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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S.

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within physics parameterizations, model resolution limitations, as well as uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and temperature, ground fluxes, and vegetation are necessary to better simulate the interactions between the land surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection. The NASA Short-term Prediction Research and Transition (SPORT) Center has been conducting studies to examine the impacts of high-resolution land surface initialization data generated by offline simulations of the NASA Land Informatiot~ System (LIS) on subsequent numerical forecasts using the Weather Research and Forecasting (WRF) model (Case et al. 2008, to appear in the Journal of Hydrometeorology). Case et al. presents improvements to simulated sea breezes and surface verification statistics over Florida by initializing WRF with land surface variables from an offline LIS spin-up run, conducted on the exact WRF domain and resolution. The current project extends the previous work over Florida, focusing on selected case studies of typical pulse convection over the southeastern U.S., with an emphasis on improving local short-term WRF simulations over the Mobile, AL and Miami, FL NWS county warning areas. Future efforts may involve examining the impacts of assimilating remotely-sensed soil moisture data, and/or introducing weekly greenness vegetation fraction composites (as opposed to monthly climatologies) into ol'fline NASA LIS runs. Based on positive impacts, the offline LIS runs could be transitioned into an operational mode, providing land surface initialization data to NWS forecast offices in real time.

Case, Jonathan L.↗
DOE OSTI2020

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

58 GEOSCIENCES↗
NASA NTRS2003

Impact of Flow-Dependent Error Correlations and Tropospheric Chemistry on Assimilated Ozone

The presentation compares different versions of a global three-dimensional ozone data assimilation system developed at NASA's Data Assimilation Office. The Solar Backscatter Ultraviolet/2 (SBUV/2) total and partial ozone column retrievals are the sole data assimilated in all of the experiments presented. We study the impact of changing the forecast error covariance model from a version assuming static correlations with a one that captures a short-term Lagrangian evolution of those correlations. This is further combined with a study of the impact of neglecting the tropospheric ozone production, loss and dry deposition rates, which are obtained from the Harvard GEOS-CHEM model. We compare statistical characteristics of the assimilated data and the results of validation against independent observations, obtained from WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres. On the other hand, the main sensitivity to tropospheric chemistry is in the Tropics and sub-Tropics. The best agreement between the assimilated ozone and the in-situ sonde data is in the experiment using both flow-dependent error covariances and tropospheric chemistry.

Wargan, K.↗
DOE OSTI2026

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
DOE OSTI2020

Capturing non-Gaussianity of the large-scale structure with weighted skew-spectra

The forthcoming generation of wide-field galaxy surveys will probe larger volumes and galaxy densities, thus allowing for a much larger signal-to-noise ratio for higher-order clustering statistics, in particular the galaxy bispectrum. Extracting this information, however, is more challenging than using the power spectrum due to more complex theoretical modeling, as well as significant computational cost of evaluating the bispectrum signal and the error budget. To overcome these challenges, several proxy statistics have been proposed in the literature, which partially or fully capture the information in the bispectrum, while being computationally less expensive than the bispectrum. One such statistics are weighted skew-spectra, which are cross-spectra of the density field and appropriately weighted quadratic fields. Using Fisher forecasts, we show that the information in these skew-spectra is equivalent to that in the bispectrum for parameters that appear as amplitudes in the bispectrum model, such as galaxy bias parameters or the amplitude of primordial non-Gaussianity. We consider three shapes of the primordial bispectrum: local, equilateral and that due to massive particles with spin two during inflation. In conclusion, to obtain constraints that match those from a measurement of the full bispectrum, we find that it is crucial to account for the full covariance matrix of the skew-spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2024

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Demand Response Optimization and Management System for Real-TIme (DROMS-RT)

To design and demonstrate DROMS-RT, a highly distributed Demand Response Optimization and Management System for Real-Time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. AutoGrid developed a novel control and communications platform to allow highly dispatchable demand response (DR) services in time frames suitable for providing ancillary services to the transmission grid. These services will be substantially less expensive and more efficient than other forms of ancillary services options currently available to manage the intermittency associated with large-scale renewable integration. DROMSRT successfully leveraged Automated Demand Response (ADR) by fundamentally re-thinking the architecture of the DR platform from the ground up and by developing innovative new technologies in a number of areas related to DR. DROMS-RT leveraged the low-cost, open, interoperable DR signaling technology, OpenADR, and low-cost, internet-protocol based telemetry solutions to reduce the cost of hardware. This allowed DROMS-RT to provide dynamic price signals to millions of OpenADR clients. Statistically rigorous signal processing techniques were developed to reliably detect even small load reductions in the presence of noisy baseline profiles. Novel forecasting engines based on modern online machine learning algorithms enabled accurate individualized forecasts for customer loads in the presence of dynamic pricing signals, and a real-time decision engines enabled continuous optimization and optimal dispatch of DR resources across a large portfolio of heterogeneous loads that respond at varying time-scales. Moreover, the real-time optimization conducted by the decision engine can utilize grid physics to maximize load reduction at the transmission system in addition to the distribution sites, for more efficient grid operation. Finally, the Software-as-a-Service (SaaS) availability of the DROMS-RT platform has reduced the cost of deployment and enable participation of small commercial and residential customers in DR who otherwise would not be able to do so.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Software for Allocating Resources in the Deep Space Network

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Wang, Yeou-Fang↗
NASA NTRSDate not supplied

Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs

Land surface models are crucial tools for many earth science applications including numerical weather prediction, water resource and crop monitoring, and climatological analysis. Given a set of atmospheric forcings, seasonal data, and static parameters, models like Noah-LSM solve for land surface quantities including skin temperature, sensible heat flux, and soil moisture. While these calculations are theoretically robust, they are often computationally expensive. Since artificial neural networks (ANNs) are universal function approximators, they can learn to emulate the output of a deterministic numerical model given a time series of input forcings, with the learned ANN having substantially shorter execution time. The ANN could efficiently parameterize other models, generate ensembles, and provide first-guess inputs for retrievals. As such, with the goal of developing a model that efficiently mimics the output of Noah-LSM given NLDAS2 forcings on a region covering much of the central US, we examine and compare several neural network architectures for the multi-horizon multivariate time series forecasting problem. Recent literature includes a diverse set of approaches including autoregressive architectures like LSTM and GRU, parametric and non-parametric statistical predictors (ForecastNet and MQRNN), self-attention (LSTM-attention-LSTM), and temporal convovlution (DeepTCN). We implement several of these models for the Noah-LSM prediction task, highlighting the features and challenges for each and providing practical insight on the training process.

Mitchell Dodson↗
DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗
NASA NTRS1981

Forecast of future aviation fuels: The model

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Ayati, M. B.↗
DOE OSTI2023

Precision redshift-space galaxy power spectra using Zel'dovich control variates

Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the power spectrum or correlation function(s). Sample variance is particularly acute at large scales, which is also where analytic techniques can be highly reliable. This provides an opportunity to combine analytic and numerical techniques in a principled way to improve the dynamic range and reliability of predictions for clustering statistics. In this paper we extend the technique of Zel'dovich control variates, previously demonstrated for 2-point functions in real space, to reduce the sample variance in measurements of 2-point statistics of biased tracers in redshift space. We demonstrate that with this technique, we can reduce the sample variance of these statistics down to their shot-noise limit out to k ~ 0.2 h Mpc -1 . This allows a better matching with perturbative models and improved predictions for the clustering of e.g. quasars, galaxies and neutral Hydrogen measured in spectroscopic redshift surveys at very modest computational expense. We discuss the implementation of ZCV, give some examples and provide forecasts for the efficacy of the method under various conditions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
NASA NTRS2006

The Art and Science of Long-Range Space Weather Forecasting

Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

Hathaway, David H.↗
NASA NTRS2002

Impact of Soil Moisture Initialization on Seasonal Weather Prediction

The potential role of soil moisture initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that soil moisture initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial soil moisture anomalies, (2) a strong sensitivity of evaporation to soil moisture, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.

Koster, Randal D.↗
DOE OSTI2024

Tropical Interbasin Interaction as Effective Predictors of Late-Spring Precipitation Variability in the Southern Great Plains

Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.

Chikamoto, Yoshimitsu↗
NASA NTRS1999

Data Assimilation Into a Coupled Ocean Atmosphere Model: Application to the 1997-1998 El Nino

As part of JPL's ocean data assimilation effort to study ocean circulation and seasonal-interannual climate variability, sea level anomaly observed by TOPEX altimeter, together with sea surface temperature and wind stress data, are assimilated into a simple coupled ocean atmosphere model of the tropical Pacific. Model-data consistency is examined. Impact of the assimilation (as initialization) on El Nino Southern Oscillation (ENSO) forecasts is evaluated. The coupled model consists of a shallow water component with two baroclinic modes, an Ekman shear layer, a simplified mixed-layer temperature equation, and a statistical atmosphere based on dominant correlations between historical surface temperature and wind stress anomaly data. The adjoins method is used to fit the coupled model to the data over various six-month periods from late 1996 to early 1998 by optimally adjusting the initial state, model parameters, and basis functions of the statistical atmosphere. On average, the coupled model can be fitted to the data to approximately within the data and representation errors (5 cm, 0.5 C, and 10 sq m/sq m for sea level, surface temperature, and pseudo wind stress anomalies, respectively). The estimated fields resemble observed spatio-temporal structure reasonably well. Hindcasts/forecasts of the 1997/1998 El Nino initialized from forced estimated ocean states and parameters are much more realistic than those simply initialized from ocean states (see figure below). In particular, the ability of the model to produce significant warming beyond the initial state is dramatically improved. Parameter estimation, which compensates for some model errors, is found to be important to obtaining better fits of the model to data and to improving forecasts.

Lee, Tong↗