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DOE OSTI2022

Raw tensiometer soil matric potential data, from Oct 2017-Jun 2020 in Manaus, Brazil

This data package contains raw output from two Campbell Scientific CR1000 data loggers each connected to three UMS T4e tensiometer sensors in Manaus, Brazil. Tensiometers at each data logger location were installed to depths of 10 cm, 20 cm and 50 cm below the ground surface for the purpose of measuring soil matric potential at each depth. “Station 3” tensiometers were installed at approximately 2°36'31.68"S, 60°12'34.70"W. “Station 4” tensiometers were installed at approximately 2°36'32.46"S, 60°12'34.92"W. The files “tensio3.edit.202109.csv” and “tensio4.edit.202109.csv” contain raw data (with invalid data removed). A text editor will be required to view the raw files in .csv (comma-separated values) format. Data processing was limited to: 1) Editing column headers for clarity; 2) Applying a correction that converts voltage read by the data loggers to pressure units (kPa); 3) removing all data greater than 50 kPa and less than -115 kPa; 4) Adding columns entitled “Caution.10cm”, “Caution.20cm” and “Caution.50cm” that contains values of either “NA” or “Caution”, the latter of which is applied when data exceeds the manufacturer reported data validity range of less than -85 kPa to suggest use of these data with caution; 5) Adding columns entitled “Invalid.10cm”, “Invalid.20cm”, and “Invalid.50cm” that contains values of either “NA” or “Invalid”, the latter of which is applied when data accuracy is deemed compromised due to the introduction of air bubbles into the instrument. Data marked “Invalid” may still indicate general trends of wet/high matric potential and dry/low matric potential, but values are inaccurate and dry/low matric potential values are likely not low enough. Wet/high matric potential values are likely to be more accurate than dry/low matric potential values; 6) Data format changed from .dat to .csv. This research was performed as a part of the NGEE Tropics project, which aims to advance model predictions of tropical forest carbon cycle responses to a changing climate over the 21st Century.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

On the determination of haze levels from Landsat data

The paper describes two methods for determining haze levels (specified by haze optical depth at a wavelength of 0.5 microns) from Landsat multispectral scanner data. The channel correlation method relates the haze level to the y-intercept of the regression line through a plot of the data in the plane of the multispectral channels MSS 4 and MSS 5. The minimum value method relates haze level to the minimum value of individual lines in the MSS 4 data set.

Potter, J. F.↗

NASA NTRS2012

Using the XMM-Newton Optical Monitor to Study Cluster Galaxy Evolution

We explore the application of XMM Newton Optical Monitor (XMM-OM) ultraviolet (UV) data to study galaxy evolution. Our sample is constructed as the intersection of all Abell clusters with z < 0.05 and having archival XMM-OM data in either the UVM2 or UVW1 filters, plus optical and UV photometry from the Sloan Digital Sky Survey and GALEX, respectively. The 11 resulting clusters include 726 galaxies with measured redshifts, 520 of which have redshifts placing them within their parent Abell clusters. We develop procedures for manipulating the XMM-OM images and measuring galaxy photometry from them, and we confirm our results via comparison with published catalogs. Color-magnitude diagrams (CMDs) constructed using the XMM-OM data along with SDSS optical data show promise for evolutionary studies, with good separation between red and blue sequences and real variation in the width of the red sequence that is likely indicative of differences in star formation history. This is particularly true for UVW1 data, as the relative abundance of data collected using this filter and its depth make it an attractive choice. Available tools that use stellar synthesis libraries to fit the UV and optical photometric data may also be used, thereby better describing star formation history within the past billion years and providing estimates of total stellar mass that include contributions from young stars. Finally, color-color diagrams that include XMM-OM UV data appear useful to the photometric identification of both extragalactic and stellar sources.

Miller, Neal A.↗

DOE OSTI2026

A Deep Look at the Ultra-Faint Milky Way Satellite Virgo III with Rubin Observatory Data Preview 2

We analyze the ultra-faint Milky Way satellite Virgo III using data from the Vera C. Rubin Observatory Data Preview 2 (DP2). Virgo III was observed in the Rubin "Cosmic Treasure Chest" (M49) First Look field, which contains 924 visits in the u,g,r,i bands comprising ~10.5hrs of exposure time with LSSTCam. These data are considerably deeper than the majority of DP2, with a $5σ$ limiting magnitude that approaches the expected 10-year depth of LSST (~25.2-26.5mag, depending on band). We report the morphological and stellar population parameters of Virgo III measured with the maximum-likelihood-based package ugali. The depth of the Rubin imaging yields more than a factor of four increase in the number of candidate member stars ($N_* = 114^{+11}_{-11}$) relative to the Virgo III discovery results ($N_* = 25^{+5}_{-4}$), enabling significantly more precise morphological constraints. Our best-fit parameters broadly agree with previous measurements, further confirming that Virgo III has properties that are consistent with an ultra-faint dwarf galaxy ($M_V = -2.72^{+0.49}_{-0.70}$; $r_{1/2} = 53^{+10}_{-8}$) located at a heliocentric distance of $D_\odot = 151^{+8}_{-8}$. We also demonstrate that the depth and photometric quality of the DP2 data are sufficient to separate metal-poor and metal-rich stars in color-color space. We further present period estimates for the three known RR Lyrae member stars derived from the DP2 forced photometry; we use theoretical Period-Luminosity-Metallicity (PLZ) and Period-Wesenheit-Metallicity (PWZ) relations to obtain independent distance estimates. We find that our period and distance estimates are broadly consistent with previous measurements for these RR Lyrae. These results demonstrate the power of LSST data for the discovery and characterization of ultra-faint dwarf galaxies and motivate future searches for new satellites across the southern sky.

Pai, Aashay [Chicago U.; Chicago U., KICP; SkAI, C↗

DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗

NASA NTRS2022

Developing Data-Driven Artificial Neural Network for A High Throughput Retrieval of Aerosol Optical Depth and Surface Temperature of Mars

In this work, we aim to developartificial neural network(ANN)techniquesto reproduce the retrieval results ofphysical quantities from spacecraft observations of solar system bodiesusing radiative transfer methods. The particular application here is the retrieval of dust optical depth, water-ice optical depth, and surface temperatureonMarsusingdaytime observationsobtained by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor.Compared against the results obtained from traditional radiative transfer retrieval techniques, our ANN successfully recoveredthe three quantitiesusingdaytime observations. The principal advantage of thesemachine learning(ML) algorithms istheir complete automation and highthroughput. Therefore, the algorithms presented here would be useful for very large datasets andwould make practical the sampling of many different approximations or boundary conditions related to a given observation dataset and retrieval problem.

Rafael Moreno↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1989

Vertical aerosol structure of Neptune - Constraints from center-to-limb profiles

New center-to-limb (CTL) equatorial region profiles of Neptune have been extracted from spatially well-resolved images. The 8900-A CTL profile entails the presence of material above the 5-mbar level whose optical depth is less than 0.5. CH4-band data admit of thin scattering hazes in the 0.4-1.5 bar pressure level, although optical depths must be lower than 0.05; so thin a haze falls short of the reflected flux at 6340 A, thereby implying the presence of another scattering layer deeper than 1.5 bars. A good fit to the observed reflectivity is obtained by a bright cloud layer with an optical depth of 3.0.

Hammel, H. B.↗

DOE OSTI2022

Intra-canopy leaf trait variation facilitates high LAI and compensatory growth in a clonal woody encroaching shrub in the tallgrass prairie

This data package contains the data for Tooley et al. (2022) Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub. The purpose of this project was to evaluate the leaf ecophysiological traits of Cornus drummondii C.A. Mey to better understand how C. drummondii maximizes photosynthetic rates across large gradients of light availability in its short, dense canopies. We also evaluate how C. drummondii modifies its allocation of leaf traits in response to grassland disturbance. High LAI canopies and a compensatory growth response to disturbance are two key factors contributing to the success of C. drummondii and other woody species responsible for the encroachment of grasslands.The data package contains ecophysiological measurements, including leaf morphological, physiological, and chemical traits; photosynthetic measurements, leaf carbon and nitrogen stable isotopes, leaf area index, and photosynthetically active radiation. All measurements were taken vertically across canopies of the clonal woody-encroaching shrub Cornus drummondii under browsed, grazed, and control (unbrowsed and ungrazed) conditions.Files:SeasonalMeasurements_Archive.csv - Data for all seasonal measurements used in the study.LeafPhotoSeasonalRaw_Archive.zip - zip file containing raw data from an li-6400 for each sampling period in the study.LAI_Archive.xlsx - Data for LAI Measurements.ACiandLightCurveResults_Archive.xlsx - Contains parameters extracted from A-ci and Light Response curves along with the morphological and chemical traits for each leaf.ACiRaw Archive.xlsx - Raw data from Li-6400 for A-ci response curvesLightCurves_Raw.zip - zip file containing raw data for Li-6400 light response curves. Excel file format: "IslandID"-"Depth"-lc.xlsx (or .csv)DataCollectionMethods.docx - file containing the methods of data collection, processing, and QA/QC for all data files. File also contains a list of excel file headers, abbreviations, and their units.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Chemical Data Assimilation: A Case Study of Solar Occultation Data From the Atlas 1 Mission of the Atmospheric Trace Molecule Spectroscopy Experiment

A key advantage of using data assimilation is the propagation of information from data-rich regions to data-poor regions, which is particularly relevant to the use of solar occultation data such as from ATMOS. For the first time an in depth uncertainty analyses is included in a photochemical model-data intercomparison including observation, representativeness, and theoretical uncertainty. Chemical data assimilation of solar occultation measurements can be used to reconstruct full diurnal cycles and to evaluate their chemical self-consistency. This paper considers as an example the measurements made by the Atmospheric Trace Molecule Spectroscopy Experiment (ATMOS) instrument Atlas-1 during March 1992 for a vertical profile flow tracking coordinates at an equivalent PV latitude of 38 S. ATMOS was chosen because it simultaneously observes several species. This equivalent PV latitude was chosen as it was where ATMOS n observed the atmosphere's composition over the largest range of altitudes. A single vertical profile was used so that the detailed diurnal information that assimilation utilizes could be highlighted. There is generally good self-consistency between the ATMOS Atlas-1 observations and photochemical theory.

Lary, D. J.↗

NASA NTRS2022

Predicting the Penetration of a Shielded TPS Tile

All spacecraft are subject to the possibility of high-speed particle impacts during their mission life. In low earth orbit, those impacts could be the result of collisions with pieces of orbital debris or with meteorites. Beyond LEO, and especially beyond GEO, those impacts will likely be caused by meteorites. Such high-speed impacts on spacecraft surfaces create debris clouds that travel towards and eventually impact other downstream spacecraft components. In addition to the impulsive load that such debris clouds would impart to the spacecraft elements with which they subsequently collide, the largest fragment in these debris clouds poses a significant threat on its own to those spacecraft elements. In order to be able to assess the severity of the threat posed by such a fragment, it is important to be able to predict the extent of damage sustained by the impacted spacecraft element. In this paper, we present a new process for determining the penetration depth in a shielded TPS tile system. The requirements for the process were that it be applicable across a full spectrum of impact low velocities (i.e. from ~ 7 km/s to above 50 km/s), that it be applicable over a wide range of projectile materials densities (i.e. ranging from water to steel, for example), and that it be applicable over a wide range of trajectory obliquities (i.e. not just normal impacts). Furthermore, the process developed should be sufficiently flexible so that it can be used for shield design parameter and / or system configuration trade studies (i.e. be written in terms of as many shield and TPS tile system parameters as possible). Following the initial impact of the projectile on the outer wall of the dual-wall shield, a debris cloud (i.e. the primary debris cloud) is created that travels towards and impacts the inner wall of the shield. Within this debris cloud is a combination of solid, liquid, and vaporized material, depending on the impact velocity and the impedance mismatch of the projectile and outer wall materials. The impact of this debris cloud on the inner wall creates another debris cloud (i.e. the secondary debris cloud) that then travels towards and impacts the TPS tile. Within the particulate distribution of the material in each of these debris clouds is a so-called largest fragment. The process used to calculate the penetration depth in a shielded TPS tile system developed herein consists of the following three steps: 1. Does the initial projectile perforate the outer wall of the shield? If NO, the TPS tile remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the outer wall of the shield and traveling towards the shield’s inner wall. 2. Does the largest fragment in the debris cloud exiting the outer wall of the shield perforate the inner wall of the shield? If NO, the TPS tile again remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the rear of the shield inner wall. 3. Calculate the penetration depth of the largest fragment in the debris cloud exiting the inner wall into the TPS. Perforation of the inner and outer shield walls is predicted using the Cour-Palais penetration depth equation and an associated failure criterion [1]. The largest debris cloud fragment diameters and associated velocities are calculated using regression equations developed from a mix of hydrocode and experimental diameter and velocity information [2]. Finally, TPS penetration depths are calculated using an empirical penetration depth predictor equation based on high-speed impact test data and are calibrated using a limited number of hydrocode penetration depth predictions for water, nylon, aluminum, Dunite, and steel projectiles The predictions of the penetration depth calculation process are compared against the predictions of more than 60 SPHC hydrocode runs. These comparisons show that the process used to calculate TPS tile penetration depths usually yields values that are within 1 mm of the predictions of hydrocode simulations. If a maximum allowable TPS penetration depth is known, the process developed herein can now be used to develop a failure limit equation that would predict whether or not an impacting particle would penetrate deeply enough into the TPS to result in a “failed” or “not failed” end state. References [1] E.L. Christiansen, “Design and performance equations for advanced meteoroid and debris shields”, International Journal of Impact Engineering, Vol. 14, 1993, pp. 145-156. [2] W.P. Schonberg, “Predicting the size of the largest particle fragment in a debris cloud created by an orbital debris impact and its associated velocity”, 2021 Applied Space Environments Conference, NASA Jet Propulsion Laboratory, Pasadena, California, November, 2021.

Meteroid↗

NASA NTRS2016

More Than the Sum of the Parts: Satellite Aerosol Remote Sensing, and Its Relationship to Sub-Orbital Measurements and Models

Space-borne instruments are providing increasing amounts of data relating to global aerosol spectral optical depth, horizontal and vertical distribution, and very loose, but spatially and temporally extensive, constraints on particle micro-physical properties. The data sets, and many of the underlying techniques, are evolving rapidly. They represent a vast amount of information, potentially useful to the AAAR community. However, there are also issues, some quite subtle, that scientific users must take into consideration. This tutorial will provide one view of the answers to the following four questions: 1) What satellite-derived aerosol products are available? 2) What are their strengths and limitations? 3) How are they being used now? 4) How might they be used in conjunction with each other, with sub-orbital measurements, and with models to address cutting-edge aerosol questions?

satellite remote sensing↗

NASA NTRSDate not supplied

Predicting GPS Fidelity in Heavily Forested Areas

There is a compelling need to advance the safety of low altitude flight in forested areas. Signal scattering by trees can interfere with GNSS signal reception and can cause navigation loss within and adjacent to woodlands. An estimate of the signal loss vs. foliage depth is needed to quantify navigational degradation by trees at low altitudes. A previous report described a method which attempts to quantify satellite signal degradation caused by foliage by comparing carrier-to-noise ratio (C/N0) to foliage depth along geometric rays cast from the receiver location to the orbital position of GNSS satellites through a 3D matrix of terrain data. A characteristic curve of attenuation vs. foliage depth was found for both L1 and L2 signals at a single forested site. The current study replicates this result at three additional sites, describes refinements to the method, and explores inherent uncertainties that arise from radiofrequency fading and from receiver limitations for weak signals. For the sites surveyed, 60% and 90% of navigational signal is lost at 10m and 20m of foliage depth, respectively. Since this technique uses low-cost hardware and readily available data collection software, it can allow aviators to estimate GNSS position fidelity in flight ranges near trees.

GPS↗

NASA NTRSDate not supplied

Predicting GPS Fidelity in Heavily Forested Areas

GPS↗

NASA NTRS2020

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are simply absent in many major cities across sub-Saharan Africa (SSA). Satellites provide snapshots of regional air pollution but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We investigate the utility of such a multi-source data integration approach using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system, and a linear conversion factor is developed to convert AOD to surface fine particulate matter mass concentration (as PM2.5). With 10 or more ground monitors in Pittsburgh, there is a 2-fold reduction in surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, we assess the ability of combined regional-scale satellite retrievals and local-scale low-cost sensor measurements to improve surface PM2.5 estimation at several urban sites in SSA. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement in surface PM2.5 estimation accuracy with respect to using low-cost ground monitoring data alone. A linear AOD-to-surface-PM2.5 conversion factor developed in Kigali, Rwanda, did not generalize well to other parts of SSA and varied seasonally for the same location, emphasizing the need for ongoing and localized ground-based monitoring, which can be facilitated by low-cost sensors. Overall, we find that combining ground-based low-cost sensor and satellite data, even without including additional meteorological or land use information, can improve and expand spatiotemporal air quality data coverage, especially in data-sparse regions.

AOD↗

DOE OSTI2020

Planck intermediate results

We present the NPIPE processing pipeline, which produces calibrated frequency maps in temperature and polarization from data from the Planck Low Frequency Instrument (LFI) and High Frequency Instrument (HFI) using high-performance computers. NPIPE represents a natural evolution of previous Planck analysis efforts, and combines some of the most powerful features of the separate LFI and HFI analysis pipelines. For example, following the LFI 2018 processing procedure, NPIPE uses foreground polarization priors during the calibration stage in order to break scanning-induced degeneracies. Similarly, NPIPE employs the HFI 2018 time-domain processing methodology to correct for bandpass mismatch at all frequencies. In addition, NPIPE introduces several improvements, including, but not limited to: inclusion of the 8% of data collected during repointing manoeuvres; smoothing of the LFI reference load data streams; in-flight estimation of detector polarization parameters; and construction of maximally independent detector-set split maps. For component-separation purposes, important improvements include: maps that retain the CMB Solar dipole, allowing for high-precision relative calibration in higher-level analyses; well-defined single-detector maps, allowing for robust CO extraction; and HFI temperature maps between 217 and 857 GHz that are binned into 0'.9 pixels (Nside = 4096), ensuring that the full angular information in the data is represented in the maps even at the highest Planck resolutions. The net effect of these improvements is lower levels of noise and systematics in both frequency and component maps at essentially all angular scales, as well as notably improved internal consistency between the various frequency channels. Based on the NPIPE maps, we present the first estimate of the Solar dipole determined through component separation across all nine Planck frequencies. The amplitude is (3366.6 ± 2.7) μK, consistent with, albeit slightly higher than, earlier estimates. From the large-scale polarization data, we derive an updated estimate of the optical depth of reionization of τ = 0.051 ± 0.006, which appears robust with respect to data and sky cuts. There are 600 complete signal, noise and systematics simulations of the full-frequency and detector-set maps. As a Planck first, these simulations include full time-domain processing of the beam-convolved CMB anisotropies. The release of NPIPE maps and simulations is accompanied with a complete suite of raw and processed time-ordered data and the software, scripts, auxiliary data, and parameter files needed to improve further on the analysis and to run matching simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Nutrient and carbon concentrations in dated soil cores at US-OWC Ameriflux wetland site (OWC NERR)

Soil cores were sampled at the Old Woman Creek (OWC) National Estuarine Research Reserve at the south shore of Lake Erie, near Huron, Ohio, USA. OWC is a temperate mineral soil marsh. We dated the soil cores using lead isotope analysis and measured the concentrations of carbon and nutrients (nitrogen, phosphorus) throughout the core depths. We analyzed 36 cores, sampled along three transects at areas of the wetland with different hydrological regimes. Each transect included three coring sites at different water depth categories (shallow, intermediate deep) with four core samples per coring site. The data can be used to determine the carbon sequestration rates and nutrient accumulation rates, at multiple locations throughout OWC wetland.Dataset, in csv format, with data variables in columns, and different cores and core depths slices in rows, includes results from 36 sediment cores (0-30 cm depth) taken at 9 locations (4 replicates at each location) along 3 gradients of water depth (shallow, intermediate, deep), each at a different hydrologic location (outflow, backflow, middle). At each depth slice within a core, we provide depth, date (using 210Pb), bulk density, and the concentrations of carbon, nitrogen, phosphorus, d13C, and d15N.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

CFD Data on a Vertical Axis Wave Turbine

In this study January to July, 2023, different variations of the original geometry of a vertical-axis wave turbine (VAWT) were generated and evaluated for hydrodynamic power efficiency using computational fluid dynamics (CFD). The key geometrical parameters considered in this parametric study included the chord length of the rotor blades and the horizontal semi-axis length. The immersion depth of the rotor was also examined as a key deployment parameter for the wave turbine. The CFD simulation results revealed that a medium chord length of the blade (i.e., the same as that of the baseline design) and a shorter horizontal semi-axis for the guide curve of the blade than that of the baseline design resulted in higher hydrodynamic power to extract. With the most efficient turbine rotor geometry identified in this study, a deployment depth that could assure full submergence of the rotor in waves but as close to the free surface as possible led to a higher hydrodynamic power. These findings revealed a pathway for the improvement of the wave turbine energy efficiency.

16 TIDAL AND WAVE POWER↗