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DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗

NASA NTRS1979

Extreme Mean and Its Applications

Extreme value statistics obtained from normally distributed data are considered. An extreme mean is defined as the mean of p-th probability truncated normal distribution. An unbiased estimate of this extreme mean and its large sample distribution are derived. The distribution of this estimate even for very large samples is found to be nonnormal. Further, as the sample size increases, the variance of the unbiased estimate converges to the Cramer-Rao lower bound. The computer program used to obtain the density and distribution functions of the standardized unbiased estimate, and the confidence intervals of the extreme mean for any data are included for ready application. An example is included to demonstrate the usefulness of extreme mean application.

Swaroop, R.↗

DOE OSTI2024

Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave

Abstract In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causing hundreds of fatalities. The observed 2021 daily maximum temperature across much of the U.S. Pacific Northwest exceeded upper bound estimates obtained from single-station temperature records even after accounting for anthropogenic climate change, meaning that the event could not have been predicted under standard univariate extreme value analysis assumptions. In this work, we utilize a flexible spatial extremes model that considers all stations across the Pacific Northwest domain and accounts for the fact that many stations simultaneously experience extreme temperatures. Our analysis incorporates the effects of anthropogenic forcing and natural climate variability in order to better characterize time-varying changes in the distribution of daily temperature extremes. We show that greenhouse gas forcing, drought conditions and large-scale atmospheric modes of variability all have significant impact on summertime maximum temperatures in this region. Our model represents a significant improvement over corresponding single-station analysis, and our posterior medians of the upper bounds are able to anticipate more than 96% of the observed 2021 high station temperatures after properly accounting for extremal dependence. Supplementary materials accompanying this paper appear online.

Zhang, Likun (ORCID:0000000154584556)↗

DOE OSTI2021

Extreme Water Vapor Transport During the March 2021 Sydney Floods in the Context of Climate Projections

During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. The maximum daily mean column integrated water vapor transport (IVT) over Sydney during this event was within the top 0.3% of all days since 1980, and the 10-day mean IVT was in the top 0.2%. Here, in this study, we have examined the change in frequency of extreme IVT events over Sydney in 16 climate models from the Coupled Model Intercomparison Project 6 under two Shared Socioeconomic Pathways (SSP245 and SSP585). Generalized Extreme Value modeling was used to further analyze the change in frequency of extreme IVT events. We found the probability of long duration high IVT events is projected to increase by 80% at the end of the century, but the future change in IVT is correlated to the rate of global and regional warming in each model.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Using the VAHIRR Radar Algorithm to Investigate Lightning Cessation

Accurately determining the threat posed by lightning is a major area for improved operational forecasts. Most efforts have focused on the initiation of lightning within a storm, with far less effort spent investigating lightning cessation. Understanding both components, initiation and cessation, are vital to improving lightning safety. Few organizations actively forecast lightning onset or cessation. One such organization is the 45th Weather Squadron (45WS) for the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45WS has identified that charged anvil clouds remain a major threat of continued lightning and can greatly extend the window of a potential lightning strike. Furthermore, no discernable trend of total lightning activity has been observed consistently for all storms. This highlights the need for more research to find a robust method of knowing when a storm will cease producing lightning. Previous lightning cessation work has primarily focused on forecasting the cessation of cloud-to -ground lightning only. A more recent, statistical study involved total lightning (both cloud-to-ground and intracloud). Each of these previous works has helped the 45WS take steps forward in creating improved and ultimately safer lightning cessation forecasts. Each study has either relied on radar data or recommended increased use of radar data to improve cessation forecasts. The reasoning is that radar data is able to either directly or by proxy infer more about dynamical environment leading to cloud electrification and eventually lightning cessation. The authors of this project are focusing on a two ]step approach to better incorporate radar data and total lightning to improve cessation forecasts. This project will utilize the Volume Averaged Height Integrated Radar Reflectivity (VAHIRR) algorithm originally developed during the Airborne Field Mill II (ABFM II) research project. During the project, the VAHIRR product showed a trend of increasing values with increases in the electric field magnitude above 3 kV/m. An extreme value analysis showed that VAHIRR values less than or equal to 10 dBZ-km showed that the probability of having an electric field magnitude larger than 3 kV/m was less than one in ten thousand. VAHIRR also was found to be sensitive at indicating anvil clouds that posed a threat of initiating a lightning flash. This project seeks to use VAHIRR to analyze its utility as a lightning cessation tool, particularly dealing with the threat posed by detached anvils. The results from this project will serve as a baseline effectiveness of radar ]based lightning cessation algorithms. This baseline will be used in the second, and concurrent work by the co ]author fs who are developing a lightning cessation algorithm based on dual ]polarimetric radar data. Ultimately, an accurate method for identifying lightning cessation can save money on lost manpower time as well as greatly improve lightning safety.

Stano, Geoffrey T.↗

NASA NTRS1980

A preliminary assessment of the accuracy of selected meteorological parameters determined from Nimbus 6 satellite profile data

Published rms errors in rawinsonde data and discrepancies between satellite and rawinsonde profile data for temperature, dewpoint temperature, mixing ratio, and wind speed. Satellite rms errors were found to be 2 to 3 times as large as those for rawinsonde data. Gradients of the preceding parameters were computed for both rawinsonde and satellite data and compared with means and near extreme values computed from the AVE 2 and AVE 4 experiments. In all cases, it was found that satellite data can be used to determine with relatively good accuracy the near extreme gradients but not those whose value does not exceed the average. Synoptic charts were prepared to show that patterns of temperature could be determined with relatively good accuracy, while those of dew point were not as good as those for temperature. Winds represented by cloud motion vectors (satellite winds) were compared with rawinsonde winds, and it was found that large gaps exist in satellite values for a given pressure level and that errors in the satellite determined concluded that satellite profile data are very useful in synoptic analysis, particularly in data sparse regions as well as regions where near extreme gradients exist in the measured parameters.

Scoggins, J. R.↗

NASA NTRS1988

Ascent structural wind loads for the National Space Transportation System (NSTS)

An aerospace vehicle must withstand the design ascent structural load or be within any flight constraint to safely reach orbit. For the NSTS an assessment for flight readiness is made for each mission. This assessment for ascent loads and performance parameters is stated in terms of launch probability derived from samples of Jimsphere wind profiles. For the day of launch operations an allowance is made for the ascent load variability due to the wind profile variability over a 3.5-hour period. This is the current time period required to obtain a Jimsphere measurement and perform a complete load simulation prior to launch time. The load variability is derived from a sample of 3.5-hour Jimsphere wind profile pairs. It is applied as a conditional percentile value given the wind load at 3.5 hours prior to launch time. Probability models are used for these analyses. Application of the univariate, bivariate and conditional forms of the extreme value (Gumbel) probability distribution to the analysis of extreme wind speeds, wind shears and vehicle loads is described.

Smith, O. E.↗

NASA NTRS1988

IRAS observations of an optically selected sample of interacting galaxies

IRAS observations of a large, morphologically selected sample of strongly interacting disk-type galaxies have demonstrated that galaxy-galaxy collisions can lead to enhanced infrared emission, but not in all cases. Infrared luminosities of the interacting galaxies span a large range, but are about a factor of 2 higher, on average, than those of isolated disk galaxies. The data suggest the existence of a cutoff in blue luminosity, below which no galaxies show markedly enhanced infrared emission. Only the most strongly interacting systems in the sample show extreme values of infrared excess, suggesting that deep, interpenetrating collisions are necessary to drive infrared emission to extreme levels. Comparisons with optical indicators of star formation show that infrared excess and color temperatures correlate with the level of star-formation activity in the interacting galaxies. All interacting galaxies in our sample that exhibit an infrared excess and have higher than normal color temperatures also have optical indicators of high levels of star formation. It is not necessary to invoke processes other than star formation to account for the enhanced infrared luminosity in this sample of interacting galaxies.

Bushouse, Howard A.↗

NASA NTRS2015

Regional-Scale High-Latitude Extreme Geoelectric Fields Pertaining to Geomagnetically Induced Currents

Motivated by the needs of the high-voltage power transmission industry, we use data from the high-latitude IMAGE magnetometer array to study characteristics of extreme geoelectric fields at regional scales. We use 10-s resolution data for years 1993-2013, and the fields are characterized using average horizontal geoelectric field amplitudes taken over station groups that span about 500-km distance. We show that geoelectric field structures associated with localized extremes at single stations can be greatly different from structures associated with regionally uniform geoelectric fields, which are well represented by spatial averages over single stations. Visual extrapolation and rigorous extreme value analysis of spatially averaged fields indicate that the expected range for 1-in-100-year extreme events are 3-8 V/km and 3.4-7.1 V/km, respectively. The Quebec reference ground model is used in the calculations.

polar regions↗

NASA NTRS1972

Surface atmospheric extremes (Launch and transportation areas)

The effects of extreme values of surface and low altitude atmospheric parameters on space vehicle design, tests, and operations are discussed. Atmospheric extremes from the surface to 150 meters for geographic locations of interest to NASA are given. Thermal parameters (temperature and solar radiation), humidity, pressure, and atmospheric electricity (lighting and static) are presented. Weather charts and tables are included.

Source record↗

NASA NTRS2004

On the Positive Bias of Peak Horizontal Velocity from an Idealized Doppler Profiler

In the presence of 3-D turbulence, peak horizontal velocity estimates from an idealized Doppler profiler are found to be positively biased due to an incomplete specification of the vertical velocity field. The magnitude of the bias was estimated by assuming that the vertical and horizontal velocities can be separated into average and perturbation values and that the vertical and horizontal velocity perturbations are normally distributed. Under these assumptions, properties of the Type-I Extreme Value Distribution for maxima, known as the Gumbel distribution, can be used to obtain an analytical solution of the bias. The bias depends on geometric properties of the profiler configuration, the variance in the horizontal velocity, and the unresolved variance in the vertical velocity. When these variances are normalized by the average horizontal velocity, the bias can be mapped as a simple function of the normalized variances.

Short, David A.↗

NASA NTRS1986

Heat flow and heat generation in greenstone belts

Heat flow has been measured in Precambrian shields in both greenstone belts and crystalline terrains. Values are generally low, reflecting the great age and tectonic stability of the shields; they range typically between 30 and 50 mW/sq m, although extreme values of 18 and 79 mW/sq m have been reported. For large areas of the Earth's surface that are assumed to have been subjected to a common thermotectonic event, plots of heat flow against heat generation appear to be linear, although there may be considerable scatter in the data. The relationship is expressed as: Q = Q sub o + D A sub o in which Q is the observed heat flow, A sub o is the measured heat generation at the surface, Q sub o is the reduced heat flow from the lower crust and mantle, and D, which has the dimension of length, represents a scale depth for the distribution of radiogenic elements. Most authors have not used data from greenstone belts in attempting to define the relationship within shields, considering them unrepresentative and preferring to use data from relatively homogeneous crystalline rocks. A discussion follows.

Drury, M. J.↗

NASA NTRS1989

Analysis of extreme wind shear

New methods utilizing extreme value statistical theory are applied in the analysis of the largest wind component shear in a wind profile as a function of shear layer thickness and season. Seasonal variability of extreme shear decreases as the shear layer thickness decreases. Wind profile measurement system smoothing and its effect upon extreme wind shear statistics is simulated by application of digital low-pass filters to Jimsphere wind profiles.

Adelfang, Stanley I.↗

NASA NTRS1994

Lower mantle thermal structure deduced from seismic tomography, mineral physics and numerical modelling

The long-wavelength thermal anomalies in the lower mantle have been mapped out using several seismic tomographic models in conjunction with thermodynamic parameters derived from high-pressure mineral physics experiments. These parameters are the depth variations of thermal expansivity and of the proportionality factor between changes in density and seismic velocity. The giant plume-like structures in the lower mantle under the Pacific Ocean and Africa have outer fringes with thermal anomalies around 300-400 K, but very high temperatures are found in the center of the plumes near the base of the core-mantle boundary. These extreme values can exceed +1500 K and may reflect large hot thermal anomalies in the lower mantle, which are supported by recent measurements of high melting temperatures of perovskite and iron. Extremely cold anomalies, around -1500 K, are found for anomalies in the deep mantle around the Pacific rim and under South America. Numerical simulations show that large negative thermal anomalies in the mid-lower mantle have modest magnitudes of around -500 K. correlation pattern exists between the present-day locations of cold masses in the lower mantle and the sites of past subduction since the Cretaceous. Results from correlation analysis show that the slab mass-flux in the lower mantle did not conform to a steady-state nature but exhibited time-dependent behavior.

Cadek, O.↗

NASA NTRS1990

Analysis of wind perturbation activity for the NSTS launches

A method for statistical analysis of wind perturbation activity is proposed for application in NSTS prelaunch assessment of the wind environment. Methods for comparing day-of-launch wind perturbation standard deviations with historical data at the launch site are described. A statistical model based on the Gumbel extreme value distribution is developed for the largest vector and component perturbations in a wind profile as a function of high-pass filter cutoff wavelength. Application of the model is proposed for assessing extreme perturbations within prelaunch sequential Jimsphere wind profiles.

Adelfang, Stanley I.↗

DOE OSTI2026

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The 'ABCD method' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two smoothly varying, statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCD with distance correlation (ABCDisCo) method was developed to construct two learned variables via a neural network trained to provide strong signal-background discrimination with small values of the distance correlation (DisCo) measure between the two learned variables. However, relying solely on minimizing the DisCo can result in learned variables that may not have distributions of background events that are smoothly varying and localized at extreme values, as necessary for the validity of the background estimation. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to solve this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN Large Hadron Collider. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2025

Mechanisms of Projected Changes in Thunderstorm Downburst Environments Across the United States

Responses of downdraft convective available potential energy (DCAPE) to global warming were investigated using the Community Earth System Model (CESM2) under a high‐emission scenario through the year 2100. DCAPE is projected to increase by 5%–12% on average in most areas, independently of wind shear. A diagnostic of downdraft buoyancy is introduced to understand the mechanisms of DCAPE responses. Much of the increase in mean DCAPE is temperature‐driven, with additional contributions from changes in relative humidity and downdraft origin heights. However, extreme values increase at much faster rates than can be explained by local warming. In winter, the latitude of significant DCAPE and CAPE shifts poleward by more than 5° due to larger changes in downburst environments within midlatitude cyclones. The projected increase in cold‐season extremes indicates an interaction between weather events and warming trends that increases the potential for downbursts and straight‐line winds in winter.

Williams, Ian N. [Iowa State Univ., Ames, IA (Unit↗

DOE OSTI2024

Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least $τ$% times the minimum refractivity gradient magnitude, where $τ$ is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different $τ$ values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes.

54 ENVIRONMENTAL SCIENCES↗