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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. 

In recent years, several locations in the United States have been experiencing a significant increase in seismicity that has been attributed to oil and gas production. As oil and natural gas production in the United States continues to increase, it is expected that the seismic hazard in these locations will continue to experience a corresponding upsurge. However, many urban structures in these locations are not designed to withstand these increasing levels of seismicity. Accordingly, it is crucial to develop methodologies that can help us quantify the seismic performance of these structures, establish their risk levels, and identify optimal retrofit strategies that will enhance the seismic resilience of these structures. In this context, structural health monitoring (SHM) plays an important role in understanding the seismic performance of structures. SHM can be used, in conjunction with finite element modelling, to provide a realistic representation of the structural performance during a seismic event. In this paper, a framework for seismic risk assessment of reinforced concrete buildings based on SHM is presented. The framework combines nonlinear finite element modeling and SHM data to establish the seismic fragility profile of the structure. The approach is illustrated on a multi- story reinforced concrete structure located on the Oklahoma State University Campus.