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Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The “AR Recon” program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost.

 

Abstract In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors. 

In this study, we analyze 44 terrestrial gamma-ray flashes (TGFs) detected by the Fermi Gamma-ray Burst Monitor (GBM) occurring in 2014–2016 in conjunction with data from the U.S. National Lightning Detection Network (NLDN). We examine the characteristics of magnetic field waveforms measured by NLDN sensors for 61 pulses that occurred within 5 ms of the start-time of the TGF photon flux. For 21 (out of 44) TGFs, the associated NLDN pulse occurred almost simultaneously with (that is, within 200 μs of) the TGF. One TGF had two NLDN pulses within 200 μs. The median absolute time interval between the beginning of these near-simultaneous pulses and the TGF flux start-time is 50 μs. We speculate that these RF pulses are signatures of either TGF-associated relativistic electron avalanches or currents traveling in conducting paths “preconditioned” by TGF-associated electron beams. Compared to pulses that were not simultaneous with TGFs (but within 5 ms of one), simultaneous pulses had higher median absolute peak current (26 kA versus 11 kA), longer median threshold-to-peak rise time (14 μs versus 2.8 μs), and longer median peak-to-zero time (15 μs versus 5.5 μs). A majority (77%) of our simultaneous RF pulses had NLDN-estimated peak currents less than 50 kA indicating that TGF emissions can be associated with moderate-peak-amplitude processes. The lightning flash associated with one of the TGFs in our data set was observed by a Lightning Mapping Array, which reported a relatively high-power source at an altitude of 25 km occurring 101 μs after the GBM-reported TGF discovery-bin start-time. 

Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.