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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Effect of precipitates in oxide film on hydrogen pick-up of zirconium alloys

In this study, we examined the effect of alloying elements on hydrogen pick-up. The alloying elements of Fe, Cr, and Ni form precipitates in Zircaloy because of their low dissolvability in Zirconium. The precipitates in oxide film are presumed to play an important role in the hydrogen pick-up process because oxide film is a pathway of hydrogen entry. A corrosion test was performed in pure water at 561 K, and the relation between the additive elements and the hydrogen pick-up was estimated by using Zircaloy-2 and Zircaloy-4. The behavior of precipitates in oxide film was estimated using TEM observation and molecular dynamic simulation. The results show that the un-oxidized precipitates had a tendency to substitute for the oxidized precipitates with distance from the metal-oxide film boundary. Additionally, precipitates containing a high concentration of oxygen were observed. The oxidation of the precipitates was confirmed. It was also shown that the Fe/Cr fraction of precipitates had a tendency to decrease with distance from the metal-oxide film boundary. It was confirmed that the diffusion coefficients of H{sup +} in the iron- or chromium-containing ZrO{sub 2} are smaller than those of the pure ZrO{sub 2} by molecular dynamics simulation. These results lead to the conclusion that the diffusion of Cr and Fe in oxide film contributes to the control of hydrogen pick-up on zirconium alloys as does the oxidation of precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)

Convective clouds play an important role in Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region’s mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection-permitting resolution. This study spans a 2-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr –1 ). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

MOSAiC instantaneous snowfall rate estimates

The data files provide high temporal (~ 30 sec) resolution time series of MOSAiC liquid equivalent snowfall rate estimates at heights of ~ 170 and 230 m above the ground. The estimates are retrieved from vertically pointing Ka-band radar measurements.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Overall assessment of needs and benefits in controlled structures

The potential benefits of employing controlled structures technology in spacecraft design are illustrated using an example of a Mission-to-Planet Earth multisensor geostationary platform. The platform has a precipitation mapping sensor which requires large antennas to meet the science requirement of resolution cell size and dynamic measurement range. A series approach and an integrated approach to designing a controlled structure are discussed.

Newsom, Jerry R.↗
NASA NTRSDate not supplied

Tracking the Large-scale Tropical Convective Systems and MJO Modulation

Science questions that motivate us − How well do Cloud-Precipitation Hybrid Regimes identify tropical convective systems? − Can we examine the life cycle of convective systems using the hybrid regimes? − What are the effects of MJO on the evolution of convective systems?

tropical convective systems↗
NASA NTRS2021

Aerosol Properties in Cloudy Environments from Remote Sensing Observations: A Review of the Current State of Knowledge

Aerosol properties are fundamentally different near clouds than away from clouds. This paper reviews the current state of knowledge of aerosol properties in the near-low-cloud environment and quantitatively compares them with aerosols far from clouds, according to remote sensing observations. It interprets observations of aerosol properties from different sensors using satellite, aircraft, and ground-based observations. The correlation (and anticorrelation) between proximity to cloud and aerosol properties is discussed. Retrieval artifacts in the near-cloud environment are demonstrated and quantified for different sensor attributes and environmental conditions. Finally, the paper describes the possible corrections for near-cloud enhancement in remote sensing retrievals. This study is timely in view of science definition studies for NASA’s Aerosol, Cloud, Convection and Precipitation (ACCP) mission, which will also seek to directly link aerosol properties to nearby clouds.

A. Marshak↗
NASA NTRSDate not supplied

NASA’s Earth System Observatory Formulation Overview

NASA’s Earth System Observatory (ESO) is an array of Earth-focused, interconnected satellite missions focused on five core study areas: Surface Biology and Geology, Mass Change, Aerosols, Surface Deformation and Change, and Clouds, Convection, and Precipitation. Observatory development follows recommendations from the National Academies of Sciences, Engineering and Medicine’s 2017 Earth Science Decadal Survey. Data gathered by ESO missions will provide actionable science to inform decisions related to climate change, disaster mitigation, and wildfires, improve real-time agricultural processes, and many other applications. Targeting launch dates in the late 2020s and early 2030s, each ESO satellite will deliver valuable information, but by working together as a single observatory system, their combined data and imagery will provide the global community with a 4D, holistic view of Earth, from bedrock to atmosphere. The ESO is also building on the legacy of international collaboration in Earth science, with initial participation and collaboration on these missions across space agency partners, including the Japanese Aerospace Exploration Agency (JAXA), Centre National D'Etudes Spatiales (CNES), Canadian Space Agency (CSA), Deutsches Zentrum für Luft- und Raumfahrt (DLR), and Agenzia Spaziale Italiana (ASI). This paper provides an overview of ESO, as well as an update on ESO missions currently in formulation – the Atmosphere Observing System, Mass Change, and Surface Biology and Geology elements – including current mission architectures, international partner collaboration, science community engagement, and applications efforts, as well as how NASA and its partners will make ESO data accessible to users all over the world.

NASA Earth Science↗
DOE OSTI2023

Changing Characteristics of Tropical Extreme Precipitation–Cloud Regimes in Warmer Climates

In this study, we investigated the changing characteristics of climatic scale (monthly) tropical extreme precipitation in warming climates using the Energy Exascale Earth System Model (E3SM). The results are from Atmospheric Model Intercomparison Project (AMIP)-type simulations driven by (a) a control experiment with the present-day sea surface temperature (SST) and CO 2 concentration, (b) P4K, the same as in (a) but with a uniform increase of 4K in the SST globally, and (c) the same as in (a), but with an imposed SST and CO 2 concentration from the outputs of the coupled E3SM forced by a 4xCO 2 concentration. We found that as the surface warmed under P4K and 4xCO 2 , both convective and stratiform rain increased. Importantly, there was an increasing fractional contribution of stratiform rain as a function of the precipitation intensity, with the most extreme but rare events occurring preferentially over land more than the ocean, and more so under 4xCO 2 than P4K. Extreme precipitation was facilitated by increased precipitation efficiency, reflecting accelerated rates of recycling of precipitation cloud water (both liquid and ice phases) in regions with colder anvil cloud tops. Changes in the vertical profiles of clouds, condensation heating, and vertical motions indicate increasing precipitation–cloud–circulation organization from the control and P4K to 4xCO 2 . The results suggest that large-scale ocean warming, that is, P4K, was the primary cause contributing to an organization structure resembling the well-known mesoscale convective system (MCS), with increased extreme precipitation on shorter (hourly to daily) time scales. Additional 4xCO 2 atmospheric radiative heating and dynamically consistent anomalous SST further amplified the MCS organization under P4K. Analyses of the surface moist static energy distribution show that increases in the surface moisture (temperature) under P4K and 4xCO 2 was the key driver leading to enhanced convective instability over tropical ocean (land). However, a fast and large increase in the land surface temperature and lack of available local moisture resulted in a strong reduction in the land surface relative humidity, reflecting severe drying and enhanced convective inhibition (CIN). It is argued that very extreme and rare “record-breaking” precipitation events found over land under P4K, and more so under 4xCO 2 , are likely due to the delayed onset of deep convection, that is, the longer the suppression of deep convection by CIN, the more severe the extreme precipitation when it eventually occurs, due to the release of a large amount of stored surplus convective available potential energy in the lower troposphere during prolonged CIN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Phosphate Sorption Speciation and Precipitation Mechanisms on Amorphous Aluminum Hydroxide

Aluminum (Al) oxides are important adsorbents for phosphate in soils and sediments, and significantly limit Phosphate (P) mobility and bioavailability, but the speciation of surface-adsorbed phosphate on Al oxides remains poorly understood. Here, phosphate sorption speciation on amorphous Al hydroxide (AAH) was determined under pH 3–8 and P concentration of 0.03 mM–15 mM using various spectroscopic approaches, and phosphate precipitation mechanisms were discussed as well. AAH exhibits an extremely high phosphate sorption capacity, increasing from 3.80 mmol/g at pH 7 to 4.63 mmol/g at pH 3. Regardless of reaction pH, with increasing P sorption loading, the sorption mechanism transits from bidentate binuclear (BB) surface complexation with dP-Al of 3.12 Å to surface precipitation of analogous amorphous AlPO4 (AAP), possibly with ternary complexes, such as (≡Al-O) 2 -PO 2 -Al, as intermediate products. Additionally, the percentage of precipitated phosphate occurring in AAP linearly and positively correlates with P sorption loading. Compared to phosphate reaction with ferrihydrite, phosphate adsorbs and precipitates more readily on AAH due to the higher solubility product (K sp ) of AAH. The formation of AAP particles involves Al III release, which is promoted by phosphate adsorption, and its subsequent precipitation with phosphate at AAH surfaces or in the bulk solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Intermittency in Precipitation: Duration, Frequency, Intensity, and Amounts Using Hourly Data

Abstract Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2–2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%–85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12–15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1–2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Can nudging be used to quantify model sensitivities in precipitation and cloud forcing?

Efficient simulation strategies are crucial for the development and evaluation of high resolution climate models. This paper evaluates simulations with constrained meteorology for the quantification of parametric sensitivities in the Community Atmosphere Model version 5 (CAM5). Two parameters are perturbed as illustrating examples: the convection relaxation time scale (TAU), and the threshold relative humidity for the formation of low-level stratiform clouds (rhminl). Results suggest that the fidelity and computational efficiency of the constrained simulations depend strongly on 3 factors: the detailed implementation of nudging, the mechanism through which the perturbed parameter affects precipitation and cloud, and the magnitude of the parameter perturbation. In the case of a strong perturbation in convection, temperature and/or wind nudging with a 6-hour relaxation time scale leads to non-negligible side effects due to the distorted interactions between resolved dynamics and parameterized convection, while a 1-year free running simulation can satisfactorily capture the annual mean precipitation sensitivity in terms of both global average and geographical distribution. In the case of a relatively weak perturbation the large-scale condensation scheme, results from 1-year free-running simulations are strongly affected by noise associated with internal variability, while nudging winds effectively reduces the noise, and reasonably reproduces the response of precipitation and cloud forcing to parameter perturbation. These results indicate that caution is needed when using nudged simulations to assess precipitation and cloud forcing sensitivities to parameter changes in general circulation models. We also demonstrate that ensembles of short simulations are useful for understanding the evolution of model sensitivities.

54 ENVIRONMENTAL SCIENCES↗