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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Use of NASA Satellite Data to Improve Coastal Cypress Forest Management

Problem: Information gaps exist regarding health status and location of cypress forests in coastal Louisiana (LA). Such information is needed to aid coastal forest conservation and restoration programs. Approach to Issue Mitigation: Use NASA data to revise cypress forest cover type maps. Landsat and ASTER data. Use NASA data to identify and track cypress forest change. Landsat, ASTER, and MODIS data. Work with partners and end-users to transfer useful products and technology.

Spurce, Joseph↗
DOE OSTI2020

Observed metallization of hydrogen interpreted as a band structure effect

A recent experimental study of the metallization of hydrogen tracked the direct band gap and vibron frequency via infrared measurements up to ~425 GPa (Loubeyre et al (2020 Nature 577 631). Above this pressure, the direct gap has a discontinuous drop to below the minimum experimentally accessible energy (~0.1 eV). The authors suggested that this observation is caused by a structural phase transition between the C2/c-24 molecular phase to another molecular phase such as Cmca-12. Here, through ab initio calculations of pressure dependent vibron frequency and direct band gap, we find that the experimental data is consistent with the C2/c-24 phase up to 425 GPa, and suggest that this consistency extends beyond that pressure. Specifically, we find that qualitative changes in the band structure of the C2/c-24 phase lead to a discontinuous drop of the direct band gap, which can explain the observed drop without a structural transition. In conclusion, this alternative scenario, which naturally explains the absence of hysteresis in the measurements, will hopefully motivate further experimental studies to ascertain the structure of the phase above the high pressure 'phase transition'.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2009

Human Research Program Human Health Countermeasures Element Bone and Muscle Risk Standing Review Panel (SRP)

The Bone and Muscle Risk Standing Review Panel (SRP) met at the NASA Johnson Space Center (JSC) on October 4-6, 2009 to discuss the areas of current and future research targeted by the Human Health Countermeasures (HHC) Element of the Human Research Program (HRP). Using evidence-based knowledge as a background for identified risks to astronaut health and performance, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Bone and Muscle Risk SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the Panel's review. The Bone and Muscle Risk SRP consisted of scientists who are experts in muscle, bone, or both and could evaluate the existing evidence with sufficient knowledge of the potential effects of long duration exposure to microgravity. More important, although expertise in basic science is important, the SRP was requested to evaluate the practicality of the proposed efforts in light of the realistic demands placed on the HRP. In short, all tasks presented in the Integrated Research Plan (IRP) should address specific questions related to the challenges faced by the astronauts as a result of prolonged exposure to microgravity. All tasks proposed to fill the gaps in knowledge should provide applied, translational data necessary to answer the specific questions. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the panel charge, either as a group or in separate sessions for the Bone and Muscle Risk subgroups. The SRP made a final debriefing to the HRP Program Scientist, Dr. John B. Charles, on October 6, 2009. Taking the evidence and identified risks as givens, the SRP concluded that 1) integration of information should lead to a more comprehensive approach to identifying the gaps, 2) not all tasks addressed the gaps as stated in the IRP, 3) better access should be given to the SRP to existing data to include in its review, and 4) there were some missing gaps and tasks. As a result the SRP recommended 1) combining certain gaps, which in some cases were addressed with identical tasks, 2) additional gaps and tasks to address some of the gaps, 3) deleting some gaps and tasks to better focus the efforts of NASA s HRP in prioritizing their efforts, and 4) prioritizing gaps to address significant issues needing resolution in the short term while maintaining an awareness of long-term goals. The SRP commended the efforts by the HHC Element to integrate knowledge gaps and design tasks to address the higher order questions. This integration did not apply to the Bone and Muscle Risk alone, but the musculoskeletal system as its functions integrate with issues in

Glowacki, Julie↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
NASA NTRS1994

Optoelectronic Tool Measures Steps And Gaps Between Tiles

Optoelectronic tool measures gaps between tiles and steps between faces of tiles. Eliminates need for difficult, tedious, time-consuming, and error-prone manual measurements. Automates measurements of steps and gaps to within 0.006 in. and records data automatically. Developed for inspecting thermal-insulation tiles on Space Shuttle. Adaptable to inspection of any other settings where steps and gaps critical.

Davis, Dick↗
DOE OSTI2020

Gap-filled water quality, Normalized Differenced Vegetation Index, total nitrogen (nitrate and ammonia) deposition, and land cover data trends for the Continental United States

The purpose of this work was to quantify trends in water quality, total nitrogen deposition, vegetation, and land-use trends for the Continental United States (CONUS). This dataset contains gap-filled daily and yearly in-stream carbon (C) and nitrogen (N) concentrations, discharge, and calculated exports from United States Geological Survey (USGS) National Water Information System (NWIS) stations across the United States of six different nitrogen parameters (Supplementary Table A1), one carbon parameter, and temperature (USGS, 2016, 2018) (https://waterdata.usgs.gov/nwis). The final water quality dataset is merged with the normalized differenced vegetation index (NDVI), total deposition (TDEP), landcover, and elevation datasets. All data are available at the CONUS scale as well as at the scale of hydrologic unit codes: (HUC) HUC8, HUC6, HUC4, and HUC2 watersheds. There are two zip files associated with this data package. I split the data into two equal sized zip files to comply with the data size limits, however the datasets are the same within each zip file. All data files can be read in directly into the R software.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗
NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗
DOE OSTI2023

Probing FeSi, a d -electron topological Kondo insulator candidate, with magnetic field, pressure, and microwaves

Recently, evidence for a conducting surface state (CSS) below 19 K was reported for the correlated d -electron small gap semiconductor FeSi. In the work reported herein, the CSS and the bulk phase of FeSi were probed via electrical resistivity ρ measurements as a function of temperature T , magnetic field B to 60 T, and pressure P to 7.6 GPa, and by means of a magnetic field-modulated microwave spectroscopy (MFMMS) technique. The properties of FeSi were also compared with those of the Kondo insulator SmB 6 to address the question of whether FeSi is a d -electron analogue of an f -electron Kondo insulator and, in addition, a “topological Kondo insulator” (TKI). The overall behavior of the magnetoresistance of FeSi at temperatures above and below the onset temperature T S = 19 K of the CSS is similar to that of SmB 6 . The two energy gaps, inferred from the ρ( T ) data in the semiconducting regime, increase with pressure up to about 7 GPa, followed by a drop which coincides with a sharp suppression of T S . Several studies of ρ( T ) under pressure on SmB 6 reveal behavior similar to that of FeSi in which the two energy gaps vanish at a critical pressure near the pressure at which T S vanishes, although the energy gaps in SmB 6 initially decrease with pressure, whereas in FeSi they increase with pressure. The MFMMS measurements showed a sharp feature at T S ≈ 19 K for FeSi, which could be due to ferromagnetic ordering of the CSS. However, no such feature was observed at T S ≈ 4.5 K for SmB 6 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗
NASA NTRS1986

Analysis of Jovian decametric data: Study of radio emission mechanisms

Catalogues of approx. 200 decametric arcs and approx. 200 gaps between arcs were studied, in an effort to reconcile the data with predictions for the model wherein reflections of Io-induced currents each emit in a conical pattern and yield a distinct radio arc. The most recent interpretations of this data suggest that these Io-produced Alfven waves persist for at least one or two passages of Io, and that the emission cone half angles are approx. 40 to 90 deg., varying from arc to arc. Below 1.2 MHz it was discovered that Jupiter emits radiation strongly modulated in frequency with periods of approx. 200 kHz; this quasi-sinusoidal emission (MSA) can shift more than 180 deg. in phase over periods of 6 seconds, although these shifts are usually much smaller. MSA is not strongly correlated with the longitudes of Io or Jupiter, and typically occurs in patches covering approx. 500 kHz or more for periods of a few minutes. Furthermore, this modulation sometimes resembles a train of impulses in frequency with exponential decays toward high frequencies. Comparison of these results with the previous studies of V-shaped S-bursts is suggestive of an emission mechanism.

Staelin, D. H.↗
DOE OSTI2022

Nuclear Data for High Energy Ion Interactions and Secondary Particle Production

Understanding the harmful effects of galactic cosmic rays (GCRs) on space exploration requires a substantial amount of nuclear data. Specifically, the interaction of energetic GCR charged particles with spacecraft materials generates secondary radiations that, through energy deposition, can harm astronauts and electronic systems. By identifying the gaps in our knowledge of the relevant nuclear data – interaction cross sections – and identifying ways to fill those gaps – with measurements, compilations, evaluations, disseminations, reaction modeling, sensitivity studies, and uncertainty quantification – the safety and viability of space exploration can be improved. This work surveys the state of the art in this interdisciplinary field and identifies promising collaborative research topics that have significant potential to advance our understanding of the effects of the space radiation environment on space exploration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Carbon Storage Technical Viability Approach and National Data Assessment

Carbon Storage Technical Viability Approach is a comprehensive evaluation of potential geologic carbon storage sites that includes classic subsurface resource assessment components such as reservoir suitability in addition to hazard, environmental, social, regulatory, and jurisdiction components that may enhance or hinder successful carbon storage. Data for assessments of technically viable carbon storage span many categories, types; are disparate and numerous. Variables and types of data required to support CS assessments were previously not clearly defined and documented. Contextualizing available data indicates their utility, potential uncertainty, and gaps. This presentation details the Carbon Storage Technical Viability Approach, the associated matrix and database, and initial results from a portion of the national data availability assessment that contextualizes the data available to support carbon storage projects. Presented at the American Association of Petroleum Geologists' Carbon Capture Utilization and Storage (SPE-AAPG-SEG CCUS 2024) conference in Houston, TX, March 11-13, 2024.

Mark-Moser, Mackenzie K.↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Estimating IDP Origins Using ACLED Political Violence Events: Validation with Lebanon IDP Flow Data

A key input in modeling population distribution and flows following conflict events is the inclusion of Internally Displaced Person (IDP) flows between administrative units within a country. These flows are critical for capturing population movement and redistribution driven by current events, particularly conflict. In some cases, IDP destination data are available while origin data is incomplete or unavailable. This creates a gap in understanding where displacement is occurring, limiting the ability to model population redistribution accurately. Without origin data, it is not possible to reallocate population flows or accurately represent where displacement is occurring within the country. This report evaluates whether Armed Conflict Location Event Data (ACLED) political violence event data can be used to estimate IDP origin distributions when direct origin data are unavailable. The approach is validated using historical IDP flow data from Lebanon, where both origins and destinations are observed. Results show that ACLED event distributions strongly correspond to observed IDP-origin patterns, particularly when using cumulative 60-day event windows. The method is most reliable for identifying major origin districts and approximating proportional origin shares. However, it is not intended to reconstruct exact individual displacement flows, but rather to provide a probabilistic spatial allocation of displacement origins.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1989

Low temperature measurement of the vapor pressures of planetary molecules

Interpretation of planetary observations and proper modeling of planetary atmospheres are critically upon accurate laboratory data for the chemical and physical properties of the constitutes of the atmospheres. It is important that these data are taken over the appropriate range of parameters such as temperature, pressure, and composition. Availability of accurate, laboratory data for vapor pressures and equilibrium constants of condensed species at low temperatures is essential for photochemical and cloud models of the atmospheres of the outer planets. In the absence of such data, modelers have no choice but to assume values based on an educated guess. In those cases where higher temperature data are available, a standard procedure is to extrapolate these points to the lower temperatures using the Clausius-Clapeyron equation. Last summer the vapor pressures of acetylene (C2H2) hydrogen cyanide (HCN), and cyanoacetylene (HC3N) was measured using two different methods. At the higher temperatures 1 torr and 10 torr capacitance manometers were used. To measure very low pressures, a technique was used which is based on the infrared absorption of thin film (TFIR). This summer the vapor pressure of acetylene was measured the TFIR method. The vapor pressure of hydrogen sulfide (H2S) was measured using capacitance manometers. Results for H2O agree with literature data over the common range of temperature. At the lower temperatures the data lie slightly below the values predicted by extrapolation of the Clausius-Clapeyron equation. Thin film infrared (TFIR) data for acetylene lie significantly below the values predicted by extrapolation. It is hoped to bridge the gap between the low end of the CM data and the upper end of the TFIR data in the future using a new spinning rotor gauge.

Kraus, George F.↗
DOE OSTI2025

Ten questions on future and extreme weather data for building simulation and analysis in a changing climate

Weather plays a significant role in building operations as it directly influences HVAC loads and in turn the building energy and thermal performance. In a changing climate, future trends and extreme weather events become critical concerns in the global building decarbonization and clean energy transition. This paper aims to address ten key questions concerning extreme and future weather data for building applications, and more importantly to identify research gaps and guide the curation and selection of future and extreme weather data for use in building performance simulation and assessment. The paper intends to inform architects and engineers, operators, owners, policy makers, and other stakeholders on considering the impacts of future and extreme weather data and adopting strategies for selecting and applying this data in various use cases related to building design, operation, and retrofit for energy efficiency, electrification, and climate resilience.

Yan, Da↗