A179-0004
Simulating Observations from Loon Balloons for Atmospheric River Reconnaissance
Tuesday, 15 December 2020
Poster
Emma Renee Robertson, Pennsylvania State University Main Campus, University Park, PA, United States, Julianna Cativo, University of California Los Angeles, Los Angeles, CA, United States, Jennifer S Haase, UCSD, La Jolla, CA, United States, Bing Cao, Scripps Institution of Oceanography, La Jolla, CA, United States, Michael J Murphy, UCSD, La Jolla, United States, Anna Maria Wilson, Scripps Institution of Oceanography, Center for Western Weather and Water Extremes (CW3E), La Jolla, CA, United States, Nikolai Chernyy, Loon, LLC, Mountain View, United States and James Antifaev, Loon, Mountain View, CA, United States
Abstract:
Atmospheric river (AR) reconnaissance (AR Recon) is a multi-year research and operations partnership that has routinely sampled ARs with aircraft off the coast of the western USA in order to aid in numerical weather prediction (NWP). It is integral to the collaboration between stakeholders and the Center for Western Weather and Water Extremes to aid in the management of water resources and flood control in the Western US. Loon LLC has launched a fleet of high-altitude balloons with the objective of providing internet connectivity to rural areas, principally in near-equatorial regions. Monitoring ARs using Global Navigation Satellite System (GNSS) radio occultation (RO) atmospheric profiling from receivers on Loon balloons could contribute to AR Recon by improving the initial NWP analysis by supplementing the existing suite of observational tools and increasing the spatial and temporal extent of observations.
We investigate the viability of RO observations using Loon’s fleet through simulations of balloon trajectories to estimate the location and number of profiles that could be collected in an AR. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) product to represent atmospheric conditions and raytracing the raypaths using realistic balloon trajectories, we show where profiles would occur relative to the AR. We provide estimates of the number of profiles over the flight duration. These simulations can also allow us to predict the expected accuracy of RO retrievals in challenging AR conditions. Further analyses will assess the capacity of Loon’s fleet to provide data in the AR region during flights for their existing operational objectives or as on-demand flights for AR Recon.
