Relative humidity and midtropospheric wind speed variables dominate in this case. Greater success with infrared is probably attributable to a failure to adequately estimate the effect of low-topped clouds, which impact the albedo differentially. Over land, patterns of year-to-year changes of visible and infrared fluxes (surrogates for anomalies) are predicted well and are consistent with observed changes in rainfall and cloudiness. Over the means the skill for year-to-year changes is low, possibly because low-topped clouds are more common, but also because analyses are poorer there.
Cahir John J.
Fromm Michael D.
Panofsky A. A. H.
Penn Lanning M.
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