GNSS Airborne Radio Occultation (ARO) 📂 Data Access

How ARO Works

GNSS Airborne Radio Occultation (ARO) is a remote sensing technique that measures how signals from Global Navigation Satellite System (GNSS) satellites—such as GPS, Galileo, and GLONASS—bend as they propagate through the atmosphere. From the observed bending angles, ARO retrieves high-vertical-resolution profiles of atmospheric refractivity, which are then used to derive pressure, temperature, and water vapor information. These profiles are densely distributed along the aircraft flight track, enabling targeted and enhanced atmospheric sampling. ARO observations are particularly valuable for improving the understanding and forecasting of localized extreme weather events, such as atmospheric rivers (ARs) and tropical cyclones.

ARO Characteristics

• Non-invasive and passive: Tracks existing GNSS signals without transmitting, capable of sampling over land and in restricted areas.
• Slanted geometry: Profiles extend laterally up to ~400 km from the aircraft track, sampling the broader environment.
• High vertical resolution: Better than 400 meters, from flight altitude down into the middle troposphere.
• Densely distributed: Typically 4–6 profiles per hour, or 30–45 profiles per 7–8 hour flight.
• Targeted sampling: Complements dropsondes by observing the surrounding moisture and temperature fields.
• Flexible operation: The system could potentially be deoployed on commercial airliners.

Current and Past Deployments

Current Deployments

• NOAA G-IV: Operational since 2018, with near-real-time data delivery.
• USAF WC-130J: Operational since 2023.
• NOAA WP-3D: Experimental deployment during hurricane field program since 2024.

Past Deployments

• NSF/NCAR G-V (HIAPER): Deployed in the 2010 PREDICT campaign.
• NOAA/G-IV: Deployed in the 2015 CalWater campaign.
• NASA ER-2: Deployed in the 2024 WHyMSIE field campaign.

Planned Future Deployments

• NOAA new G-550: Planned for 2026.
• NASA G-III: Planned for 2026 NURTURE campaign.
• DLR G-550 (HALO): Planned for 2026 NAWDIC campaign.

Available Datasets

The ARO dataset from Atmospheric River Reconnaissance (AR Recon) and Hurricane Field Program (HFP) campaigns, documentation, and other information are publicly available.

AR Recon is a recurring winter-season observational campaign (November–March) focused on improving forecasts of atmospheric rivers over the eastern Pacific. ARO observations collected during AR Recon flights contribute high-vertical-resolution thermodynamic profiles over data-sparse ocean regions.
🔗 More about AR Recon: Center for Western Weather and Water Extremes (CW3E)

HFP flights are conducted in the Atlantic, Caribbean, and Gulf of Mexico during the summer hurricane season (July–November). ARO provides complementary observations to dropsondes for investigating tropical cyclone structure and improving tropical cyclone forecasting.
🔗 More about tropical cyclones: National Hurricane Center (NHC)

Available Datasets (AR=Atmospheric River, TC=Tropical Cyclone as of July 2025)

🔗 Access the ARO dataset: https://agsweb.ucsd.edu/gnss-aro/
🔁 Backup server: https://cw3e-datashare.ucsd.edu/gnss-aro/

Data Quality

ARO observations are particularly valuable in cloud-covered and oceanic regions where satellite radiances may be limited or biased. Over 50% of ARO profiles extend below 4 km, though phase-locked receiver limitations mean fewer profiles penetrate below 2 km. Improved retrieval methods such as open-loop tracking and wave optics inversion are under development to extend lower tropospheric coverage in future deployments.

ARO vs. ERA5

ARO profiles show excellent agreement with both in situ dropsonde observations and ERA5 reanalysis data. Across the mid-to-upper troposphere (4–14 km), the mean refractivity difference is generally less than 0.5%, and the standard deviation remains below 1.5%, validating ARO’s reliability for thermodynamic structure retrievals.

Machine Learning Clustering

To make the best use of ARO data, it’s important to understand how reliable these measurements are—especially since the quality of radio occultation observations can be affected by environmental conditions in the lower atmosphere. Using refractivity anomaly (N’) derived from dropsondes, we train a Gaussian Mixture Model (GMM) to identify different air masses within ARs. We then apply this trained model to data from weather analyses, such as NASA’s GEOS model, to label regions of the atmosphere based on their characteristics. Read More »

Observation Operator

To support numerical weather prediction (NWP) and improve atmospheric river forecasts, a dedicated observation operator for ARO data assimilation has been developed and validated.

This two-dimensional forward model simulates ARO bending angles by accounting for both horizontal moisture gradients and the drift of the occultation tangent point. It has been specifically tested in the context of Atmospheric River Reconnaissance (AR Recon) flights and shows clear benefits in capturing key AR features in the middle and lower troposphere.

The ARO operator is implemented within the JEDI (Joint Effort for Data assimilation Integration) framework, making it readily available to operational and research modeling centers interested in assimilating ARO observations.

🔗 Access the ARO operator GitHub: https://github.com/jhaaseresearch/sio-aro-ropp

Publications

ARO instrumentation and datasets

Cao, B., Haase, J. S., Murphy Jr., M. J., & Wilson, A. M. (2025). Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation. Atmospheric Measurement Techniques, 18, 3361–3392. https://doi.org/10.5194/amt-18-3361-2025

ARO data assimilation operator

Hordyniec, P., Haase, J. S., Murphy, M. J., Jr., Cao, B., Wilson, A. M., & Banos, I. H. (2025). Forward modeling of bending angles with a two-dimensional operator for GNSS airborne radio occultations in atmospheric rivers. Journal of Advances in Modeling Earth Systems, 17, e2024MS004324. https://doi.org/10.1029/2024MS004324

ARO data assimilation impact

Do, P.-N., Haase, J. S., Baños, I. H., Hordyniec, P., & Cao, B. (2025). Impact of airborne radio occultation observations on short term precipitation forecasts of an atmospheric river. Geophysical Research Letters, 52, e2025GL115639. https://doi.org/10.1029/2025GL115639

Haase, J. S., Murphy, M. J., Cao, B., Ralph, F. M., Zheng, M., & Delle Monache, L. (2021). Multi-GNSS airborne radio occultation observations as a complement to dropsondes in atmospheric river reconnaissance. Journal of Geophysical Research: Atmospheres, 126, e2021JD034865. https://doi.org/10.1029/2021JD034865

Funding and Acknowledgments

The development, deployment, and operation of GNSS Airborne Radio Occultation (ARO) has been made possible through support from multiple agencies and collaborators. We gratefully acknowledge funding from:

• Office of Naval Research (ONR)
• National Science Foundation (NSF)
• National Aeronautics and Space Administration (NASA)
• National Oceanic and Atmospheric Administration (NOAA)

We also thank the Center for Western Weather and Water Extremes (CW3E), with support from the Atmospheric River Program funded by the California Department of Water Resources and the U.S. Army Corps of Engineers.

ARO deployments during the AR Recon and HFP campaigns were made possible through collaboration with the NOAA Aircraft Operations Center (G-IV and WP-3D) and the USAF 53rd Weather Reconnaissance Squadron (WC-130J). They provided essential technical, logistical, and operational support.

Computational resources and model development were supported in part by:

• The Joint Effort for Data assimilation Integration (JEDI) framework, developed and maintained by the Joint Center for Satellite Data Assimilation (JCSDA).
• The National Center for Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology (MMM) Laboratory, for assistance with MPAS-Workflow and MPAS-JEDI.
• The Derecho high-performance computing system, provided by the Computational and Information Systems Laboratory (CISL) at NCAR, and sponsored by the NSF.