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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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NASA NTRSDate not supplied

Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

Sana Khan↗
DOE OSTI2023

Sensitivity of Near-Surface Variables in the RUC Land Surface Model in the Weather Research and Forecasting Model

In this study, we investigate the parametric sensitivity of near-surface variables, such as sensible heat flux, latent heat flux, ground heat flux, hub-height wind speed and land surface temperature, to the parameters used in the Rapid Update Cycle (RUC) land surface model (LSM) during a wintertime and summertime period. The model simulations are compared with observations collected from the second Wind Forecast Improvement Project (WFIP2) field campaign. The results suggest that parameters related to snow/ice and thermal processes can have significant impact on the simulated near-surface variables. Out of the 11 examined parameters, only 6 of them have considerable influences on the model behaviors and explain about 60 ~ 80 % of the estimated total variance of the simulated variables. In addition, the magnitude of the parametric sensitivity varies with season. For instance, parameters associated with snow/ice processes are dominant during the wintertime whereas those associated with thermal processes are more important during the summertime. Furthermore, the impact of the identified parameters on the simulated variables is highly related to the topography. There is a high degree of sensitivity to the parameter values over the slope region. This points out the importance of collecting field observations over steep areas to better quantity the appropriate values of key parameters. Overall, our findings provide a better understanding of the RUC LSM behavior associated with parameter uncertainties and can be used to improve the forecasting skill of land surface processes via calibration of the most uncertain model parameters.

17 WIND ENERGY↗
NASA NTRS1980

Application of snowcovered area to runoff forecasting in selected basins of the Sierra Nevada, California

The author has identified the following significant results. Direct overlay onto 1:1,000,000 prints takes about one third the time of 1:500,000 zone transfer scope analysis using transparencies, but the consistency of the transparencies reduce the time for data analysis. LANDSAT data received on transparencies is better and more easily interpreted than the near real-time data from Quick Look, or imagery from other sources such as NOAA. The greatest potential for water supply forecasting is probably in improving forecast accuracy and in expanding forecast services during the period of snowmelt. Problems of transient snow line and uncertainties in future weather are the main reasons that snow cover area appears to offer little in water supply forecast accuracy improvement during the peroid snowpack accumulation.

Brown, A. J.↗
DOE OSTI2023

Managing Uncertainty and Flexibility in Day-Ahead Electricity Markets

Net load imbalances from day ahead forecasts can lead to significant grid operations costs and are expected to increase as variable renewable energy adoption grows. We propose a new wholesale market product to manage the risk of net load imbalances called Flexibility Options. This product relies on probabilistic forecasts to estimate flexibility demand and would be co-optimized in the day-ahead market. We also propose stochastic methods that enable DER and flexible load aggregators to participate in flexibility markets while considering the uncertainty in weather and occupant behavior.

day-ahead market↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2022

Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Robust Scheduling of Microgrids Considering Unintentional Islanding Conditions

This paper proposes a robust scheduling model for microgrids considering the stochastic unintentional islanding conditions. The proposed model minimizes the total operating cost of the microgrid by efficiently coordinating the supply of power from local distributed energy resources and the main grid. To capture the prevailing uncertainties in renewable generation and demand as well as unintentional islanding conditions, a two-stage adaptive robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (C&CG) method is used to solve the problem in an iterative manner. The solution of the proposed scheduling model ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, two micro-turbines, a diesel generator and a battery demonstrate the effectiveness of the proposed approach.

Liu, Guodong↗
NASA NTRS2019

Advances in Space Radiation Physics and Transport

The space radiation environment is a complex mixture of particle types and energies originating from sources inside and outside of the galaxy. These environments may be modified by the heliospheric and geomagnetic conditions as well as planetary bodies and vehicle or habitat mass shielding. In low Earth orbit (LEO), the geomagnetic field deflects a portion of the galactic cosmic rays (GCR) and all but the most intense solar particle events (SPE). There are also dynamic belts of trapped electrons and protons with low to medium energy and intense particle count rates. In deep space, the GCR exposure is more severe than in LEO and varies inversely with solar activity. Unpredictable solar storms also present an acute risk to astronauts if adequate shielding is not provided. Near planetary surfaces such as the Earth, moon or Mars, secondary particles are produced when the ambient deep space radiation environment interacts with these surfaces and/or atmospheres. These secondary particles further complicate the local radiation environment and modify the associated health risks. Characterizing the radiation fields in this vast array of scenarios and environments is a challenging task and is currently accomplished with a combination of computational models and dosimetry. The computational tools include models for the ambient space radiation environment, mass shielding geometry, and atomic and nuclear interaction parameters. These models are then coupled to a radiation transport code to describe the radiation field at the location of interest within a vehicle or habitat. Many new advances in these models have been made in the last decade, and the present review article focuses on the progress and contributions made by workers and collaborators at NASA in the same time frame. Although great progress has been made, and models continue to improve, significant gaps remain and are discussed in the context of planned future missions. Of particular interest is the juxtaposition of various review committee findings regarding the accuracy and gaps of combined space radiation environment, physics, and transport models with the progress achieved over the past decade. While current models are now fully capable of characterizing radiation environments in the broad range of forecasted mission scenarios, it should be remembered that uncertainties still remain and need to be addressed.

Space radiation↗
NASA NTRS2019

An Automatic Light Rain Detection Algorithm on NASA MPLNET Lidar Observations in the Frame of WMO GALION Project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Simone Lolli↗
NASA NTRS2021

Features and Characteristics of the New NASA MicroPuLse NETwork (MPLNET) Automatic Rain Detection Algorithm

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics.

S. Lolli↗
NASA NTRSDate not supplied

Global Solar Activity Data Portal for Studying 3D Dynamics and Activity of the Sun

The main problems with understanding and predicting solar activity are tightly linked to limitations in describing the global evolution of the Sun from the deep interior to the corona. Because of the complexity of interactions in a wide range of dynamical, turbulent, and spatial scales and dramatic changes in thermodynamic and magnetic field conditions, only physics-based models can provide essential background to generate reliable solar activity forecasts. However, performing accurate model calibration and estimating uncertainties is often challenging due to the unavailability of a long time series of observations. To mitigate these limitations, we have developed the Global Solar Activity (GSA) Data Portal, which enables convenient access to a variety of modern and historical data. The portal supports a dynamic visualization for 1D time series (such as the sunspot number and solar irradiance) and quick-look visualization for 2D datasets (e.g., synoptic magnetograms, the solar internal rotation, and flows). The GSA portal includes a search engine that enables data retrieval from user-specified data sources and time intervals. In this presentation, we will discuss the current and upcoming capabilities of the data portal and its potential applications for space weather studies.

SMD↗
DOE OSTI2024

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

17 WIND ENERGY↗
DOE OSTI2021

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Wang, Chen↗
NASA NTRS2016

Impact of the Timing of a SAR Image Acquisition on the Calibration of a Flood Inundation Model

Synthetic Aperture Radar (SAR) data have proven to be a very useful source of information for the calibration of flood inundation models. Previous studies have focused on assigning uncertainties to SAR images in order to improve flood forecast systems (e.g. Giustarini et al. (2015) and Stephens et al. (2012)). This paper investigates whether the timing of a SAR acquisition of a flood has an important impact on the calibration of a flood inundation model. As no suitable time series of SAR data exists, we generate a sequence of consistent SAR images through the use of a synthetic framework. This framework uses two available ERS-2 SAR images of the study area, one taken during the flood event of interest, the second taken during a dry reference period. The obtained synthetic observations at different points in time during the flood event are used to calibrate the flood inundation model. The results of this study indicate that the uncertainty of the roughness parameters is lower when the model is calibrated with an image taken before rather than during or after the flood peak. The results also show that the error on the modeled extent is much lower when the model is calibrated with a pre-flood peak image than when calibrated with a near-flood peak or a post-flood peak image. It is concluded that the timing of the SAR image acquisition of the flood has a clear impact on the model calibration and consequently on the precision of the predicted flood extent.

Flood inundation↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗