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Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for theunderstanding of Earth's energy budget and climate system. Clouds, aerosols,water vapor, and ozone (O<sub>3</sub>) are among the most important atmosphericagents impacting the Earth's shortwave (SW) radiation budget. There areseveral sensors in orbit that provide independent information related tothese parameters. Having coincident information from these sensors isimportant for understanding their potential contributions. The A-trainconstellation of satellites provides a unique opportunity to analyze datafrom several of these sensors. In this paper, retrievals of cloud/aerosolparameters and total column ozone (TCO) from the Aura Ozone MonitoringInstrument (OMI) have been collocated with the Aqua Clouds and Earth'sRadiant Energy System (CERES) estimates of total reflected TOA outgoing SWflux (SWF). We use these data to develop a variety of neural networks thatestimate TOA SWF globally over ocean and land using only OMI data and otherancillary information as inputs and CERES TOA SWF as the output for trainingpurposes. OMI-estimated TOA SWF from the trained neural networks reproducesindependent CERES data with high fidelity. The global mean daily TOA SWFcalculated from OMI is consistently within ±1 % of CERES throughoutthe year 2007. Application of our neural network method to other sensors thatprovide similar retrieved parameters, both past and future, can producesimilar estimates TOA SWF. For example, the well-calibrated Total OzoneMapping Spectrometer (TOMS) series could provide estimates of TOA SWF datingback to late 1978

atmospheric measurement techniques

Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation