Improvement of Satellite Cloud Vertical Cross-section Products for Aviation Weather Applications

Abstract:

Knowledge of three-dimensional cloud structures is critical to numerous aviation applications. Satellites have provided valuable cloud observations over decades, but the data is mostly biased at/near cloud top or integrated through a vertical atmospheric column. We have developed a statistical Cloud Base Height (CBH) algorithm using active and passive sensor observations from NASA A-Train satellite data, which is currently operational as part of the NOAA Enterprise Cloud Algorithms and also used to improve Cloud Cover/Layers (CCL) products. These products allow us to provide vertically extended cloud height fields beyond a typical plain-view image, which has been challenging with conventional passive sensor observations. Tied with the NOAA JPSS Proving Ground and Risk Reduction (PGRR) Aviation Initiative effort, we have provided VIIRS Cloud Vertical Cross-sections (CVC) for flight routes over Alaska, attempting to maximize the use of satellite cloud products for aviation weather applications. Cloud phase, temperatures (NUCAPS or NWP model), and PIREPs (icing and turbulence) are also implemented in the vertical cloud view for aviation users. The CVCs are now extended to CONUS with the addition of GOES-16 ABI. We continue to obtain and incorporate feedback from operational forecasters and general aviation users including pilots to provide relevant training tools and display capabilities. Further refinements are ongoing to provide optimized retrievals for nighttime and multilayered clouds where IR-only-based cloud retrievals are generally degraded. We have been exploring various approaches with traditional statistics and machine learning for improved products, taking advantage of JPSS and GOES research. The results are evaluated through intercomparisons between multiple satellite observations and using surface measurements at the US Department of Energy Atmospheric Radiation Measurement (ARM) sites. This study presents our current accomplishments and continuing efforts on both sides of scientific and user-engaged improvements.