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Atmospheric Rivers (ARs) are narrow filaments of high moisture flux responsible for most of the horizontal transport of water vapor from the tropics to mid-latitudes. Improving forecasts of ARs through numerical weather prediction (NWP) is important for increasing the resilience of the western US to flooding and droughts. These NWP forecasts rely on the improved understanding of AR physics and dynamics from satellite, radar, aircraft, and in situ observations, and now airborne radio occultation (ARO) can contribute to those goals. The ARO technique is based on precise measurements of Global Navigation Satellite Systems (GNSS) signal delays collected from a receiver onboard an aircraft from setting or rising GNSS satellites. ARO inherits the advantages of high vertical resolution and all-weather capability of spaceborne RO observations and has the additional advantage of continuous and dense sampling of the targeted storm area. This work presents a comprehensive ARO dataset recovered from four years of AR Reconnaissance (AR Recon) missions over the eastern Pacific. The final dataset is comprised of ∼ 1700 ARO profiles from 39 flights (∼ 260 flight hours) from multiple GNSS constellations. Profiles extend from aircraft cruising altitude (13–14 km) down into the lower troposphere, with more than 50 % of the profiles extending below 4 km, below which the receiver loses or cannot initiate lock. The horizontal drift of the tangent points that comprise a given ARO profile greatly extends the area sampled from just underneath the aircraft to both sides of the flight track (up to ∼ 400 km). The estimated refractivity accuracy with respect to dropsondes is ∼ 1.2 %, in the upper troposphere where the sample points are closely collocated. For the lower troposphere, the agreement is within ∼ 7 % which is the level of consistency expected given the nature of atmospheric variations over the 300–700 km separation between the lowest point and the dropsonde. [ABSTRACT FROM AUTHOR]

Earthquakes pose a major threat to the people of Haiti, as tragically shown by the catastrophic 2010 MW 7.0 earthquake and more recently by the 2021 MW 7.2 earthquake. Both events occurred within the transpressional Enriquillo-Plantain Garden fault zone (EPGFZ), which runs through the southern peninsula of Haiti and is a major source of seismic hazard for the region. Satellite-based Interferometric Synthetic Aperture Radar (InSAR) data are used to illuminate the ground deformation patterns associated with the 2021 event. The analysis of Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 InSAR data shows (1) the broad coseismic deformation field; (2) detailed secondary fault structures as far as 12 km from the main Enriquillo-Plantain Garden fault (EPGF), which are active during and after the earthquake; and (3) postseismic shallow slip, which migrates along an ~40 km unruptured section of the EPGF for approximately two weeks following the earthquake. The involvement of secondary faults in this rupture requires adjustments to the representation of hazard that assumes a simple segmented strike-slip EPGF. This work presents the first successful use of phase gradient techniques to map postseismic deformation in a vegetated region, which opens the door to future studies of a larger number of events in a wider variety of climates. [ABSTRACT FROM AUTHOR]

Rapidly growing societal needs in urban areas are increasing the demand for tall buildings with complex structural systems. Many of these buildings are located in areas characterized by high seismicity. Quantifying the seismic resilience of these buildings requires comprehensive fragility assessment that integrates iterative nonlinear dynamic analysis (NDA). Under these circumstances, traditional finite element (FE) analysis may become impractical due to its high computational cost. Soft-computing methods can be applied in the domain of NDA to reduce the computational cost of seismic fragility analysis. This study presents a framework that employs nonlinear autoregressive neural networks with exogenous input (NARX) in fragility analysis of multi-story buildings. The framework uses structural health monitoring data to calibrate a nonlinear FE model. The model is employed to generate the training dataset for NARX neural networks with ground acceleration and displacement time histories as the input and output of the network, respectively. The trained NARX networks are then used to perform incremental dynamic analysis (IDA) for a suite of ground motions. Fragility analysis is next conducted based on the results of the IDA obtained from the trained NARX network. The framework is illustrated on a twelve-story reinforced concrete building located at Oklahoma State University, Stillwater campus. [ABSTRACT FROM AUTHOR]

Current climate models have difficulty representing realistic wave-mean flow interactions, partly because the contribution from waves with fine-vertical scales is poorly known. There are few direct observations of these waves and most models have difficulty resolving them. This observational challenge cannot be addressed by satellite or sparse ground-based methods. The Strateole-2 long-duration stratospheric superpressure balloons that drift with the horizontal wind on constant density surfaces provide a unique platform for wave observations across a broad range of spatial and temporal scales. For the first time, balloon-borne Global Navigation Satellite System (GNSS) radio occultation (RO) is used to provide high vertical resolution equatorial wave observations. By tracking navigation signal refractive delays from GPS satellites near the horizon, 40-50 temperature profiles were retrieved daily, from balloon flight altitude (~20 km) down to 6-8 km altitude, forming an orthogonal pattern of observations over a broad area (±400-500 km) surrounding the flight track. The refractivity profiles show an excellent agreement of better than 0.2 % with co-located radiosonde, spaceborne COSMIC-2 RO and reanalysis products. The 200-500 m vertical resolution and consecutive sampling in time and space make it possible to extract properties of Kelvin waves to shorter period gravity waves with vertical wavelengths as short as 2-3 km. A short dataset from the extra Galileo and GLONASS constellations demonstrates the feasibility to nearly double the sampling density in planned follow-on campaigns. The results will contribute to improved estimates of momentum forces for wave driving of the Quasi-biennial Oscillation (QBO) and improved QBO representation in models, and illustrate the importance of measuring intrinsic period in the Lagrangian reference frame. [ABSTRACT FROM AUTHOR]

Current climate models have difficulty representing realistic wave-mean flow interactions, partly because the contribution from waves with fine-vertical scales is poorly known. There are few direct observations of these waves and most models have difficulty resolving them. This observational challenge cannot be addressed by satellite or sparse ground-based methods. The Strateole-2 long-duration stratospheric superpressure balloons that drift with the horizontal wind on constant density surfaces provide a unique platform for wave observations across a broad range of spatial and temporal scales. For the first time, balloon-borne Global Navigation Satellite System (GNSS) radio occultation (RO) is used to provide high vertical resolution equatorial wave observations. By tracking navigation signal refractive delays from GPS satellites near the horizon, 40–50 temperature profiles were retrieved daily, from balloon flight altitude (∼20 km) down to 6–8 km altitude, forming an orthogonal pattern of observations over a broad area (±400–500 km) surrounding the flight track. The refractivity profiles show an excellent agreement of better than 0.2 % with co-located radiosonde, spaceborne COSMIC-2 RO and reanalysis products. The 200–500 m vertical resolution and consecutive sampling in time and space make it possible to extract properties of Kelvin waves to shorter period gravity waves with vertical wavelengths as short as 2–3 km. A short dataset from the extra Galileo and GLONASS constellations demonstrates the feasibility to nearly double the sampling density in planned follow-on campaigns. The results will contribute to improved estimates of momentum forces for wave driving of the Quasi-biennial Oscillation (QBO) and improved QBO representation in models, and illustrate the importance of measuring intrinsic period in the Lagrangian reference frame. [ABSTRACT FROM AUTHOR]