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Abstract Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite‐based AR catalog is then combined with the satellite‐based precipitation (Integrated Muti‐SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR‐induced precipitation in reanalysis products. Our results show that the spreads in AR frequency and AR length distribution are generally small across data sets, while the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and extreme AR‐related precipitation. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.
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This content will become publicly available on April 1, 2026
Forward Modeling of Bending Angles With a Two‐Dimensional Operator for GNSS Airborne Radio Occultations in Atmospheric Rivers
Abstract The Global Navigation Satellite System (GNSS) airborne radio occultation (ARO) technique is used to retrieve profiles of the atmosphere during reconnaissance missions for atmospheric rivers (ARs) on the west coast of the United States. The measurements of refractive bending angle integrate the effects of variations in refractive index over long near‐horizontal ray‐paths from a spaceborne transmitter to a receiver onboard an aircraft. A forward operator is required to assimilate ARO observations, which are sensitive to pressure, temperature, and humidity, into numerical weather prediction models to support forecasting of ARs. A two‐dimensional (2D) bending angle operator is proposed to enable capturing key atmospheric features associated with strong ARs. Comparison to a one‐dimensional (1D) forward model supports the evidence of large bending angle departures within 3–7 km impact heights for observations collected in a region characterized by the integrated water vapor transport (IVT) magnitude above 500 kg . The assessment of the 2D forward model for ARO retrievals is based on a sequence of six flights leading up to a significant AR precipitation event in January 2021. Since the observations often sample regions outside the AR where moisture is low, the significance of horizontal variations is obscured in the average bending angle statistics. Examples from individual flights sampling the cross‐section of an AR support the need for the 2D forward model. Additional simulation experiments are performed to quantify forward modeling errors due to tangent point drift and horizontal gradients suggesting contributions on the order of 5% and 20%, respectively.
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- Award ID(s):
- 1642650
- PAR ID:
- 10629852
- Publisher / Repository:
- Journal of Advances in Modeling Earth Systems (JAMES)
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 17
- Issue:
- 4
- ISSN:
- 1942-2466
- Subject(s) / Keyword(s):
- airborne radio occultation radio occultation observation operator data assimilation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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