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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]

Atmospheric rivers (ARs) are long, narrow corridors of water vapor transport that serve as the primary mechanism to advect moisture into midlatitude continental regions, including the U.S. West Coast. Associated [...]

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]

Global Positioning System (GPS) radio occultation (RO) sounding, with its high vertical resolution temperature and humidity profiling capability, is revolutionizing atmospheric science, particularly through assimilation in numerical weather prediction (NWP) models. Currently, the observations are derived from GPS receivers onboard low Earth orbiting satellites. However, with the current number of satellites, it is difficult to provide dense sounding measurements in a specific region within a limited time period. With a GPS receiver onboard an airplane, the GPS RO technique offers such an opportunity while retaining the high vertical resolution sounding capability. The GNSS Instrument System for Multistatic and Occultation Sensing is currently under development for the National Science Foundation's High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) aircraft. This paper presents a sensitivity analysis of the airborne occultation technique that will be used for the HIAPER system. The results demonstrate an anticipated overall accuracy of better than 0.5 % for the retrieved refractivity from the surface to about 1 km below the airplane, where the expected airplane velocity errors of up to 5 mm/s limit the accuracy. The effects on the retrievals due to horizontal variations in atmospheric refractivity are significant, and retrieval errors may reach several percent inside frontal systems when the front is perpendicular to the ray paths and within 200 km of the tangent point. In general, the airborne GPS RO system provides a promising new data source for NWP and targeted observational studies. Index Terms--Airborne, aircraft, atmospheric measurements, Global Positioning System (GPS), microwave radio propagation, radio occultation (RO).

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]

Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse. Satellite radiances are affected by the presence of clouds and heavy precipitation, which impact their distribution in the lower atmosphere and in precipitating areas. The goal of this study is to document a data gap in existing observations of ARs in the northeastern Pacific, and to investigate how a targeted field campaign called AR Reconnaissance (AR Recon) can effectively fill this gap. When reconnaissance data are excluded, there is a gap in AR regions from near the surface to the middle troposphere (below 450 hPa), where most water vapor and its transport are concentrated. All-sky microwave radiances provide data within the AR object, but their quality is degraded near the AR core and its leading edge, due to the existence of thick clouds and precipitation. AR Recon samples ARs and surrounding areas to improve downstream precipitation forecasts over the western United States. This study demonstrates that despite the apparently extensive swaths of modern satellite radiances, which are critical to estimate large-scale flow, the data collected during 15 AR Recon cases in 2016, 2018, and 2019 supply about 99% of humidity, 78% of temperature, and 45% of wind observations in the critical maximum water vapor transport layer from the ocean surface to 700 hPa in ARs. The high-vertical-resolution dropsonde observations in the lower atmosphere over the northeastern Pacific Ocean can significantly improve the sampling of low-level jets transporting water vapor to high-impact precipitation events in the western United States. [ABSTRACT FROM AUTHOR]

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort's core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the "National Winter Season Operations Plan" to support improved outcomes for emergency preparedness and water management in the West. [ABSTRACT FROM AUTHOR]