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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AI Automated Discovery of New Climate Water System Knowledge from Models and Observations

This paper addresses focus area 3, “Insight gleaned from complex data.” Science Challenge: The climate-water system is highly complex, containing a multitude of positive and negative feedbacks, time scales stretching many orders-of-magnitudes, and non-linear, connected processes. Powerful artificial intelligence (AI) methods can revolutionize and automate the discovery of new knowledge and relationships in the climate-water system, which will improve understanding and predictability of extreme hydrological events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗
DOE OSTI2025

Sensitivity of Regional WRF‐Chem Air Quality and Weather Simulations to Biomass‐Burning Emission Data Sets: A Case Study of the Impact of Canadian Wildfire on the US°

This study focuses on the period from June 26 to 29, 2023, when record‐breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF‐Chem) and four biomass‐burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI‐VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting PM 2.5 , O 3 , CO and aerosol weather feedback. Model simulations were compared with ground‐based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. Our findings show that the movement of a low‐pressure system from the Great Lakes to the Atlantic, combined with the high‐pressure system over the Atlantic, caused the transport of aerosols from Canadian wildfires to the US. Results show WRF‐Chem significantly underestimated key atmospheric components: aerosol optical depth (AOD) by over 50%, PM 2.5 by 65%–90% and peak O 3 concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and NO 2 concentrations were underpredicted. The substantial underestimation of PM 2.5 led to an overestimation of temperature by up to 3.6 °C primarily due to excessive downward shortwave radiation, which resulted from the underestimation of direct aerosol effects and an increase in sensible heat flux. Among the biomass‐burning data sets, QFED produced the most accurate AOD and PM 2.5 predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5 °C reduction in temperature overestimation during the daytime. These findings underscore the need for improving wildfire emission estimates for trace gases and aerosols to enhance air quality and weather feedback predictions.

WRF-chem model↗
DOE OSTI2026

REDI – Readiness Engine for Data Integration

The Readiness Engine for Data Integration (REDI) is an open-source framework for automating, standardizing, and assessing the process of preparing scientific data for AI training. REDI implements a five-stage pipeline (ingest, preprocess, transform, structure, output) with per-stage provenance instrumentation via Flowcept, domain-aware transformation logic (PII anonymization, regridding, graph encoding, and more), and built-in readiness assessment and validation modes. REDI has been evaluated across climate, proteomics, materials science, and nuclear fusion datasets, demonstrating near-ideal parallel scaling to 100 nodes on OLCF's Frontier system. REDI is deployable as an agent-callable skill in coding environments such as Claude Code and OpenAI Codex, and is complemented by SetGo for FAIR compliance and catalog publication.

Brewer, Wesley [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

CROCUS Air Quality Dataset from the University of Illinois Chicago (UIC), July 2024

This dataset was collected by the measurement system in the Atmosphere, Climate, and Ecosystems (ACE) Lab at the University of Illinois Chicago (UIC) from July 12 to July 31, 2024, as part of the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory.To enhance understanding of urban air quality dynamics in Chicago, and as part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), several instruments were set up to provide continuous measurements of air quality parameters in Chicago during July 2024. These measurements cover both aerosols and gas-phase species. It focuses on particle size distribution (2.5–478 nm) measured by two Scanning Mobility Particle Sizers (SMPS) at a 4-min resolution, total particle number concentrations at a 1-s resolution, and chemical composition from a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS) at a 1-min resolution. Key gas-phase species, including NO, NO₂, SO₂, and O₃, are measured at a 1-min resolution, along with high-resolution NO and dimethyl sulfide (DMS) data from a Chemical Ionization Mass Spectrometer (CIMS). Volatile organic compound (VOC) data for toluene, isoprene, and benzene are provided by a GC-PID with a time resolution of 25 minutes.The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python. Each parameter is stored in an individual dataset, which includes detailed instrument information in the header, as well as the corresponding sample start time and concentration/distribution data for each sample.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CROCUS 3-D wind data from Argonne Deployable Mast during Urban Canyon IOP July 2024

During the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory, this dataset was collected by METEK uSonic-3 Class A MP sonic anemometer at 10 meter height of the Argonne Deployable Mast (ADM) from July 26 to July 28, 2024, .As part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), 3-D sonic anemometer on the ADM was set up to provide continuous measurements of wind and temperature in Chicago during July 2024. These measurements cover winds in X-, Y- and Z- direction and sonic temperature at 30-Hz resolution. The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

PCMDI - An Earth System Model Evaluation Project Lawrence Livermore National Laboratory Annual Report

This annual report describes the activities of PCMDI – An Earth System Model Evaluation Project for the period November 1, 2019 – June 30, 2020. PCMDI is a project at Lawrence Livermore National Laboratory that is funded by the Climate and Environmental Sciences Division (CESD) of the Department of Energy. PCMDI is funded by two efforts within CESD: The Regional and Global Model Analysis program area (Dr. Renu Joseph, program manager) and the Atmospheric Systems Research program (Dr. Shaima Nasiri and Jeffrey Stehr, program managers). As instructed by Drs. Joseph, Nasiri, and Stehr, we provide an annual report addressing sections 4 (Progress and Plans) and 5 (Staffing and Budget Summary) of the SFA Program Management and Performance Reporting Criteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Report of the panel on the land surface: Process of change, section 5

The panel defined three main areas of study that are central to the Solid Earth Science (SES) program: climate interactions with the Earth's surface, tectonism as it affects the Earth's surface and climate, and human activities that modify the Earth's surface. Four foci of research are envisioned: process studies with an emphasis on modern processes in transitional areas; integrated studies with an emphasis on long term continental climate change; climate-tectonic interactions; and studies of human activities that modify the Earth's surface, with an emphasis on soil degradation. The panel concluded that there is a clear requirement for global coverage by high resolution stereoscopic images and a pressing need for global topographic data in support of studies of the land surface.

Adams, John B.↗
NASA NTRS1993

The mysterious atmosphere of the Earth. ATLAS: Instruction guide with activities

This teacher's guide presents lesson plans addressing various aspects of atmospheric physics and chemistry; the interactions between land, air, and sea; air pollution; and the mechanisms of climate change. The science concepts in this guide have been designed to complement the middle school curriculum. However, many activities can be used with younger or older students.

Source record↗
NASA NTRS2002

Fabry-Perot Interferometer for Column CO2

Global atmospheric CO2 measurements are essential to resolving significant discrepancies in our understanding of the global carbon budget and, hence, humankind's role in global climate change. The science measurement requirements for CO2 are extremely demanding (precision is less than .3%). No atmospheric chemical species has ever been measured from space with this precision. We are developing a novel application of a Fabry-Perot interferometer to detect spectral absorption of reflected sunlight by CO2 and O2 in the atmosphere. Preliminary design studies indicate that the method will be able to achieve the sensitivity and signal-to-noise detection required to measure column CO2 at the target specification. The objective of this program is to construct a prototype instrument for deployment on an aircraft to test the instrument performance and our ability to retrieve the data in the real atmosphere. To date we have assembled a laboratory bench system to begin testing the optical and electronic components. We are also undertaking some measurements of signal and noise levels for actual sunlight reflecting from the ground in order to evaluate the potential of some components to meet the exacting requirements of this measurement.

Heaps, William S.↗
NASA NTRS2002

Fabry-Perot Interferometer for Column CO2

Global atmospheric CO2 measurements are essential to resolving significant discrepancies in our understanding of the global carbon budget and, hence, humankind's role in global climate change. The science measurement requirements for CO2 are extremely demanding (precision approx. 0.3%). No atmospheric chemical species has ever been measured from space with this precision. We are developing a novel application of a Fabry-Perot interferometer to detect spectral absorption of reflected sunlight by CO2 and O2 in the atmosphere. Preliminary design studies indicate that the method will be able to achieve the sensitivity and signal-to-noise detection required to measure column CO2 at the target specification. The objective of this program is to construct a prototype instrument for deployment on an aircraft to test the instrument performance and our ability to retrieve the data in the real atmosphere. To date we have assembled a laboratory bench system to begin testing the optical and electronic components. We are also measuring signal and noise levels in actual sunlight to evaluate component performance.

Heaps, William S.↗
NASA NTRS2002

Progress on Passive Sensor for Ultra-Precise Measurement of Carbon Dioxide from Space

Global measurements of atmospheric carbon dioxides (CO2) are needed to resolve significant discrepancies that exist in our understanding of the global carbon budget and, therefore, man's role in global climate change. The science measurement requirements for CO2 are extremely demanding (precision c .3%) No atmospheric chemical species has ever been measured from space with this precision. We are developing a novel application of a Fabry-Perot interferometer to detect spectral absorption of reflected sunlight by CO2 and O2 in the atmosphere. Preliminary design studies indicate that the method will be able to achieve the sensitivity and signal-to-noise required to measure column CO2 at the target specification. We are presently engaged in the construction of a prototype instrument for deployment on an aircraft to test the instrument performance and our ability to retrieve the data in the real atmosphere. In the first 6 months we have assembled a laboratory bench system to begin testing the optical and electronic components. We are also undertaking some measurements of signal and noise levels for actual sunlight reflecting from the ground. We shall present results from some of these ground based studies and discuss their implications for a space based system.

Heaps, William S.↗
NASA NTRS2002

Fabry-Perot Interferometer for Column CO2: Airborne

Global atmospheric CO2 measurements are essential to resolving significant discrepancies in our understanding of the global carbon budget and, hence, humankind's role in global climate change. The science measurement requirements for CO2 are, however, extremely demanding (precision approximately 0.3%). We are developing a novel application of a Fabry-Perot interferometer to detect spectral absorption of reflected sunlight by CO2 and O2 in the atmosphere that should be able to achieve sufficient sensitivity and signal-to-noise to measure column CO2 at the target specification. We are currently constructing a prototype instrument for deployment on aircraft. The aircraft version will measure total column CO2 and CO2 below the aircraft as well as O2, which allows normalization of CO2 column amounts for varying surface height and pressure. This instrument will be a valuable asset in carbon budget field studies as well as a useful tool for evaluating existing and future space-based CO2 measurements. We will present the instrument concept, sensitivity calculations, and the results of testing a bench system in the laboratory and outdoors on the ground. We will also discuss our plan for deployment on the aircraft and potential flight applications to the CO2 budget problem.

Kawa, S. R.↗
NASA NTRS1997

Research in Satellite-Fiber Network Interoperability

This four part report evaluated the performance of high data rate transmission links using the ACTS satellite, and to provide a preparatory test framework for two of the space science applications that have been approved for tests and demonstrations as part of the overall ACTS program. The test plan will provide guidance and information necessary to find the optimal values of the transmission parameters and then apply these parameters to specific applications. The first part will focus on the satellite-to-earth link. The second part is a set of tests to study the performance of ATM on the ACTS channel. The third and fourth parts of the test plan will cover the space science applications, Global Climate Modeling and Keck Telescope Acquisition Modeling and Control.

Edelson, Burt↗
NASA NTRS2007

Global Warming - Are We on Thin Ice?

The evidence for global warming is very conclusive for the past 400-500 years. Prior to the 16th century, proxy surface temperature data are regionally good but lack a global distribution. The speaker will review surface temperature reconstruction based upon ice cores, coral cores, tree rings, deep sea sediments, and bore holes and discuss the controversy surrounding global warming. This will be contrasted with the excellent data we have from the satellite era of earth observations the past 30+ years that enables the quantitative study of climate across earth science disciplines.

Tucker, Compton J.↗
NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗