First posted January 17, 2024 For additional information, contact: Director, New York Water Science CenterU.S. Geological Survey425 Jordan RoadTroy, NY 12180–8349 Between October 31 and November 3, 2019, historic flooding in localized areas of the Mohawk Valley and southern Adirondack region in central New York State resulted in one fatality and an estimated $33 million in damages. Flooding resulted from high-intensity, hyperlocal rainfall in the region within a 24-hour period between October 31 and November 1, 2019, at the end of a much wetter than average October. In that 24-hour period, rainfall amounts largely ranged from 2 to 5 inches in the most heavily affected parts of the region, but a maximum rainfall amount for the region of 7 inches was recorded in Speculator, New York. This rainfall total for a 24-hour period for this location is estimated to have between a 200- and 500-year recurrence interval. The most severe flooding to result from the rainfall was mainly in the Sacandaga River basin, which is within the upper Hudson River basin, and in the East and West Canada Creek basins, which are within the Mohawk River basin.Streamflow, stage, and reservoir elevation data, collected by the U.S. Geological Survey, are documented in this report. Flooding resulted in new peak streamflow records at five of six U.S. Geological Survey streamgages in the region that have periods of record of at least 20 years, including at three streamgages that have been in operation for about 100 years. At the sixth streamgage, this flooding resulted in the second highest peak streamflow in its 71-year period of record. For all six streamgages, estimates of flood magnitudes for selected annual exceedance probabilities were updated using the peak streamflows from the flooding. Additionally, the annual exceedance probabilities for the six respective peak streamflows were all estimated to be less than 1 percent (greater than a 100-year recurrence interval). At three of those six streamgages, however, previous annual peak streamflows of comparable magnitudes (within 10 percent) have also happened within the past 20 years.

Our recent studies departed from the conventional description of polymer fracture behavior while maintaining consistency with the principles of material mechanics including linear elastic fracture mechanics (LEFM). In traditional fracture mechanics of brittle materials, crack resistance is quantified in terms of toughness G(c), which represents the critical energy release per unit fracture surface area. This perspective suggests that high G(c) involved high energy dissipation. A new perspective has surfaced, proposing a fundamental yet underexplored, seemingly universal fracture mechanism and explaining the origin of high G(c) within an alternative framework. According to this view, for unfilled plastics and elastomers, fracture initiates when local tensile stress surpasses the polymer's fracture strength sigma(F(inh)), and high toughness is a consequence of high fracture strength. Remarkably, for polymers in both plastic and elastomeric states, their inherent strength sigma(F(inh)) appears to be of a comparable magnitude to the nominal tensile strength sigma(b). Spatial-temporal resolved polarized optical microscopic (str-POM) measurements have started to provide insight into the fracture mechanism and unveil a concealed length scale (P) representing the size of a stress saturation zone at the crack tip. The two-parameter theoretical framework shows (a) toughness of brittle plastics increases quadratically with its strength sigma(F(inh)) and linearly with P and (b) elastomers exhibit significantly greater toughness and higher tensile strength at lower temperatures due to the increased stability of covalent bonds given the lower thermal energy and plausible frictional effect on bond vibration frequency. Embracing this emerging paradigm where we recognize polymer strength to be time-dependent, we anticipate new advancements in polymer design and a clearer understanding of the fracture behavior of polymeric materials.

To rationalize the comprehensive phenomenology in Paper 1 (this volume, p. 125878), we present a qualitative description for the mechanical behavior of semicrystalline polymers (SCPs) through a synthesis of prior understandings about the mechanical characteristics of glassy and semicrystalline polymers. Based on the central idea to regard an SCP as a crystalline chain network (CCN), in which a test chain can have crystalline and amorphous strands, we emphasize several key concepts: (a) crystallization determines the structure of the CCN, which is generally weaker than the pre-crystallization chain network in the molten state formed by interchain uncrossability, (b) ductility in SCPs is afforded by a sufficiently robust CCN, and (c) yielding and plastic deformation of SCPs require a successful structural transformation involving shape-change of crystalline phases through appreciable chain pull-out from the crystalline phases without breakdown of the CCN. In other words, it is the CCN that drives ductile SCPs to undergo yielding and subsequent large deformation through massive pull-out of load bearing strands (LBSs), which are the tie and entangling strands. Brittle fracture and lack of drawability occur in those SCPs where the CCN is too weak (due to a sparse population of LBSs) to cause sufficient meltdown of crystalline phases: There are not enough LBSs to undergo pull-out that is necessary for yielding and crystal transformation. Based on these concepts we can explore the processing-structure-property (P–S–P) relationship by demonstrating how pre-deformation in either crystalline or molten state produces more favorable structures for stronger mechanical characteristics.

Moist static energy (MSE) and ocean heat content (OHC) in the tropics are inextricably linked. The processes by which sources and sinks of OHC modulate column integrated MSE in the Indian Ocean (IO) are explored through a reformulation of the MSE budget using atmosphere and ocean reanalysis data. In the reframed MSE budget, interfacial air-sea turbulent and radiative fluxes are replaced for information on upper ocean dynamics, thus "mooring" the MSE tendency to the subsurface ocean. On subseasonal time scales, ocean forcing is largely responsible for the amplification of MSE anomalies across the IO, with basin average growth rates of 10% day(-1). Local OHC depletion is the leading contributor to anomalous MSE amplification with average rates of 12% day(-1). Along the equator, MSE is amplified by OHC vertical advection. Ocean forcing only weakly reduces the propagation tendency of MSE anomalies (-2% day(-1)), with propagation predominantly resulting from atmosphere forcing (10% day(-1)). OHC in the IO acts as an MSE reservoir that is expended during periods of enhanced intraseasonal atmosphere convection and recharged during periods of suppressed convection. Because OHC is an MSE source during enhanced intraseasonal convection periods, it largely offsets the negative MSE tendency produced by horizontal advection in the atmosphere. The opposite effect occurs during suppressed convection periods, where OHC is a sink of MSE and counters the positive MSE tendency produced by horizontal advection in the atmosphere.

The current understanding of mechanical behavior of semicrystalline polymers (SCPs) is insufficient even at a qualitative level. Since a disproportional extent of efforts have been made to describe yielding rather than brittle fracture or lack of drawability in SCPs, we have avoided basic questions such as when and why tensile extension of SCPs result in brittle failure. Consequently, it has remained ambiguous just exactly what crystallization does, e.g., whether crystallization favors ductile or brittle mechanical response, what polymer entanglement does in the crystalline state, and exactly how and why tie-strands play an essential role to bring about yielding and ductility in SCPs. The present work (in a sequence of two papers) builds a coherent phenomenological basis to propose a chain-level qualitative framework where answers to these questions start to emerge. We first present extensive data in Paper 1 to (a) show how ductility depends on the state of crystallization, (b) describe a ductile-to-brittle (non-drawable) transition as a function of temperature and drawing rate, including the anomalous temperature and rate dependences, and (c) demonstrate how the structures of SCPs may be affected by pre-deformation to improve ductility and increase tensile strength. While some observations appear familiar, the total phenomenology has never been holistically compiled to depict a comprehensive (qualitative) molecular picture in Paper 2 that elucidates the effect of crystallization and the roles of chain entanglement and tie-strands regarding drawability.

This study carries out spatial-resolved optical birefringence observations to quantify, for mode I (tensile opening) loading, the stress intensification at crack tip of brittle and ductile glassy polymers (polymethyl methacrylate-PMMA, polyethylene terephthalate-PET) as well as one type of elastomer: (ethylene propylene diene monomer-EPDM). We measure the stress buildup in a precut specimen by correlating retardation with the corresponding tensile stress. Given the adequate spatial resolution under 10 mu m and natural bluntness of intentional through-cuts, we are able to show that (a) during drawing at different stages up to the onset of fracture in precut PMMA and EPDM, the local stress saturates, namely, ceases to increase as r(-1/2) upon approaching the cut tip (with r reaching r(ss) in a range of 0.05-0.15 mm), (b) tip stress sigma(tip), i.e., the tensile stress in the stress saturation zone (r <= r(ss)), linearly grows with (operationally defined) stress intensity factor K-I until fracture, reaching a level below the breaking stress sigma(b) observed of uncut specimen. Thus, the inherent strength sigma F(inh) under plane strain, taken to be the tip stress at fracture is only comparable to sigma b. Moreover, a characteristic length P, involved in the observed linearity between K-I and sigma tip, i.e., in K-I = sigma P-tip(1/2), is found to be comparable to 2 pi r(ss). Here r(ss) appears to depend on the tip sharpness, which may be characterized by a radius of curvature rho(tip). Thus, toughness given by the critical stress intensity factor K-Ic is determined by the product of sigma F(inh) and rho(1/2)(tip), and the critical energy release rate G(Ic) is given by the product of specific work of fracture w(F) = [sigma(F(inh))](2)/2E and rho(tip). (C) 2022 Elsevier Ltd. All rights reserved.

While storm identification and tracking algorithms are used both operationally and in research, there exists no single standard technique to objectively determine performance of such algorithms. Thus, a comparative skill score is developed herein that consists of four parameters, three of which constitute the quantification of storm attributes-size consistency, linearity of tracks, and mean track duration-and the fourth that correlates performance to an optimal postevent reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one among the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms that are efficient at severe storm detection, i.e., high-scoring algorithms should detect storms that have higher current or future severe threat and minimize detection of weaker, short-lived storms with low severe potential. The skill score is shown to be capable of successfully ranking a large number of algorithms, both between varying settings within the same base algorithm and between distinct base algorithms. Through a comparison with manually created user datasets, high-scoring algorithms are verified to match well with hand analyses, demonstrating appropriate calibration of skill score parameters. Significance StatementWith the growing number of options for storm identification and tracking techniques, it is necessary to devise an objective approach to quantify performance of different techniques. This study introduces a comparative skill score that assesses size consistency, linearity of tracks, mean track duration, and correlation to an optimal postevent reanalysis to rank diverse algorithms. This paper will show the capability of the skill score at highlighting algorithms that are efficient at detecting storms with higher severe potential, as well as those that closely resemble human-perceived storms through a comparison with manually created user datasets. The novel methodology will be useful in improving systems that rely on such algorithms, for both operational and research purposes focusing on severe storm detection.

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) data set blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS data set uses the WSR-88D archive starting in 1998 through 2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1-km by 1-km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe storm related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5-km by 0.5-km. The initial period of record for the data set is 1998-2011. The data set underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall data set, but which caused large data errors in some cases. This data set has applications towards radar-based climatologies, post-event analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.

The processing-structure-property relationship using poly(lactic acid) (PLA) and poly(ethylene terephthalate) (PET) is explored. Specifically, both pre-extension and preshear of amorphous PLA and PET above their glass transition temperatures Tg , carried out in the affine deformation limit, can induce a specific type of cold crystallization during annealing, i.e., nanoconfined crystallization (NCC) where crystal sizes are limited to a nanoscopic scale in all dimensions so as to render the processed PLA and PET optically transparent. The new polymer structure after premelt deformation can show considerably enhanced mechanical properties. For example, premelt stretching produces geometric condensation of the chain network. This structural alternation can profoundly change the mechanical characteristics, e.g., turning brittle PLA ductile. In contrast, after preshear of amorphous PLA above Tg , the NCC containing PLA remains brittle, showing the importance to have geometric condensation from processing. Both AFM imaging and SAXS measurements are performed to verify that premelt deformation of PLA and PET indeed results in NCC from annealing that permits the strain-induced cold crystallization to take place on the length scale of the mesh size of the deformed chain network.

A promising solution to address the challenges in plastics sustainability is to replace current polymers with chemically recyclable ones that can depolymerise into their constituent monomers for circular use of materials. Despite the progress, few depolymerisable polymers exhibit the excellent thermal stability and strong mechanical properties of traditional polymers. Here we report a series of chemically recyclable polymers that show excellent thermal stability (decomposition temperature > 370 ºC) and tunable mechanical properties. The polymers are formed via ring-opening metathesis polymerisation of cyclooctene with a trans-cyclobutane installed at the 5,6-positions. The additional ring converts the non-depolymerisable polycyclooctene into a depolymerisable polymer by reducing the ring strain energy in the monomer (from 8.2 kcal/mol in unsubstituted cyclooctene to 4.9 kcal/mol in the fused ring). The fused-ring monomer enables a broad scope of functionalities to be incorporated, providing access to chemically recyclable elastomers and plastics that show promise as next-generation sustainable materials.

Theoretical analyses and experiments have been carried out to investigate fracture behavior of glassy polymers. Our birefringence measurements quantify the local stress buildup at cut tip during different stages of drawing. Based on polymethyl methacrylate (PMMA), bisphenol A polycarbonate (PC) and polyethylene terephthalate (PET), we find several key results beyond the existing knowledge base. (1) The inherent fracture and yield strengths sigma_F(inh) and sigma_Y(inh) differ little in magnitude from the breaking and yield stress (sigma_b and sigma_y). (2) Stress intensification (SI) near a pre-through-cut builds up deviates from the theoretical description of linear elastic fracture mechanics (LEFM) upon approaching the cut tip. (3) SI meets a natural cutoff below which stress ceases to increase. (4) The stress stip at cut tip shows a trend of approximate linear increase with the far-field load s0 for all three polymers and different cut size a. (5) A characteristic length scale P emerges from the linear relation between stip and KI. For these glassy polymers, P is on the order of 0.1 mm. (6) Fracture toughness of brittle polymers is characterized by critical stress intensity factor K_Ic = sigma_F(inh)(2*pi*P)1/2, revealing relevance of the two crucial quantities. (7) The critical energy release rate GIc for brittle glass polymers such as PMMA is determined by the product of its work of fracture wF (of uncut specimen) and P. (8) The elusive fractocohesive length Lfc defined in the literature as G_Ic/w_F naturally arises from the new expression for G_Ic as stated in (7), i.e., it is essentially P. These results suggest that a great deal of future work is required to acquire additional understanding with regards to fracture and failure behaviors of plastics.

NOAA’s Hazardous Weather Testbed (HWT) is a physical space and research framework to foster collaboration and evaluate emerging tools, technology, and products for NWS operations. The HWT’s Experimental Warning Program (EWP) focuses on research, technology, and communication that may improve severe and hazardous weather warnings and societal response. The EWP was established with three fundamental hypotheses: 1) collaboration with operational meteorologists increases the speed of the transition process and rate of adoption of beneficial applications and technology, 2) the transition of knowledge between research and operations benefits both the research and operational communities, and 3) including end users in experiments generates outcomes that are more reliable and useful for society. The EWP is designed to mimic the operations of any NWS Forecast Office, providing the opportunity for experiments to leverage live and archived severe weather activity anywhere in the United States. During the first decade of activity in the EWP, 15 experiments covered topics including new radar and satellite applications, storm-scale numerical models and data assimilation, total lightning use in severe weather forecasting, and multiple social science and end-user topics. The experiments range from exploratory and conceptual research to more controlled experimental design to establish statistical patterns and causal relationships. The EWP brought more than 400 NWS forecasters, 60 emergency managers, and 30 broadcast meteorologists to the HWT to participate in live demonstrations, archive events, and data-denial experiments influencing today’s operational warning environment and shaping the future of warning research, technology, and communication for years to come.

The hydrolysis of poly(lactic acid), PLA, was investigated considering the changes in the three-phase model structures, the mobile amorphous, crystalline (CF), and rigid amorphous fractions (RAF). Amorphous PLA films with different L-lactide were crystallized by cold-crystallization and melt-stretching crystallization to promote the three-phase structural variation in the PLA films. The changes in the phase structure and molecular weight during hydrolysis were investigated. As a result, PLA with higher CF had a slow hydrolysis rate due to limited water diffusion into the crystalline structure. Conversely, the initial amount of RAF fasten the hydrolysis affected by the higher water diffusion and more hydrophilic end groups at the early stage. Moreover, the distinct structure of the nanoconfined crystals from the melt-stretching method could limit the diffusion of water molecules into the PLA film and accordingly add more stability to the hydrolysis.

Unexpected hydrogel and coacervate are observed for dilute (1 mM) uranyl peroxide molecular cluster (Li68K12(OH)20[UO2(O2)(OH)]60, U60) solution in the presence of di- or trivalent salts. We report the mechanism as the formation of anisotropic two-dimensional (2-D) single-layer nanosheets, driven by counterion-mediated attraction due to the size disparity between U60 and small counterions. With weak monovalent cations, the nanosheets are bendable, resulting in hollow, spherical blackberry-type supramolecular assemblies in a homogeneous solution. With extra strong divalent or trivalent cations, the tough, free-standing sheets lead to gelation at ∼1 mM U60. These stiff nanosheets are difficult to bend into spherical blackberry-type structures; instead, they stay in solution and form hydrogel based on their significant excluded volumes. At higher ionic strength, the large, thin filmlike nanosheet structures stack together more compactly and consequently lead to the transition from gel phase to a coacervate phase, another surprise since it was formed without the presence of bulky polycations.

This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly rotating tornadic supercells and weak nontornadic cells in mesoscale convective systems, respectively. The worst predictions (false positives and negatives) occur for strongly rotating nontornadic supercells and tornadic cells in quasi-linear convective systems, respectively. The performance of our CNNs is comparable to an operational machine-learning system for severe weather prediction, which suggests that they would be useful for real-time forecasting.

We demonstrate that machine learning (ML) can skillfully classify thunderstorms into three categories: supercell, part of a quasi-linear convective system, or disorganized. These classifications are based on radar data and environmental information obtained through a proximity sounding. We compare the performance of five ML algorithms: logistic regression with the elastic-net penalty, random forests, gradient-boosted forests, and support-vector machines with both a linear and nonlinear kernel. The gradient-boosted forest performs best, with an accuracy of 0.77 ± 0.02 and a Peirce score of 0.58 ± 0.04. The linear support-vector machine performs second best, with values of 0.70 ± 0.02 and 0.55 ± 0.05, respectively. We use two interpretation methods, permutation importance and sequential forward selection, to determine the most important predictors for the ML models. We also use partial-dependence plots to determine how these predictors influence the outcome. A main conclusion is that shape predictors, based on the outline of the storm, appear to be highly important across ML models. The training data, a storm-centered radar scan and modeled proximity sounding, are similar to real-time data. Thus, the models could be used operationally to aid human decision-making by reducing the cognitive load involved in manual storm-mode identification. Also, they could be run on historical data to perform climatological analyses, which could be valuable to both the research and operational communities.