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Automated weather observing systems have provided gradual replacement of manual observers since 1992. New automated systems implemented the use of new observation sampling frequencies, temporal averaging for reported values, real-time quality assessment of observations and instrumentation that include both advantages and disadvantages over manual observers. These differences between instrumentation and methods used by manual observers and automated systems introduced biases into observations that users of these data must be aware of. These include negative biases for dry-bulb temperature and wind speed observed by the U.S. National Weather Service’s Automated Surface Observation Systems (ASOS). Additionally, the inability of ASOS to detect cloud cover above 12,600 feet is one of the most influential differences when using weather observations for building energy calculations.

A model that uses observations from ASOS to estimate hourly global horizontal, direct normal and diffuse horizontal solar radiation has been developed. Previously developed solar radiation models require input data from human meteorological observers and do not account for biases introduced when using data from observing systems such as ASOS. Nearly twice as many hours with clear conditions are reported by ASOS because of the spatial limitations of ceilometers. Likewise, manual observers report a higher frequency of cloud cover for most sky conditions. Also, greater vertical resolution of cloud detection gives ASOS the ability to distinguish cloud layers that are separated by small heights. Changes such as these resulting from the use of ASOS instead of manual observers made it necessary to calculate new model transmissivities used for solar radiation estimation. In addition to new transmissivities, updated methods are used to account for conditions when multiple cloud layer interactions occur.