An improved parameterization for estimating effective atmospheric emissivity for use in calculating daytime downwelling longwave radiation

An improved parameterization is presented for estimating effective atmospheric emissivity for use in calculating downwelling longwave radiation based on temperature, humidity, pressure, and solar radiation observations. The first improvement is the incorporation of an annual sinusoidal variation in effective clear-sky atmospheric emissivity, based on typical climatological variations in near-surface vapor pressure. The second is the continuous estimation of fractional cloudiness by taking the ratio of observed solar radiation to a modeled clear-sky solar radiation. Previous methods employed observer-estimated fractional cloudiness. Data from the Atmospheric Radiation Measurement (ARM) program were used to develop these improvements. The estimation of cloudiness was then used to modify the effective clear-sky atmospheric emissivity in order to calculate 30-min averages of downwelling longwave radiation. Monthly mean bias errors (mbe's) of -9 to +4 W m(-2) and root-mean-square errors (rmse's) of 11-22 W m(-2) were calculated based on ARM data over a 1-yr period. These mbe's were smaller overall than any of the six previous methods tested, while the rmse's were similar to the best previous methods. The improved parameterization was then tested on FIFE data from the summer of 1987. Although the monthly mbe's were larger, the rmse's were smaller. It is also shown that data from upper-air soundings can be used to calculate the effective atmospheric emissivity rather than specifying the aforementioned sinusoidal variation. Using ARM upper-air soundings, this method resulted in larger mbe's, -7 to +11 W m(-2), especially during the summer months, and similar rmse's. The success of the method suggests that it has application at any observing site within reasonable proximity of an upper-air sounding, while removing the empiricism used to specify the annual sinusoidal variation in emissivity.