Search NASA⌕ Search

SEARCH · Search NASA

Results for “RMSE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗
DOE OSTI2021

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2022

Synergistic Retrieval of Temperature and Humidity Profiles from Space-Based and Ground-Based Infrared Sounders Using an Optimal Estimation Method

The atmospheric temperature and humidity profiles of the troposphere are generally measured by radiosondes and satellites, which are essential for analyzing and predicting weather. Nevertheless, the insufficient observation frequencies and low detection accuracy of the boundary layer restricts the description of atmospheric state changes by the temperature and humidity profiles. Therefore, this work focus on retrieving the temperature and humidity profiles using observations of the FengYun-4 (FY-4) Geostationary Interferometric Infrared Sounder (GIIRS) combined with ground-based infrared spectral observations from the Atmospheric Emitted Radiance Interferometer (AERI), which are more accurate than space-based individual retrieval results and have a wider effective retrieval range than ground-based individual retrieval results. Based on the synergistic observations, which are made by matching the space-based and ground-based data with those of different spatial and temporal resolutions, a synergistic retrieval process is proposed to obtain the temperature and humidity profiles at a high frequency under clear-sky conditions based on the optimal estimation method. In this research, using the line-by-line radiative transfer model (LBLRTM) as the forward model for observing simulations, a retrieval experiment was carried out in Qingdao, China, where an AERI is situated. Taking radiosonde data as a reference for comparing the retrieval results of the temperature and humidity profiles of the troposphere, the root-mean-square error (RMSE) of the synergistic retrieval algorithm below 400 hPa is within 2 K for temperature and within 12% for relative humidity. Compared with the GIIRS individual retrieval, the RMSE of temperature and relative humidity for the synergistic method is reduced by 0.13–1.5 K and 2.7–4.4% at 500 hPa, and 0.13–2.1 K and 2.5–7.2% at 900 hPa. Moreover, the forecast index (FI) calculated from the retrieval results shows reasonable consistency with the FIs calculated from the ERA5 reanalysis and from radiosonde data. The synergistic retrieval results have higher temporal resolution than space-based retrieval results and can reflect the changes in the atmospheric state more accurately. Overall, the results demonstrated the promising potential of the synergistic retrieval of temperature and humidity profiles at high accuracy and high temporal resolution under clear-sky conditions from FY-4/GIIRS and AERI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

MTL_TX: A Multi-Task Transformer Model for Improved Radiation Time-Series Estimation

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed model: hierarchical feature embedding (HFE) and multi-level decomposition attention (MDA). Additionally, the multi-task learning (MTL) framework effectively leverages correlations among multiple sensors, enabling individual estimations for each sensor. MTL_TX achieved outstanding results on data collected in 2018, with an MSE of 0.1464, an RMSE of 0.2353, and an R 2 score of 0.8584. Furthermore, when trained on 2018 data, MTL_TX exhibited excellent generalization capability to unseen datasets from 2016 to 2019, achieving an MSE of 0.1407, an RMSE of 0.2263, and an R 2 score of 0.8831. These results demonstrate a significant improvement over existing state-of-the-art models.

Transformer↗
DOE OSTI2022

Modeling Monthly Nitrate Concentration in a Karst Spring with and without Discrete Conduit Flow

Understanding the coupled continuum pipe-flow framework for modeling contaminant transport in karst systems is critical for protecting water resources therein. This study simulated point and non-point source nitrate concentration in a karst spring and investigated the results generated from the flow and transport model with and without discrete conduit flow. CFPv2 and CMT3D models were integrated to address the changes in nitrate concentration at a monthly scale in a karst spring, and the results were compared with that from an equivalent porous media (EPM) model with high-hydraulic conductivity (K) zones set in the main karstified area to represent conduits. The results show that the CFPv2+CMT3D model is able to describe well the recession of nitrate concentration in spring discharge, and the relatively larger deviation (slower nitrate recession) from the observed trend for the EPM model is probably a result of the limitation of utilizing high hydraulic conductivity cells to represent conduit. Moreover, simulated hydraulic heads in poorly karstified areas from the two models both show slight differences from the observations (the head RMSE values of calibration/validation for CFPv2 and MODFLOW models are 0.16 m/0.25 m and 0.26 m/0.17 m, respectively), indicating the inclusion of conduits may not affect the simulation considerably, and the lower the proportion of karstic area, the slight effects brought from the inclusion of conduits in the model. For highly karstified areas, the CFPv2+CMT3D model could provide more accurate results (head RMSE of calibration/validation for CFPv2 and MODFLOW are 0.22 m/0.06 and 0.52 m/0.47 m, respectively), showing the coupled continuum pipe-flow framework may be more appropriate for applying to highly and maturely karstified areas where the variations in the behavior of flow and contaminant transport are more affected by turbulent flow regime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Surface radiation trends at North Slope of Alaska influenced by large-scale circulation and atmospheric rivers

Arctic amplification manifests as a pervasive warming trend emerging over the past century in near-surface air temperature throughout the Arctic that is double the globally averaged temperature increase throughout most of the year. It results from complex processes involving oceanic, atmospheric and terrestrial components which require detailed study to discern roles of the fundamental processes involved to improve predictions of the Arctic environment. We report on signals that are beginning to emerge, on a timescale predicted by recent satellite remote sensing studies, from the unique 25 year record of detailed surface-based radiometer measurements obtained by the US Department of Energy Atmospheric Radiation Measurement (ARM) Facility North Slope of Alaska (NSA) site at Utqiaġvik, Alaska. Statistically significant warming trends are found at the site in the boreal fall, while a decrease in net radiation occurs in late summer. This decrease is driven primarily by the decrease in shortwave radiation resulting from increasing cloud liquid water path as observed by the microwave radiometer. The magnitude of the liquid water path trends is slightly larger than the root mean square error (RMSE) due to natural variability, while the magnitude of the net radiation trends is smaller than the RMSE natural variability, emphasizing that these signals are only just emerging. Analysis of prevailing meteorological regimes linking NSA with the Arctic Ocean and subarctic latitudes, and atmospheric rivers, suggests that specific changing circulation patterns are the primary driver for these summertime trends.

Lubin, Dan [Univ. of California, San Diego, CA (Un↗
DOE OSTI2022

Evaluating alternative ebullition models for predicting peatland methane emission and its pathways via data–model fusion

Abstract. Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)

We present a new 3D unstructured-grid global ocean model to study both tidal and nontidal processes, with a focus on the total water elevation. Unlike existing global ocean models, the new model resolves estuaries and rivers down to ~8 m without the need for grid nesting. The model is validated with both satellite and in situ observations for elevation, temperature, and salinity. Tidal elevation solutions have a mean complex root-mean-square error (RMSE) of 4.2 cm for M2 and 5.4 cm for all five major constituents in the deep ocean. The RMSEs for the other four constituents, S2, N2, K1, and O1, are, respectively, 2.05, 0.93, 2.08, and 1.34 cm). The nontidal residual assessed by a tide gauge dataset (GESLA) has a mean RMSE of 7 cm. For the first time ever, we demonstrate the potential for seamless simulation on a single mesh from the global ocean into several estuaries along the US West Coast. The model is able to accurately capture the total elevation, even at some upstream stations. The model can therefore potentially serve as the backbone of a global tide surge and compound flooding forecasting framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2

Abstract. A new weakly coupled land data assimilation (WCLDA) system based on the four-dimensional ensemble variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the adjoint technique. With an interest in providing initial conditions for decadal climate predictions, monthly mean anomalies of soil moisture and temperature from the Global Land Data Assimilation System (GLDAS) reanalysis from 1980 to 2016 are assimilated into the land component of E3SMv2 within the coupled modeling framework with a 1-month assimilation window. The coupled assimilation experiment is evaluated using multiple metrics, including the cost function, assimilation efficiency index, correlation, root-mean-square error (RMSE), and bias, and compared with a control simulation without land data assimilation. The WCLDA system yields improved simulation of soil moisture and temperature compared with the control simulation, with improvements found throughout the soil layers and in many regions of the global land. In terms of both soil moisture and temperature, the assimilation experiment outperforms the control simulation with reduced RMSE and higher temporal correlation in many regions, especially in South America, central Africa, Australia, and large parts of Eurasia. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The WCLDA system is intended to be a foundational resource for research to investigate land-derived climate predictability.

58 GEOSCIENCES↗
DOE OSTI2025

Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2

The development, implementation, and evaluation of a new weakly coupled ocean data assimilation (WCODA) system for the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2) utilizing the four-dimensional ensemble variational (4DEnVar) method are presented in this study. The 4DEnVar method, based on the dimension-reduced projection four-dimensional variational (DRP-4DVar) approach, replaces the adjoint model with the ensemble technique, thereby reducing computational demands. Monthly mean ocean temperature and salinity data from the EN4.2.1 reanalysis are integrated into the ocean component of E3SMv2 from 1950 to 2021 with the goal of providing realistic initial conditions for decadal predictions and predictability studies. The performance of the WCODA system is assessed using various metrics, including the reduction rate of the cost function, root mean square error (RMSE) differences, correlation differences, and model biases. Results indicate that the WCODA system effectively assimilates the reanalysis data into the climate model, consistently achieving negative reduction rates of the cost function and notable improvements in RMSE and correlation across various ocean layers and regions. Significant enhancements are observed in the upper ocean layers across the majority of global ocean regions, particularly in the north Atlantic, north Pacific, and Indian Ocean. Model biases in sea surface temperature and salinity are also substantially reduced. For sea surface temperature, cold biases in the north Pacific and north Atlantic are diminished by about 1–2 °C, and warm biases in the Southern Ocean are corrected by approximately 1.5–2.5 °C. In terms of salinity, improvements are observed with bias reductions of about 0.5–1 psu in the north Atlantic and north Pacific and up to 1.5 psu in parts of the Southern Ocean. The ultimate goal of the WCODA system is to advance the predictive capabilities of E3SM for subseasonal to decadal climate predictions, thereby supporting research on strategic energy-sector policies and planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Validating Irradiance Models for High-Latitude Vertical Bifacial Photovoltaic Systems

Bifacial photovoltaic systems oriented vertically facing east-west are an emerging design, targeting production in morning and afternoon hours and providing competitive annual energy yield to traditional south-tilted modules for high latitude locations. The accuracy of existing bifacial PV models when modules are oriented vertically has yet to be examined in detail. Here, we compare four bifacial PV irradiance models in ~150 locations between 15-80 degrees N on the utility-scale, finding higher inter-model deviations for vertical PV systems than south-tilted across all latitudes less than 75 degrees N. We validate model-predicted irradiance with test-site data collected in Golden, Colorado and Fairbanks, Alaska for E-W vertical and south-tilted arrays. View factor models agree with E-W vertical test-site data in Golden with RMSE=15%. Modelling error increases for the Alaskan test-site to RMSE values between 21-30%, driven in part by high albedo measurement uncertainty during snowy months.

bifacial↗
DOE OSTI2022

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

54 ENVIRONMENTAL SCIENCES↗