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NASA NTRS2007

An Attempt to Observe Debris from the Breakup of a Titan 3C-4 Transtage

In February 2007 dedicated observations were made of the orbital space predicted to contain debris from the breakup of the Titan 3C-4 transtage back on February 21, 1992. These observations were carried out on the Michigan Orbital DEbris Survey Telescope (MODEST) in Chile with its 1.3deg field of view. The search region or orbital space (inclination and right ascension of the ascending node (RAAN) was predicted using NASA#s LEGEND (LEO-to-GEO Environment Debris) code to generate a Titan debris cloud. Breakup fragments are created based on the NASA Standard Breakup Model (including fragment size, area-to-mass (A/M), and delta-V distributions). Once fragments are created, they are propagated forward in time with a subroutine GEOPROP. Perturbations included in GEOPROP are those due to solar/lunar gravity, radiation pressure, and major geopotential terms. Barker, et. al, (AMOS Conference Proceedings, 2006, pp. 596-604) used similar LEGEND predictions to correlate survey observations made by MODEST (February 2002) and found several possible night-to-night correlations in the limited survey dataset. One conc lusion of the survey search was to dedicate a MODEST run to observing a GEO region predicted to contain debris fragments and actual Titan debris objects (SSN 25000, 25001 and 30000). Such a dedicated run was undertaken with MODEST between February 17 and 23, 2007 (UT dates). MODEST#s limiting magnitude of 18.0 (S\N approx.10) corresponds to a size of 22cm assuming a diffuse Lambertian albedo of 0.2. However, based on observed break-up data, we expect most debris fragments to be smaller than 22cm which implies a need to increase the effective sensitivity of MODEST for smaller objects. MODEST#s limiting size can be lowered by increasing the exposure time (20 instead of 5 seconds) and applying special image processing. The special processing combines individual CCD images to detect faint objects that are invisible on a single CCD image. Sub-images are cropped from six consecutive CCD images with pixel shifts between images being consistent with the predicted movement of a Titan object. A median image of all the sub-images is then created leaving only those objects with the proper Titan motion. Limiting the median image in this manner brings the needed computer time to process all images taken on one night down to about 50 hours of CPU time.

Barker, E. S.↗

NASA NTRS1995

SeaWiFS technical report series. Volume 31: Stray light in the SeaWiFS radiometer

Some of the measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) will not be useful as ocean measurements. For the ocean data set, there are procedures in place to mask the SeaWiFS measurements of clouds and ice. Land measurements will also be masked using a geographic technique based on each measurment's latitude and longitude. Each of these masks involves a source of light much brighter than the ocean. Because of stray light in the SeaWiFS radiometer, light from these bright sources can contaminate ocean measurements located a variable number of pixels away from a bright source. In this document, the sources of stray light in the sensor are examined, and a method is developed for masking measurements near bright targets for stray light effects. In addition, a procedure is proposed for reducing the effects of stray light in the flight data from SeaWiFS. This correction can also reduce the number of pixels masked for stray light. Without these corrections, local area scenes must be masked 10 pixels before and after bright targets in the along-scan direction. The addition of these corrections reduces the along-scan masks to four pixels before and after bright sources. In the along-track direction, the flight data are not corrected, and are masked two pixels before and after. Laboratory measurements have shown that stray light within the instrument changes in a direct ratio to the intensity of the bright source. The measurements have also shown that none of the bands show peculiarities in their stray light response. In other words, the instrument's response is uniform from band to band. The along-scan correction is based on each band's response to a 1 pixel wide bright sources. Since these results are based solely on preflight laboratory measurements, their successful implementation requires compliance with two additional criteria. First, since SeaWiFS has a large data volume, the correction and masking procedures must be such that they can be converted into computationally fast algorithms. Second, they must be shown to operate properly on flight data. The laboratory results, and the corrections and masking procedures that derive from them, should be considered as zeroeth order estimates of the effects that will be found on orbit.

Hooker, Stanford B.↗

NASA NTRS2001

Tropical Ocean Evaporation/SST Sensitivity and It's Link to Water and Energy Budget Variations During ENSO

The continuing debate over feedback mechanisms governing tropical sea surface temperatures (SSTs) and tropical climate in general has highlighted the diversity of potential checks and balances within the climate system. Competing feedbacks due to changes in surface evaporation, water vapor, and cloud long- and shortwave radiative properties each may serve critical roles in stabilizing or destabilizing the climate system. It is also intriguing that even those climate variations having origins internal to the climate system - changes in ocean heat transport for example, apparently require complementary equilibrating effects by changes in atmospheric energy fluxes. Perhaps the best observational evidence of this is the relatively invariant nature of tropically averaged net radiation exiting the top-of-atmosphere (TOA) as measured by broadband satellite sensors over the past two decades. Thus, analyzing how these feedback mechanisms are operating within the context of current interannual variability may offer considerable insight for anticipating future climate change. In this paper we focus primarily on interannual variations of ocean evaporative fluxes and their significance for coupled water and energy cycles within the tropical climate system. In particular, we use both the da Silva estimates of surface fluxes (based on the Comprehensive Ocean Atmosphere Data Set, COADS) and numerical simulations from several global climate models to examine evaporation sensitivity to perturbations in SST associated with warm and cold ENSO events. The specific questions we address are as follows: (1) What recurring patterns of surface wind and humidity anomalies are present during ENSO and how do they combine to yield systematic evaporation anomalies?, (2) What is the resulting tropical ocean mean evaporation-SST sensitivity associated with this climate perturbation?, and (3) What role does this evaporation play in tropical heat and water balance over tropical oceanic regions? We use the da Silva ocean flux data to identify composite structure of departures of latent heat flux from climatology. We also show how these patterns arise out of associated wind and humidity anomaly distributions. Our preliminary work shows that evaporation sensitivity estimates from the da Silva / COADS data, computed for the tropical oceans (30 degrees N/S) are in the neighborhood of 5 to 6 W/square m K. Model estimates are also quite close to this figure. This rate is only slightly less than a rate corresponding to constant relative humidity; however, substantial regional departures from constant relative humidity are present. These patterns are robust and we relate the associated wind and humidity fluctuations noted in previous investigations to the derived evaporation anomalies. Finally, these results are interpreted with other data from the Earth radiation Budget Experiment (ERBE), Global Precipitation Climatology Project (GPCP) and NASA's Surface Radiation Budget (SRB) data set to characterize the tropical energetics of ENSO-related climate variability.

Robertson, Franklin R.↗

NASA NTRS2016

Ice-Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel

Icing simulation tools and computational fluid dynamics codes are reaching levels of maturity such that they are being proposed by manufacturers for use in certification of aircraft for flight in icing conditions with increasingly less reliance on natural-icing flight testing and icing-wind-tunnel testing. Sufficient high-quality data to evaluate the performance of these tools is not currently available. The objective of this work was to generate a database of ice-accretion geometry that can be used for development and validation of icing simulation tools as well as for aerodynamic testing. Three large-scale swept wing models were built and tested at the NASA Glenn Icing Research Tunnel (IRT). The models represented the Inboard (20 percent semispan), Midspan (64 percent semispan) and Outboard stations (83 percent semispan) of a wing based upon a 65 percent scale version of the Common Research Model (CRM). The IRT models utilized a hybrid design that maintained the full-scale leading-edge geometry with a truncated afterbody and flap. The models were instrumented with surface pressure taps in order to acquire sufficient aerodynamic data to verify the hybrid model design capability to simulate the full-scale wing section. A series of ice-accretion tests were conducted over a range of total temperatures from -23.8 to -1.4 C with all other conditions held constant. The results showed the changing ice-accretion morphology from rime ice at the colder temperatures to highly 3-D scallop ice in the range of -11.2 to -6.3 C. Warmer temperatures generated highly 3-D ice accretion with glaze ice characteristics. The results indicated that the general scallop ice morphology was similar for all three models. Icing results were documented for limited parametric variations in angle of attack, drop size and cloud liquid-water content (LWC). The effect of velocity on ice accretion was documented for the Midspan and Outboard models for a limited number of test cases. The data suggest that there are morphological characteristics of glaze and scallop ice accretion on these swept-wing models that are dependent upon the velocity. This work has resulted in a large database of ice-accretion geometry on large-scale, swept-wing models.

Wing Aerodynamics↗

NASA NTRS2013

Cloud Absorption Radiometer Autonomous Navigation System - CANS

CAR (cloud absorption radiometer) acquires spatial reference data from host aircraft navigation systems. This poses various problems during CAR data reduction, including navigation data format, accuracy of position data, accuracy of airframe inertial data, and navigation data rate. Incorporating its own navigation system, which included GPS (Global Positioning System), roll axis inertia and rates, and three axis acceleration, CANS expedites data reduction and increases the accuracy of the CAR end data product. CANS provides a self-contained navigation system for the CAR, using inertial reference and GPS positional information. The intent of the software application was to correct the sensor with respect to aircraft roll in real time based upon inputs from a precision navigation sensor. In addition, the navigation information (including GPS position), attitude data, and sensor position details are all streamed to a remote system for recording and later analysis. CANS comprises a commercially available inertial navigation system with integral GPS capability (Attitude Heading Reference System AHRS) integrated into the CAR support structure and data system. The unit is attached to the bottom of the tripod support structure. The related GPS antenna is located on the P-3 radome immediately above the CAR. The AHRS unit provides a RS-232 data stream containing global position and inertial attitude and velocity data to the CAR, which is recorded concurrently with the CAR data. This independence from aircraft navigation input provides for position and inertial state data that accounts for very small changes in aircraft attitude and position, sensed at the CAR location as opposed to aircraft state sensors typically installed close to the aircraft center of gravity. More accurate positional data enables quicker CAR data reduction with better resolution. The CANS software operates in two modes: initialization/calibration and operational. In the initialization/calibration mode, the software aligns the precision navigation sensors and initializes the communications interfaces with the sensor and the remote computing system. It also monitors the navigation data state for quality and ensures that the system maintains the required fidelity for attitude and positional information. In the operational mode, the software runs at 12.5 Hz and gathers the required navigation/attitude data, computes the required sensor correction values, and then commands the sensor to the required roll correction. In this manner, the sensor will stay very near to vertical at all times, greatly improving the resulting collected data and imagery. CANS greatly improves quality of resulting imagery and data collected. In addition, the software component of the system outputs a concisely formatted, high-speed data stream that can be used for further science data processing. This precision, time-stamped data also can benefit other instruments on the same aircraft platform by providing extra information from the mission flight.

Kahle, Duncan↗

NASA NTRS2016

Ice-Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel

Icing simulation tools and computational fluid dynamics codes are reaching levels of maturity such that they are being proposed by manufacturers for use in certification of aircraft for flight in icing conditions with increasingly less reliance on natural-icing flight testing and icing-wind-tunnel testing. Sufficient high-quality data to evaluate the performance of these tools is not currently available. The objective of this work was to generate a database of ice-accretion geometry that can be used for development and validation of icing simulation tools as well as for aerodynamic testing. Three large-scale swept wing models were built and tested at the NASA Glenn Icing Research Tunnel (IRT). The models represented the Inboard (20% semispan), Midspan (64% semispan) and Outboard stations (83% semispan) of a wing based upon a 65% scale version of the Common Research Model (CRM). The IRT models utilized a hybrid design that maintained the full-scale leading-edge geometry with a truncated afterbody and flap. The models were instrumented with surface pressure taps in order to acquire sufficient aerodynamic data to verify the hybrid model design capability to simulate the full-scale wing section. A series of ice-accretion tests were conducted over a range of total temperatures from -23.8 deg C to -1.4 deg C with all other conditions held constant. The results showed the changing ice-accretion morphology from rime ice at the colder temperatures to highly 3-D scallop ice in the range of -11.2 deg C to -6.3 deg C. Warmer temperatures generated highly 3-D ice accretion with glaze ice characteristics. The results indicated that the general scallop ice morphology was similar for all three models. Icing results were documented for limited parametric variations in angle of attack, drop size and cloud liquid-water content (LWC). The effect of velocity on ice accretion was documented for the Midspan and Outboard models for a limited number of test cases. The data suggest that there are morphological characteristics of glaze and scallop ice accretion on these swept-wing models that are dependent upon the velocity. This work has resulted in a large database of ice-accretion geometry on large-scale, swept-wing models.

Wind-Tunnel Tests↗

DOE OSTI2025

Exploring the interaction between the MW and LMC with a large sample of blue horizontal branch stars from the DESI survey

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7σ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 deg away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is –24 km s –1 ⁠, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first 2 yr of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW–LMC interaction will improve significantly.

Galaxy: evolution↗

DOE OSTI2021

A mathematical framework for ejecta cloud dynamics with application to source models and piezoelectric mass measurements

We present a mathematical framework for describing the dynamical evolution of an ejecta cloud generated by a generic ejecta source model. We consider a piezoelectric sensor fielded in the path of an ejecta cloud, for experimental configurations in which the ejecta are created at a singly shocked planar surface and fly ballistically through vacuum to the stationary sensor. To do so, we introduce the concept of a time- and velocity-dependent ejecta “areal mass function.” We derive expressions for the analytic (“true”) accumulated ejecta areal mass at the sensor and the measured (“inferred”) value obtained via the standard method for analyzing piezoelectric voltages. In this way, we derive an exact expression and upper bound for the error imposed upon a piezoelectric ejecta mass measurement (in a perfect system) by the assumption of instantaneous creation, which is commonly required for momentum diagnostic analyses. This error term is zero for truly instantaneous source models; otherwise, the standard piezoelectric analysis is guaranteed to overestimate the true mass. When combined with a piezoelectric dataset, this framework provides a unique solution for the ejecta particle velocity distribution, subject to the assumptions inherent in the data analysis. The framework also leads to strong boundary conditions that any ejecta source model must satisfy in order to be consistent with apparently global properties of piezoelectric measurements from a wide range of experiments. We demonstrate this methodology by applying it to the Richtmyer–Meshkov instability+self-similar velocity distribution ejecta source model currently under development at Los Alamos National Laboratory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.

14 SOLAR ENERGY↗

NASA NTRS2021

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

Goutam Konapala↗

DOE OSTI2013

Automated Cache Performance Analysis And Optimization

While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for particular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory addresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this software, we worked to develop core capabilities for gathering enhanced memory usage performance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercomputer and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters, cache behavior could only be measured reliably in the aggregate across tens or hundreds of thousands of instructions. With the newest iteration of PEBS technology, cache events can be tied to a tuple of instruction pointer, target address (for both loads and stores), memory hierarchy, and observed latency. With this information we can now begin asking questions regarding the efficiency of not only regions of code, but how these regions interact with particular data structures and how these interactions evolve over time. In the short term, this information will be vital for performance analysts understanding and optimizing the behavior of their codes for the memory hierarchy. In the future, we can begin to ask how data layouts might be changed to improve performance and, for a particular application, what the theoretical optimal performance might be. The overall benefit to be produced by this effort was a commercial quality easy-to- use and scalable performance tool that will allow both beginner and experienced parallel programmers to automatically tune their applications for optimal cache usage. Effective use of such a tool can literally save weeks of performance tuning effort. Easy to use. With the proposed innovations, finding and fixing memory performance issues would be more automated and hide most to all of the performance engineer expertise ”under the hood” of the Open|SpeedShop performance tool. One of the biggest public benefits from the proposed innovations is that it makes performance analysis more usable to a larger group of application developers. Intuitive reporting of results. The Open|SpeedShop performance analysis tool has a rich set of intuitive, yet detailed reports for presenting performance results to application developers. Our goal was to leverage this existing technology to present the results from our memory performance addition to Open|SpeedShop. Suitable for experts as well as novices. Application performance is getting more difficult to measure as the hardware platforms they run on become more complicated. This makes life difficult for the application developer, in that they need to know more about the hardware platform, including the memory system hierarchy, in order to understand the performance of their application. Some application developers are comfortable in that sceario, while others want to do their scientific research and not have to understand all the nuances in the hardware platform they are running their application on. Our proposed innovations were aimed to support both experts and novice performance analysts. Useful in many markets. The enhancement to Open|SpeedShop would appeal to a broader market space, as it will be useful in scientific, commercial, and cloud computing environments. Our goal was to use technology developed initially at the and Lawrence Livermore National Laboratory combined with the development and commercial software experience of the Argo Navis Technologies, LLC (ANT) to form a powerful combination to delivery these objectives.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗

NASA NTRS1998

Stratospheric Sulfuric Acid and Black Carbon Aerosol Measured During POLARIS and its Role in Ozone Chemistry

Stratospheric aerosol can affect the environment in three ways. Sulfuric acid aerosol have been shown to act as sites for the reduction of reactive nitrogen and chlorine and as condensation sites to form Polar Stratospheric Clouds, under very cold conditions, which facilitate ozone depletion. Recently, modeling studies have suggested a link between BCA (Black Carbon Aerosol) and ozone chemistry. These studies suggest that HNO3, NO2, and O3 may be reduced heterogeneously on BCA particles. The ozone reaction converts ozone to oxygen molecules, while HNO3 and NO2 react to form NOx. Finally, a buildup of BCA could reduce the single-scatter albedo of aerosol below a value of 0.98, a critical value that has been postulated to change the effect of stratospheric aerosol from cooling to warming. Correlations between measured BCA amounts and aircraft usage have been reported. Attempts to link BCA to ozone chemistry and other stratospheric processes have been hindered by questions concerning the amount of BCA that exists in the stratosphere, the magnitude of reaction probabilities, and the scarcity of BCA measurements. The Ames Wire Impactors (AWI) participated in POLARIS as part of the complement of experiments on the NASA ER-2. One of our main objectives was to determine the amount of aerosol surface area, particularly BCA, available for reaction with stratospheric constituents and assess if possible, the importance of these reactions. The AWI collects aerosol and BCA particles on thin Palladium wires that are exposed to the ambient air in a controlled manner. The samples are returned to the laboratory for subsequent analysis. The product of the AWI analysis is the size, surface area, and volume distributions, morphology and elemental composition of aerosol and BCA. This paper presents results from our experiments during POLARIS and puts these measurements in the context of POLARIS and other missions in which we have participated. It describes modifications to the AWI data analysis procedures in which the collection of BCA is modeled as a fractal aggregate. The new analysis results in an increase in BCA surface area of approximately 24 and an increase in mass of 7-10 from the previous method. For the current study, BCA surface area is used in computer models that attempt to predict measured NOx/NOy ratios and O3 depletion rates. Inclusion of the HNO3 reaction with BCA in one model tends to improve the agreement of calculated to measured NOx/NOy ratio. However, it was found that these trends are viable only if the reactions are catalytic.

Strawa, Anthony W.↗

DOE OSTI2012

ON THE ORIGIN OF THE HIGH COLUMN DENSITY TURNOVER IN THE H I COLUMN DENSITY DISTRIBUTION

We study the high column density regime of the H I column density distribution function and argue that there are two distinct features: a turnover at N{sub H{sub I}} Almost-Equal-To 10{sup 21} cm{sup -2}, which is present at both z = 0 and z Almost-Equal-To 3, and a lack of systems above N{sub H{sub I}} Almost-Equal-To 10{sup 22} cm{sup -2} at z = 0. Using observations of the column density distribution, we argue that the H I-H{sub 2} transition does not cause the turnover at N{sub H{sub I}} Almost-Equal-To 10{sup 21} cm{sup -2} but can plausibly explain the turnover at N{sub H{sub I}} {approx}> 10{sup 22} cm{sup -2}. We compute the H I column density distribution of individual galaxies in the THINGS sample and show that the turnover column density depends only weakly on metallicity. Furthermore, we show that the column density distribution of galaxies, corrected for inclination, is insensitive to the resolution of the H I map or to averaging in radial shells. Our results indicate that the similarity of H I column density distributions at z = 3 and 0 is due to the similarity of the maximum H I surface densities of high-z and low-z disks, set presumably by universal processes that shape properties of the gaseous disks of galaxies. Using fully cosmological simulations, we explore other candidate physical mechanisms that could produce a turnover in the column density distribution. We show that while turbulence within giant molecular clouds cannot affect the damped Ly{alpha} column density distribution, stellar feedback can affect it significantly if the feedback is sufficiently effective in removing gas from the central 2-3 kpc of high-redshift galaxies. Finally, we argue that it is meaningful to compare column densities averaged over {approx} kpc scales with those estimated from quasar spectra that probe sub-pc scales due to the steep power spectrum of H I column density fluctuations observed in nearby galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

Overview of NASA's Ocean Color Instrument Solar Calibration Architecture, Pre-Launch Tests and Preliminary On-Orbit Results

Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. Like its predecessors, OCI will provide two day global coverage of TOA radiances. Unlike its predecessors, OCI will cover a spectral range from 340nm to 2260nm. Below 900nm, OCI will include two spectrometers that continuously span the ultraviolet to 600nm and 600nm to near-infrared spectral regions to provide hyperspectral radiances sampled every 2.5 nm, with a bandwidth of 5 nm for each channel. Wavelengths above 900nm are measured in seven discrete multispectral bands of varying bandwidths, six of which are at similar wavelengths to those on heritage missions to support both atmospheric and ocean color applications. Nominal spatial resolution is similar to the SeaWiFS instrument with 1050 m at nadir. As for SeaWiFS, the pixel size increases due to a ~20 degree tilt and as a function of scan angle. Variations in the radiometric sensitivity of each OCI channel over time will be monitored by solar diffuser measurements for short term instrument gain adjustments and independent lunar measurements for trend adjustments of long time periods, similar to the approach employed for the VIIRS instrument [4]. The OCI flight-unit was built at NASA’s Goddard Space Flight Center. At the time of this writing, OCI has completed on-orbit commissioning activities and normal science operations have begun. A key aspect of the OCI architecture is the capability to trend absolute and relative calibration changes over the course of mission life with solar calibration. Every 24 hours, the PACE spacecraft performs an inertial hold as the ground track nears the North Pole which orients a Quasi-Volume Diffuser (QVD) mounted on OCI towards the sun. By knowing the irradiance of the sun and the reflectivity of the target, the absolute radiance at the input to the OCI aperture can be computed as OCI scans the target. The allowable absolute uncertainty budget for each solar calibration measurement is 1.6% 1-sigma below 900nm at beginning of life (BOL) and the allowable relative uncertainty budget is ~0.26% 1-sigma. The Solar Calibration Assembly (SCA) consists of three targets selectable via a single mechanism which also opens a door. The targets consist of a Daily Bright Target (DBT), Monthly Bright Target (MBT), and Daily Dim Target (DDT). The bright targets are quartz QVDs with the monthly target being used to track the degradation of the daily target. The dim target is used to track CCD linearity using Progressive Time-Delay Integration (PTDI). A composite baffle is attached to the SCA housing aperture to block Earth shine and stray light from the spacecraft. The SCA assembly is mounted to a view port ~90° from OCI nadir. This paper provides an overview of driving solar calibration requirements, error-budgets and early trade studies which drove the solar calibration assembly (SCA) architecture and on-orbit maneuver. Measurements of the diffuser Bidirectional Reflectance Distribution Function (BRDF) at TNO, Netherlands and GSFC are briefly described. Optical modelling and test results at the sub-system and instrument level are included. Finally, preliminary measurements on-orbit are compared to pre-launch predictions.

Joseph J Knuble↗

NASA NTRSDate not supplied

Overview of NASA's Ocean Color Instrument Solar Calibration Architecture, Pre-Launch Tests and Preliminary On-Orbit Results

ocean color↗