Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site

• Main Authors: Zwink, A. B., Turner, D. D.
• Other Authors: United States. Department of Energy. Office of Science.
• Format: Report
• Language: English
• Published: Pacific Northwest National Laboratory (U.S.) 2012
• Subjects: 99 General And Miscellaneous//Mathematics; Computing; And Information Science; Clouds; Mirrors; Sky; Fouling; Switches; Algorithms; 54 Environmental Sciences; Neural Networks; Precipitation; Interferometers; 47 Other Instrumentation; Barrow; north slope; Alaska
• Online Access: https://doi.org/10.2172/1036531; http://digital.library.unt.edu/ark:/67531/metadc830765/

The fore-optics of the Atmospheric Emitted Radiance Interferometer (AERI) are protected by an automated hatch to prevent precipitation from fouling the instrument's scene mirror (Knuteson et al. 2004). Limit switches connected with the hatch controller provide a signal of the hatch state: open, closed, undetermined (typically associated with the hatch being between fully open or fully closed during the instrument's sky view period), or an error condition. The instrument then records the state of the hatch with the radiance data so that samples taken when the hatch is not open can be removed from any subsequent analysis. However, the hatch controller suffered a multi-year failure for the AERI located at the ARM North Slope of Alaska (NSA) Central Facility in Barrow, Alaska, from July 2006-February 2008. The failure resulted in misreporting the state of the hatch in the 'hatchOpen' field within the AERI data files. With this error there is no simple solution to translate what was reported back to the correct hatch status, thereby making it difficult for an analysis to determine when the AERI was actually viewing the sky. As only the data collected when the hatch is fully open are scientifically useful, an algorithm was developed to determine whether the hatch was open or closed based on spectral radiance data from the AERI. Determining if the hatch is open or closed in a scene with low clouds is non-trivial, as low opaque clouds may look very similar spectrally as the closed hatch. This algorithm used a backpropagation neural network; these types of neural networks have been used with increasing frequency in atmospheric science applications.