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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Development of performance specifications for hybrid modeling of floating wind turbines in wave basin tests

Hybrid modeling—combining physical testing and numerical simulation in real time$-$opens new opportunities in floating wind turbine research. Wave basin testing is an important validation step for floating support structure design, but the conventional approaches that use physical wind above the basin are limited by scaling problems in the aerodynamics. Applying wind turbine loads with an actuation system that is controlled by a simulation responding to the basin test in real time offers a way to avoid scaling problems and reduce cost barriers for floating wind turbine design validation in realistic coupled wind and wave conditions. This paper demonstrates the development of performance specifications for a system that couples a wave basin experiment with a wind turbine simulation. Two different points for the hybrid coupling are considered: the tower-base interface and the aero-rotor interface (the boundary between aerodynamics and the rotor structure). Analyzing simulations of three floating wind turbine designs across seven load cases reveals the motion and force requirements of the coupling system. By simulating errors in the hybrid coupling system, the sensitivity of the floating wind turbine response to coupling quality can be quantified. The sensitivity results can then be used to determine tolerances for motion tracking errors, force actuation errors, bandwidth limitations, and latency in the hybrid coupling system. These tolerances can guide the design of hybrid coupling systems to achieve desired levels of accuracy. An example demonstrates how the developed methods can be used to generate performance specifications for a system at 1:50 scale. Results show that sensitivities vary significantly between support structure designs and that coupling at the aero-rotor interface has less stringent requirements than those for coupling at the tower base. As a result, the methods and results presented here can inform design of future hybrid coupling systems and enhance understanding of how test results are affected by hybrid coupling quality.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

A radio-frequency sheath model for complex waveforms

Plasma sheaths driven by radio-frequency voltages occur in contexts ranging from plasma processing to magnetically confined fusion experiments. An analytical understanding of such sheaths is therefore important, both intrinsically and as an element in more elaborate theoretical structures. Radio-frequency sheaths are commonly excited by highly anharmonic waveforms, but no analytical model exists for this general case. We present a mathematically simple sheath model that is in good agreement with earlier models for single frequency excitation, yet can be solved for arbitrary excitation waveforms. As examples, we discuss dual-frequency and pulse-like waveforms. The model employs the ansatz that the time-averaged electron density is a constant fraction of the ion density. In the cases we discuss, the error introduced by this approximation is small, and in general it can be quantified through an internal consistency condition of the model. This simple and accurate model is likely to have wide application.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Thermographic process monitoring in powderbed based additive manufacturing

Selective Laser Melting is utilized to build metallic parts directly from CAD-Data by solidification of thin powder layers through application of a fast scanning laser beam. In this study layerwise monitoring of the temperature distribution is used to gather information about the process stability and the resulting part quality. The heat distribution varies with different kinds of parameters including scan vector length, laser power, layer thickness and inter-part distance in the job layout which in turn influence the resulting part quality. By integration of an off-axis mounted uncooled thermal detector the solidification as well as the layer deposition are monitored and evaluated. Errors in the generation of new powder layers usually result in a locally varying layer thickness that may cause poor part quality. For effect quantification, the locally applied layer thickness is determined by evaluating the heat-up of the newly deposited powder. During the solidification process space and time-resolved data is used to characterize the zone of elevated temperatures and to derive locally varying heat dissipation properties. Potential quality indicators are evaluated and correlated to the resulting part quality: Thermal diffusivity is derived from a simplified heat dissipation model and evaluated for every pixel and cool-down phase of a layer. This allows the quantification of expected material homogeneity properties. Maximum temperature and time above certain temperatures are measured in order to detect hot spots or delamination issues that may cause a process breakdown. Furthermore, a method for quantification of sputter activity is presented. Since high sputter activity indicates unstable melt dynamics this can be used to identify parameter drifts, improper atmospheric conditions or material binding errors. The resulting surface structure after solidification complicates temperature determination on the one hand but enables the detection of potential surface defects on the other hand. These issues and proper key figures for thermographic monitoring of the Selective Laser Melting process are discussed in the paper. Even though microbolometric temperature measurement is limited to repetition rates in the Hz-regime and sub megapixel resolution, current results show the feasibility of process surveillance by thermography for a limited section of the building platform in a commercial system.

42 ENGINEERING↗
DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Viscosity of Iron Oxide Aluminosilicate Melts

A structure-based viscosity model has been developed for the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems. A critical analysis of the experimental data for iron oxide aluminosilicate systems has been conducted and used in the optimization of a model, which provides a link between the viscosity and the structures of the melts. The structural characteristics of aluminosilicate melt can be described by four types of oxygen: bridging oxygen O{sub i}{sup 0}, non-bridging oxygen O{sub j}{sup −}, free oxygen O{sup 2−}, and excess bridging oxygen O{sup *}. The present model is capable of predicting the viscosities in the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems over wide ranges of composition and temperature above the liquidus within experimental uncertainties, and the average of relative errors for this model was found to be 19.94 pct. This model includes FeO-SiO{sub 2}, CaO-SiO{sub 2}-FeO, Al{sub 2}O{sub 3}-SiO{sub 2}-FeO, MgO-SiO{sub 2}-FeO, CaO-SiO{sub 2}-MgO-FeO, CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO, and CaO-MgO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO. It was found that the effects of different metal cations on the viscosity can be determined, and the ability to reduce viscosity in the silicate melts follows the order: FeO > CaO > MgO. In the present model, the predicted viscosity decreased when CaO was initially initial added to the Al{sub 2}O{sub 3}-MgO-FeO-SiO{sub 2} system at a fixed SiO{sub 2} content because Ca{sup 2+} has the highest priority for charge compensation and FeO is the strongest modifier for this slag.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Lignin is a natural source of aromatic chemicals with significant potential as an abundant, renewable feedstock for value-added products. Deep eutectic solvents (DES)–solvents composed of a hydrogen bond donor (HBD) and acceptor (HBA) in varying ratios–have emerged as a highly tunable class of solvents for lignin solubilization. However, the variety of possible DES compositions and limited molecular-scale understanding of lignin solubility makes solvent selection a challenge without laborious trial-and-error experimentation. To address these challenges, we use classical molecular dynamics (MD) simulations to study the interactions of lignin model compounds with various DES–water systems. Quantitative parameters (descriptors) were calculated by postprocessing the MD results and used to train a regression model that predicts experimentally determined solubilities of lignin model compounds. This approach revealed that the most important descriptors of solubility are the system temperature, solute hydrophilicity, and metrics quantifying hydrogen bonding. Maximizing the interactions between solute–HBD (hydrophobic group), water–HBD (hydrophilic group), and water–HBA molecules led to the highest model compound solubility. Our results support a hydrotropic mechanism in which extensive DES–water hydrogen bonding and favorable HBD interactions with the solute promote high solubility. We applied the regression model derived using model compounds to predict the solubility of representative lignin oligomers. The model predicted lignin oligomers’ solubilities in good agreement with experiments, indicating that the simulations of model compounds can be extended to predict the solubility of larger lignin compounds across a range of solvent compositions and temperatures. Furthermore, these findings provide new molecular-scale insight into lignin solubilization mechanisms and a new method for computationally screening potential solvent systems for lignin valorization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

THE MEGAMASER COSMOLOGY PROJECT. IV. A DIRECT MEASUREMENT OF THE HUBBLE CONSTANT FROM UGC 3789

In Papers I and II from the Megamaser Cosmology Project, we reported initial observations of H{sub 2}O masers in an accretion disk of a supermassive black hole at the center of the galaxy UGC 3789, which gave an angular-diameter distance to the galaxy and an estimate of H{sub 0} with 16% uncertainty. We have since conducted more very long baseline interferometric observations of the spatial-velocity structure of these H{sub 2}O masers, as well as continued monitoring of its spectrum to better measure maser accelerations. These more extensive observations, combined with improved modeling of the masers in the accretion disk of the central supermassive black hole, confirm our previous results, but with significantly improved accuracy. We find H{sub 0} = 68.9 {+-} 7.1 km s{sup -1} Mpc{sup -1}; this estimate of H{sub 0} is independent of other methods and is accurate to {+-}10%, including sources of systematic error. This places UGC 3789 at an angular-diameter distance of 49.6 {+-} 5.1 Mpc, with a central supermassive black hole of (1.16 {+-} 0.12) Multiplication-Sign 10{sup 7} M{sub Sun }.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Revisiting surface core-level shifts for ionic compounds

The shifts of core-level binding energies can provide powerful information about the electronic structure of a material. Understanding the physical origin of these shifts for catalytically relevant oxides may provide important insight into their properties. This requires reliable theoretical methods which are able to relate the binding energy shifts to the electronic structure. In order to establish such a methodology, the CaO(100) surface to bulk core-level binding energy shifts have been studied with Hartree-Fock and density-functional theory methods using both cluster and periodic slab models. The shifts obtained from the different theoretical methods are compared with each other and with data from synchrotron-based x-ray photoelectron spectroscopy (XPS) measurements. With a common approximation for the slab model treatment of XPS, the predicted binding energy shifts are seriously in error. In this work, the origin of the error is identified as arising from a flawed treatment of the surface atom binding energies, and a method for correcting the failure is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Tiling Framework for Heterogeneous Computing of Matrix based Tiled Algorithms

Tiling matrix operations can improve the load balancing and performance of applications on heterogeneous computing resources. Writing a tile-based algorithm for each operation with a traditional, hand-tuned tiling approach that uses for loops in C/C++ is cumbersome and error prone. Moreover, it must enable and support the heterogeneous memory management of data objects and also explore architecture-supported, native, tiled-data transfer APIs instead of copying the tiled data to continuous memory before the data transfer. The tiling framework provides a tiled data structure for heterogeneous memory mapping and parameterization to a heterogeneous task specification API. We have integrated our tiled framework into MatRIS (Math kernels library using IRIS). IRIS is a heterogeneous run-time framework with a heterogeneous programming model, memory model, and task execution model. Experiments reveal that the tiled framework for BLAS operations has improved the programmability of tiled BLAS and improved performance by ~20% when compared against the traditional method that copies the data to continuous memory locations for heterogeneous computing.

Miniskar, Narasinga Rao↗
DOE OSTI2012

Modeling the Structural Response from a Propagating High Explosive Using Smooth Particle Hydrodynamics

This report primarily concerns the use of two massively parallel finite element codes originally written and maintained at Lawrence Livermore National Laboratory. ALE3D is an explicit hydrodynamics code commonly employed to simulate wave propagation from high energy scenarios and the resulting interaction with nearby structures. This coupled response ensures that a structure is accurately applied with a blast loading varying both in space and time. Figure 1 illustrates the radial outward propagation of a pressure wave due to a center detonated spherical explosive originating from the lower left. The radial symmetry seen in this scenario is lost when instead a cylindrocal charge is detonated. Figure 2 indicates that a stronger, faster traveling pressure wave occurs in the direction of the normal axis to the cylinder. The ALE3D name is derived because of the use of arbitrary-Lagrange-Eulerian elements in which the mesh is allowed to advect; a process through which the mesh is modified to alleviate tanlging and general mesh distortion often cuased by high energy scenarios. The counterpart to an advecting element is a Lagrange element, whose mesh moves with the material. Ideally all structural components are kept Lagrange as long as possible to preserve accuracy of material variables and minimize advection related errors. Advection leads to mixed zoning, so using structural Lagrange elements also improves the visualization when post processing the results. A simplified representation of the advection process is shown in Figure 3. First the mesh is distorted due to material motion during the Lagrange step. The mesh is then shifted to an idealized and less distorted state to prevent irregular zones caused by the Lagrange motion. Lastly, the state variables are remapped to the elements of the newly constructed mesh. Note that Figure 3 represents a purely Eulerian mesh relaxation because the mesh is relocated back to the pre-Lagrange position. This is the case when the material flows through a still mesh. This is not typically done in an ALE3D analysis, especially if Lagrange elements exist. Deforming Lagrange elements would certainly tangle with a Eulerian mesh eventually. The best method in this case is to have an advecting mesh positioned as some relaxed version of the pre and post Lagrange step; this gives the best opportunity of modeling a high energy event with a combination of Lagrange and ALE elements. Dyne3D is another explicit dynamic analysis code, ParaDyn being the parallel version. ParaDyn is used for predicting the transient response of three dimensional structures using Lagrangian solid mechanics. Large deformation and mesh tangling is often resolved through the use of an element deletion scheme. This is useful to accommodate component failure, but if it is done purely as a means to preserve a useful mesh it can lead to problems because it does not maintain continuity of the material bulk response. Whatever medium exists between structural components is typically not modeled in ParaDyn. Instead, a structure either has a known loading profile applied or given initial conditions. The many included contact algorithms can calculate the loading response of materials if and when they collide. A recent implementation of an SPH module in which failed or deleted material nodes are converted to independent particles is currently being utilized for a variety of spall related problems and high velocity impact scenarios. Figure 4 shows an example of a projectile, given an initial velocity, and how it fails the first plate which generates SPH particles which then interact with and damage the second plate.

36 MATERIALS SCIENCE↗
DOE OSTI2019

van der Waals Correction to the Physisorption of Graphene on Metal Surfaces

Adsorption is a scientifically and technologically important interfacial phenomenon, which however presents challenges to conventional density functional theory (DFT) due to the long-range van der Waals (vdW) interactions. We have developed a model of long-range vdW correction for physisorption of graphene (G) on metals with the Lifshitz–Zaremba–Kohn second-order perturbation theory, by incorporating dipole- and quadrupole-surface interactions and screening effects. The physisorption energies calculated by the model between graphene and eight metal surfaces (Al, Ni, Co, Pd, Pt, Cu, Ag, and Au), and the adsorption energies for the same G/metal structures from self-consistent DFT PBE (Perdew–Burke–Ernzerhof) calculations, are obtained in a range of distances between G and the metal surfaces. The sum of these two parts is the total adsorption energy as a function of the distance, from which the equilibrium distance and the binding energy are determined simultaneously. The results show high accuracy, with the mean absolute error (MAE) of binding energy of 7 meV and the MAE of equilibrium distance of 0.2 Å, significantly improving upon other vdW methods. Here, the PBE + vdW binding energy curves give better fits to the random phase approximation curves around the equilibrium distances than do the curves of other methods considered here. The higher-order quadrupole-surface correction is important and accounts for about 30% of the total vdW correction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale Physics of Atomic Nuclei from First Principles

Atomic nuclei exhibit multiple energy scales ranging from hundreds of MeV in binding energies to fractions of an MeV for low-lying collective excitations. As the limits of nuclear binding are approached near the neutron and proton drip lines, traditional shell structure starts to melt with an onset of deformation and an emergence of coexisting shapes. It is a long-standing challenge to describe this multiscale physics starting from nuclear forces with roots in quantum chromodynamics. Here, we achieve this within a unified and nonperturbative quantum many-body framework that captures both short- and long-range correlations starting from modern nucleon-nucleon and three-nucleon forces from chiral effective field theory. The short-range (dynamic) correlations which account for the bulk of the binding energy are included within a symmetry-breaking framework, while long-range (static) correlations (and fine details about the collective structure) are included by employing symmetry projection techniques. Our calculations accurately reproduce—within theoretical error bars—available experimental data for low-lying collective states and the electromagnetic quadrupole transitions in 20−30 Ne. In addition, we reveal coexisting spherical and deformed shapes in 30 Ne, which indicates the breakdown of the magic neutron number 𝑁 = 20 as the key nucleus 28 O is approached, and we predict that the drip line nuclei 32,34 Ne are strongly deformed and collective. By developing reduced-order models for symmetry-projected states, we perform a global sensitivity analysis and find that the subleading singlet 𝑆-wave contact and a pion-nucleon coupling strongly impact nuclear deformation in chiral effective field theory. The techniques developed in this work clarify how microscopic nuclear forces generate the multiscale physics of nuclei spanning collective phenomena as well as short-range correlations and allow one to capture emergent and dynamical phenomena in finite fermion systems such as atom clusters, molecules, and atomic nuclei.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

SU-F-P-05: Initial Experience with an Independent Certification Program for Stereotactic Radiosurgery and Stereotactic Body Radiation Therapy

Purpose: The ASTRO document “Safety is no accident: A FRAMEWORK FOR QUALITY RADIATION ONCOLOGY AND CARE” recommends external reviews of specialized modalities. The purpose of this presentation is to describe the implementation of such a program for Stereotactic Radiosurgery (SRS) and Stereotactic Body radiation Therapy (SBRT). Methods: The margin of error for SRS and SBRT delivery is significantly smaller than that of conventional radiotherapy and therefore requires special attention and diligence. The Novalis Certified program was created to fill an unmet need for specialized SRS / SBRT credentialing. A standards document was drafted by a panel of experts from several disciplines, including medical physics, radiation oncology and neurosurgery. The document, based on national and international standards, covers requirements in program structure, personnel, training, clinical application, technology, quality management, and patient and equipment QA. The credentialing process was modeled after existing certification programs and includes an institution-generated self-study, extensive document review and an onsite audit. Reviewers generate a descriptive report, which is reviewed by a multidisciplinary expert panel. Outcomes of the review may include mandatory requirements and optional recommendations. Results: 15 institutions have received Novalis Certification, including 3 in the US, 7 in Europe, 4 in Australia and 1 in Asia. 87 other centers are at various stages of the process. Nine reviews have resulted in mandatory requirements, however all of these were addressed within three months of the audit report. All reviews have produced specific recommendations ranging from programmatic to technical in nature. Institutions felt that the credentialing process addressed a critical need and was highly valuable to the institution. Conclusion: Novalis Certification is a unique peer review program assessing safety and quality in SRS and SBRT, while recognizing international practice standards. The approach is capable of highlighting outstanding requirements and providing recommendations to enhance both new and established programs. Timothy Solberg is co-owner of Global Radiosurgery services, LLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗
DOE OSTI2025

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗
DOE OSTI2022

Analysis of Integrated Vapor Transport Biases

Abstract High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.

Reynolds, Carolyn A.↗