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NASA NTRS2011

Optical Component Performance for the Ocean Radiometer for Carbon Assessment (ORCA)

The Ocean Radiometer for Carbon Assessment (ORCA) is a new design for the next generation remote sensing of ocean biology and biogeochemistry. ORCA is configured to meet all the measurement requirements of the Decadal Survey Aerosol, Cloud, and Ecology (ACE), the Ocean Ecosystem (OES) radiometer and the Pro-ACE climate data continuity mission (PACE). Under the auspices of a 2007 grant from NASA Research Opportunity in Space and Earth Science (ROSES) and the Instrument Incubator Program (IIP), a team at the Goddard Space Flight Center (GSFC) has been working on a functional prototype with flight-like fore and aft optics and scan mechanisms. As part of the development efforts to bring ORCA closer to a flight configuration and in order to reduce cost, we have conducted component-level optical testing using standard spectrophotometers and system-level characterizations using non-flight commercial off-the-shelf (COTS) focal plane array detectors. Although these arrays would not be able to handle flight data rates, they are adequate for optical alignment and performance testing. The purpose of this presentation is to describe the results of this testing performed at GSFC and the National Institute of Standards and Technology (NIST) at the component and system level. Specifically, we show results for ORCA's spectral calibration ranging from the near UV, visible, and near-infrared spectral region.

Quijada, Manuel A.↗

NASA NTRS2021

Subcommittee on Space and Aeronautics Committee on Science, Space, and Technology: Statement by Dr. Gavin A. Schmidt Senior Advisor on Climate to the Administrator (Acting) National Aeronautics and Space Administration 117th Congress

NASA has long recognized that climate science and climate change are fundamental parts of our mandate, and for decades we have worked on fundamental and applied research in these areas. With the increasing evidence for serious impacts and the elevation of this topic by the Administration, it is time for a renewed focus on all the aspects of climate change that NASA works on and a commitment to ensure that the data and understanding that NASA has and will continue to accumulate will be used for the benefit of society.

Gavin A Schmidt↗

NASA NTRS1992

Our Changing Planet: The FY 1993 US Global Change Research Program. A report by the Committee on Earth and Environmental Sciences, a supplement to the US President's fiscal year 1993 budget

The U.S. Global Change Reasearch Program (USGCRP) was established as a Presidential initiative in the FY-1990 Budget to help develop sound national and international policies related to global environmental issues, particularly global climate change. The USGCRP is implemented through a priority-driven scientific research agenda that is designed to be integrated, comprehensive, and multidisciplinary. It is designed explicitly to address scientific uncertainties in such areas as climate change, ozone depletion, changes in terrestrial and marine productivity, global water and energy cycles, sea level changes, the impact of global changes on human health and activities, and the impact of anthropogenic activities on the Earth system. The USGCRP addresses three parallel but interconnected streams of activity: documenting global change (observations); enhancing understanding of key processes (process research); and predicting global and regional environmental change (integrated modeling and prediction).

Source record↗

DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗

NASA NTRS1995

Universities Earth System Scientists Program

This document constitutes the final technical report for the National Aeronautics and Space Administration (NASA) Grant NAGW-3172. This grant was instituted to provide for the conduct of research under the Universities Space Research Association's (USRA's) Universities Earth System Scientist Program (UESSP) for the Office of Mission to Planet Earth (OMTPE) at NASA Headquarters. USRA was tasked with the following requirements in support of the Universities Earth System Scientists Programs: (1) Bring to OMTPE fundamental scientific and technical expertise not currently resident at NASA Headquarters covering the broad spectrum of Earth science disciplines; (2) Conduct basic research in order to help establish the state of the science and technological readiness, related to NASA issues and requirements, for the following, near-term, scientific uncertainties, and data/information needs in the areas of global climate change, clouds and radiative balance, sources and sinks of greenhouse gases and the processes that control them, solid earth, oceans, polar ice sheets, land-surface hydrology, ecological dynamics, biological diversity, and sustainable development; (3) Evaluate the scientific state-of-the-field in key selected areas and to assist in the definition of new research thrusts for missions, including those that would incorporate the long-term strategy of the U.S. Global Change Research Program (USGCRP). This will, in part, be accomplished by study and evaluation of the basic science needs of the community as they are used to drive the development and maintenance of a global-scale observing system, the focused research studies, and the implementation of an integrated program of modeling, prediction, and assessment; and (4) Produce specific recommendations and alternative strategies for OMTPE that can serve as a basis for interagency and national and international policy on issues related to Earth sciences.

Estes, John E.↗

NASA NTRSDate not supplied

Arctic Amplification: Process Drivers and Sources of Uncertainty

Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This presentation synthesizes current knowledge of AA and describes a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA.

Patrick C Taylor↗

DOE OSTI2025

Advancing the Representation of Human Actions in Large‐Scale Hydrological Models: Challenges and Future Research Directions

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi-basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human-water interactions in large-scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (~10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper-resolution” hydrological models (~0.1–1 km), multi-sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human-water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper-resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort.

catchment hydrology↗

DOE OSTI2026

CMIP7 Data Request: atmosphere priorities and opportunities

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data unlocking key research avenues in atmospheric science and provides justification for the resources needed to produce this data. Topics within the CMIP7 Atmosphere Theme centre around processes and feedbacks in atmospheric science such as clouds, aerosols and atmospheric chemistry, atmospheric circulation, temperature variability and extremes, radiative forcings, and Earth system model evaluation. These topics are summarised in this paper as scientific “opportunities” which will be realised through CMIP7 experiments and Earth system model outputs. These opportunities were submitted by a thematic group of atmospheric science community representatives combined with an extended consultation process. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making, including supporting the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). As an author group, we also reflect on the process used to collate this data request and make recommendations to future CMIP governance on implementing a consultation on this scale in the future.

58 GEOSCIENCES↗

DOE OSTI2021

Climate Extremes and Variability Surrounding Chesapeake Bay: Past, Present, and Future

Measures of extreme climate variability around Chesapeake Bay have changed over the past century (1895–2014), resulting in the need to establish new baselines for understanding future change. Here, observed climate variability is compared with Coupled Model Intercomparison Project fifth assessment climate models to evaluate ensemble model skill in this region. Observed trends include annual and seasonal declines in the percentage of cold days. Similarly, increases in the annual and seasonal percentages of warm days occurred in all seasons of the North Chesapeake (>38.2°N) but were only significant in spring and summer in the South (<38.2°N) demonstrating regional differences even at local scales. Precipitation intensity increased over the past century. Models using emissions scenarios RCP4.5 and 8.5 project these trends will continue, though they have little skill in precipitation extremes. Pacific and Atlantic climate modes are generally more correlated with climate extremes than mean temperature and precipitation suggesting potential predictability in the synoptic patterns underlying the extremes. However, they also drive the need for time series at least 60-years long for establishing climate trends. This local analysis differs from regional, state level, and local 2.5° × 3.75° grid analyses, highlighting the importance of local climate assessments that consider topographic and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Impacts of phase state and water content on secondary organic aerosol formation and partitioning (Final Technical Report)

Secondary organic aerosols (SOA) account for a major fraction of particulate matter in the atmosphere, affecting climate, air quality, and public health. SOA formation and evolution are highly complex processes involving both chemical reactions and transport of molecules in air, at particle surfaces, and within particles. The state of the matter, or “phase state,” comprising SOA can vary from a liquid, over an amorphous semi-solid, to a glassy solid, depending on chemical composition, water content, relative humidity (RH), and temperature. The occurrence of glassy and amorphous semi-solid states can pose limitations on the rate of transport of molecules, affecting gas-particle interactions and challenging the treatment of SOA in atmospheric models. The objective of this project was to improve fundamental understanding of the interplay of the phase state of particles and water content on the evolution of SOA formation. We have developed a method to estimate the glass transition temperature (T g ) of organic compounds to predict the phase state and viscosity of SOA. This method has been applied to high resolution mass spectrometry data of various types of SOA, achieving a good agreement with viscosity measurements. In addition, the viscosity estimation method was implemented into a regional model CMAQ to simulate diel and seasonal variations of SOA phase state over the U.S. Applying kinetic multilayer modeling, we analyzed laboratory experiments and ARM field measurements (e.g., HI-SCALE) on kinetic limitations of amine uptake. We have also conducted numerical simulations to estimate equilibration timescales of SOA partitioning. Overall, we have evaluated the impacts of phase state and water content on SOA lifecycle that should contribute to reducing the uncertainty of SOA representation in regional climate and air quality predictions. The project has yielded 17 publications, in which DE-SC0018349 is explicitly acknowledged. Please see the list of publications as below, followed by summary of research activities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multi-Scale Modeling of the Evolution of Structure and Properties in Materials for Nuclear Energy Applications [Slides]

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1994

A modular electric power system test bed for small spacecraft

In the new climate of smaller, faster, and cheaper space science satellites, a new power system topology has been developed at the NASA Lewis Research Center. This new topology is based on a series connected boost converter (SCBC) and can greatly affect the size, weight, fault tolerance, and cost of any small spacecraft using photovoltaic solar arrays. The paper presents electric power system design factors and requirements as background information. The series connected boost converter topology is discussed and several advantages over existing technologies are illustrated. Besides being small, lightweight, and efficient, this topology has the added benefit of inherent fault tolerance. A positive ground power system test bed has been developed for the TROPIX spacecraft program. Performance of the SCBC in the test bed is described in detail. SCBC efficiencies of 95 percent to 98 percent have been measured. Finally, a modular, photovoltaic regulator 'kit' concept is presented. Two SCBC's are used to regulate solar array charging of batteries and to provide 'utilitytype' power to the user loads. The kit's modularity will allow a spacecraft electric power system to be built from off-the-shelf hardware; resulting in smaller, faster, and cheaper spacecraft.

Button, Robert M.↗

NASA NTRS1998

Optical Detection of Lightning from Space

Optical sensors have been developed to detect lightning from space during both day and night. These sensors have been fielded in two existing satellite missions and may be included on a third mission in 2002. Satellite-hosted, optically-based lightning detection offers three unique capabilities: (1) the ability to reliably detect lightning over large, often remote, spatial regions, (2) the ability to sample all (IC and CG) lightning, and (3) the ability to detect lightning with uniform (i.e., not range-dependent) sensitivity or detection efficiency. These represent significant departures from conventional RF-based detection techniques, which typically have strong range dependencies (biases) or range limitations in their detection capabilities. The atmospheric electricity team of the NASA Marshall Space Flight Center's Global Hydrology and Climate Center has implemented a three-step satellite lightning research program which includes three phases: proof-of-concept/climatology, science algorithm development, and operational application. The first instrument in the program, the Optical Transient Detector (OTD), is deployed on a low-earth orbit (LEO) satellite with near-polar inclination, yielding global coverage. The sensor has a 1300 x 1300 sq km field of view (FOV), moderate detection efficiency, moderate localization accuracy, and little data bias. The OTD is a proof-of-concept instrument and its mission is primarily a global lightning climatology. The limited spatial accuracy of this instrument makes it suboptimal for use in case studies, although significant science knowledge has been gained from the instrument as deployed.

Boccippio, Dennis J.↗

NASA NTRS2001

Center for Aerosol Research (AEROCENTER)

The newly established Center for Aerosol Research (AEROCENTER) located at the NASA/Goddard Space Flight Center in Greenbelt MD is dedicated to fostering interdisciplinary research in all aspects of aerosol science. AEROCENTER will be an incubator for innovative new analysis of existing data and ideas for new space missions. The plan is to tap and harvest ideas from a broad international and interdisciplinary science community and to incorporate these ideas into NASA's aerosol research effort for understanding and predicting the aerosol effect on climate and the environment. In order to achieve this goal the center aims to host several established and developing scientists for a period of 3-6 months each year. AEROCENTER will also develop a new technical infrastructure that will integrate the present aerosol research activities and data resources of GSFC/Greenbelt and GSFC/GISS, increase efficiency in the use of NASA remote sensing data, and increase the involvement of a larger national and international scientific community. The center aims to institutionalize and extend the present knowledge base within NASA into a national resource for the education and research communities.

Kleidman, Richard↗

NASA NTRS2006

Spatial and Temporal Distribution of Clouds as Observed by MODIS Onboard the Terra and Aqua Satellites

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched onboard the Terra spacecraft on December 18,1999 and Aqua spacecraft on May 4, 2002. It achieved its final orbit and began Earth observations on February 24,2000 for Terra and June 24,2002 for Aqua. A comprehensive set of remote sensing algorithms for cloud masking and the retrieval of cloud physical and optical properties has been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, and fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Over the last year, extensive improvements and enhancements in the global cloud products have been implemented, and reprocessing of all MODIS data on Terra has commenced since first light in February 2000. In the cloud mask algorithm, the most extensive improvements were in distinguishing clouds at nighttime, including the challenging polar darkness regions of the world. Additional improvements have been made to properly distinguish sunglint from clouds in the tropical ocean regions, and to improve the identification of clouds from snow during daytime in Polar Regions. We will show global monthly mean cloud fraction for both Terra and Aqua, and show how similar the global daytime cloud fraction is from these morning and afternoon orbits, respectively. We will also show the zonal distribution of cloud fraction over land and ocean regions for both Terra and Aqua, and show the time series of global cloud fraction from July 2002 through June 2006.

King, Michael D.↗

NASA NTRS2011

NPP After Launch: Characterizing ATMS Performance

The NPOESS Preparatory Project (NPP) mission is scheduled to launch in the fall of 2011. Although several teams from the government and the instrument contractor will be assessing and characterizing the performance of the Advanced Technology Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) sounding suite, the NASA NPP Science Team will be paying particular attention to the aspects of these sensors that affect their utility for atmospheric and climate research. In this talk we discuss relevant aspects of ATMS and our post launch analysis approach.

climate research↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

David M. Giles↗