Remote Sensing (Nov 2022)

A Framework for Satellite-Based 3D Cloud Data: An Overview of the VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications

  • Yoo-Jeong Noh,
  • John M. Haynes,
  • Steven D. Miller,
  • Curtis J. Seaman,
  • Andrew K. Heidinger,
  • Jeffrey Weinrich,
  • Mark S. Kulie,
  • Mattie Niznik,
  • Brandon J. Daub

DOI
https://doi.org/10.3390/rs14215524
Journal volume & issue
Vol. 14, no. 21
p. 5524

Abstract

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Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era.

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