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All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
NASA NTRS2021

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products from MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03),central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals thanMOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereasMOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

MAIAC↗
DOE OSTI2024

Dashboard for Marine Energy Site Assessment and Monitoring

The marine energy (ME) industry presently relies upon fragmented site assessment solutions that require high resource expenditure for deployment at each site and do not leverage the wealth of readily available tools and information. A wave energy resource assessment dashboard, currently in development, will substantially improve siting, permitting, operations, and maintenance of ME projects by providing an integrated solution that is a one-stop-shop for a developer’s needs. The Site Energy Assessment and MOnitoring Dashboard (SEAMOD) will be of commercial interest to anyone seeking to deploy an ME project and is easily expandable to include tidal and wind energy site assessments. The integrated dashboard is being developed using state-of-the-art database and cloud computing methods and data-assimilative modeling tools that can be coupled with low-cost, rapidly deployable wave buoys and environmental sensing hardware. The combined software and hardware dashboard will reduce wave energy site characterization and wave climate monitoring costs by more than 60 percent and provide assessments that meet international industry standards. To realize a thriving global ME industry, the physical environment at a potential deployment site must be understood, not only for resource characterization, but also for optimization of device and power conversion performance. SEAMOD directly addresses these needs with a commercially marketable product. SEAMOD is a low-cost solution that provides comprehensive ME resource assessments, baseline environmental monitoring, and offshore characterizations required for successful ME development. The key technical objectives for Phase I were a series of software development goals, which when implemented with monitoring solutions, produced an initial proof-of-concept low-cost wave energy resources dashboard. In Phase II, the development of the prototype SEAMOD continued. The basic framework employed was the development of a revised dashboard and monitoring tool customized for ME applications by focusing on IEC site assessment and method requirements. Development was focused on the integration of full hindcast metocean products to provide hindcast resource characterization and environmental information. The final integrated dashboard provides a low-cost solution that delivers comprehensive, scalable, industry-standard energy resource assessments and offshore characterizations required for successful ME development. The integrated dashboard offers visibility of the most recent site modeling, measurements, and historical data. The application and integration of consensus-based standards for wave energy resource assessment, as determined by the International Electrotechnical Commission (IEC), are crucial for the impact and value of SEAMOD. SEAMOD includes monthly, seasonal, and yearly statistics, as well as the total 30-year record, offering temporal resolution of the IEC parameters to aid potential developers in determining the available wave energy resources in their area of interest.

16 TIDAL AND WAVE POWER↗
NASA NTRS1995

Implementation of Combined Feather and Surface-Normal Ice Growth Models in LEWICE/X

Experimental observations have shown that discrete rime ice growths called feathers, which grow in approximately the direction of water droplet impingement, play an important role in the growth of ice on accreting surfaces for some thermodynamic conditions. An improved physical model of ice accretion has been implemented in the LEWICE 2D panel-based ice accretion code maintained by the NASA Lewis Research Center. The LEWICE/X model of ice accretion explicitly simulates regions of feather growth within the framework of the LEWICE model. Water droplets impinging on an accreting surface are withheld from the normal LEWICE mass/energy balance and handled in a separate routine; ice growth resulting from these droplets is performed with enhanced convective heat transfer approximately along droplet impingement directions. An independent underlying ice shape is grown along surface normals using the unmodified LEWICE method. The resulting dual-surface ice shape models roughness-induced feather growth observed in icing wind tunnel tests. Experiments indicate that the exact direction of feather growth is dependent on external conditions. Data is presented to support a linear variation of growth direction with temperature and cloud water content. Test runs of LEWICE/X indicate that the sizes of surface regions containing feathers are influenced by initial roughness element height. This suggests that a previous argument that feather region size is determined by boundary layer transition may be incorrect. Simulation results for two typical test cases give improved shape agreement over unmodified LEWICE.

Matthew T Velazquez↗
DOE OSTI2025

Implementation of Sub‐Grid Scale Temperature Perturbations Induced by Non‐Orographic Gravity Waves in WACCM6

Abstract Atmospheric gravity waves can play a significant role on atmospheric chemistry through temperature fluctuations. A recent modeling study introduced a method to implement subgrid‐scale orographic gravity‐wave‐induced temperature perturbations in the Whole Atmosphere Community Climate Model (WACCM). The model with a wave‐induced temperature parameterization was able to reproduce for example, the influence of mountain wave events on atmospheric chemistry, as highlighted in previous literature. Here we extend the subgrid‐scale wave‐induced temperature parameterization to also include non‐orographic gravity waves arising from frontal activity and convection. We explore the impact of these waves on middle atmosphere chemistry, particularly focusing on reactions that are strongly sensitive to temperature. The non‐orographic gravity waves increase the variability of chemical reaction rates, especially in the lower mesosphere. As an example, we show that this, in turn, leads to increases in the daytime ozone variability. To demonstrate another impact, we briefly investigate the role of non‐orographic gravity waves in cirrus cloud formation in this model. Consistent with findings from the previous study focusing on orographic gravity waves, non‐orographic waves also enhance homogeneous nucleation and increase cirrus clouds. The updated method used enables the global chemistry‐climate model to account for both orographic and non‐orographic gravity‐wave‐induced subgrid‐scale dynamical perturbations in a consistent manner.

Yook, Simchan [Department of Earth Atmospheric and↗
DOE OSTI2022

Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2007

Geostationary Lightning Mapper for GOES-R

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Goodman, Steven↗
NASA NTRS2021

On-Orbit Calibration and Performance Assessments of Terra and Aqua Modis Thermal Emissive Bands

Terra and Aqua MODIS have successfully operated for more than 20 and 18 years, respectively, and far exceeded their designed lifetimes of 6 years. MODIS has 36 spectral bands, among which 16 are the thermal emissive bands (TEB) covering a wavelength range from 3.75 to 14.24 μm. Observations from the MODIS TEB have been used to generate a number of data products, such as surface/cloud/atmospheric temperatures, cloud top altitude, and water vapor properties. To a large extent, the quality of the MODIS L1B radiance product has been maintained through the entirety of each mission via extensive on-orbit calibration and validation efforts. MODIS TEB calibration uses a quadratic algorithm and is referenced to an on-board blackbody with its temperature measurements traceable to the NIST standard. We provide an overview of the MODIS instrument operations, key TEB calibration activities, and algorithms used in the latest L1B data Collection 6.1 (C6.1) and describe the TEB on-orbit performance for both Terra and Aqua MODIS. The TEB gain trends have been stable to within 5% over the Terra mission after 2003 under the same electronic configuration, except for bands 27 to 30, and within 3% over the Aqua mission. The Terra MODIS optical crosstalk correction implemented right after launch has been very effective in removing the ghost images in its photoconductive detector bands 32 to 36. Starting from C6.1, an electronic crosstalk correction algorithm has also been applied to the Terra MODIS photovoltaic detector long-wave infrared bands 27 to 30. The noise characterization performance remains stable with most TEB detectors continuously meeting their design requirements. A gradual loss of the Aqua MODIS radiative cooler margin reached its maximum in 2013 and has been slowly recovering since then. On-orbit changes in TEB response versus scan angle are extremely small based on pitch maneuver observations and assessments using vicarious approaches. The remaining challenges in TEB calibration and proposed improvements for the upcoming data collection with L1B data production and reprocessing are discussed.

Terra↗
DOE OSTI2016

Marine ARM GPCI Investigation of Clouds Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) Field Campaign Report

Sea surface temperature (SST) is one of the most appropriate and important climate parameters: a widespread increase is an indicator of global warming and modifications of the geographical distribution of SST are an extremely sensitive indicator of climate change. There is high demand for accurate, reliable, high-spatial-and-temporal-resolution SST measurements for the parameterization of ocean-atmosphere heat, momentum, and gas (SST is therefore critical to understanding the processes controlling the global carbon dioxide budget) fluxes, for detailed diagnostic and process-orientated studies to better understand the behavior of the climate system, as model boundary conditions, for assimilation into climate models, and for the rigorous validation of climate model output. In order to achieve an overall net flux uncertainty < 10 W/m 2 (Bradley and Fairall, 2006), the sea surface (skin) temperature (SSST) must be measured to an error < 0.1 C and a precision of 0.05 C. Anyone experienced in shipboard meteorological measurements will recognize this is a tough specification. These demands require complete confidence in the content, interpretation, accuracy, reliability, and continuity of observational SST data—criteria that can only be fulfilled by the successful implementation of an ongoing data product validation strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗
NASA NTRSDate not supplied

Calibrating CERES and VIIRS using CLARREO Pathfinder: Adjustment for the Viewing Geometry Mismatch

The Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission on the International Space Station (ISS) will provide, for the first-time, an SI-traceable on-orbit calibration reference with a reflectance uncertainty of 0.3% (1 sigma). One major mission objective is to demonstrate the capability of transferring CPF’s radiometric accuracy to other satellite-based instruments, such as Clouds and Earth's Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. CPF inter-calibration measurements will be planned to obtain inter-calibration samples that closely match satellite-based target sensor observations in time, space, angle, and wavelength. The inter-calibration event observations cover a wide range of viewing geometry angles with potential angular mismatch between CPF and the target sensors which can introduce non-negligible errors against the mission’s inter-calibration uncertainty budget. We will introduce the CPF approach to adjust for angular mismatch differences, thereby reducing the incurred uncertainties to within the inter-calibration uncertainty budget. The angular adjustment algorithm has been developed and validated based on the high-fidelity simulations of the CPF spectra using the Principal Component based Radiative Transfer Model (PCRTM). The implementation of the algorithm on event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗
NASA NTRS2001

Sensing, Spectra and Scaling: What's in Store for Land Observations

Bill Pecora's 1960's vision of the future, using spacecraft-based sensors for mapping the environment and exploring for resources, is being implemented today. New technology has produced better sensors in space such as the Landsat Thematic Mapper (TM) and SPOT, and creative researchers are continuing to find new applications. However, with existing sensors, and those intended for launch in this century, the potential for extracting information from the land surface is far from being exploited. The most recent technology development is imaging spectrometry, the acquisition of images in hundreds of contiguous spectral bands, such that for any pixel a complete reflectance spectrum can be acquired. Experience with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has shown that, with proper attention paid to absolute calibration, it is possible to acquire apparent surface reflectance to 5% accuracy without any ground-based measurement. The data reduction incorporates in educated guess of the aerosol scattering, development of a precipitable water vapor map from the data and mapping of cirrus clouds in the 1.38 micrometer band. This is not possible with TM. The pixel size in images of the earth plays and important role in the type and quality of information that can be derived. Less understood is the coupling between spatial and spectral resolution in a sensor. Recent work has shown that in processing the data to derive the relative abundance of materials in a pixel, also known is unmixing, the pixel size is an important parameter. A variance in the relative abundance of materials among the pixels is necessary to be able to derive the endmembers or pure material constituent spectra. In most cases, the 1 km pixel size for the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is too large to meet the variance criterion. A pointable high spatial and spectral resolution imaging spectrometer in orbit will be necessary to make the major next step in our understanding of the solid earth surface and its changing face.

Goetz, Alexander F. H.↗
NASA NTRS2014

A Flexible Parameterization for Shortwave Optical Properties of Ice Crystals

A parameterization is presented that provides extinction cross section sigma (sub e), single-scattering albedo omega, and asymmetry parameter (g) of ice crystals for any combination of volume, projected area, aspect ratio, and crystal distortion at any wavelength in the shortwave. Similar to previous parameterizations, the scheme makes use of geometric optics approximations and the observation that optical properties of complex, aggregated ice crystals can be well approximated by those of single hexagonal crystals with varying size, aspect ratio, and distortion levels. In the standard geometric optics implementation used here, sigma (sub e) is always twice the particle projected area. It is shown that omega is largely determined by the newly defined absorption size parameter and the particle aspect ratio. These dependences are parameterized using a combination of exponential, lognormal, and polynomial functions. The variation of (g) with aspect ratio and crystal distortion is parameterized for one reference wavelength using a combination of several polynomials. The dependences of g on refractive index and omega are investigated and factors are determined to scale the parameterized (g) to provide values appropriate for other wavelengths. The parameterization scheme consists of only 88 coefficients. The scheme is tested for a large variety of hexagonal crystals in several wavelength bands from 0.2 to 4 micron, revealing absolute differences with reference calculations of omega and (g) that are both generally below 0.015. Over a large variety of cloud conditions, the resulting root-mean-squared differences with reference calculations of cloud reflectance, transmittance, and absorptance are 1.4%, 1.1%, and 3.4%, respectively. Some practical applications of the parameterization in atmospheric models are highlighted.

parameterization↗
NASA NTRS2009

NPP Clouds and the Earth's Radiant Energy System (CERES) Predicted Sensor Performance Calibration and Preliminary Data Product Performance

Continuation of the Earth Radiation Budget (ERB) Climate Data Record (CDR) has been identified as critical in the 2007 NRC Decadal Survey, the Global Climate Observing System WCRP report, and in an assessment titled Impacts of NPOESS Nunn-McCurdy Certification on Joint NASA-NOAA Climate Goals. In response, NASA, NOAA and NPOESS agreed in early 2008 to fly the final existing CERES Flight Model (FM-5) on the NPP spacecraft for launch in 2010. Future opportunities for ERB CDR continuity consist of procuring an additional CERES Sensor with modest performance upgrades for flight on the NPOESS C1 spacecraft in 2013, followed by a new CERES follow-on sensor for flight in 2018 on the NPOESS C3 spacecraft. While science goals remain unchanged for the long-term ERB Climate Data Record, it is now understood that the task of achieving these goals is more difficult for two reasons. The first is an increased understanding of the dynamics of the Earth/atmosphere system which demonstrates that rigorous separation of natural variability from anthropogenic change on decadal time scales requires higher accuracy and stability than originally envisioned. Secondly, future implementation scenarios involve less redundancy in flight hardware (1 vs. 2 orbits and operational sensors) resulting in higher risk of loss of continuity and reduced number of independent observations to characterize performance of individual sensors. Although EOS CERES CDR's realize a factor of 2 to 4 improvement in accuracy and stability over previous ERBE CDR's, future sensors will require an additional factor of 2 improvement to answer rigorously the science questions moving forward. Modest investments, defined through the CERES Science Team s 30-year operational history of the EOS CERES sensors, in onboard calibration hardware and pre-flight calibration and test program will ensure meeting these goals while reducing costs in re-processing scientific datasets. The CERES FM-5 pre-flight radiometric characterization program benefited from the 30-year operational experience of the CERES EOS sensors, as well as a stronger emphasis of radiometric characterization in the Statement of Work with the sensor provider. Improvements to the pre-flight program included increased spectral, spatial, and temporal sampling under vacuum conditions as well as additional tests to characterize the primary and transfer standards in the calibration facility. Future work will include collaboration with NIST to further enhance the understanding of the radiometric performance of this equipment prior to flight. The current effort summarizes these improvements to the CERES FM-5 pre-flight sensor characterization program, as well as modifications to inflight calibration procedures and operational tasking. In addition, an estimate of the impacts to the system level accuracy and traceability is presented.

Priestly, Kory↗
DOE OSTI2026

Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware

The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution due to their ability to dynamically adapt to changing environments and consider delayed consequences. In many real-world applications, RL policies must produce actions in real time, often within microseconds to milliseconds, imposing significant constraints on system latency and computational overhead that conventional machine learning libraries are not designed to handle. To control phenomena in real time at these timescales, RL needs to be deployed on-the-edge, namely on dedicated hardware located near the system it controls, without relying on a host CPU or cloud-based inference. In this work we present the design and deployment of an experience accumulator system in a particle accelerator. In this system, deep-RL algorithms run using hardware acceleration and act within a few microseconds, enabling the use of RL for control of phenomena like beam instabilities. The training uses the collected data offline to reduce the number of operations carried out on the acceleration hardware. The proposed architecture was tested in real experimental conditions at the Karlsruhe research accelerator, a synchrotron light source, where the system was used to control artificially induced horizontal betatron oscillations in real-time, with a control loop period of just 2.7 μs. The results showed a performance comparable to the commercial feedback system available at the accelerator, demonstrating the viability and potential of this approach. Due to the self-learning and reconfiguration capability of this implementation, a seamless application to other control problems is possible. Applications range from particle accelerators to large-scale research and industrial facilities.

FPGA↗
NASA NTRS2012

Highlights of TOMS Version 9 Total Ozone Algorithm

The fundamental basis of TOMS total ozone algorithm was developed some 45 years ago by Dave and Mateer. It was designed to estimate total ozone from satellite measurements of the backscattered UV radiances at few discrete wavelengths in the Huggins ozone absorption band (310-340 nm). Over the years, as the need for higher accuracy in measuring total ozone from space has increased, several improvements to the basic algorithms have been made. They include: better correction for the effects of aerosols and clouds, an improved method to account for the variation in shape of ozone profiles with season, latitude, and total ozone, and a multi-wavelength correction for remaining profile shape errors. These improvements have made it possible to retrieve total ozone with just 3 spectral channels of moderate spectral resolution (approx. 1 nm) with accuracy comparable to state-of-the-art spectral fitting algorithms like DOAS that require high spectral resolution measurements at large number of wavelengths. One of the deficiencies of the TOMS algorithm has been that it doesn't provide an error estimate. This is a particular problem in high latitudes when the profile shape errors become significant and vary with latitude, season, total ozone, and instrument viewing geometry. The primary objective of the TOMS V9 algorithm is to account for these effects in estimating the error bars. This is done by a straightforward implementation of the Rodgers optimum estimation method using a priori ozone profiles and their error covariances matrices constructed using Aura MLS and ozonesonde data. The algorithm produces a vertical ozone profile that contains 1-2.5 pieces of information (degrees of freedom of signal) depending upon solar zenith angle (SZA). The profile is integrated to obtain the total column. We provide information that shows the altitude range in which the profile is best determined by the measurements. One can use this information in data assimilation and analysis. A side benefit of this algorithm is that it is considerably simpler than the present algorithm that uses a database of 1512 profiles to retrieve total ozone. These profiles are tedious to construct and modify. Though conceptually similar to the SBUV V8 algorithm that was developed about a decade ago, the SBUV and TOMS V9 algorithms differ in detail. The TOMS algorithm uses 3 wavelengths to retrieve the profile while the SBUV algorithm uses 6-9 wavelengths, so TOMS provides less profile information. However both algorithms have comparable total ozone information and TOMS V9 can be easily adapted to use additional wavelengths from instruments like GOME, OMI and OMPS to provide better profile information at smaller SZAs. The other significant difference between the two algorithms is that while the SBUV algorithm has been optimized for deriving monthly zonal means by making an appropriate choice of the a priori error covariance matrix, the TOMS algorithm has been optimized for tracking short-term variability using month and latitude dependent covariance matrices.

Bhartia, Pawan↗