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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Decadal Prediction Skill in the GEOS-5 Forecast System

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office?s GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). The hindcasts are initialized every December from 1959 to 2010 following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from the atmospheric reanalysis (MERRA, the Modern-Era Retrospective Analysis for Research and Applications) generated using the GEOS-5 atmospheric model. The forecast skill of a three-member-ensemble mean is compared to that of an experiment without initialization but forced with observed CO2. The results show that initialization acts to increase the forecast skill of Northern Atlantic SST compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. The annual-mean Atlantic Meridional Overturning Circulation (AMOC) index is predictable up to a 5-year lead time, consistent with the predictable signal in upper ocean heat content over the Northern Atlantic. While the skill measured by Mean Squared Skill Score (MSSS) shows 50% improvement up to 10-year lead forecast over the subtropical and mid-latitude Atlantic, however, prediction skill is relatively low in the subpolar gyre, due in part to the fact that the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region appears to be unrealistic. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Ham, Yoo-Geun↗
NASA NTRS2012

Using M and S to Improve Human Decision Making and Achieve Effective Problem Solving in an International Environment

In the international arena, decision makers are often swayed away from fact-based analysis by their own individual cultural and political bias. Modeling and Simulation-based training can raise awareness of individual predisposition and improve the quality of decision making by focusing solely on fact vice perception. This improved decision making methodology will support the multinational collaborative efforts of military and civilian leaders to solve challenges more effectively. The intent of this experimental research is to create a framework that allows decision makers to "come to the table" with the latest and most significant facts necessary to determine an appropriate solution for any given contingency.

Christie, Vanessa L.↗
NASA NTRS2013

Decadal Prediction Skill in the GEOS-5 Forecast System

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Decadal Prediction↗
NASA NTRS2016

High Upward Fluxes of Formic Acid from a Boreal Forest Canopy

Eddy covariance fluxes of formic acid, HCOOH, were measured over a boreal forest canopy in spring/summer 2014. The HCOOH fluxes were bidirectional but mostly upward during daytime, in contrast to studies elsewhere that reported mostly downward fluxes. Downward flux episodes were explained well by modeled dry deposition rates. The sum of net observed flux and modeled dry deposition yields an upward gross flux of HCOOH, which could not be quantitatively explained by literature estimates of direct vegetative soil emissions nor by efficient chemical production from other volatile organic compounds, suggesting missing or greatly underestimated HCOOH sources in the boreal ecosystem. We implemented a vegetative HCOOH source into the GEOS-Chem chemical transport model to match our derived gross flux and evaluated the updated model against airborne and spaceborne observations. Model biases in the boundary layer were substantially reduced based on this revised treatment, but biases in the free troposphere remain unexplained.

forest↗
NASA NTRS2017

An Intercomparison of Changes Associated with Earth's Lower Tropospheric Temperature Using Traditional and AMIP-Style Reanalyses

Reanalyses have become an integral tool for evaluating regional and global climate variations, and an important component of this is modifications to the energy budget. Reductions in Arctic Sea ice extent has induced an albedo feedback, causing the Arctic to warm more rapidly than anywhere else in the world, referred to as "Arctic Amplification." This has been demonstrated by observations and numerous reanalyses, including the Modern Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). However, the Arctic Amplification signal is non-existent in a ten member ensemble of the MERRA-2 Atmospheric Model Intercomparison Project (M2AMIP) simulations, using the same prescribed climate forcing, including Sea Surface Temperature (SST) and ice. An evaluation of the temperature tendency within the lower troposphere due to radiation, moisture, and dynamics as well as the surface energy budget in MERRA-2 and M2AMIP will demonstrate that despite identical prescribed SSTs and sea ice in both versions, enhanced warming in the Arctic in MERRA-2 is in response to the analysis increment tendency due to temperature observations. Furthermore, the role of boundary conditions, model biases and changes in observing systems on the Arctic Amplification signal will be assessed. Literature on the topic of Arctic Amplification demonstrates that the enhanced warming begins in the mid-1990s. Anomalously warm Arctic SST in the early time period of MERRA-2 can mute the trend in Arctic lower troposphere temperature without the constraint of observations in M2AMIP. Additionally, MERRA-2 uses three distinct datasets of SST and sea ice concentration, which also plays a role.

Arcti↗
NASA NTRS2018

Winter Precipitation in North America and the Pacific-North America Pattern in GEOS-S2Sv2 Seasonal Hindcast

Reliable prediction of precipitation remains one of the most pivotal and complex challenges in seasonal forecasting. Previous studies show that various large-scale climate modes, such as ENSO, PNA and NAO play significant role in winter precipitation variability over the Northern America. The influences are most pronounced in years of strong indices of such climate modes. This study evaluates model bias, predictability and forecast skills of monthly winter precipitation in GEOS5-S2S 2.0 retrospective forecast from 1981 to 2016, with emphasis on the forecast skill of precipitation over North America during the extreme events of ENSO, PNA and NAO by applying EOF and composite analysis.

Li, Zhao↗
NASA NTRS2018

Climate Reanalysis: Progress and Future Prospects

Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of observations, typically spanning an extended segment of the historical data record. The process relies on an underlying model to combine often-disparate observations in a physically consistent manner, enabling production of gridded data sets for a broad range of applications including the study of historical weather events, preparation of climatologies, business sector development and, more recently, climate monitoring. Over the last few decades, several generations of reanalyses of the global atmosphere have been produced by various operational and research centers, focusing more or less on the period of regular conventional and satellite observations beginning in the mid to late twentieth century. There have also been successful efforts to extend atmospheric reanalyses back to the late nineteenth and early twentieth centuries, using mostly surface observations. Much progress has resulted from (and contributed to) advancements in numerical weather prediction, especially improved models and data assimilation techniques, increased computing capacity, the availability of new observation types and efforts to recover and improve the quality of historical ones. The recent extension of forecast systems that allow integrated modeling of meteorological, oceanic, land surface, and chemical variables provide the basic elements for coupled data assimilation. This has opened the door to the development of a new generation of coupled reanalyses of the Earth system, or integrated Earth system analyses (IESA). Evidence so far suggests that this approach can improve the analysis of currently uncoupled components of the Earth system, especially at their interface, and lead to increased predictability. However, extensive analysis coupling as envisioned for IESA, while progressing, still presents significant challenges. These include model biases that can be exacerbated when coupled, component systems with different physical characteristics and different spatial and temporal scales, and component observations in different media with different spatial and temporal frequencies and different latencies. Quantification of uncertainty in reanalyses is also a critical challenge and is important for expanding their utility as a tool for climate change assessment. This talk provides a brief overview of the progress of reanalysis development during recent decades, and describes remaining challenges in the progression toward coupled Earth system reanalyses.

IES↗
NASA NTRS2025

Pose Estimation in the Geometric Algebra G (3, 0, 1)

Using techniques from Geometric Algebra, methods for solving attitude determination problems are extended to the problem of simultaneously estimating attitude and location, i.e. pose, from direct or indirect measurements of geometric objects including points, lines, and planes. Wahba’s problem is generalized for the solution of initial pose. The multiplicative extended Kalman filter is generalized for the maintenance of pose solutions. Farrankopf’s model for biases in rotation rate sensors is generalized to biases in twist rate measurements. A multiplicative extended Kalman filter for pose in the presence of such biases is developed.

Sensor Bias Modeling↗
DOE OSTI2024

Validation of Weather Forecasting Products

Weather forecasting is a tool in which the Savannah River Site relies on to complete daily tasks. The importance and integrity of forecasting systems and models is of the utmost importance to safety at SRS. By comparing and contrasting model bias, the meteorologists and employees of the Atmospheric Technologies Group will be equipped with bias data to create more accurate forecasts. Thus, furthering the safety of individuals at SRS.

Gray, Avery↗
DOE OSTI2026

Dark Energy Survey Year 6 Results: Galaxy-galaxy lensing

We present galaxy--galaxy lensing (GGL) measurements from the full six years of data from the Dark Energy Survey (DES Y6), covering $4031\,\mathrm{deg}^2$ and used in the DES Y6 $3\times2$pt cosmological analysis. We use the MagLim++ lens sample, containing $\sim 9$ million galaxies divided into six redshift bins, and the Metadetection source catalog, including $\sim 140$ million galaxies divided into four redshift bins. The mean tangential shear signal achieves a total signal-to-noise ratio (S/N) of $173$, corresponding to a $17\%$ improvement over DES Y3. After applying the scale cuts used in the cosmological analysis, with $R_{\min}=6\,\mathrm{Mpc}/h$ ($4\,\mathrm{Mpc}/h$) for the linear (nonlinear) galaxy-bias model, the S/N is reduced to $75$ (90). A comprehensive suite of validation tests demonstrates that the measurement is robust against observational and astrophysical systematics at the statistical precision required for the DES Y6 analysis. Although not used in the main cosmological analysis, we extract high--signal-to-noise geometric shear-ratio measurements from the galaxy--galaxy lensing signal on small angular scales. These measurements provide an internal consistency check on the photometric redshift distributions and shear calibration used in the $3\times2$pt analysis.

Giannini, G. [Barcelona, IEEC; Chicago U., KICP; B↗
DOE OSTI2024

The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity

Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. Here we diagnose these biases in a cohort of nine CMIP6 models with sub-daily output, assessing conditional instability in profiles of equivalent potential temperature θ e and saturation equivalent potential temperature θ es in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θ es vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θ es measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θ es lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θ e /θ es structure (those using variants of the Tiedtke Scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

SPICE Modeling of Body Bias Effect in 4H-SiC Integrated Circuit Resistors

The DC electrical behavior of n-type 4H-SiC resistors used for realizing 500C durable integrated circuits (ICs) is studied as a function of substrate bias and temperature. Improved fidelity electrical simulation is described using SPICE NMOS model to simulate resistor substrate body bias effect that is absent from the SPICE semiconductor resistor model.

Neudeck, Philip G.↗
DOE OSTI2022

Mechanisms behind the Springtime North Pacific ENSO Teleconnection Bias in Climate Models

Previous studies have shown that models overestimate the strength of ENSO teleconnections to the North Pacific during springtime, but the underlying reasons for this bias remain unknown. Here, in this work, the relative contributions from basic-state and thermodynamic/dynamic forcing factors are disentangled through idealized experiments with the Community Earth System Model and a range of stationary wave modeling experiments. It is revealed that in CESM1 the diabatic heating biases over the tropical Indian Ocean and tropical central-western Pacific jointly favor a cyclonic (anticyclonic) circulation bias to occur in the North Pacific during the springtime of El Niño (La Niña) events. On one hand, the difference in the modeled and observed climatological basic state does not lead to the bias formation directly, as the diabatic heating biases are the primary cause. On the other hand, the springtime basic state is conducive to a more vigorous stationary wave response to the biased diabatic heating than the wintertime state, and this explains why the teleconnection bias occurs during springtime but not in winter. An iterative bias-correction approach is then implemented in the atmospheric model component of CESM1 to verify the linkage between the tropical diabatic heating bias and the teleconnection bias. Moreover, this explanation is shown to be relevant in other models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) as a strong relationship is found between biases in ENSO-related tropical central-western Pacific/Indian Ocean precipitation and North Pacific circulation across models in spring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Probabilistic Deep Learning Methods for Uncertainty Quantification of Precipitation Bias Correction

Climate models often exhibit biases in their precipitation predictions, particularly underestimating high-intensity events and overestimating low precipitation. Deep learning approaches offer promising solutions, but their epistemic uncertainty associated with a deep learning–based bias correction method has not previously been quantified for reliable downstream climate impact studies. While methods for capturing the epistemic uncertainty in deep learning frameworks exist, there is currently no consensus on the best method. In this work, we compare three uncertainty quantification (UQ) methods—Deep Ensembles (DEns), Monte Carlo Dropout (MCD), and Flipout—by assessing the reliability of their uncertainty estimates using standard measures such as sharpness and calibration. These UQ methods are applied to an existing deep learning precipitation bias correction model known as UFNet: a coupled U-Net and fully connected neural network. The methods utilized to assess the models’ uncertainties are 1) calibration, which ensures that the expected probabilities of the model align with reality and 2) sharpness, which is a measure of the precision of the model’s probabilistic predictions. Of the three UQ methods evaluated, the DEns and MCD methods demonstrated the best-calibrated performance (expected calibration error of 0.36 and 0.35, respectively), compared to Flipout (0.58). In contrast, Flipout had the sharpest predictions and the highest metric performance in bias correcting precipitation—especially for higher-order moments such as kurtosis with a spatial correlation of 72% compared to 32% and 55% spatial correlation for DEns and MCD, respectively. Of the three UQ methods, MCD was found to be the most suitable method for UQ purposes based on its calibration, sharpness, and computational requirements.

Bayesian methods↗
NASA NTRS2010

Multiple Scattering of Laser Pulses in Snow Over Ice: Modeling the Potential Bias in ICESat Altimetry

The primary goal of NASA's current ICESat and future ICESat2 missions is to map the altitude of the Earth's land ice with high accuracy using laser altimetry technology, and to measure sea ice freeboard. Ice however is a highly transparent optical medium with variable scattering and absorption properties. Moreover, it is often covered by a layer of snow with varying depth and optical properties largely dependent on its age. We describe a modeling framework for estimating the potential altimetry bias caused by multiple scattering in the layered medium. We use both a Monte Carlo technique and an analytical diffusion model valid for optically thick media. Our preliminary numerical results are consistent with estimates of the multiple scattering delay from laboratory measurements using snow harvested in Greenland, namely, a few cm. Planned refinements of the models are described.

Davis, A. B.↗
NASA NTRS2016

Foreground Bias from Parametric Models of Far-IR Dust Emission

We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB plus foreground emission to precision 0.1 percent or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by (Delta)(Beta)(sub d) = 0.2 and the inflationary B-mode amplitude by (Delta)(r) = 0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by (Delta)(r) greater than 0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple superposition of emission at different temperatures, but fails at the level (Delta)(r) = 0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.

cosmic microwave background↗
DOE OSTI2025

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Bogenschutz, Peter A. [Lawrence Livermore National↗