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DOE OSTI2023

Earth Filter Mathematica Toolbox V4 User’s Guide: Simplifying, Visualizing and Quick Modeling of All Fission Products from 60 Nuclear Sources

The Earth Filter Mathematica toolbox is a set of user-friendly routines that accurately calculate the ingrowth and decay of any of the 908 fission products for an infinite combination of sources over a large dynamic range and large time scales. This toolbox was developed by Dr. W. P. Brug at Los Alamos National Laboratory and eliminates the need for the user to define and solve differential equations by automatically scanning the ENDF-349 dataset and creating a complete set of differential equations (including delayed neutron branches to both the ground and metastable states) for every fission product. Additionally, this code allows the user to choose the data source for half-lives from several key references (i.e., TORI, IAEA, ENDF or NUDAT.) The complete set of ENDF-349 independent and cumulative fission yields for all 60 isotopic targets and 4 different neutron spectrums are included. This allows nearly any nuclear fission source to be accurately modeled as a linear combination of endpoint sources. The Earth Filter Toolbox has no limit as to the length of split list, and split lists of 5 or more endpoint sources can be quite common.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1999

Systematic Differences between Satellite-Based Presipitation Climatologies over the Tropical Oceans

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. It is clear however that there still remain significant uncertainties with satellite precipitation retrievals which limit their usefulness for many purposes. Systematic differences i'A tropical precipitation estimates have been brought to light in comparison activities such as the GPCP Algorithm Intercomparison Project and more recent Wetnet Precipitation Intercomparison Project 3. These uncertainties are assuming more importance because of the demands for validation associated with global climate modeling and data assimilation methodologies. The objective of the present study is to determine the physical basis for systematic differences in spatial structure of tropical precipitation as portrayed by several different satellite-based data sets. The study is limited to oceanic regions only and deals primarily with aspects of spatial variability. We are specifically interested in why MSU channel 1 and GPI precipitation differences are so striking over the Eastern Pacific ITCZ and why they both differ from other microwave emission-based precipitation estimates from SSM/I and a scattering-based deep convective ice index from MSU channel 2. Our results to date have shown that MSU channel I precipitation estimates are biased high over the Eastern Pacific ITCZ because of two factors: (1) the hypersensitivity of this frequency to cloud water in contrast to falling rain drops, and (2) unaccounted for scattering effects by precipitation-size ice which depresses the signal of the liquid water emission. Likewise, cold cloud top climatologies such as the GPI show an excess (a deficit) in estimated rainfall over the E. Pacific ITCZ (Warm Pool region). We show that these algorithms need to account for regionally varying heights (or temperatures) at which tropical convection detrains to form cirrus shields. A second objective we pursue is to identify variations in the macroscale cloud physical and thermodynamic properties of precipitation regimes" and relate these differences to tropical dynamical mechanisms of tropical heat and moisture balance. Finally, we interpret the algorithm differences and their associations with tropical dynamics in terms of WCRP GPCP goals for constructing precipitation climatologies.

Robertson, Frankin R.↗

NASA NTRS2007

Cooling of the North Atlantic by Saharan Dust

Using aerosol optical depth, sea surface temperature, top-of-the-atmosphere solar radiation flux, and oceanic mixed-layer depth from diverse data sources that include NASA satellites, NCEP reanalysis, in situ observations, as well as long-term dust records from Barbados, we examine the possible relationships between Saharan dust and Atlantic sea surface temperature. Results show that the estimated anomalous cooling pattern of the Atlantic during June 2006 relative to June 2005 due to attenuation of surface solar radiation by Saharan dust remarkably resemble observations, accounting for approximately 30-40% of the observed change in sea surface temperature. Historical data analysis show that there is a robust negative correlation between atmospheric dust loading and Atlantic SST consistent with the notion that increased (decreased) Saharan dust is associated with cooling (warming) of the Atlantic during the early hurricane season (July- August-September).

Lau, K. M.↗

NASA NTRS2011

Validation of Real-Time Data Processing for the Ground and Air-MSPI Systems

JPL is currently developing the multi-angle spectro-polarimetric imager (MSPI), targeted for the Aerosol-Cloud-Ecosystems (ACE) mission, as defined in the National Academies 2007 Decadal Survey. In preparation for the space instrument, the MSPI team has built two incremental camera systems (Ground- and Air-MSPI) to improve understanding of the proposed architecture. Ground-MSPI is a gimballed instrument used primarily for stationary observation and characterization of the imager and optics. The ER-2 based Air-MSPI operates in a step-and-stare mode, providing multi-angle imaging of a static target. This mode-of-operation simulates the observation scenario of the space instrument. Physically, MSPI is a pushbroom camera with a specialized frontend. Before imaging, light entering the camera passes through a pair of photoelastic modulators and a set of pattern polarizers. These optical elements act on the light to make polarimetric extraction computationally feasible. Calculating polarimetric parameters from the imager's data stream requires a real-time least-squares computation that produces coefficients of a truncated time-series expansion of the image. As reported in, the data processing algorithm can operate in real-time on a Xilinx Virtex-5 FPGA. Moving beyond verification with an onboard data source, the algorithm has been validated on a commercial development board interfaced with the ground camera. In addition, the algorithm has been instantiated within the Air-MSPI electronics board's FPGA, and in situ first-light has been achieved.

real-time data processing↗

DOE OSTI2021

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real-Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re-analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root-mean-squared-error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface-based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Thunderstorm cloud top observations using satellite stereoscopy

The present investigation has as objective to take a detailed look at the intense squall line over Oklahoma on May 2-3, 1979, using GOES stereoscopy combined with GOES infrared data. The synoptic situation and data sources are considered along with the stereoscopically observed cloud top ascent rates. Cloud top observations of intense thunderstorms are discussed, taking into account a contouring technique, the interpretation of infrared cloud top temperature patterns, and small-scale structure and its variability. It is found that GOES IR cloud top temperatures grossly underestimate the actual cloud top height observed stereoscopically, especially for immature storms. It is difficult to define growing storms below about 10 km in the GOES infrared data.

Mack, R. A.↗

NASA NTRS1993

An investigation of the role of current and future remote sensing data systems in numerical meteorology

The goals of this research endeavor have been to develop a flexible and relatively complete framework for the investigation of current and future satellite data sources in numerical meteorology. In order to realistically model how satellite information might be used for these purposes, it is necessary that Observing System Simulation Experiments (OSSEs) be as complete as possible. It is therefore desirable that these experiments simulate in entirety the sequence of steps involved in bringing satellite information from the radiance level through product retrieval to a realistic analysis and forecast sequence. In this project we have worked to make this sequence realistic by synthesizing raw satellite data from surrogate atmospheres, deriving satellite products from these data and subsequently producing analyses and forecasts using the retrieved products. The accomplishments made in 1991 are presented. The emphasis was on examining atmospheric soundings and microphysical products which we expect to produce with the launch of the Advanced Microwave Sounding Unit (AMSU), slated for flight in mid 1994.

Diak, George R.↗

NASA NTRS2015

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

Monitoring groundwater drought using land surface models is a valuable alternative given the current lack of systematic in situ measurements at continental and global scales and the low resolution of current remote sensing based groundwater data. However, uncertainties inherent to land surface models may impede drought detection, and thus should be assessed using independent data sources. In this study, we evaluated a groundwater drought index (GWI) derived from monthly groundwater storage output from the Catchment Land Surface Model (CLSM) using a GWI similarly derived from in situ groundwater observations. Groundwater observations were obtained from unconfined or semi-confined aquifers in eight regions of the central and northeastern U.S. Regional average GWI derived from CLSM exhibited strong correlation with that from observation wells, with correlation coefficients between 0.43 and 0.92. GWI from both in situ data and CLSM was generally better correlated with the Standard Precipitation Index (SPI) at 12 and 24 month timescales than at shorter timescales, but it varied depending on climate conditions. The correlation between CLSM derived GWI and SPI generally decreases with increasing depth to the water table, which in turn depends on both bedrock depth (a CLSM parameter) and mean annual precipitation. The persistence of CLSM derived GWI is spatially varied and again shows a strong influence of depth to groundwater. CLSM derived GWI generally persists longer than GWI derived from in situ data, due at least in part to the inability of coarse model inputs to capture high frequency meteorological variability at local scales. The study also showed that groundwater can have a significant impact on soil moisture persistence where the water table is shallow. Soil moisture persistence was estimated to be longer in the eastern U.S. than in the west, in contrast to previous findings that were based on models that did not represent groundwater. Assimilation of terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission improved the correlation between CLSM based regional average GWI and that based on in situ data in six of the eight regions. Practical issues regarding the application of GRACE assimilated groundwater storage for drought detection are discussed. An important conclusion of this study is that model parameters that control the depth to the water table, including bedrock depth, strongly influence the evolution and persistence of simulated groundwater and require careful configuration for drought monitoring.

Evaluation↗

NASA NTRS2014

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

hydrology↗

NASA NTRS2017

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data.

da/night band↗

NASA NTRSDate not supplied

Assessment of New Radio Occultation Measurements at the Global Modeling and Assimilation Office

Recent advances at the Global Modeling and Assimilation Office (GMAO) have focused on the assimilation of additional radio occultation (RO) bending angle measurements in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system. These efforts targeted the GEOS near-real-time forward processing system and may serve as the backbone for the next atmospheric reanalysis of the 21st century. In the most recent system upgrade, the assimilated RO counts increased by a factor of four with the addition of the COSMIC-2 constellation. Furthermore, with the advent of near-real-time commercial RO measurements, even more growth is expected. This presentation will quantify the impacts of new public and commercially-sourced data available to the GEOS system. The RO data assimilated in these experiments are from both the routinely acquired operational data streams as well as those from Spire Global, Inc. available via the Commercial SmallSat Data Acquisition (CSDA) Program.

Will McCarty↗

NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗

NASA NTRS2013

Space Shuttle Orbiter Drag Chute Summary

This paper summarizes the development history and technical highlights of the Space Shuttle Orbiter Drag Chute Program. Data and references are given on the design, development, and testing of the system, plus several interesting operational issues and solutions. The last Shuttle flight was completed in 2011 and all the Orbiters have now become museum pieces. Before all the data from system development and the 86 Orbiter Drag Chute (ODC) operational landings is lost or forgotten, it may be useful to summarize it here and to identify data sources for future reference. Much has been written about various aspects of the program, and this summary has attempted to cite many such references to make available more detailed information. The ODC program was a high-visibility NASA program that afforded the opportunity to thoroughly engineer and test the chute system, far beyond so many of today s tight-budget programs. So the ODC program was extremely informative--it provided a wide scope of information including protective door jettison issues and solutions, wind tunnel data and analyses on chute stability and drag behind a huge and rather blunt forebody, component and system reuse, and chute cleaning methods. Technology and data created have aided several current and past parachute programs, and will continue to do so in the future. The original Orbiter preliminary design included a drag parachute-- it was deleted early to save weight. But after the 1987 Challenger accident and during the program redefinition phase that followed, Astronaut John Young presented a strong case for enhancing landing safety by adding nosegear steering, brake improvements, and reviving the drag chute.

Lowry, Charles H.↗

DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗

NASA NTRS1982

Low flight speed acoustic results for a supersonic inlet with auxiliary inlet doors

A model supersonic inlet with auxiliary inlet doors and bounday layer bleeds was acoustically tested in simulated low speed flight up to Mach 0.2 in the NASA Lewis 9x15 Anechoic Wind Tunnel and statically in the NASA Lewis Anechoic Chamber. A JT8D refan model was used as the noise source. Data were also taken for a CTOL inlet and for an annular inlet with simulated centerbody support struts. Inlet operation with open auxiliary doors increased the blade passage tone by about 10 dB relative to the closed door configuration although noise radiation was primarily through the main inlet rather than the doors. Numerous strong spikes in the noise spectra were associated with the bleed system, and were strongly affected by the centerbody location. The supersonic inlet appeared to suppress multiple pure tone (MPT) generation at the fan source. Inlet length and the presence of support struts were shown not to cause this MPT suppression.

Woodward, R. P.↗

NASA NTRS1983

Low flight speed acoustic results for a supersonic inlet with auxiliary inlet doors

A model supersonic inlet with auxiliary inlet doors and boundary layer bleeds was acoustically tested in simulated low speed flight up to Mach 0.2 in the NASA Lewis 9 x 15 Anechoic Wind Tunnel and statically in the NASA Lewis Anechoic Chamber. A JT8D refan model was used as the noise source. Data were also taken for a CTOL inlet and for an annular inlet with simulated centerbody support struts. Inlet operation with open auxiliary doors increased the blade passage tone by about 10 dB relative to the closed door configuration although noise radiation was primarily through the main inlet rather than the doors. Numerous strong spikes in the noise spectra were associated with the bleed system, and were strongly affected by the centerbody location. The supersonic inlet appeared to suppress multiple pure tone (MPT) generation at the fan source. Inlet length and the presence of support struts were shown not to cause this MPT suppression. Previously announced in STAR as N83-27794

Woodward, R. P.↗

DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. Here, we have verified the proposed approach using real high- and low-resolution load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗