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Atmos. Meas. Tech., 9, 2813-2826, 2016
https://doi.org/10.5194/amt-9-2813-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
07 Jul 2016
Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation
Pawan Gupta1,2, Joanna Joiner2, Alexander Vasilkov3,2, and Pawan K. Bhartia2 1Universities Space Research Association, Greenbelt, MD, USA
2NASA Goddard Space Flight Center, Greenbelt, MD, USA
3Science Systems and Applications, Inc., Greenbelt, MD, USA
Abstract. Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within ±1 % of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.

Citation: Gupta, P., Joiner, J., Vasilkov, A., and Bhartia, P. K.: Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation, Atmos. Meas. Tech., 9, 2813-2826, https://doi.org/10.5194/amt-9-2813-2016, 2016.
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Short summary
The A-train constellation of satellites provides a unique opportunity to analyze near-simultaneous data from several of these sensors. In this paper, retrievals of cloud/aerosols parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been used to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data as inputs. Application of our method to other ultraviolet sensors may provide unique estimates of TOA SWF.
The A-train constellation of satellites provides a unique opportunity to analyze...
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