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At least 289 records · Page 16

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NASA NTRS2021

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.

Huanxin Zhang↗

DOE OSTI2021

Validation of Subhourly Clipping Loss Error Corrections: Preprint

Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.6\% and 1.1\% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.4\% and 2.5\% reduction in mean bias error, respectively, when the clipping correction was applied.

machine learning↗

DOE OSTI2024

Advanced Energy Scale Correction Techniques for the X-ray Transition Edge Sensors of the Athena mission

The X-ray Integral Field Unit (X-IFU) onboard the future European X-ray telescope Athena will be the first space instrument carrying an array of more than a thousand transition edge sensors. One of the key challenges of the X-IFU is the measurement of narrow X-ray atomic lines to determine velocity shifts at an unprecedented level of accuracy. For this reason, the energy scale of the instrument needs to be known with extreme accuracy, of 0.4 eV (1σ) up to 7 keV. The energy scale will be measured on the ground through a dedicated calibration campaign using fiducial X-ray sources. Though calibrated, the energy scale is extremely sensitive to the environmental conditions around the TES array, and drifts in the readout chain electronics. Uncorrected, the energy scale can naturally drift up to hundreds of eVs. Changes of the TES gain will be monitored via onboard X-ray calibration sources, and the energy scale will be corrected either per pixel, or within a small groups of pixels. Although simulations show that a 0.4 eV level can be achieved, the very high accuracy required by the X-IFU calls for experimental validation. A dedicated measurement campaign has been performed by NASA Goddard Space Flight Center to characterize the energy scale of a prototype kilo-pixel array of X-IFU-representative TESs. The analysis of the data demonstrated the ability to correct for various drifts using two fiducial lines to track the temporal gain variation. In this paper, we propose to extend this study on the same data set by investigating multi-parameter correction techniques based on both the pulse-height of the fiducial line and the prepulse baseline level, using the knowledge of the TES energy scale at reference temperature/magnetic field set points acquired on the ground. Investigations on the co-adding of pixels to perform a joint correction over pools of pixels is also explored.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

A relative radiometric correction method for airborne SWIR hyperspectral image using the side-slither technique

Stripe noise still remains in airborne short-wave infrared (SWIR) hyperspectral (HS) images after laboratory calibration due to the stray light of HS imager, nonlinear response of infrared focal plane array, and the distinct difference of equivalent color temperature between the integrating sphere and the sun. It is difficult to get a sun-like radiation source, and we apply the side-slither technique for relative radiometric correction of HS images. The calibration data corresponding to different irradiance were obtained by the side-slither technique of imager. Then, the two-point multi-section method is used for relative radiometric correction of HS images. This paper presents the principle, the experimental results, and the analysis of the proposed method. To validate the effectiveness of this method, it was compared with other methods and evaluated by quantitative quality indices. The results reveal that this method has a good performance in relative radiometric correction of HS image and is superior to the laboratory calibration based on integrating sphere. Consequently, the proposed method can successfully eliminate the adverse effect caused by the difference of equivalent color temperature between radiation sources, and also can improve the accuracy of HS applications such as absolute radiation correction and target recognition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Accelerating eigenvalue computation for nuclear structure calculations via perturbative corrections

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

CRISPR/Cas correction of muscular dystrophies

Muscular dystrophies are a heterogeneous group of monogenic neuromuscular disorders which lead to progressive muscle loss and degeneration of the musculoskeletal system. The genetic causes of muscular dystrophies are well characterized, but no effective treatments have been developed so far. The discovery and application of the CRISPR/Cas system for genome editing offers a new path for disease treatment with the potential to permanently correct genetic mutations. The post-mitotic and multinucleated features of skeletal muscle provide an ideal target for CRISPR/Cas therapeutic genome editing because correction of a subpopulation of nuclei can provide benefit to the whole myofiber. In this review, we provide an overview of the CRISPR/Cas system and its derivatives in genome editing, proposing potential CRISPR/Cas-based therapies to correct diverse muscular dystrophies, and we discuss challenges for translating CRISPR/Cas genome editing to a viable therapy for permanent correction of muscular dystrophies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Learning to Correct Climate Projection Biases

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.

58 GEOSCIENCES↗

DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Ancilla-Free Quantum Error Correction Codes for Quantum Metrology

Quantum error correction has recently emerged as a tool to enhance quantum sensing under Markovian noise. It works by correcting errors in a sensor while letting a signal imprint on the logical state. This approach typically requires a specialized error-correcting code, as most existing codes correct away both the dominant errors and the signal. To date, however, few such specialized codes are known, among which most require noiseless, controllable ancillas. We show here that such ancillas are not needed when the signal Hamiltonian and the error operators commute, a common limiting type of decoherence in quantum sensors. We give a semidefinite program for finding optimal ancilla-free sensing codes in general, as well as closed-form codes for two common sensing scenarios: qubits undergoing dephasing, and a lossy bosonic mode. Lastly, we analyze the sensitivity enhancement offered by the qubit code under arbitrary spatial noise correlations, beyond the ideal limit of orthogonal signal and noise operators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Pion-Induced Radiative Corrections to Neutron β Decay

In this work, we compute the electromagnetic corrections to neutron β decay using a low-energy hadronic effective field theory. We identify new radiative corrections arising from virtual pions that were missed in previous studies. The largest correction is a percent-level shift in the axial charge of the nucleon proportional to the electromagnetic part of the pion-mass splitting. Smaller corrections, comparable to anticipated experimental precision, impact the β-ν angular correlations and the β asymmetry. We comment on implications of our results for the comparison of the experimentally measured nucleon axial charge with first-principles computations using lattice QCD and on the potential of β decay experiments to constrain beyond-the-standard-model interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Perturbative Stability and Error-Correction Thresholds of Quantum Codes

Topologically ordered phases are stable to local perturbations, and topological quantum error-correcting codes enjoy thresholds to local errors. We connect the two notions of stability by constructing classical statistical mechanics models for decoding general Calderbank-Shor-Steane codes and classical linear codes. Our construction encodes correction success probabilities under uncorrelated bit-flip and phase-flip errors, and simultaneously describes a generalized ℤ 2 lattice-gauge theory with quenched disorder. We observe that the clean limit of the latter is precisely the discretized imaginary-time path integral of the corresponding quantum code Hamiltonian when the errors are turned into a perturbative 𝑋 or 𝑍 magnetic field. Motivated by error-correction considerations, we define general order parameters for all such generalized ℤ 2 lattice-gauge theories, and show that they are generally lower bounded by success probabilities of error correction. For CSS codes satisfying the low-density parity-check condition and with a sufficiently large code distance, we prove the existence of a low-temperature ordered phase of the corresponding lattice-gauge theories, particularly for those lacking Euclidean spatial locality and/or when there is a nonzero code rate. We further argue that these results provide evidence for stable phases in the corresponding perturbed quantum Hamiltonians, obtained in the limit of continuous imaginary time. To do so, we distinguish space- and timelike defects in the lattice-gauge theory. A high free-energy cost of spacelike defects corresponds to a successful “memory experiment” and suppresses the energy splitting among the ground states, while a high free-energy cost of timelike defects corresponds to a successful “stability experiment” and points to a nonzero gap to local excitations.

quantum error correction↗

DOE OSTI2017

Hybrid Cascading Outage Analysis of Extreme Events with Optimized Corrective Actions

Power system are vulnerable to extreme contingencies (like an outage of a major generating substation) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Some cascading outages are seen within minutes following a major contingency, which may not be captured exclusively using the dynamic simulation of the power system. The utilities plan for contingencies either based on dynamic or steady state analysis separately which may not accurately capture the impact of one process on the other. We address this gap in cascading outage analysis by developing Dynamic Contingency Analysis Tool (DCAT) that can analyze hybrid dynamic and steady state behavior of the power system, including protection system models in dynamic simulations, and simulating corrective actions in post-transient steady state conditions. One of the important implemented steady state processes is to mimic operator corrective actions to mitigate aggravated states caused by dynamic cascading. This paper presents an Optimal Power Flow (OPF) based formulation for selecting corrective actions that utility operators can take during major contingency and thus automate the hybrid dynamic-steady state cascading outage process. The improved DCAT framework with OPF based corrective actions is demonstrated on IEEE 300 bus test system.

Cascading outage↗

DOE OSTI2020

Corrective Actions Plan for Real Property Asset Management

The purpose of this Triad Infrastructure Programs Office (IFPROG) Real Property Asset Management (RPAM) Corrective Actions Plan (CAP) is to formally identify and document completion of the associated corrective actions. It presents IFPROG work scope in the context of executing RPAM requirements and Comprehensive Site Planning; the drivers and rationale for the corrective actions; and, a corrective actions summary table that includes the scope, owner, deliverables with dates, and implementation status.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

An Evolve-Then-Correct Reduced Order Model for Hidden Fluid Dynamics

In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics into known and unknown components. In the known part, we first utilize an intrusive Galerkin method projected on a set of basis functions obtained by proper orthogonal decomposition. We then present two variants of correction formula based on the assumption that the observed data are a manifestation of all relevant processes. The first method uses a standard least-squares regression with a quadratic approximation and requires solving a rank-deficient linear system, while the second approach employs a recurrent neural network emulator to account for the correction term. We further enhance our approach by using an orthonormality conforming basis interpolation approach on a Grassmannian manifold to address off-design conditions. The proposed framework is illustrated here with the application of two-dimensional co-rotating vortex simulations under modeling uncertainty. The results demonstrate highly accurate predictions underlining the effectiveness of the evolve-then-correct approach toward real-time simulations, where the full process model is not known a priori.

long short-term memory↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: precipitation mode, corrected

The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrpr' (precipitation) mode output from this VAP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: general mode, corrected

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: moderate sensitivity mode, corrected

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Continental-Scale Validation of Modis-Based and LEDAPS Landsat ETM + Atmospheric Correction Methods

The potential of Landsat data processing to provide systematic continental scale products has been demonstratedby several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent freeavailability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicableto large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correctionmethods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive ProcessingSystem (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmosphericcharacterization approaches. The MODIS-based method uses the MODIS Terra derived dynamicaerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions ineach coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from eachLandsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validationresults are presented comparing ETM+ atmospherically corrected data generated using these two methodswith AERONET corrected ETM+ data for 95 10 km10 km 30 m subsets, a total of nearly 8 million 30 mpixels, located across the conterminous United States. The results indicate that the MODIS-based methodhas better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.

Atmospheric correction↗