Andrew K. Heidinger

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Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many OPEN ACCESS 6718(More)
Observations and models show that northern tropical Atlantic surface temperatures are sensitive to regional changes in stratospheric volcanic and tropospheric mineral aerosols. However, it is unknown whether the temporal variability of these aerosols is a key factor in the evolution of ocean temperature anomalies. We used a simple physical model,(More)
The Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) determines, on a pixel-by-pixel basis, whether or not a given location contains cloud. The VCM serves as an intermediate product (IP) between the production of VIIRS sensor data records and 22 downstream Environmental Data Records that each depends upon the VCM output. As such, the(More)
Thirty-one years of imager data from polar orbiting satellites are composited to produce a satellite climate data set of cloud amount for the Great Lakes region. A trend analysis indicates a slight decreasing trend in cloud cover over the region during this time period. The trend is significant and largest (~2% per decade) over the water bodies. A strong(More)
An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based on a naïve Bayesian approach. The goal of this paper is to generate analysis(More)
i Acknowledgements Thanks to Andi Walther and Andrew Heidinger at NESDIS for providing assistance with the application of the DCOMP algorithm.Yuan Cheng during the design and execution of the methodology. Thank you to Justin Sieglaff for advising this project and providing indispensable guidance, expertise and encouragement during the research process.(More)
Meteorologists and other scientists rely heavily on remotely sensed data collected from instruments aboard orbiting satellites. The design of such instruments requires technical and economic trade-offs that results in certain desirable data not being directly available. One way to mitigate the lack of availability of this data is to use machine learning(More)
Continuous satellite-derived cloud records now extend over three decades, and are increasingly used for climate applications. Certain applications, such as trend detection, require a clear understanding of uncertainty as it relates to establishing statistical significance. The use of reanalysis products as sources of ancillary data could be construed as one(More)