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At least 1,261 records · Page 70

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DOE OSTI2023

Boundary layer moisture variability at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory during marine conditions

Abstract. Boundary layer moisture variability at the Eastern North Atlantic (ENA) site during marine conditions is examined at monthly and daily timescales using 5 years of ground-based observations and output from the European Center for Medium range Weather Forecast (ECMWF) reanalysis model. The annual cycle of the mixed-layer total water budgets is presented to estimate the relative contribution of large-scale advection, local moisture tendency, entrainment, and precipitation to balance the moistening due to surface latent heat flux on monthly timescales. When marine conditions prevail, advection of colder and dry air from the north acts as an important moisture sink (∼ 50 % of the overall budget) during fall and winter driving the seasonality of the budget. Entrainment and precipitation contribute to the drying of the boundary layer (∼ 25 % and ∼ 15 % respectively), and the local change in moisture contributes to a small residual part. On a daily temporal scale, moist and dry mesoscale columns of vapor (∼ 10 km) are analyzed during 10 selected days of precipitating stratocumulus clouds. Adjacent moist and dry columns present distinct mesoscale features that are strongly correlated with clouds and precipitation. Dry columns adjacent to moist columns have more frequent and stronger downdrafts immediately below the cloud base. Moist columns have more frequent updrafts, stronger cloud-top cooling, and higher liquid water path and precipitation compared to the dry columns. This study highlights the complex interaction between large-scale and local processes controlling the boundary layer moisture and the importance of spatial distribution of vapor to support convection and precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Doppler spectra from ARM Doppler Lidar during ARMing the Edge

There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. In this dataset, we provide the Doppler spectra from the ARM Doppler lidar at C1 recorded in April 2022 during ARMing the Edge.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Physics-based Induced Earthquake Forecasting: Process Understanding, and Hazards Mitigation

Disposal of saltwater co-produced with oil and gas is linked to elevated seismicity in the Central and Midwest US. There is a concern that these events may lead to widespread damage and an overall increase in seismicity. Thus an improved understanding of the spatially and temporally variable deformation and stress field associated with fluid injection operation is critically necessary for evaluating time-varying seismic hazards. Despite the improvements in seismic monitoring capacity and the resulting decrease in the magnitude detection threshold, estimates of induced earthquake probability remain elusive due to insufficient models incapable of accounting for the complex physics governing the process of induced seismicity. The proposed research effort will comprehensively analyze, integrate, and interpret geodetic, injection, and seismic data in the vicinity of the injection sites in Oklahoma to resolve the 4-dimensional distribution of pore pressure and stress in the shallow crust. This project, in particular, is focused on exploring the statistical relation between injection operation and increased earthquake hazard. The amplitude of and the extent to which pore pressure changes are determined by some factors, in particular, the hydrogeological properties of the rocks, such as diffusivity. Thus the available deformation data will be used to constrain hydrogeological properties of the medium, to accurately resolve the evolution of crustal stresses due to fluid injection. Having the time-varying models of stress changes, a statistical framework will be implemented to estimate the time-dependent probability of large earthquakes on the nearby fault systems. These data and models help to improve seismic hazard estimates and aid in constructing operational-induced earthquake forecast models. This information can also be integrated into the updated U.S. National Seismic Hazard Map, which local communities and authorities use in their earthquake risk estimates and mitigation efforts.

58 GEOSCIENCES↗

DOE OSTI2022

The RAM-SCB model and its applications to advance space weather forecasting

Energetic particle fluxes that are part of the Earth’s ring current and radiation belts can intensify significantly during space weather events like geomagnetic storms and could cause severe damage to satellite-based technologies. Understanding the physical processes that control their dynamics and improving our capability for their prediction is thus extremely important. In the context of space weather applications and user needs, this paper provides a brief description of our kinetic ring current-atmosphere interactions model with self-consistent magnetic field (RAM-SCB) and its further extension to implement a self-consistent electric (E) field. Specific examples that demonstrate RAM-SCB capabilities and limitations to reproduce the near-Earth space weather environment are given. In this paper, the current status of RAM-SCB is assessed and plans for its further improvement are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

The Second Wind Forecast Improvement Project (WFIP 2): General Overview

In 2015 the U.S. Department of Energy initiated a four-year study, the second Wind Forecast Improvement Project (WFIP 2), to improve the representation of boundary-layer physics and related processes in mesoscale models for better wind and wind power forecasts. This goal challenges numerical weather prediction models in complex terrain due to inherent assumptions underlying their boundary-layer parameterizations. The WFIP 2 effort involved the wind industry, universities, NOAA, and DOE's national laboratories in an integrated observational and modeling study. Observations spanned 18-months to assure a full annual cycle of continuously recorded observations from remote-sensing and in situ measurement systems. The study area comprised the Columbia Basin of eastern Washington and Oregon, containing more than 4 GW of installed wind capacity; nests of observational systems captured important atmospheric scales from mesoscale to NWP sub-grid scale. Model improvements targeted NOAA's High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to NWS operational forecast models. This paper describes the general WFIP 2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP 2 observational science, modeling challenges and solutions, practical transfer of boundary layer advances to industry use, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

WFIP2, boundary layer, complex terrain, forecastin↗

DOE OSTI2022

The future of Earth system prediction: Advances in model-data fusion

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗

DOE OSTI2016

The impact of wind power on electricity prices

Not Available

Brancucci Martinez-Anido, Carlo↗

DOE OSTI2019

Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

The future of the Colorado River water supply (WS) affects millions of people and the US economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020, a tendency that is confirmed by updated WS observation. Such information could aid in management decision-making in the face of near-future water shortages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)

This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).

17 WIND ENERGY↗

DOE OSTI2025

Evaluation of the 2022 West Nile virus forecasting challenge, USA

Abstract Background West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. Methods Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. Results Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. Conclusions The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases. Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Impact of natural and anthropogenic aerosols on stratocumulus and precipitation in the Southeast Pacific: a regional modelling study using WRF-Chem

Cloud-system resolving simulations with the chemistry version of the Weather Research and Forecasting (WRF-Chem) model are used to quantify the relative impacts of regional anthropogenic and oceanic emissions on changes in aerosol properties, cloud macro- and microphysics, and cloud radiative forcing over the Southeast Pacific (SEP) during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) (15 October–16 November 2008). Two distinct regions are identified. The near-coast polluted region is characterized by low surface precipitation rates, the strong suppression of non-sea-salt particle activation due to sea-salt particles, a predominant albedo effect in aerosol indirect effects, and limited impact of aerosols associated with anthropogenic emissions on clouds. Opposite sensitivities to natural marine and anthropogenic aerosol perturbations are seen in cloud properties (e.g., cloud optical depth and cloud-top and cloud-base heights), precipitation, and the top-of-atmosphere and surface shortwave fluxes over this region. The relatively clean remote region is characterized by large contributions of aerosols from non-regional sources (lateral boundaries) and much stronger drizzle at the surface. Under a scenario of five-fold increase in regional anthropogenic emissions, this relatively clean region shows large cloud responses, for example, a 13% increase in cloud-top height and a 9% increase in albedo in response to a moderate increase (25% of the reference case) in cloud condensation nuclei (CCN) concentration. The reduction of precipitation due to this increase in anthropogenic aerosols more than doubles the aerosol lifetime in the clean marine boundary layer. Therefore, the aerosol impacts on precipitation are amplified by the positive feedback of precipitation on aerosol, which ultimately alters the cloud micro- and macro-physical properties, leading to strong aerosol-cloud-precipitation interactions. The high sensitivity is also related to an increase in cloud-top entrainment rate (by 16% at night) due to the increased anthropogenic aerosols. The simulated aerosol-cloud-precipitation interactions due to the increased anthropogenic aerosols have a stronger diurnal cycle over the clean region compared to the near-coast region with stronger interactions at night. During the day, solar heating results in more frequent decoupling of the cloud and sub-cloud layers, thinner clouds, reduced precipitation, and reduced sensitivity to the increase in anthropogenic emissions. This study shows the importance of natural aerosols in accurately quantifying anthropogenic forcing within a regional modeling framework. Finally, the results of this study also imply that the energy balance perturbations from increased anthropogenic emissions are larger in the more susceptible clean environment than in already polluted environment and are larger than possible from the first indirect effect alone.

Yang, Q.↗

DOE OSTI2017

Managing time-substitutable electricity usage using dynamic controls

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Managing time-substitutable electricity usage using dynamic controls

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Managing time-substitutable electricity usage using dynamic controls

Ghosh, Soumyadip↗

DOE OSTI2025

Machine learning methods for weather forecasting

SAND2025-14466O This repository contains code for developing, training, and evaluating machine learning models for weather and climate forecasting, including forecast skill assessment, feature importance analysis, and reproducible workflows for model comparison. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Holthuijzen, Maike [Sandia National Lab. (SNL-CA),↗

DOE OSTI2022

A Regime-Switching Spatio-Temporal GARCH Method for Short-Term Wind Forecasting

The growth of wind energy poses challenges to the integration of wind energy into the power grid. Within a wind farm, the conditions of local wind exhibit sizeable variations in very short term period and temporal wind speed patterns vary from turbine to turbine. Hence, short-term wind forecasting has been adopted to assist power system operations. In this work, we propose a wind plant-level short term wind speed and power forecasting methodology considering turbine contributions. The proposed model utilizes spatio-temporal dependencies and nonstationarity to accommodate the characteristics of wind farm data by using a novel regime-switching spatiotemporal generalized autoregressive conditional heteroscedasticity (RS-stGARCH) model. Case studies based on 2 years of data from a wind farm shows that the proposed RS-stGARCH method outperforms benchmark models by up to 21.10% for wind speed forecasting and up to 58.62% for the wind power forecasting.

ENERGY PLANNING, POLICY, AND ECONOMY↗