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NASA NTRS2017

Meteor Shower Forecasting for Spacecraft Operations

Although sporadic meteoroids generally pose a much greater hazard to spacecraft than shower meteoroids, meteor showers can significantly increase the risk of damage over short time periods. Because showers are brief, it is sometimes possible to mitigate the risk operationally, which requires accurate predictions of shower activity. NASA's Meteoroid Environment Office (MEO) generates an annual meteor shower forecast that describes the variations in the near-Earth meteoroid flux produced by meteor showers, and presents the shower flux both in absolute terms and relative to the sporadic flux. The shower forecast incorporates model predictions of annual variations in shower activity and quotes fluxes to several limiting particle kinetic energies. In this work, we describe our forecasting methods and present recent improvements to the temporal profiles based on flux measurements from the Canadian Meteor Orbit Radar (CMOR).

Moorhead, Althea V.↗

NASA NTRS2017

Meteor Shower Forecasting for Spacecraft Operations

Although sporadic meteoroids are a much greater hazard to spacecraft than shower meteoroids in general, meteor showers can significantly increase the risk of damage over short time periods. Because showers are brief, it is sometimes possible to mitigate the risk operationally, which requires accurate predictions of shower activity. NASA's Meteoroid Environment Office generates an annual meteor shower forecast that describes the variations in the near-Earth meteoroid flux produced by meteor showers, which presents the shower flux both in absolute terms and relative to the sporadic ux. The shower forecast incorporates model predictions of annual variations in shower activity and quotes fluxes to several limiting particle kinetic energies. In this work, we describe our forecasting methods, compare them to actual observations, and highlight recent improvements to the temporal pro les based on flux measurements from the Canadian Meteor Orbit Radar (CMOR).

Moorhead, Althea V.↗

DOE OSTI2019

Multi-scale simulations of wind farm performance with complex terrain and weather events

Predicting the response of wind farms to local weather is necessary for optimal design and operation. In this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine wakes, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with the GAD at10 m resolution. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed turbine wake structure, velocity deficit, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD framework for simulating wind farm performance under complex atmospheric conditions.

17 WIND ENERGY↗

NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗

DOE OSTI2026

Improving Process Level Understanding of Boundary Layer Winds over the Northeast U.S. Shelf: The Third Wind Forecast Improvement Project (WFIP3)

The third Wind Forecast Improvement Project (WFIP3), a U.S. Department of Energy and National Oceanic and Atmospheric Administration sponsored investigation, sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the marine atmospheric boundary layer. WFIP3 focused on mesoscale and submesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high-use coastal zone, using a 3D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the marine atmospheric boundary layer over the ocean was done from an air-sea interaction flux tower and extended deployments of a large autonomous barge platform. Numerous critical forecasting phenomena were observed that are being evaluated within regional coupled and uncoupled modeling systems, including the National Oceanic and Atmospheric Administration's foundational High-Resolution Rapid Refresh forecast model.

Kirincich, Anthony↗

DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

DOE OSTI2015

Nuclear aircraft carrier inactivation - Execution of waste management initiatives - 15565

After 6 years of planning efforts, in June 2013, Newport News Shipbuilding (NNS) began the execution phase of the inactivation of the USS Enterprise. The USS Enterprise decommissioning involves implementation of a new approach to defueling aircraft carriers, utilizing new processes for defueling and shipment of spent nuclear fuel and waste. During planning for this complex endeavor, NNS developed several process improvement initiatives including streamlining waste management processes and technology insertion using modeling and simulation (M and S) capabilities. One of the new approaches involves the establishment of a Waste Packaging Area (WPA) onboard the ship. The process assists with maximizing waste loading and shipments directly from the ship. Another waste management initiative includes the NNS/ Waste Vendor teaming effort to facilitate more efficient loading and shipment of inactivation waste. With execution underway, these initiatives are being put to the test to see if they can withstand the rigors of day-to-day production. NNS has implemented the use of the Modeling and Simulation (M and S) technology. During the planning phase, the M and S 3D Spatial Arrangement Tool was utilized to determine the optimal equipment layout within the new M-290 facility used for spent fuel packaging and ship-out and to determine the appropriate shipping container placement on the ship to maximize waste loading. The M and S technology proved to be a beneficial application resulting in continued utilization through the execution phase of the carrier inactivation. NNS is currently expanding the M-290 Facility Model to provide a visual representation of the facility, its processes, resources, and variable parameters that can be utilized as a decision support tool through simulation of the spent fuel packaging and ship-out process through the facility's life-cycle. NNS is also expanding the application of the waste management model to effectively predict waste forecast. The simulation model is linked with the defueling schedule which captures the operational elements of the waste flow process. The model extrapolates the data to provide waste generation output to assess the most efficient and cost effective path for radiological waste shipments offsite. The ability to conduct waste forecasting allows streamlining of the waste management process in support of complex defueling operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2013

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.

Robertson, Franklin R.↗

NASA NTRS2013

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

Roberts, J. Brent↗

DOE OSTI2023

Improving the Representation of Hydropower in Production Cost Models

One of the challenges of a high-renewable, low-carbon system is that grid operators will no longer be able to count on fossil-fired generation should wind miss its forecast or modeling errors create shortage issues, and this change has operational and reliability implications that affect variable renewable energy integration. Hydropower can help in that it can provide many of the services that have been traditionally provided by fossil generation. However, getting the most from hydropower can be challenging in that its capabilities vary with time and are affected by other water users' needs, two complexities that are not well represented in most of today's production cost models (PCMs). This work is an early step in addressing these gaps and making the most of hydropower's capabilities.

cost model↗

DOE OSTI2018

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contribution of surface energy budget errors to the bias. All participating models simulate higher net shortwave and longwave radiative fluxes at the surface but there is no consistency on signs of biases in latent and sensible heat fluxes over the Central U.S. and ARM SGP. Nevertheless, biases in net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are the main contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF is affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis suggests that radiation errors are always an important source of T2m error for long-term climate runs with EF errors either of equal or lesser importance. However, for the short-term hindcasts, EF errors are more important provided a model has a substantial EF bias.

Ma, H. -Y.↗

DOE OSTI2024

A Data-Driven Decision Support Tool for Anticipating Tropical Storm Impacts to the United States Power Grid

Not Available

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets

Extreme weather events, such as hurricanes, severe thunderstorms, and floods can significantly disrupt power grid systems, leading to electrical outages that result in inconvenience, economic losses, and life-threatening situations. There is a growing need for a robust and precise predictive model to forecast power outages, which will help prioritize emergency response before, during, and after extreme weather events. In this paper, we introduce machine-learning models that predict power outage risk at the state level during and after extreme weather events. We jointly utilized two publicly available datasets: the U.S. historical power outage data collected by the Environment for Analysis of Geo-Located Energy Information (EAGLE-I™) system, and the National Weather Service historical weather alert data sets. We highlight our initial result and discuss future work aimed at enhancing the model's robustness and accuracy for real-world applications.

Lee, Sangkeun (Matt)↗

DOE OSTI2018

U.S. Navy Wave Energy Test Site: Update for International Marine Renewable Energy Conference.

Hawaii Natural Energy Institute (HNEI) at the University of Hawaii (UH), provides key research support to the Navy’s Wave Energy Test Site (WETS), off Marine Corps Base Hawaii. This is the nation’s first grid-connected site, supporting the nascent commercial wave energy sector by providing and supporting Wave Energy Conversion (WEC) device test infrastructure. Through DOE and Navy support, HNEI provides environmental monitoring, independent WEC device performance analysis, numerical modeling, wave forecasting and hindcasting, and critical marine logistical support for WEC device developers.

16 TIDAL AND WAVE POWER↗

DOE OSTI2018

Ocean Testing at WETS – Ramping Up

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Performance of Convection-Permitting and Convection-Parameterized Models in Reproducing the Extreme Precipitation Intensity Relationship with Surface Conditions

Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

Atmosphere↗

NASA NTRS2015

High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility

Mesoscale weather conditions can have an adverse effect on space launch, landing, ground processing, and weather advisories, watches, and warnings at the Eastern Range (ER) in Florida and Wallops Flight Facility (WFF) in Virginia. During summer, land-sea interactions across Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger personnel and resources. Many other weak locally-driven low-level boundaries and their interactions with the sea breeze front and each other can also initiate deep convection in the KSC/CCAFS area. These convective processes often last 60 minutes or less and pose a significant challenge to the local forecasters. Surface winds during the transition seasons (spring and fall) pose the most difficulties for the forecasters at WFF. They also encounter problems forecasting convective activity and temperature during those seasons. Therefore, accurate mesoscale model forecasts are needed to better forecast a variety of unique weather phenomena. Global and national scale models cannot properly resolve important local-scale weather features at each location due to their horizontal resolutions being much too coarse. Therefore, a properly tuned local data assimilation (DA) and forecast model at a high resolution is needed to provide improved capability. To accomplish this, a number of sensitivity tests were performed using the Weather Research and Forecasting (WRF) model in order to determine the best DA/model configuration for operational use at each of the space launch ranges to best predict winds, precipitation, and temperature. A set of Perl scripts to run the Gridpoint Statistical Interpolation (GSI)/WRF in real-time were provided by NASA's Short-term Prediction Research and Transition Center (SPoRT). The GSI can analyze many types of observational data including satellite, radar, and conventional data. The GSI/WRF scripts use a cycled GSI system similar to the operational North American Mesoscale (NAM) model. The scripts run a 12-hour pre-cycle in which data are assimilated from 12 hours prior up to the model initialization time. A number of different model configurations were tested for both the ER and WFF by varying the horizontal resolution on which the data assimilation was done. Three different grid configurations were run for the ER and two configurations were run for WFF for archive cases from 27 Aug 2013 through 10 Nov 2013. To quantify model performance, standard model output will be compared to the Meteorological Assimilation Data Ingest System (MADIS) data. The MADIS observation data will be compared to the WRF forecasts using the Model Evaluation Tools (MET) verification package. In addition, the National Centers for Environmental Prediction's Stage IV precipitation data will be used to validate the WRF precipitation forecasts. The author will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for each space launch range.

Watson, Leela R.↗