Turning night into day: the creation, validation, and application of synthetic lunar reflectance values from the day-night band and infrared sensors for use with JPSS VIIRS and GOES ABI
Date
2023
Authors
Pasillas, Chandra M., author
Kummerow, Christian, advisor
Bell, Michael, advisor
Miller, Steven D, committee member
Rasmussen, Kristen, committee member
Reising, Steven, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Investigation of the dynamics of tropical cyclone precipitation structure using radar observations and numerical modeling Satellite remote sensing revolutionized weather forecasting and observing in the 1960s providing a true bird's eye view of the weather beyond what could be achieved from balloon and aircraft reconnaissance. With advances in observing systems came the desire for more capabilities and a better understanding of the Earth system, leading to rapid increases in satellite imaging capabilities. The most popular imager products come from solar reflective radiation in the form of visible imagery as they are the most intuitive to users. Similar benefits were later made possible by equivalent nighttime imagery; first available through the operational lines can system (OLS) and then the Day/Night Band (DNB), but these sensors have limited revisit time due to their low Earth orbits. A day-night band sensor in geostationary orbit would greatly enhance the utility of this measurement for now casters, but it does not exist. Work towards a pseudo-nighttime visible imagery to fill this gap has been done with varying results (Chirokova et al., 2018; Kim et al., 2019; Kim and Hong, 2019; Mohandoss et al., 2020; Harder et al., 2020). This dissertation demonstrates the creation and implementation of a machine learning model to turn night into day by transforming satellite radiance observations into representative full moon lunar reflectance values that provide quantifiable metrics and visible-like imagery to its users. In Chapter 2, a method is described that utilizes a feed-forward neural network model to replicate DNB lunar reflectance using brightness temperatures and brightness temperature differences in the short and long-wave infrared (IR) spectrum as the primary input. The goal was to improve upon the performance of the DNB during new moon periods, and lay the foundation for transitioning the algorithm to the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI). Results from this method are the first to quantitatively validate low-light visible nighttime imagery with lunar reflectance calculated from DNB radiances. This work further demonstrated that there is a relationship between full moon lunar reflectance and IR that can be captured to create imagery that is visually consistent across the full lunar cycle regardless of moon phase and angle. In Chapter 3, the machine learning (ML) nighttime visible imagery (NVI) model is applied to the GOES ABI utilizing wavelength relationships and satellite inter-calibrations information. This demonstrates that a model trained and validated on VIIRS polar orbiting imagery can work on sensors aboard geostationary satellites. It also confirms why the 10.3μm channel is the preferred substitution for the 10.7μm centered band over the 11.2μm channel. Furthermore, it demonstrates that lunar reflectance derived from IR can be replicated across sensors with similar spectral response functions providing enhanced geographic and temporal resolution that is not possible on the JPSS platforms. The final section of the dissertation transitions into forecaster applications by examining case studies concerning tropical cyclones and fog in greater detail. Focused on low cloud detection, NVI provides additional information not possible from IR and current analysis products available. It can detect tropical cyclone low-level circulations through cirrus cloud and identify fog extent more readily. The findings in this doctoral study will advance remote sensing of clouds at night, further reducing weather now-casting errors and increasing weather-related safety. In Chapter 2, a method is described that utilizes a feed-forward neural network model to replicate DNB lunar reflectance using brightness temperatures and brightness temperature differences in the short and long-wave infrared (IR) spectrum as the primary input. The goal was to improve upon the performance of the DNB during new moon periods, and lay the foundation for transitioning the algorithm to the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI). Results from this method are the first to quantitatively validate low-light visible nighttime imagery with lunar reflectance calculated from DNB radiances. This work further demonstrated that there is a relationship between full moon lunar reflectance and IR that can be captured to create imagery that is visually consistent across the full lunar cycle regardless of moon phase and angle. In Chapter 3, the machine learning (ML) nighttime visible imagery (NVI) model is applied to the GOES ABI utilizing wavelength relationships and satellite inter-calibrations information. This demonstrates that a model trained and validated on VIIRS polar orbiting imagery can work on sensors aboard geostationary satellites. It also confirms why the 10.3 µm channel is the preferred substitution for the 10.7 µm centered band over the 11.2 µm channel. Furthermore, it demonstrates that lunar reflectance derived from IR can be replicated across sensors with similar spectral response functions providing enhanced geographic and temporal resolution that is not possible on the JPSS platforms. The final section of the dissertation transitions into forecaster applications by examining case studies concerning tropical cyclones and fog in greater detail. Focused on low cloud detection, NVI provides additional information not possible from IR and current analysis products available. It can detect tropical cyclone low-level circulations through cirrus cloud and identify fog extent more readily. The findings in this doctoral study will advance remote sensing of clouds at night, further reducing weather now-casting errors and increasing weather-related safety.
Description
Rights Access
Subject
day night band
machine learning
cloud
satellite
imagery