Abstract

The relationship between narrowband and broadband thermal radiances is explored to determine the accuracy of outgoing longwave radiation derived from narrowband data. Infrared window (10.2–12.2 μm) data from the Geostationary Operational Environmental Satellite (GOES) are correlates with longwave (5.0–50.0 μm) data from the Earth Radiation Budget Experiment (ERBE)- A simple quadratic fit between the narrowband and longwave fluxes results in standard errors of 4.4%–5.3% for data that are matched closely in time and space. The use of matched regional flux data with temporal differences up to 59 minutes yields standard errors of 4.1%–5.4%. About 30% of the error may be attributed to limb darkening and spatial and temporal differences in the matched fluxes. The relationship shows a statistically significant dependence on the relative humidity of the atmosphere above the radiating surface. Although this dependency accounts for only about 1% of the standard error, it reduces the monthly mean regional errors by more than 10%. Data taken over land produced a relationship slightly different from data taken over water. The differences appear to be primarily due to daytime heating of the land surface. Cloud amount and cloud-top height also influence the narrowband-broadband relationship. Inclusion of these statistically relevant parameters does not affect the standard errors, but it reduces the monthly mean regional errors by 9%. Better humidity and temperature data and knowledge of cloud microphysics may be required to further improve the relationship. Using the best global fits, it is concluded that the monthly mean outgoing flux may be determined with an rms uncertainty of 1.7% using a single infrared window channel with coincident cloud and humidity data. The atmospheric structure that dictates the infrared-longwave relationship does not vary randomly; it changes with climate regimes. Thus, the errors resulting from using the global fits tend to be biases concentrated in certain geographical areas. This arms biasing dampens the utility of the narrowband data for monitoring the climatic-scale changes in the longwave flux. Regressions performed on a region-by-region basis eliminate most of the monthly mean regional bias errors. Thus, the regional regressions may be useful for short-term studies requiring high temporal sampling. Because of varying atmospheric conditions, regional regressions require continual calibration with broadband instruments, thereby limiting their utility for longer-term climate applications.

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