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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗
DOE OSTI2024

The Impact of Radiative Transfer at Reduced Spectral Resolution in Large‐Eddy Simulations of Convective Clouds

Abstract Many radiative transfer schemes approximate the spectral integration over ∼10 5 to ∼10 6 wavelengths with correlated k ‐distributions methods that typically require only 10 1 –10 2 spectral integration points ( g ‐points). The exact number of g ‐points is then chosen as an optimal balance between computational costs and accuracy, normally assessed in terms of a number of radiative quantities. How this radiative accuracy propagates to simulation accuracy, however, is not straightforward. In this study, we therefore explore the sensitivity of cloud properties in large‐eddy simulations (LES) to the accuracy of radiative fluxes and heating rates. We first generate smaller sets of g ‐points from existing k ‐distributions by repeatedly combining adjacent g ‐points while maintaining the highest possible accuracy on a chosen set of radiative metrics. Next, we perform three sets of LES with varying cloud—radiation coupling pathways, and therefore different requirements for the accuracy of the radiative transfer computations, to investigate how these smaller and thus less accurate k ‐distributions affect simulation characteristics. The decrease in radiative accuracy with 3–4 times smaller k ‐distributions results in biases in cloud properties that are relative small compared to their temporal fluctuations. These results show potential for speeding up radiative transfer computations in cloud‐resolving models by reducing the resolved spectral detail. However, more statistically converged simulations and a wider set of case studies is required to fully assess the robustness of our results.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

A cloud physics investigation utilizing Skylab data

The author has identified the following significant results. The Lowtran 2 program, S191 spectral response, and solar spectrum were used to compute the expected absorption by 2.0 micron band for a variety of cloud pressure levels and solar zenith angles. Analysis of the three long wavelength data channels continued in which it was found necessary to impose a minimum radiance criterion. It was also found necessary to modify the computer program to permit the computation of mean values and standard deviations for selected subsets of data on a given tape. A technique for computing the integrated absorption in the A band was devised. The technique normalizes the relative maximum at approximately .78 micron to the solar irradiance curve and then adjusts the relative maximum at approximately .74 micron to fit the solar curve.

Alishouse, J.↗
DOE OSTI2023

A robust approach to 3D neuron shape representation for quantification and classification

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

3D neuron morphology↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
DOE OSTI2021

Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for the compilers to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNNs spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

OpenCHAMI Developer Summit [Slides]

The mission of the OpenCHAMI consortium is to steward the collaborative development and continuous evolution of cloud-like software to manage High Performance Computing capacity regardless of the size or deployment platform. We are guided by the operators and practitioners who use modern tooling and concepts to address the needs of classical HPC applications and the growing AI/ML and Data Science community that wish to leverage HPC capacity within their own workflows, to meet their needs with their own tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1969

A cloud-cover simulation procedure.

Worldwide cloud cover simulation procedure providing data for computer simulation of earth oriented space missions, describing computer program using Monte Carlo mission simulation

Brown, S. C.↗
NASA NTRS1974

Motion of magnetospheric particle clouds in a time-dependent electric field model

A computer code has been developed to study quantitatively the drift motion of magnetospheric particles in a time-dependent electric field. These calculations were applied to the case of proton and electron injections from the plasma sheet during substorms; the model predictions were checked against observations on board the geosynchronous satellite ATS 5 by DeForest and McIlwain (1971). It was found that it is possible to simulate the observed proton spectrograms with an adequate choice of a time-dependent electric field model. The resulting kinematics is physically quite simple and in its gross features does not depend too strongly on the particular fine structure of the model.

Roederer, J. G.↗
NASA NTRS1981

An Automated Technique for Estimating Daily Precipitation over the State of Virginia

Digital IR and visible imagery obtained from a geostationary satellite located over the equator at 75 deg west latitude were provided by NASA and used to obtain a linear relationship between cloud top temperature and hourly precipitation. Two computer programs written in FORTRAN were used. The first program computes the satellite estimate field from the hourly digital IR imagery. The second program computes the final estimate for the entire state area by comparing five preliminary estimates of 24 hour precipitation with control raingage readings and determining which of the five methods gives the best estimate for the day. The final estimate is then produced by incorporating control gage readings into the winning method. In presenting reliable precipitation estimates for every cell in Virginia in near real time on a daily on going basis, the techniques require on the order of 125 to 150 daily gage readings by dependable, highly motivated observers distributed as uniformly as feasible across the state.

Follansbee, W. A.↗
NASA NTRS1984

Restored methane band images of Uranus and Neptune

Charge-coupled device images of Uranus and Neptune taken in the 8900-A absorption band of methane are presented. The images have been digitally processed by means of nonlinear deconvolution techniques to partially remove the effects of atmospheric seeing. The restored Uranus images show strong limb brightening consistent with previous observations and theoretical models of the planet's atmosphere. The computer-processed images of Neptune show discrete cloud features similar to those reported previously by Smith, Reitsema, and Larson (1979). A time series of the restored Neptune images shows a continuous variation which may be due to the planet's rotation.

Heasley, J. N.↗
NASA NTRS1986

Derivatives of the Arithmetic-Geometric Mean

Developed for theoretical studies of lightning requiring estimates of electric fields in clouds, the technique is expected to reduce computation time and improve accuracy.

Taton, F. B.↗
NASA NTRS1990

The radiation budget of a Cirrus layer deduced from simultaneous aircraft observations and model calculations

Several aircraft were employed during the FIRE Cirrus IFO in order to make nearly simultaneous observations of cloud properties and fluxes. A segment of the flight data collected on 28 October 1988 during which the NASA Ames ER-2 overflew the NCAR King Air was analyzed. The ER-2 flew at high altitude making observations of visible and infrared radiances and infrared flux and cloud height and thickness. During this segment, the King Air flew just above the cloud base making observations of ice crystal size and shape, local meteorological variables, and infrared fluxes. While the two aircraft did not collect data exactly coincident in space and time, they did make observations within a few minutes of each other. For this case study, the infrared radiation balance of the cirrus layer is of primary concern. Observations of the upwelling 10 micron radiance, made from the ER-2, can be used to deduce the 10 micron optical depth of the layer. The upwelling broadband infrared flux is also measured from the ER-2. At the same time, the upwelling and downwelling infrared flux at the cloud base is obtained from the King Air measurements. Information on cloud microphysics is also available from the King Air. Using this data in conjunction with atmospheric temperature and humidity profiles from local radiosondes, the necessary inputs for an infrared radiative transfer model can be developed. Infrared radiative transfer calculations are performed with a multispectral two-stream model. The model fluxes at the cloud base and at 19 km are then compared with the aircraft observations to determine whether the model is performing well. Cloud layer heating rates can then be computed from the radiation exchange.

Ackerman, Thomas P.↗
NASA NTRS1990

Flight motor set 360L009 (STS-36). Volume 1: System overview

Flight Motor Set 360L009, as part of NASA Space Shuttle Mission STS-36, a Department of Defence mission, was launched after two launch attempts. One launch was scrubbed following the failure of a ground-based Range Safety computer and one was scrubbed due to cloud cover at the return to launch landing site. As with all previous redesigned solid rocket motor launches, overall motor performance was excellent. There were no debris concerns from either motor. All ballistic and mass property parameters that could be assessed, closely matched the predicted values and were well within the required contract item specification levels. All field joint heaters and igniter joint heaters performed without anomalies. Evaluation of the ground environment instrumentation measurements again verified thermal model analysis data and showed agreement with predicted environmental effects. No launch commit criteria violations occurred. Postflight inspection again verified nominal performance of the insulation, phenolics, metal parts, and seals. Postflight evaluation indicated that both nozzles performed as expected during flight. All combustion gas was contained by insulation in the field and case-to-nozzle joints. Recommendations were made concerning improved thermal modeling and measurements. The rationale for these recommendations and complete result details are presented.

Garecht, Diane M.↗
NASA NTRS1992

Polarization Lidar Liquid Cloud Detection Algorithm for Winter Mountain Storms

We have collected an extensive polarization lidar dataset from elevated sites in the Tushar Mountains of Utah in support of winter storm cloud seeding research and experiments. Our truck-mounted ruby lidar collected zenith, dual-polarization lidar data through a roof window equipped with a wiper system to prevent snowfall accumulation. Lidar returns were collected at a rate of one shot every 1 to 5 min during declared storm periods over the 1985 and 1987 mid-Jan. to mid-Mar. Field seasons. The mid-barrier remote sensor field site was located at 2.57 km MSL. Of chief interest to weather modification efforts are the heights of supercooled liquid water (SLW) clouds, which must be known to assess their 'seedability' (i.e., temperature and height suitability for artificially increasing snowfall). We are currently re-examining out entire dataset to determine the climatological properties of SLW clouds in winter storms using an autonomous computer algorithm.

Sassen, Kenneth↗
NASA NTRS1994

ERS-1 scatterometer estimates of annual variations of Atlantic ITCZ and Pacific NECC

A 10 m height wind vector data set, named CMODFD, from ERS-1 Active Microwave Instrument (AMI) measurements, was compared with moored buoy wind observations. The CMODFD wind vectors compared favorably with moored buoy wind observations at about 60 sites. Satisfactory results were obtained from intercomparisons between CMODFD and moored buoy estimates of horizontal wind divergence and wind stress curl. The CMODFD positions of the Intertropical Convergence Zone (ITCZ) at 28 deg west were nearly equal to those determined from satellite images of clouds. The CMODFD data were employed to compute the Ekman vertical velocity in the region of the Pacific North Equatorial Countercurrent (NECC), and a correlative relation was found between the annual cycles and longitudinal variations of the NECC and Ekman vertical motion.

Halpern, D.↗
NASA NTRS2000

Satellite Mapping of the Earth's Ozone and Sulfur Dioxide

The Total Ozone Mapping Spectrometer (TOMS) instruments are spatially-scanning UV spectrometers that have produced daily global images of total ozone over the last 21 years since the launch of the Nimbus 7 satellite. The instruments use a total ozone retrieval algorithm pioneered by J.V. Dave and C. L. Mateer for the Nimbus 4 Backscatter Ultraviolet (BUV) instrument, designed by D.F. Heath. The TOMS ozone maps have revealed the relations between total ozone and atmospheric dynamics, and shown the dramatic losses of ozone in the Antarctic ozone hole and the Northern hemisphere. The accepted long-term trends in global, regional, and local ozone are derived from data from the Nimbus 7 TOMS and three successive TOMS flights on Russian, Japanese, and American satellites. The next TOMS flight will be launched in 2000. The contiguous mapping design and fortuitous choice of TOMS wavelengths bands also permitted imaging of a second atmospheric gas, sulfur dioxide, which is transient due to its short lifetime. The importance of this measurement was first realized after the eruption of El Chichon volcano in 1982. The extreme range of sizes of volcanic eruptions and the 'associated danger require observations from a distant observing platform. The first quantitative time series of the input of sulfur dioxide by explosive volcanic eruptions into the atmosphere thus was developed from the TOMS missions. Finally, the Rayleigh and aerosol scattering spectral characteristic and reflectivity complete the four dominant pieces of information in the near UV albedo of the Earth. The four parameters are derived with a linear algorithm, the absorption coefficients of the gases, and effective paths computed from radiative transfer tables. Absorbing aerosol clouds (smoke, dust, volcanic ash) are readily identified by their deviation from a Rayleigh signature. The greatest shortcoming of the TOMS dataset is the 24 hour time resolution that is produced by the polar orbit of the satellite. Dynamic phenomena, such as upper air fronts that modulate total ozone and volcanic eruptions of sulfur dioxide and ash, cannot be adequately resolved. It is hoped that UV observations from geostationary satellites will soon be made to test the value of this unique information in weather forecasting and aviation safety.

Krueger, Arlin↗