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DOE OSTI2021

In-situ magnetic alignment model for additive manufacturing of anisotropic bonded magnets

In this work, we report a mathematical framework which predicts the degree of alignment (DoA) in an in-situ aligned additively manufactured 3D printed bonded magnets. A multiphysics model is developed which couples the harmonious interactions of magnetic particles in a viscous flowing polymer under the presence of an external magnetic field. The hydrodynamic fluid-particle interaction is paired with the magnetophoretic force to predict the particle trajectory and distribution during extrusion through a 3D printer nozzle. Succeeding the force balance, a magnetohydrodynamic torque equilibrium analysis is performed to predict the net-orientation of the magnetic particles as a function of the applied field strength, viscous forces, and particle-to-particle interactions (P2P). Experimental validation of the DoA predictions is performed using 65 vol% Nd-Fe-B+Sm-Fe-N in Nylon12 (DoA exp = 0.620 and DoA theory = 0.686) and 15 vol% Sm-Co in PLA (DoA exp = 0.830 and DoA theory = 0.863). A parametric analysis is performed to analyze the effect of operating and design parameters like alignment field strength, magnetic loading fraction, extrusion load, and particle size. The model predicts a competing behavior between particle-fluid and particle-particle interactions under the presence of an applied field. Lastly, the model provides a framework to efficiently predict the DoA in tandem with a functionalized-magnetic 3D printer and allows the user to adjust the operating parameters according to the desired DoA.

36 MATERIALS SCIENCE↗

NASA NTRS1991

Sensor-model in-situ control of the RTM composite process

The paper reports on the use of in situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model to select and control the processing properties of an epoxy resin during RTM impregnation and cure. Once correlated with viscosity and degree of cure, FDEMS monitors the advancement of the resin, viscosity, and the impregnation of the fabric, allowing a direct means of monitoring in situ in the mold throughout the fabrication process the effects of time, temperature, vacuum, and pressure.

Kranbuehl, D. E.↗

DOE OSTI2023

Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition

Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Using the Coronal Evolution to Successfully Forward Model CMEs' In Situ Magnetic Profiles

Predicting the effects of a coronal mass ejection (CME) impact requires knowing if impact will occur, which part of the CME impacts, and its magnetic properties. We explore the relation between CME deflections and rotations, which change the position and orientation of a CME, and the resulting magnetic profiles at 1 AU. For 45 STEREO-era, Earth-impacting CMEs, we determine the solar source of each CME, reconstruct its coronal position and orientation, and perform a ForeCAT (Forecasting a CME's Altered Trajectory) simulation of the coronal deï¬‚ection and rotation. From the reconstructed and modeled CME deflections and rotations, we determine the solar cycle variation and correlations with CME properties. We assume no evolution between the outer corona and 1 AU and use the ForeCAT results to drive the ForeCAT In situ Data Observer (FIDO) in situ magnetic field model, allowing for comparisons with ACE and Wind observations. We do not attempt to reproduce the arrival time. On average FIDO reproduces the in situ magnetic field for each vector component with an error equivalent to 35 percent of the average total magnetic field strength when the total modeled magnetic field is scaled to match the average observed value. Random walk best fits distinguish between ForeCAT's ability to determine FIDO's input parameters and the limitations of the simple flux rope model. These best fits reduce the average error to 30 percent.The FIDO results are sensitive to changes of order a degree in the CME latitude, longitude, and tilt, suggesting that accurate space weather predictions require accurate measurements of a CME's position and orientation.

Kay, C.↗

NASA NTRS2004

Interpretation of TOMS Observations of Tropical Tropospheric Ozone with a Global Model and In Situ Observations

We interpret the distribution of tropical tropospheric ozone columns (TTOCs) from the Total Ozone Mapping Spectrometer (TOMS) by using a global three-dimensional model of tropospheric chemistry (GEOS-CHEM) and additional information from in situ observations. The GEOS-CHEM TTOCs capture 44% of the variance of monthly mean TOMS TTOCs from the convective cloud differential method (CCD) with no global bias. Major discrepancies are found over northern Africa and south Asia where the TOMS TTOCs do not capture the seasonal enhancements from biomass burning found in the model and in aircraft observations. A characteristic feature of these northern topical enhancements, in contrast to southern tropical enhancements, is that they are driven by the lower troposphere where the sensitivity of TOMS is poor due to Rayleigh scattering. We develop an efficiency correction to the TOMS retrieval algorithm that accounts for the variability of ozone in the lower troposphere. This efficiency correction increases TTOC's over biomass burning regions by 3-5 Dobson units (DU) and decreases them by 2-5 DU over oceanic regions, improving the agreement between CCD TTOCs and in situ observations. Applying the correction to CCD TTOCs reduces by approximately DU the magnitude of the "tropical Atlantic paradox" [Thompson et al, 2000], i.e. the presence of a TTOC enhancement over the southern tropical Atlantic during the northern African biomass burning season in December-February. We reproduce the remainder of the paradox in the model and explain it by the combination of upper tropospheric ozone production from lightning NOx, peristent subsidence over the southern tropical Atlantic as part of the Walker circulation, and cross-equatorial transport of upper tropospheric ozone from northern midlatitudes in the African "westerly duct." These processes in the model can also account for the observed 13-17 DU persistent wave-1 pattern in TTOCs with a maximum above the tropical Atlantic and a minimum over the tropical Pacific during all seasons. The photochemical effects of mineral dust have only a minor role on the modeled distribution of TTOCs, including over northern Africa, due to multiple competing effects. The photochemical effects of mineral dust globally decease annual mean OH concentrations by 9%. A global lightning NOx source of 6 Tg N yr(sup -1) in the model produces a simulation that is most consistent with TOMS and in situ observations.

Martin, Randall V.↗

NASA NTRS2011

SO2 Emissions and Lifetimes: Estimates from Inverse Modeling Using In Situ and Global, Space-Based (SCIAMACHY and OMI) Observations

Top-down constraints on global sulfur dioxide (SO2) emissions are inferred through inverse modeling using SO2 column observations from two satellite instruments (SCIAMACHY and OMI). We first evaluated the S02 column observations with surface SO2 measurements by applying local scaling factors from a global chemical transport model (GEOS-Chem) to SO2 columns retrieved from the satellite instruments. The resulting annual mean surface SO2 mixing ratios for 2006 exhibit a significant spatial correlation (r=0.86, slope=0.91 for SCIAMACHY and r=0.80, slope = 0.79 for OMI) with coincident in situ measurements from monitoring networks throughout the United States and Canada. We evaluate the GEOS-Chem simulation of the SO2 lifetime with that inferred from in situ measurements to verity the applicability of GEOS-Chem for inversion of SO2 columns to emissions. The seasonal mean SO2 lifetime calculated with the GEOS-Chem model over the eastern United States is 13 h in summer and 48 h in winter, compared to lifetimes inferred from in situ measurements of 19 +/- 7 h in summer and 58 +/- 20 h in winter. We apply SO2 columns from SCIAMACHY and OMI to derive a top-down anthropogenic SO2 emission inventory over land by using the local GEOS-Chem relationship between SO2 columns and emissions. There is little seasonal variation in the top-down emissions (<15%) over most major industrial regions providing some confidence in the method. Our global estimate for annual land surface anthropogenic SO2 emissions (52.4 Tg S/yr from SCIAMACHY and 49.9 Tg S / yr from OMI) closely agrees with the bottom-up emissions (54.6 Tg S/yr) in the GEOS-Chem model and exhibits consistency in global distributions with the bottom-up emissions (r = 0.78 for SCIAMACHY, and r = 0.77 for OMI). However, there are significant regional differences.

Lee, Chulkyu↗

NASA NTRSDate not supplied

FORWARD AND INVERSE L-BAND RADIATIVE TRANSFER MODELING OVER THE DRY CHACO, USING SMOS OBSERVATIONS, LAND SURFACE MODELING AND IN SITU DATA

Passive microwave L-band remote sensing is well known for its sensitivity to surface soil moisture over land.The signal is also affected by other dynamic variables such as vegetation and soil salinity. In this research, L-band microwave observations of the Soil Moisture Ocean Salinity (SMOS) mission are used to explore soil surface salinity, moisture and vegetation, in the Argentinean Dry Chaco, an area with possible emerging dryland salinity. A Radiative Transfer Model (RTM) with inclusion of a correction to the soil’s dielectric constant for salinity was used in forward and inverse mode, using either in situ data or Catchment Land Surface Model (CLSM) simulations as RTM input. The forward analysis pointed out shortcomings in the modeled soil moisture and soil surface temperature estimates. The impact of salinity on forward simulations over the Dry Chaco was limited.The RTM inversion using 10 years of SMOS brightness temperature observations resulted in realistic estimates of vegetation and roughness, both with and withouto ptimizing a correction term for the dielectric constant in terms of salinity equivalents. However,the retrieval of the correction term to the dielectric constant was very uncertain and not representative of soil surface salinity, but rather of open water, texture uncertainty or soil moisture bias.

L-band↗

DOE OSTI2022

Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Integrated In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) connects individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. MATLAB was favorable to use as the main software integration tool due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of technologies, including but not limited to, the water processing Lunar Auger Dryer for ISRU (LADI) system and an oxygen extraction carbothermal reduction process. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing heater energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Efforts to update the MAIT tool are ongoing to accommodate and scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Advanced efforts with advanced models will include examining multiple production targets to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was updated and became the capital program in the Systems Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) project. Prior work was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 5,000 cases during its first round of parametric sweeps on the water processing architecture at multiple production targets; the system analysis produced valuable insight into the optimal LADI geometry that minimized energy demands, estimated effects to cold trap size and radiator requirements, and calculated the power dynamics of the electrolysis unit and liquid oxygen storage volume. Additional efforts are being made to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy and increase the MAIT software capability. Work is ongoing to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was updated and became the capital program in the Systems Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) project. Prior work was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 5,000 cases during its first round of parametric sweeps on the water processing architecture at multiple production targets; the system analysis produced valuable insight into the optimal LADI geometry that minimized energy demands, estimated effects to cold trap size and radiator requirements, and calculated the power dynamics of the electrolysis unit and liquid oxygen storage volume. Additional efforts are being made to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy and increase the MAIT software capability. Work is ongoing to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRS2011

Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology

The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.

Gregg, Watson↗

DOE OSTI2021

AFRL Additive Manufacturing Modeling Series: Challenge 4, In Situ Mechanical Test of an IN625 Sample with Concurrent High-Energy Diffraction Microscopy Characterization

In this work, we describe 3D characterization of an additively manufactured Inconel 625 nickel-base superalloy specimen conducted during a uniaxial tension test using a suite of nondestructive x-ray techniques. High-energy diffraction microscopy in both near- and far-field modalities are employed in situ to track evolution of the material orientation and stress–strain fields at six points during the mechanical test, and these data streams are registered with micro-computed tomography reconstructions which probe the material density. This data volume was matched to a multi-modal serial sectioning characterization of the specimen taken after loading, described in this article’s companion. Twenty-eight grains which were monitored throughout the experiment were selected to form the basis for AFRL AM Modeling Series Challenge 4, Microscale Structure-to-Properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Understanding coarsening of a post-corrosion microstructure in a molten salt by combining phase-field modeling and in situ tomography

Alloys corroding in molten salt have been observed to form bicontinuous, nanoporous microstructures via dealloying, which subsequently undergo coarsening due to facile transport in high-temperature conditions. In this work, we describe a methodology to elucidate the underlying transport mechanisms during coarsening of a bicontinuous microstructure via quantitative comparisons between phase-field simulations and four-dimensional in situ experiments, in this case X-ray nanotomography of the coarsening of a dealloyed 80 wt% Ni-20 wt% Cr microwire in molten KCl-MgCl 2 at 800°C. We conduct phase-field simulations initialized from experimental data to model coarsening via three different transport mechanisms: surface diffusion, solid bulk diffusion, and liquid bulk diffusion. These simulations reproduce key features of the experiment, such as the densification of the outer layer of the dealloyed wire and the reduction in radius over time. We quantitatively compare different microstructural characteristics between the simulations and experiment and extract temporal scaling factors that optimally match the time scales of the simulations to that of the experiment. This allows us to evaluate morphological similarity between the simulations and experiment and relate the experimental coarsening kinetics to fundamental material properties. We find that surface diffusion is most likely to be the dominant coarsening mechanism, and its kinetics imply a surface diffusivity of D S = 8.9 x 10 -20 m 3 /s, which is within the range of reported values for Ni-vacuum interfaces at 800°C. However, the difference between the experiment and the surface diffusion simulation increases substantially at late times, suggesting that other mechanisms, such as the dissolution of residual Cr, may be at play.

36 MATERIALS SCIENCE↗

NASA NTRS2020

Basin-Scale River Runoff Estimation from GRACE Gravity Satellites, Climate Models and In Situ Observations: a Case Study in the Amazon Basin

River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situgauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreementbut slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude maybe attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long-term mean evapotranspiration in the Amazon by ~2 cm/monthb ased on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ runoff observations. Seasonal variations in ERA5 simulated runoff are about twice as large as those from in situobservations and show a large phase lag as well. Water budget based runoff for the entire Amazon (~ 7,200 km3averaged 29over the 13 yr period) is significantly larger than observed runoff (~5,700 km3) at notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges.

Jianli Chen↗

DOE OSTI2020

Multivariate Linear Models for In Situ Monitoring of Low-Activity Nuclear Waste Using Infrared and Raman Spectroscopy - 20218

This paper presents the use of in situ monitoring tools for molecular analysis of non-radioactive species found in the supernatant liquid and dissolvable salt cake of nuclear waste at Hanford. We have selected two in situ spectroscopic techniques that do not require any sample preparation or consumables: infrared (in the form of attenuated total reflectance Fourier transform infrared or ATR-FTIR) and Raman spectroscopy. This work aims to evaluate their performance for liquid waste simulants representative of the Hanford DFLAW (Direct Feed Low-Activity Waste) feed. In addition, we have evaluated the performance of four multivariate linear models that extract concentrations from spectral measurements for a data set that was designed to include key molecular species found in liquid low-activity waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Ice and Supercooled Liquid Water Distributions over the Southern Ocean based on In Situ Observations and Climate Model Simulations

Three climate models are evaluated using in situ airborne observations from the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign. The evaluation targets cloud phases, microphysical properties, thermodynamic conditions, and aerosol indirect effects from –40°C to 0°C. Compared with 580-s averaged observations (i.e., 100 km horizontal scale), the Community Atmosphere Model version 6 (CAM6) shows the most similar result for cloud phase frequency distribution and allows more liquid-containing clouds below –10°C compared with its predecessor—CAM5. The Energy Exascale Earth System Model (E3SM) underestimates (overestimates) ice phase frequencies below (above) –20°C. CAM6 and E3SM show liquid and ice water contents (i.e., LWC and IWC) similar to observations from –25°C to 0°C, but higher LWC and lower IWC than observations at lower temperatures. Simulated in-cloud RH shows higher minimum values than observations, possibly restricting ice growth during sedimentation. As number concentrations of aerosols larger than 500 nm (Na 500 ) increase, observations show increases of LWC, IWC, liquid, and ice number concentrations (N liq , N ice ). Number concentrations of aerosols larger than 100 nm (Na 100 ) only show positive correlations with LWC and N liq . From –20°C to 0°C, higher aerosol number concentrations are correlated with lower glaciation ratio and higher cloud fraction. From –40°C to –20°C, large aerosols show positive correlations with glaciation ratio. CAM6 shows small increases of LWC and N liq with Na 500 and Na 100 . E3SM shows small increases of N ice with Na 500 . Altogether, CAM6 and E3SM underestimate aerosol indirect effects on ice crystals and supercooled liquid droplets over the Southern Ocean.

54 ENVIRONMENTAL SCIENCES↗