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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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Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data

This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.

Quantum Computing↗
NASA NTRS1977

Remote sensing-aided systems for snow qualification, evapotranspiration estimation, and their application in hydrologic models

The design of general remote sensing-aided methodologies was studied to provide the estimates of several important inputs to water yield forecast models. These input parameters are snow area extent, snow water content, and evapotranspiration. The study area is Feather River Watershed (780,000 hectares), Northern California. The general approach involved a stepwise sequence of identification of the required information, sample design, measurement/estimation, and evaluation of results. All the relevent and available information types needed in the estimation process are being defined. These include Landsat, meteorological satellite, and aircraft imagery, topographic and geologic data, ground truth data, and climatic data from ground stations. A cost-effective multistage sampling approach was employed in quantification of all the required parameters. The physical and statistical models for both snow quantification and evapotranspiration estimation was developed. These models use the information obtained by aerial and ground data through appropriate statistical sampling design.

Korram, S.↗
NASA NTRS2021

Strong Regional Influence of Climatic Forcing Datasets on Global Crop Model Ensembles

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS1986

Planning for the utilization of the PCDS in studying the interaction of clouds (ISCCP-C data) and the Earth radiation budget (ERBE data)

The Pilot Climate Data System (PCDS) affords an opportunity to analyze data from different but highly complementary data sets. Two of these highly complementary data sets supported by the PCDS are the International Satellite Cloud Climatology Project (ISCCP) and the Earth Radiation Budget Experiment (ERBE). Both data set sponsors are aware of the utility of one data set to the other, and both projects utilize gridded data on a 2.5 deg by 2.5 deg grid. The ISCCP data have been collected since July 1983, and the NOAA-9 data for ERBE have been collected for more than a year. Therefore, there is a good chance to use these temporally overlapping data sets to investigate hypothesized relationships. Changes in cloudiness affect both cloud albedo feedback (shortwave) and the greenhouse effect (longwave). The relative importance of the effects of clouds on albedo versus outgoing longwave radiation (OLR) in determining the radiation balance has long been a matter of controversy. Now, however, changes in cloud amount as observed by the ISCCPO can be correlated to corresponding changes in the albedo and changes in the OLR from ERBE. Monthly means can be utilized in all instances.

Jacobowitz, H.↗
NASA NTRS1986

UV albedo of clouds from TOMS data

The Pilot Climate Data System (PCDS) was found to be useful in examining a subset of data from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS). The TOMS instrument scans with six channels between .3 and .4 micrometers. It was suggested that by analyzing albedo values from the longer wavelength channels of the TOMS, the effects of increasing optical depth could be determined. It is spectral relationship of albedo-to-optical depth that is investigated here. The question can be stated: Is there spectral differentiation in albedo with changes in the optical depth of clouds? Nimbus-7 TOMS data were obtained through the PCDS for an area and time period for which correlative NOAA AVHRR data were available. The AVHRR data were important in determining the existing cloud patterns. As expected, no spectral differentiation was observed at very high albedos (associated with high optical depths). However, at lower optical depths, evidence of spectral dependence on albedo was observed. Mapping the results geographically was deemed highly desirable but was not possible through the PCDS at the time. The PCDS can be viewed as an effective research tool to access selected portions of data. Without being intimately familiar with a data set, a PCDS user can successfully manipulate data in a scientific study.

Short, D.↗
DOE OSTI2023

Mechanical ventilation and indoor air quality in recently constructed U.S. homes in marine and cold-dry climates

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and the Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO 2 , PM2.5 and radon; and time-integrated NO 2 , NO X and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2–2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO 2 and NO (NO X -NO 2 ) when operated with WHMV, and had faster PM 2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19% vs 3%) felt their IAQ could be improved and more reported dryness as a problem (58% vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1986

The CDF software package

The concepts that are fundamental to the new Common Data Format are outlined. With Pilot Climate Data System (PCDS) Version 4.0, the Common Data Format (CDF) will supersede the Climate Data File (also CDF) or earlier versions. This new format incorporates generalizations in both design and terminology that make it applicable to multidisciplinary data sets. Furthermore, the new CDF will be made available to programmers as a software package that shields them from the low-level details of file formats. The CDF interface routines create an abstract conceptual environment for the scientific programmer. The principle concept for visualizing a CDF is known as the basic grid. A basic grid is an n-dimensional block by means of which a CDF is constructed. The size of the block may vary from one CDF to another, but is consistent within any individual CDF. Thus, the basic grid serves as a fundamental uniform building block for a CDF. The number of grid dimensions and the size of each dimension are chosen by the scientist/programmer to represent the patterns by which data are structured. The uniform grid structure appears to the programmer to be propagated into each record for each variable. The CDF stores a variable value for each lattice point of the grid for each record. These data values are inserted and retrieved simply by specifying the variable's identifier, a record number, and the indices that specify the lattice point of interest.

Wilson, C. E.↗
DOE OSTI2025

Data for: Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources

Data package for manuscript "Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources" by Emily G. Wright, Ivey Wang, Yihang Fang, Elaine D. Flynn, and Jeffrey G. Catalano. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to goethite and Pt(II) adsorption to hematite and goethite, including lab experiments, X-ray absorption fine structure spectroscopy, and models of retention within a laterite. See the associated manuscript for full methods information. The file "Wright2025_PtAds_data.csv" contains the target starting Pt concentration (uM), final aqueous Pt and associated error (in uM), calculated adsorbed Pt and associated error (in umol/m2), target and measured aqueous chloride (mM), target aqueous nitrate (mM), final pH, and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, S20, and S22 in the associated manuscript. The file "Wright2025_PdAds_data.csv" contains the target starting Pd concentration (uM), final aqueous Pd and associated error (in uM), calculated adsorbed Pd and associated error (in umol/m2), target and measured aqueous chloride (mM), and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, and S20 in the associated manuscript. The file "Wright2025_MineralBatches_data.csv" contains the mineral identity and BET specific surface area (m2/g) for every mineral batch synthesized and used in experiments. The annealing time used is listed for hydrothermally annealed goethite. These data appear in Table S2 in the associated manuscript. The file "Wright2025_XRD_data.csv" contains the XRD patterns for every mineral batch synthesized as the counts as a function of two theta (in degrees). See "Wright2025_MineralBatches_data.csv" for more details on specific mineral batches. These data appear in Figure S2 in the associated manuscript. The file "Wright 2025_ZetaPotential_data.csv" contains the measured zeta potentials for samples of goethite (batch G2) at pH 4 the presence of varying amounts of sodium chloride. These data appear in Table S3 in the associated manuscript. The file "Wright2025_XAFSSamples_data.csv" contains the specific mineral batch, measured final aqueous Pd or Pt (uM), measured final aqueous chloride (mM), and estimated adsorbed Pd or Pt (umol/m2) of all XAFS samples. These data appear in Tables S4, S7, S8, and S10 in the associated manuscript. The files "Wright2025_PdXAFS_data.csv" and "Wright2025_PtXAFS_data.csv" contain the normalized spectra of Pd and Pt, respectively, adsorbed to minerals at varying chloride concentrations. See "Wright2025_XAFSSamples_data.csv" for a guide to sample names. Note that "05" in a sample name is equivalent to "0.5". These data appear in Figures 2, S6, S7, S8, S12, S13, and S14 in the associated manuscript. The file "Wright2025_LateriteProfileProfileModelParameters_data.csv" include the ratio of hematite to hematite and goethite in two synthetic, modeled profiles, as well as the modeled surface areas of goethite and hematite as a function of relative depth within the modeled weathering zone. These data were used, in conjunction with equations presented in the paper, to calculate the theoretical concentrations of Pd and Pt (and the resulting Pt/Pd ratio) within the profiles. These data appear in Figure 3 in the associated manuscript. The file "Wright2025_Imagery_data.zip" is a zipped folder containing the TEM and STEM images appear in Figures S18 and S19. Individual files are labeled as either STEM (Fig. S18) or TEM (Fig. S19) with a letter representing the part of the multipart figure.

58 GEOSCIENCES↗
DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra: Supporting Data

Climate warming may accelerate decomposition of Arctic soil carbon, but few controlled experiments have manipulated the entire active layer. To determine surface-atmosphere fluxes of carbon dioxide and methane under anticipated end-of-century warming, we used heating rods to warm soil (by 3.8 °C) to the depth of permafrost in polygonal tundra over two growing seasons at the Barrow Environmental Observatory in Utqiaġvik (formerly Barrow), Alaska. This data product includes supporting data for the companion paper entitled "Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra", published in Nature Communications by Torn et al. This data package contains seven data files in csv format with corresponding data dictionaries and file-level metadata, describing vegetation biomass dry weight ("Utqiagvik_Vegetation_Biomass_2014.csv"), radiocarbon measurements of respired carbon dioxide ("Utqiagvik_Radiocarbon_2015_2016.csv"), surface-atmosphere fluxes of carbon dioxide and methane and associated soil temperature ("Utqiagvik_Fluxes_Temperature_2015_2016.csv"), and soil temperature measurements averaged every 15-min and 1-hr for 2015 and 2016 ("Utqiagvik_Temperature_15min_2015.csv", "Utqiagvik_Temperature_15min_2016.csv", "Utqiagvik_Temperature_1hr_2015.csv", "Utqiagvik_Temperature_1hr_2016.csv"). There are no specific software requirements to use these data. UIC Science Native Corporation facilitated our scientific research on the Barrow Environmental Observatory, which is Iñupiat land.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal: Representative Agricultural Pathways, Climate, Crop and Economic Datasets

This paper describes the datasets that were used to implement an AgMIP Regional Integrated Assessment for the Nioro region of Senegal to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin and to assess adaptations of that system to climate change under current as well as future climate and socio-economic conditions. This dataset includes the Representative Agricultural Pathways developed for Nioro from 2000-2050; the climate data that were used to implement crop yield simulations; the data that were used to parameterize the DSSAT and APSIM crop models, including historical climate data and future climate scenarios; and the data that were used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOAMD) economic simulation model, as well as simulated model outputs.

AgMIP↗
DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Summary Data

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

Naasko, Katherine I↗
DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Sequence Data

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

Naasko, Katherine I↗
NASA NTRS2019

Web-based Decision Support System Tools: the Soil and Water Assessment Tool Online Visualization and Analyses (SWATOnline) and NASA Earth Observation Data Downloading and Reformatting Tool (NASAaccess)

The current influx of climate related information required scientists to communicate their findings to decision makers in governments, disaster preparedness organizations, and the general public. The Soil and Water Assessment Tool (SWAT) is a powerful modelling tool that allows scientists to simulate many of the physical processes involved in the water cycle. This article presents the design, methods and development efforts to overcome some of the limitations of the previously developed SWAT visualization software programs by creating a set of modular web applications that can be duplicated, customized, and run. Moreover, this article features a web application development tool for climate data retrieval. The NASAaccess fetches, extracts and reformats climate data from the National Aeronautics and Space Administration servers and outputs data compatible with hydrological models. This work has the potential to increase the SWAT's model impact on non-technically trained stakeholders and decision makers charged with water and climate management.

Tethys↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
NASA NTRS2012

Compilation of Abstracts for SC12 Conference Proceedings

1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets.

HPC↗
DOE OSTI2023

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools . In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

14 SOLAR ENERGY↗