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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Aerosol and Cloud Optical Properties from the ARM Raman Lidars: The Feature Detection and Extinction (RLPROF-FEX) Value-Added Product

Aerosols and their interactions and influence on clouds are among the main sources of uncertainties in radiative direct and indirect forcing (IPCC 2013). Continuous height-resolved measurements of cloud and aerosol optical properties are needed to reduce these uncertainties. Here we describe the Raman Lidar Profiles – Feature detection and Extinction (RLPROF-FEX) Value-Added Product (VAP) derived using Raman lidar data at multiple U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility sites. RLPROF-FEX provides estimates of extinction, backscatter, and depolarization using the algorithm described by Thorsen et al. 2015 and Thorsen and Fu 2015. This document provides a description of the FEX algorithm, its input and output data, and related details about the Raman lidar (RL) system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches

Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Usage of Observing System Simulation Experiments and Reinforcement Learning to Optimize Experimental Design and Operation

Various organizations regularly conduct field campaigns across the globe designed to probe and improve atmospheric process understanding. However, these campaigns are mostly designed ad-hoc, and rely on anecdotes and knowledge of what was successful in previous campaigns. Such ad-hoc experiment design results in sub-optimal instrument siting and operation strategies. Inadequate targeted data collection of extreme weather is a major limitation to Earth and Environmental Systems Science Division (EESSD)’s goals of data model integration (4.5.3), which limits the knowledge gained through the collected observations, holding back significant advances in predictability. While advances in instrument design are always chipping away at these limitations, we are not optimally utilizing the instrumentation we currently possess.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ARM FY2021 Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) radar facility is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. ARM has a complement of 32 scanning and zenith pointing radars, not including the radar wind profilers. This large number of radars and deployment locales require an enormous commitment of effort to keep operational. It has been the experience in ARM that all these radars cannot be always operated at a high level, and priorities need to be developed to properly manage operations every fiscal year. This document provides an overview of the FY20 activities and documents the priorities for FY21.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ARM FY2022 Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) radar facility is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. ARM has a complement of 32 radars, including scanning and zenith pointing varieties (Table 1). The effort to maintain and operate these radars is significant and requires a large, distributed staffing commitment. However, at available staffing levels, ARM’s experience dictates that it is not feasible to ensure the continuous, high-level operations expected by the ARM user community for all radars at all locations at all times. Therefore, priorities must be established to routinely manage select radar operations every fiscal year.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ARM FY2023 Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s radar capability is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. ARM has a complement of 32 radars, including scanning and zenith-pointing varieties (Table 1). The effort to maintain and operate these radars is significant and requires a large, distributed staffing commitment. It has not been feasible to ensure the continuous, high-level operations expected by the ARM user community for all radars at all times, so priorities are established to routinely manage select radar operations every fiscal year. These priorities for FY23 are listed herein.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Translator Plan: A Coordinated Vision for Fiscal Years 2023-2025

Translators serve a unique role in the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility, offering scientific input through various leadership and service roles. The Translators direct the creation of value-added products (VAPs) and analysis tools that make ARM measurements more accessible to the scientific community. Translators also serve as liaisons between users and the ARM infrastructure, collecting information about priorities and communicating ARM data and services. A key group focus is supporting the DOE Atmospheric System Research (ASR) program scientists and ASR’s efforts towards a process-level understanding of cloud-aerosol interactions, and in reducing uncertainty in global climate model projections. The ARM Translator Group (Table 1) consists of the five Translators, a representative of software development, and one from the Data Quality Office (DQO). Additionally, the ARM Engineering and Process Manager participates in this group and provides input and direction from ARM and its programmatic priorities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Questionable Benchmarks [Slides]

An investigation and initial review of ‘questionable benchmarks’ was performed at LANL. Multiple methods were used, including machine learning techniques, to identify 'questionable benchmarks' and/or benchmarks with low uncertainties. Those benchmarks were then reviewed for obvious errors. This is not a recommendation to ICSBEP, but can be used as a starting point for a more comprehensive review. A journal paper is being written on this work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

COMBLE Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility recently concluded its Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), with its campaign emphasis on marine boundary-layer clouds and mixed-phase clouds during cold-air outbreaks. The COMBLE campaign featured the deployment of the first ARM Mobile Facility (AMF1) to northern Scandinavia (Andenes, Norway), including its standard complement of ARM cloud radars. In keeping with user demands stemming from the previous AMF Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, a post-campaign radar mentor effort was initiated for COMBLE. This activity was intended to improve the usability of the ARM cloud radar data sets in response to overall demands for calibrated, corrected data sets for downstream studies and retrieval applications. In addition to the terrain complexities previously important to CACTI data sets (e.g., clutter designation and/or removal), COMBLE presented new challenges to existing ARM radar mentor capabilities. These included the extension of existing methodologies (e.g., relative calibration adjustment [RCA] target techniques) to frozen environments and the potential issues with their applicability when considering mixed-phase precipitation conditions. As in the previous CACTI documentation (Hardin et al. 2020), the overall calibration and conditioning process in ARM nomenclature is referred to as generating a “b1” datastream. For the radars, these “b1” standards refer to a datastream that has been calibrated (and cross-calibrated), with effort to deliver the highest-quality (well-characterized) data possible. The “b1” radar mentor reporting (this current document) is intended to detail (i) the status/quality of the original “a1” (raw) data sets during the COMBLE AMF campaign, (ii) the corrections and calibrations that are applied to generate the b1 datastreams available on ARM’s Data Discovery, and (iii) the details of the applied methods, e.g., how radar offset/calibration numbers were determined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ARM FY2024 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s radar capability aims to provide high-quality radar observations to the scientific community, advancing the understanding of clouds and precipitation processes and ultimately improving climate models. ARM operates radars across a spectrum of frequencies with diverse scanning capacities (Table 1) to meet these scientific goals. The maintenance, operations, and management of these distributed radars at sites in various climate regimes require a substantial staffing commitment. However, the existing staffing resources are constrained, making it impractical to sustain the consistent high-quality operations that are expected by the ARM user community. In response, ARM needs to develop priorities for the radar team each year. This report lays out the forthcoming priorities for Financial Year 2024 (FY24), aiming to establish a structure for the ongoing management of radar operations and data processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MOSAiC Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed many instruments on board a German ice breaker, the Research Vessel (RV) Polarstern, for one year from October 2019 to October 2020. The purpose of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign was to study the decline in the sea-ice pack around the North Pole, and what factors may be at play. After the campaign ended, efforts were undertaken to provide a calibrated radar data set for future studies. MOSAiC presented new challenges to this process, as existing methodologies often were not applicable for the frozen environment with little to no ground clutter, and concurrent engineering updates or calibrations could not be accomplished once the ship set off. Like the previous Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) documentation (Hardin et al. 2020, Matthews et al. 2023), the data correction and calibration process is known in ARM as creating a “b1” datastream. This means that the radar datastreams have been well characterized to the best possible quality. This report will detail the status of the raw “a1” level data sets during the MOSAiC campaign, the corrections that were applied to create the “b1” data files, and the details of the applied methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Introgressions of novel diseases resistance genes from Miscanthus into energycane

Sugarcane (Saccharum) is among the world’s leading bioenergy crops. However, modern sugarcane cultivars are derived from a relatively small set of founder genotypes, which has contributed to cultivar susceptibility to diseases. Miscanthus is a close relative of sugarcane that is genetically diverse and a potential source of genes for improving sugarcane. We found that Miscanthus is a source of resistance to four major diseases of sugarcane and we crossed these disease-resistance genes into a predominantly sugarcane genetic background (BC1 generation). Additionally, we found that Miscanthus could also confer genes for chilling-tolerant photosynthesis to sugarcane, which would be highly advantageous for production of energycane in subtropical environments, like the southern coastal plain of the US. Using advanced modeling techniques, we found that standard marker-assisted selection could be effective for breeding resistance conferred by a small number of genes each with large effect (i.e. vertical resistance), but to breed for many genes each of small effect (i.e. horizontal resistance), genomic selection would be the better strategy. Lastly, we learned that Miscanthus can be induced to flower sooner by giving the plants short days (long nights) but that the ideal day length for a given accession depends on its adaptation to its latitude of origin, with tropical accessions requiring shorter days to flower than accessions from high latitudes. This information will facilitate plant breeders’ ability to make crosses between Miscanthus and sugarcane, and enable greater use of Miscanthus as a genetic resource to improve sugarcane.

09 BIOMASS FUELS↗
DOE OSTI2024

2023 Atmospheric Radiation Measurement (ARM) (Annual Report)

For more than 30 years, the Atmospheric Radiation Measurement (ARM) user facility, which is managed by the U.S. Department of Energy (DOE) Office of Science, has provided freely available data and resources to scientists worldwide. Providing these services effectively requires getting out in the field and engaging with the science community. With COVID travel restrictions behind us, we are focused on connecting in person with ARM users, establishing new partnerships, and supporting fieldwork that might have waited during the pandemic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the first ARM Mobile Facility (AMF1) to Houston, Texas for the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign. The TRACER campaign was conducted from October 1, 2021 to September 30, 2022, with an intensive operational period (IOP) from June 1 to September 30, 2022.

54 ENVIRONMENTAL SCIENCES↗