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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗
DOE OSTI2023

Using CRISPR/Cas13a technologies to detect miRNA [DOE SULI Internship Report]

Micro ribonucleic acids (miRNA) give our immune systems the ability to recognize viruses and other pathogens by their complementary single-stranded RNA (ssRNA) produced in the reproduction of the pathogen in our cells. When miRNA of a specific sequence is detected in a cell sample, it can be assumed that the immune system is activated and attempting to track down the infection. This pathway can be utilized to diagnose infection from a pathogen before the individual even develops symptoms, aiding in early disease detection and proper treatment. One of the ways that we can detect miRNA is through an assay of clustered regularly interspaced short palindromic repeats or “CRISPR” and the bacterial protein Cas13a. This report details discoveries made while attempting to optimize this assay for miRNA detection. After looking at several different factors within the assay, it was determined that some factors, such as reporter type and metallic ion concentration, are more impactful on the overall assay sensitivity than other factors, such as the overall concentration of Cas13a, CRISPR RNA (crRNA), or ssRNA reporter. It was also discovered that different sequences with different lengths require renewed optimization efforts, as each target has a unique binding affinity determined by the sequence length and composition. This information is crucial in the development of point of care molecular detection devices as they become sensitive enough to identify pathogens before they spread.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Detection of trace contaminants released from rapidly heated pulsed power electrodes by laser diagnostics

Development of a capability to measure trace gas contaminants released from pulsed power electrodes would immediately impact Sandia pulsed power research. These releases occur during ultra-fast heating of metal electrodes during pulsed power discharges and can lead to substantial power losses through plasma formation. Detection of contaminants is a formidable challenge due to the need for in-situ spatially and temporally resolved measurements of trace gases in the extreme environment of ultra-fast heated metal surfaces. We investigate the feasibility of laser diagnostics for detecting contaminants, including H-atom, OH, and H 2 O. Laser-induced fluorescence and photofragmentation fluorescence showed significant plasma emission interferences and did not yield any detectable H-atom, OH, or H 2 O. Our newly developed H-atom detection using femtosecond degenerate four-wave mixing suppressed interferences and enabled detection of H-atoms. A few shots showed large signals in the near-surface region of metal foils, suggesting the formation of a wave of H-atoms from the metal.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Analysis of Warped April Tag Impacts on Detection and Pose Estimation

This report evaluates the impact of geometric deformation on an April Tag, particularly when warped due to attachment on a curved surface, on its detectability and pose estimation performance. A comparative analysis was conducted using a flat April Tag as a control under identical experimental conditions, which involved recording video sequences with varying viewing angles. For detectability, the warped tag exhibited consistent detection failures at viewing angles beyond 40° and complete failures beyond 60°, whereas the flat tag maintained reliable detection across all angles. For pose estimation, measured by pose jitter (variation in rotation and translation), differences between the warped and flat tags were minimal and statistically insignificant, indicating robust performance even for the deformed tag. These findings suggest that while geometric warping reduces an April Tag’s detectability, its pose estimation accuracy remains relatively unaffected under the tested conditions.

42 ENGINEERING↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Nucleic Acid-Based Detection Protease Activity

Proteases include clinically relevant markers for clotting disorders, certain cancers as well as toxins. Assays for protease activity often use designed peptides mimicking natural substrates and detection with colorometric and fluorescence-based detection that is difficult to multiplex without expensive and resource demanding instruments. This work demonstrates detection of proteolytic activity using PCR and sequencing-readable reporter molecules. The assay development focused on binding the constructed peptide-oligonucleotide chimera to immobilized streptavidin. Thrombin, an essential component of the clotting cascade, was used as a model system for testing peptide substrate recognition and release of a designed oligonucleotide for detection. Detection of protease activity was demonstrated in a concentration-dependent manner using MALDI-MS, RT-PCR and DNA sequencing.

Wunschel, David S [Pacific Northwest National Labo↗
DOE OSTI2024

Rapid detection of mexX in Pseudomonas aeruginosa based on CRISPR-Cas13a coupled with recombinase polymerase amplification

The principal pathogen responsible for chronic urinary tract infections, immunocompromised hosts, and cystic fibrosis patients is Pseudomonas aeruginosa , which is difficult to eradicate. Due to the extensive use of antibiotics, multidrug-resistant P. aeruginosa has evolved, complicating clinical therapy. Therefore, a rapid and efficient approach for detecting P. aeruginosa strains and their resistance genes is necessary for early clinical diagnosis and appropriate treatment. This study combines recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats-association protein 13a (CRISPR-Cas13a) to establish a one-tube and two-step reaction systems for detecting the mexX gene in P. aeruginosa . The test times for one-tube and two-step RPA-Cas13a methods were 5 and 40 min (including a 30 min RPA amplification reaction), respectively. Both methods outperform Quantitative Real-time Polymerase Chain Reactions (qRT-PCR) and traditional PCR. The limit of detection (LoD) of P. aeruginosa genome in one-tube and two-step RPA-Cas13a is 10 aM and 1 aM, respectively. Meanwhile, the designed primers have a high specificity for P. aeruginosa mexX gene. These two methods were also verified with actual samples isolated from industrial settings and demonstrated great accuracy. Furthermore, the results of the two-step RPA-Cas13a assay could also be visualized using a commercial lateral flow dipstick with a LoD of 10 fM, which is a useful adjunt to the gold-standard qRT-PCR assay in field detection. Taken together, the procedure developed in this study using RPA and CRISPR-Cas13a provides a simple and fast way for detecting resistance genes.

Zhu, Xiao-Xuan↗
DOE OSTI2023

Diagnostic Evaluation of the IS1081-Targeted Real-Time PCR for Detection of Mycobacterium bovis DNA in Bovine Milk Samples

The ability of Mycobacterium bovis (M. bovis) to survive in bovine milk has emerged as a serious public health concern. The first objective of this study was to evaluate the diagnostic utility of IS1081-targeted real-time PCR for the detection of M. bovis DNA in different fractions of bovine milk. In a model study, bovine milk samples were spiked with serially diluted M. bovis BCG to investigate the detection limit of M. bovis DNA in whole milk and milk fractions (cream, pellet, and pellet + cream combined) using IS1081 real-time PCR. The assay was then used to detect M. bovis DNA in whole milk and milk fractions from naturally infected animals. The results showed that the IS1081 real-time PCR was more sensitive when detecting M. bovis DNA in the cream layer alone and cream + pellet combined compared to whole milk or the pellet alone. While PCR-based diagnostic assays for the detection of M. bovis in milk samples provide a quicker diagnostic tool for bovine tuberculosis, safe processing, and handling of M. bovis-infected milk samples remain a challenge and pose a human health risk. PrimeStore Molecular Transport Medium (MTM) has been shown to rapidly inactivate infected specimens while preserving nucleic acid for subsequent Molecular analysis. Therefore, the secondary objective of this study was to evaluate the ability of MTM to inactivate M. bovis BCG in spiked milk samples as well as its ability to preserve BCG DNA for the PCR assay. The results showed that MTM can successfully inactivate BCG alone or in spiked milk samples while preserving DNA for the PCR assay. The CT values of M. bovis BCG alone and spiked milk samples aliquoted in MTM and without MTM were similar at various dilutions. Taken together, our results indicate that using DNA extracted from the milk cream fraction alone or combined milk cream and pellet improved the recovery rate of M. bovis DNA in bovine milk samples. MTM has the potential to provide a safe and rapid sample processing tool for M. bovis inactivation in milk samples and preserve DNA for molecular diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ΔR. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2022

Multiple Mutations Associated with Emergent Variants Can Be Detected as Low-Frequency Mutations in Early SARS-CoV-2 Pandemic Clinical Samples

Genetic analysis of intra-host viral populations provides unique insight into pre-emergent mutations that may contribute to the genotype of future variants. Clinical samples positive for SARS-CoV-2 collected in California during the first months of the pandemic were sequenced to define the dynamics of mutation emergence as the virus became established in the state. Deep sequencing of 90 nasopharyngeal samples showed that many mutations associated with the establishment of SARS-CoV-2 globally were present at varying frequencies in a majority of the samples, even those collected as the virus was first detected in the US. A subset of mutations that emerged months later in consensus sequences were detected as subconsensus members of intra-host populations. Spike mutations P681H, H655Y, and V1104L were detected prior to emergence in variant genotypes, mutations were detected at multiple positions within the furin cleavage site, and pre-emergent mutations were identified in the nucleocapsid and the envelope genes. Because many of the samples had a very high depth of coverage, a bioinformatics pipeline, “Mappgene”, was established that uses both iVar and LoFreq variant calling to enable identification of very low-frequency variants. This enabled detection of a spike protein deletion present in many samples at low frequency and associated with a variant of concern.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Detection of Cyclopropenylidene on Titan with ALMA

We report the first detection on Titan of the small cyclic molecule cyclopropenylidene (c-C{sub 3}H{sub 2}) from high-sensitivity spectroscopic observations made with the Atacama Large Millimeter/submillimeter Array. Multiple lines of cyclopropenylidene were detected in two separate data sets: ∼251 GHz in 2016 (Band 6) and ∼352 GHz in 2017 (Band 7). Modeling of these emissions indicates abundances of 0.50 ± 0.14 ppb (2016) and 0.28 ± 0.08 (2017) for a 350 km step model, which may either signify a decrease in abundance, or a mean value of 0.33 ± 0.07 ppb. Inferred column abundances are (3–5) × 10{sup 12} cm{sup −2} in 2016 and (1–2) × 10{sup 12} cm{sup −2} in 2017, similar to photochemical model predictions. Previously the C{sub 3}H{sub 3}{sup +} ion has been measured in Titan’s ionosphere by Cassini’s Ion and Neutral Mass Spectrometer (INMS), but the neutral (unprotonated) species has not been detected until now, and aromatic versus aliphatic structure could not be determined by the INMS. Our work therefore represents the first unambiguous detection of cyclopropenylidene, the second known cyclic molecule in Titan’s atmosphere along with benzene (C{sub 6}H{sub 6}) and the first time this molecule has been detected in a planetary atmosphere. We also searched for the N-heterocycle molecules pyridine and pyrimidine finding nondetections in both cases, and determining 2σ upper limits of 1.15 ppb (c-C{sub 5}H{sub 5}N) and 0.85 ppb (c-C{sub 4}H{sub 4}N{sub 2}) for uniform abundances above 300 km. These new results on cyclic molecules provide fresh constraints on photochemical pathways in Titan’s atmosphere, and will require new modeling and experimental work to fully understand the implications for complex molecule formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Mitigating Shear-dependent Object Detection Biases with Metacalibration

M ETACALIBRATION is a new technique for measuring weak gravitational lensing shear that is unbiased for isolated galaxy images. Here we test M ETACALIBRATION with overlapping, or "blended" galaxy images. Using standard M ETACALIBRATION , we find a few percent shear measurement bias for galaxy densities relevant for current surveys, and that this bias increases with increasing galaxy number density. We show that this bias is not due to blending itself, but rather to shear-dependent object detection. If object detection is shear-independent, no deblending of images is needed, in principle. We demonstrate that detection biases are accurately removed when including object detection in the M ETACALIBRATION process, a technique we call M ETADETECTION . This process involves applying an artificial shear to images of small regions of sky and performing detection on the sheared images, as well as measurements that are used to calculate a shear response. We demonstrate that the method can accurately recover weak shear signals even in highly blended scenes. In the M ETACALIBRATION process, the space between objects is sheared coherently, which does not perfectly match the real universe in which some, but not all, galaxy images are sheared coherently. We find that even for the worst case scenario, in which the space between objects is completely unsheared, the resulting shear bias is at most a few tenths of a percent for future surveys. We discuss additional technical challenges that must be met in order to implement M ETADETECTION for real surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

MEGASIM: Distribution and Detection of Earth Trojan Asteroids

Abstract Using N -body simulation results from the MEGASIM data set, we present spatial distributions of Earth Trojan Asteroids and assess the detectability of the population in current and next-generation ground-based astronomical surveys. Our high-fidelity Earth Trojan Asteroid (ETA) distribution maps show never-before-seen high-resolution spatial features that evolve over timescales up to 1 Gyr. The simulation was synchronized to start times and timelines of two observational astronomy surveys: (1) the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and (2) the Zwicky Transient Facility (ZTF). We calculate upper limits for the number of ETAs potentially observable with both the ZTF and LSST surveys. Due to the Yarkovsky Effect, we find no stable ETAs on billion-year timescales likely to be detected by any ETA survey, as no C-type or S-type ETAs (with H < 22 and H < 24, respectively) are likely to be stable on billion-year timescales, and ETAs large enough to remain stable on billion-year timescales are very rare relative to the rest of the ETA population. We find that a twilight ETA survey will not drastically increase the likelihood of individual ETA detection, but it would provide orders of magnitude more observations of select ETA populations. The null detection to date from ZTF restricts the potential ETA population to hundreds of objects larger than 100 m (at H ≈ 22), while a null detection by LSST will further restrict the ETA population to tens of objects larger than 100 m.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Systematic Search for MeV–GeV Pulsar Wind Nebulae without Gamma-Ray Detected Pulsars

An increasing number of pulsar wind nebulae (PWNe) are being identified in the TeV band by ground-based Imaging Air Cherenkov Telescopes such that they constitute the dominant source class of Galactic TeV emitters. However, MeV–GeV PWN counterparts are still largely lacking. To date, only a dozen PWNe are identified by the Fermi–Large Area Telescope (LAT) in the MeV–GeV band. Most PWNe are located along the Galactic plane embedded within the prominent, diffuse Galactic γ-ray emission, which makes these sources difficult to disentangle from the bright diffuse background. We present a systematic search for γ-ray counterparts to known PWNe in the 300 MeV–2 TeV energy band using the Fermi–LAT. We target the locations of previously identified PWNe that lack detected Fermi–LAT pulsars to minimize associated pulsar contamination. The sample includes six previously identified Fermi PWNe and eight Fermi–LAT sources associated with PWNe. We report the analysis of 58 regions of interest and classify Fermi–LAT detected sources as either a likely PWN or a candidate PWN counterpart based on their morphological and spectral characteristics across the broadband spectrum. There are nine unidentified Fermi–LAT sources that we consider as likely PWN counterparts, which, if confirmed to be PWNe, would greatly increase the PWN population detected by the Fermi–LAT from 12 to 21. The remaining Fermi–LAT detected sources are considered weaker PWN candidates. A second approach in the systematic search for γ-ray emitting PWNe will involve studying the off-pulse phases of Fermi–LAT detected pulsars for the presence of an obscured PWN and will be reported in a subsequent paper.

Acharyya, A. [Univ. of Southern Denmark, Odense (D↗
DOE OSTI2020

Advanced Intra-Cycle Detection of Pre-Ignition Events through Phase-Space Transforms of Cylinder Pressure Data

The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs. Furthermore, phase space transforms through time-delay embedding of cylinder pressure and principal component analysis were applied to same-cycle detection of pre-ignition and shown to enable detection on the order of a crank degree earlier than deviation in cylinder pressure can be identified through direct statistical observation of the pressure data. Additionally, it appears that the deviation of the trajectory in phase space may offer the opportunity to extend this method to further extend the detection window and allow more time for mitigation actions to occur.

42 ENGINEERING↗
DOE OSTI2025

Remote-sensing detectability of airborne Arctic dust

Remote-sensing (RS)-based estimates of Arctic dust are oftentimes overestimated due to a failure in separating out the dust contribution from that of spatially homogeneous clouds or low-altitude cloud-like plumes. A variety of illustrations are given with a particular emphasis on questionable claims of using brightness temperature differences (BTDs) as a signature indicator of Arctic dust transported from mid-latitude deserts or generated by local Arctic sources. While there is little dispute about the presence of both Asian and local dust across the Arctic, the direct RS detectability of airborne dust, as ascribed to satellite (MODIS and AVHRR) measurements of significantly negative brightness temperature differences at 11 and 12 µm (BTD 11–12 ), has been misrepresented in certain cases. While it is difficult to account for all examples of strongly negative BTD 11–12 values in the Arctic, it is unlikely that airborne dust plays a significant role. One much more likely contributor would be water clouds in the Arctic inversion layer. The RS detectability of the impact of Arctic dust (notably due to Arctic dust from local sources) can, however, be of significance. Sustained dust deposition can substantially decrease (visible to shortwave IR) snow and ice reflectance albedo (pan-chromatic reflectance) and the signal measured by satellite sensors. Significantly negative BTD 11–12 values would, however, only represent a limited area near the drainage basin sources according to our event-level case studies. The enhanced ice-nucleating particle (INP) role of local Arctic dust can, for example, induce significant changes in the properties of low-level mixed-phase clouds (cloud optical depth changes <~ 1) that can readily be detected by active and passive RS instruments. It is critical that the distinction between the RS detectability of airborne Arctic dust versus the RS detectability of the impacts of that dust be understood if we are to appropriately parameterize, for example, the radiative forcing influence of dust in this climate-sensitive region.

54 ENVIRONMENTAL SCIENCES↗