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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Initial Stage of Nanoscale Imaging in Positive Tone Extreme UV Photoresists: The Influence of the Polymer Sequence

Photolithographic patterning using extreme ultraviolet (EUV, 92.5 eV) light is a radiolytic process that initially forms electrons, radical cations, anions, and neutral radicals in the polymeric photoresist matrix. These species may participate in the chemical reactions that define the ultimate resolution of the printed image, and their concentrations and nanometer-scale stochastic variations in their formation influence printed image quality. Proposals have been made that polymer chain uniformity may be advantageous in reducing stochastics due to spatial inhomogeneities, and this aspect of radiolysis is examined in this work. We have simulated the initial subpicosecond stages of the imaging process for a series of photoresist films that are identical in composition but vary in their polymer chain structures. We use detailed, physically accurate stochastic reaction-diffusion calculations to evaluate the influence of defined sequence and random copolymer structures on radiolytic spur formation, i.e., a cluster of species formed by electron-polymer interactions that defines the initial spatial characteristic of the imaging process. Predictions of electron thermalization in the present work are shown to be consistent with the literature, indicating that our overall computational approach for ultrafast nanoscale processes is sound. The computational results show that the polymer sequence has no significant effect on the spur composition. This suggests that any potential imaging improvements to be gained by sequence control must originate from postimaging lithographic process steps.

Absorption↗
NASA NTRS2002

Satellites as Shared Resources for Caribbean Climate and Health Studies

Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring, risk mapping, and surveillance of parameters useful to such problems as vector-borne and infectious diseases, air and water quality, harmful algal blooms, UV (ultraviolet) radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIS), global positioning systems (GPS), improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. Collaborative efforts among scientists from health and Earth sciences together with local decision-makers are enabling increased understanding of the relationships between changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and the occurrence and patterns of diseases (especially, infectious and vector-borne diseases) and other health problems. This increased understanding through improved information and data sharing, in turn, empowers local health and environmental officials to better predict health problems, take preventive measure, and improve response actions. This paper summarizes the remote sensing systems most useful for climate, environment and health studies of the Caribbean region and provides several examples of interdisciplinary research projects in the Caribbean currently using remote sensing technologies. These summaries include the use of remote sensing of algal blooms, pollution transport, coral reef monitoring, vectorborne disease studies, and potential health effects of African dust on Trinidad and Barbados.

Maynard, Nancy G.↗
DOE OSTI2016

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

MERRA↗
DOE OSTI2025

Directed self-assembly of block copolymers for high-precision patterning in the era of extreme ultraviolet lithography

Extreme ultraviolet (EUV) lithography enables unprecedented resolution in semiconductor patterning but faces critical challenges in developing resist materials that achieve high-precision at economically viable throughput. Directed self-assembly (DSA) of block copolymers (BCPs) offers a promising solution for pattern rectification by leveraging thermodynamically determined domain structures to decouple BCP pattern quality from the imperfect original lithographic pattern. This prospective presents an overview of recent progress on the EUV + DSA strategy, covering advances in BCP material design, processing, metrology, and pattern transfer. We highlight recent advances in high-χ BCPs with perpendicular orientation and domain spacings compatible with EUV dimensions, leveraging A-b-(B-r-C) architectures. We also discuss progress in chemical pre-pattern fabrication using both positive- and negative tone resists, along with processing strategies to minimize defects and roughness based on BCP thermodynamics and assembly kinetics. We further examine metrology platforms for characterizing the thermodynamics of BCP materials and quantifying the size and shape of BCP domains. Lastly, we review pattern transfer strategies for generating functional inorganic masks suitable for semiconductor manufacturing. Together, these advances highlight the potential of DSA to complement EUV lithography, offering a pathway to address critical challenges in achieving high-precision patterning for the semiconductor industry.

Lee, Kyunghyeon [Univ. of Chicago, IL (United Stat↗
DOE OSTI2018

Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration

In this study, we quantified the relationship between atmospheric rivers (ARs) and occurrence and magnitude of extreme precipitation in western U.S. watersheds, using AR tracking results of Atmospheric River Tracking Method Intercomparison Project and precipitation from a high-resolution regional climate simulation. Our analysis indicates that ARs have the potential of predicting extreme precipitation events at daily scale, with Gilbert Skill Score of ~0.2, and monthly extreme precipitation amount in the west coast watershed is closely related to AR intensity, with correlation coefficients of up to 0.6. The relationship between ARs and precipitation is most significant in the Pacific Northwest and California. Lastly, using K-means clustering algorithm, AR events can be classified into three categories with distinct features: weak ARs, flash ARs, and prolonged ARs. Flash ARs and prolonged ARs, though accounting for less than 50% of total AR events, are more important in controlling regional extreme precipitation patterns, and should be prioritized for future studies of hydrological extreme events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Implications of Arctic Sea Ice Decline for the Earth System

Arctic sea ice decline has led to an amplification of surface warming and is projected to continue to decline from anthropogenic forcing, although the exact timing of ice-free summers is uncertain owing to large natural variability. Sea ice reductions affect surface heating patterns and the atmospheric pressure distribution, which may alter midlatitude extreme weather patterns. Increased light penetration and nutrient availability during spring from earlier ice breakup enhances primary production in the Arctic Ocean and its adjacent shelf seas. Ice-obligate marine mammals may be losers, whereas seasonally migrant species may be winners from rapid sea ice decline. Tundra greening is occurring across most of the Arctic, driven primarily by warming temperatures, and is displaying complex spatial patterns that are likely tied to other factors. Sea ice changes are affecting greenhouse gas exchanges as well as halogen chemistry in the Arctic. This review highlights the heterogeneous nature of Arctic change, which is vital for researchers to better understand.

Decline↗
DOE OSTI2019

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defned based on the characteristic spatial variations of a meteorological feld or on the remote circulation response to a known forcing, LSMPs are defned relative to the occurrence of a specifc phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary infuence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to defnitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many diferent defnitions of “extreme” are in use, making it difcult to directly compare diferent studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identifed and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Barlow, Mathew↗
DOE OSTI2025

Implications of Changes in Water Stress and Precipitation Extremes for Cocoa Production in Côte D'ivoire and Ghana

ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

Obahoundje, Salomon [International Water Managemen↗
NASA NTRS1975

Fractionation in the solar nebula - Condensation of yttrium and the rare earth elements

The condensation of Y and the rare earth elements (REE) from the solar nebula may be controlled by thermodynamic equilibrium between gas and condensed solids. Highly fractionated REE patterns may result if condensates are removed from the gas before condensation is complete. It is found that the fractionation is not a smooth function of REE ionic radius but varies in an extremely irregular pattern. Both Yb and Eu are predicted to be extremely depleted in the early condensate without the requirement of condensation in the divalent state. The model is discussed with respect to a highly fractionated pattern observed by Tanaka and Masuda (1973), in a pink Ca-Al-rich inclusion from the Allende meteorite and can account for the abundances of each REE determined. According to the model this inclusion represents a condensate from a previously fractionated gas rather than from a gas of solar composition. Before the condensation of this inclusion, an earlier condensate was formed and was removed from equilibrium with the gas.

Boynton, W. V.↗
DOE OSTI2021

Additive Lithography–Organic Monolayer Patterning Coupled with an Area-Selective Deposition

The combination of area-selective deposition (ASD) with a patternable organic monolayer provides a versatile additive lithography platform, enabling the generation of a variety of nanoscale feature geometries. Stearate hydroxamic acid self-assembled monolayers (SAMs) were patterned with extreme ultraviolet (λ = 13.5 nm) or electron beam irradiation and developed with ASD to achieve line space patterns as small as 50 nm. Density functional theory was employed to aid in the synthesis of hydroxamic acid derivatives with optimized packing density to enhance the imaging contrast and improve dose sensitivity. Near-edge X-ray absorption fine structure spectroscopy and infrared spectroscopy reveal that the imaging mechanism is based on improved deposition inhibition provided by the cross-linking of the SAM to produce a more effective barrier during a subsequent deposition step. With patterned substrates composed of coplanar copper lines and silicon spacers, hydroxamic acids selectively formed monolayers on the metal portions and could undergo a pattern-wise exposure followed by ASD in the first combination of a patternable monolayer with ASD. This material system presents an additional capability compared to traditional ASD approaches that generally reflect a starting patterned surface. Finally, this bottoms-up additive approach to lithography may be a viable alternative to subtractive nanoscale feature generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A stochastic model of future extreme temperature events for infrastructure analysis

Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%-90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850-1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov-Smirnov -value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67 degrees C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

The violent hard X-ray variability of MRK 421 observed by NuSTAR in 2013 April

The well-studied blazar Markarian 421 (Mrk 421, z = 0.031) was the subject of an intensive multi-wavelength campaign when it flared in 2013 April. The recorded X-ray and very high-energy (E > 100 GeV) γ-ray fluxes are the highest ever measured from this object. At the peak of the activity, it was monitored by the hard X-ray focusing telescope Nuclear Spectroscopic Telescope Array (NuSTAR) and the Swift X-Ray Telescope (XRT). In this work, we present a detailed variability analysis of NuSTAR and Swift-XRT observations of Mrk 421 during this flaring episode. We obtained the shortest flux doubling time of 14.01 ± 5.03 minutes, which is the shortest hard X-ray (3–79 keV) variability ever recorded from Mrk 421, and is on the order of the light-crossing time of the black hole's event horizon. A pattern of extremely fast variability events superposed on slowly varying flares is found in most of the NuSTAR observations. We suggest that these peculiar variability patterns may be explained by magnetic energy dissipation and reconnection in a fast-moving compact emission region within the jet. Based on the fast variability, we derive a lower limit on the magnetic field strength of B⩾0.73δ{sub 1}{sup −2/3} ν{sub 19}{sup 1/3} G, where δ{sub 1} is the Doppler factor in units of 10, and ν{sub 19} is the characteristic X-ray synchrotron frequency in units of 10{sup 19} Hz.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales

Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Variational AutoEncoders Reveal Intensifying GPP Extremes in Continental United States based on CESM2 Simulations

Climate extremes significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under SSP5-8.5 scenario. The VAE architecture employs three dense layers and a latent space with input sequence length of 12 months, training on normalized GPP time series to reconstruct the GPP and identify anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE consistently producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. This research demonstrates the potential of deep learning approaches for extremes detection and provides a foundation for improved understanding of future carbon cycle risks under future conditions.

Sharma, Bharat [ORNL] (ORCID:0000000266982487)↗
DOE OSTI2015

TECA: Petascale pattern recognition for climate science

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

54 ENVIRONMENTAL SCIENCES↗