UMBC Joint Center for Earth Systems Technology (JCET)

Permanent URI for this collectionhttp://hdl.handle.net/11603/7732

The Joint Center for Earth Systems Technology (JCET) was formed under a Cooperative agreement between the Earth Science Division of NASA Goddard Space Flight Center (GSFC) and the University of Maryland, Baltimore County (UMBC) in 1995. The JCET family consists of its business staff, students, research faculty, GSFC Sponsors, appointed fellows and affiliated tenured/tenure-track faculty. JCET is an innovative center where these scientists interact in a seamless fashion with the support of an efficient business and administrative unit.

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Now showing 1 - 20 of 1424

A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates
(AMS, 2024-11-13) Grecu, Mircea; Heymsfield, Gerald M.; Nicholls, Stephen; Lang, Stephen; Olson, William S.
In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
Tutorial on Causal Inference with Spatiotemporal Data
(ACM, 2024-11-04) Ali, Sahara; Wang, Jianwu
Spatiotemporal data, which captures how variables evolve across space and time, is ubiquitous in fields such as environmental science, epidemiology, and urban planning. However, identifying causal relationships in these datasets is challenging due to the presence of spatial dependencies, temporal autocorrelation, and confounding factors. This tutorial provides a comprehensive introduction to spatiotemporal causal inference, offering both theoretical foundations and practical guidance for researchers and practitioners. We explore key concepts such as causal inference frameworks, the impact of confounding in spatiotemporal settings, and the challenges posed by spatial and temporal dependencies. The paper covers synthetic spatiotemporal benchmark data generation, widely used spatiotemporal causal inference techniques, including regression-based, propensity score-based, and deep learning-based methods, and demonstrates their application using synthetic datasets. Through step-by-step examples, readers will gain a clear understanding of how to address common challenges and apply causal inference techniques to spatiotemporal data. This tutorial serves as a valuable resource for those looking to improve the rigor and reliability of their causal analyses in spatiotemporal contexts.
Evolution of Reactive Organic Compounds and Their Potential Health Risk in Wildfire Smoke
(ACS, 2024-10-22) Pye, Havala O. T.; Xu, Lu; Henderson, Barron H.; Pagonis, Demetrios; Campuzano-Jost, Pedro; Guo, Hongyu; Jimenez, Jose L.; Allen, Christine; Skipper, T. Nash; Halliday, Hannah S.; Murphy, Benjamin N.; D’Ambro, Emma L.; Wennberg, Paul O.; Place, Bryan K.; Wiser, Forwood C.; McNeill, V. Faye; Apel, Eric C.; Blake, Donald R.; Coggon, Matthew M.; Crounse, John D.; Gilman, Jessica B.; Gkatzelis, Georgios I.; Hanisco, Thomas F.; Huey, L. Gregory; Katich, Joseph M.; Lamplugh, Aaron; Lindaas, Jakob; Peischl, Jeff; St Clair, Jason; Warneke, Carsten; Wolfe, Glenn; Womack, Caroline
Wildfires are an increasing source of emissions into the air, with health effects modulated by the abundance and toxicity of individual species. In this work, we estimate reactive organic compounds (ROC) in western U.S. wildland forest fire smoke using a combination of observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign and predictions from the Community Multiscale Air Quality (CMAQ) model. Standard emission inventory methods capture 40–45% of the estimated ROC mass emitted, with estimates of primary organic aerosol particularly low (5–8×). Downwind, gas-phase species abundances in molar units reflect the production of fragmentation products such as formaldehyde and methanol. Mass-based units emphasize larger compounds, which tend to be unidentified at an individual species level, are less volatile, and are typically not measured in the gas phase. Fire emissions are estimated to total 1250 ± 60 g·C of ROC per kg·C of CO, implying as much carbon is emitted as ROC as is emitted as CO. Particulate ROC has the potential to dominate the cancer and noncancer risk of long-term exposure to inhaled smoke, and better constraining these estimates will require information on the toxicity of particulate ROC from forest fires.
Evaluation of 10-m Wind Speed From ISD Meteorological Stations and the MERRA-2 Reanalysis: Impacts on Dust Emission in the Arabian Peninsula
(AGU, 2024-10-30) Faber, Emily; Rocha-Lima, Adriana; Colarco, P.; Baker, Barry
Mineral dust is one of the most important aerosols when studying the radiative balance and climate of the planet. There are different dust emission schemes utilized by the atmospheric modeling communities, many of which disagree on basic output quantities such as mass of dust emitted and distribution of mass among size bins. In this work, we examined mineral dust emission from a leading model scheme, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), as utilized in the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Reanalysis and compared it to dust emissions calculated using wind measurements from ground based weather stations located in the Arabian Peninsula that are included in the National Oceanic and Atmospheric Administration’s (NOAA) integrated surface database (ISD). An intercomparison of 10-m wind speed is shown for the Arabian Peninsula region, differences of the observed and modeled wind field are quantified, and impacts of differences on dust emissions are calculated. This analysis shows 10-m winds in the ISD were generally lower than MERRA-2 winds, which propagated to dust emissions errors. Our estimate of one of the most significant mass impacts in dust emission is 0.178 Tg/year/grid box with a percent change of over 200% to the recalculated dust emissions from MERRA-2. These differences in wind speed propagated to a difference in dust mass emitted by the use of a static source function which aids in scaling the mass emitted by the availability of dust in each grid. Additionally, the magnitude of these differences varies seasonally.
Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models
(2024) Cham, Mostafa; Tabassum, Tartela; Shakeri, Ehsan; Wang, Jianwu
On the high accuracy to test dragging of inertial frames with the LARES 2 space experiment
(Springer Nature, 2024-10-05) Ciufolini, Ignazio; Paris, Claudio; Pavlis, Erricos C.; Ries, John C.; Matzner, Richard; Deka, Darpanjeet; Ortore, Emiliano; Kuzmicz-Cieslak, Magdalena; Gurzadyan, Vahe; Penrose, Roger; Paolozzi, Antonio; Goncalves, Juan Pablo Sellanes
In this paper we treat some aspects of the LARES 2 space experiment to test the general relativistic phenomenon of dragging of inertial frames, or frame-dragging, in particular we discuss some aspects of its relative accuracy which can approach one part in a thousand. We then, once again respond to the criticisms of the author of a recent paper about the accuracy in the measurement of frame-dragging with LARES 2. The claims of such a paper are not reproducible in any independent analyses. Indeed, it claims that the accuracy in the test of frame-dragging, which can be reached by the LARES 2 space experiment, is several orders of magnitude larger than previously estimated in a number of papers. Here we show that such a paper is based on a number of significant misunderstandings and conceptual mistakes. Furthermore, it is puzzling to observe that previous papers by the same author contained completely opposite statements about the accuracy which can be reached using two satellites with supplementary inclinations, such as in the LARES 2 space experiment, and in general with laser-ranged satellites.
Coastal-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) Science Plan
(2024-08-01) Davis, Kenneth; Zaitchik, Benjamin; Asa-Awuku, Akua; Bou-Zeid, Elie; Baidar, Sunil; Boxe, Christopher; Brewer, W. Alan; Chiao, Sen; Damoah, Richard; DeCarlo, Peter; Demoz, Belay; Dickerson, Russ; Giometto, Marco; Gonzalez-Cruz, Jorge; Jensen, Michael; Kuang, Chongai; Lamer, Katia; Li, Xiaowen; Lombardo, Kelly; Miles, Natasha; Niyogi, Dev; Pan, Ying; Peters, John; Ramamurthy, Prathap; Peng, Wei; Richardson, Scott; Sakai, Ricardo; Waugh, Darryn; Zhang, Jie
Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations regarding the impacts of climate change and associated mitigation and adaptation measures. Earth system (climate and weather) models have not yet been adapted to provide accurate predictions of climate and weather variability within cities, nor do they provide well-tested representations of the impacts of urban systems on the atmospheric environment. These limitations are largely due to limited field data available for testing and development of these models. We will deploy the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s first Mobile Facility (AMF1) to the mid-Atlantic region surrounding the city of Baltimore for the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE). This deployment will create a four-node regional atmospheric observatory network including Baltimore and its three primary surrounding environments – rural, urban, and bay. CoURAGE investigators will study the interactions among the Earth’s surface, the atmospheric boundary layer, aerosols and atmospheric composition, clouds, radiation, and precipitation at each site, and examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. This study will determine the degree to which Baltimore’s atmospheric environment depends on interactive feedbacks in the atmospheric system and the degree to which conditions in Baltimore depend on the surrounding environment. Some topics of interest include how urban land management exacerbates heat waves, the impact of regional mesoscale winds (nocturnal jet, bay breeze) on urban air pollution and cloud cover, and the impact of the urban heat island and aerosol production on heavy precipitation events. Understanding this integrated coast-urban-rural system quantitatively and with good accuracy and precision is critical to informing climate adaptation and mitigation efforts in the city of Baltimore. The understanding gained should be applicable to many similar coastal, mid-latitude urban centers. Another important objective of CoURAGE is to improve the representation of the climate of coastal cities in Earth systems models (ESMs). CoURAGE investigators will use the observations to test current ESMs, identify weaknesses and work towards improved simulations of this complex environment. The ARM core facility will be deployed in the city of Baltimore, complementing the Baltimore Social-Environmental Collaborative (BSEC), a DOE urban integrated field laboratory (UIFL). Ancillary sites will be deployed to rural Maryland northwest of Baltimore, and to the southern end of Kent Island within Chesapeake Bay. The fourth node will be a long-term atmospheric observatory operated in Beltsville, Maryland by Howard University and the Maryland Department of the Environment. Measurements will be conducted for one year, starting in December of 2024. There will be two intensive operational periods (IOPs), one in summer and one in winter, when the ancillary sites will be enhanced with additional balloon launches, tethered balloon system (TBS) operation, and added atmospheric composition measurements.
Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features
(2024-09-09) Khanjani, Zahra; Ale, Tolulope; Wang, Jianwu; Davis, Lavon; Mallinson, Christine; Janeja, Vandana
Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with sociolinguistics experts to label spoofed audio samples with Expert Defined Linguistic Features (EDLFs) that can be discerned by the human ear: pitch, pause, word-initial and word-final release bursts of consonant stops, audible intake or outtake of breath, and overall audio quality. It is established that there is an improvement in several deepfake detection algorithms when they augmented the traditional and common features of audio data with these EDLFs. In this paper, using a hybrid dataset comprised of multiple types of spoofed audio augmented with sociolinguistic annotations, we investigate causal discovery and inferences between the discernible linguistic features and the label in the audio clips, comparing the findings of the causal models with the expert ground truth validation labeling process. Our findings suggest that the causal models indicate the utility of incorporating linguistic features to help discern spoofed audio, as well as the overall need and opportunity to incorporate human knowledge into models and techniques for strengthening AI models. The causal discovery and inference can be used as a foundation of training humans to discern spoofed audio as well as automating EDLFs labeling for the purpose of performance improvement of the common AI-based spoofed audio detectors.
Developing Small Satellite Ground Support Software for Orbit Tracking and Target Acquisition of the HARP Cubesat
(Frontiers, 2024-10-10) Sienkiewicz, Noah; Martins, J. Vanderlei; Xu, Xiaoguang; McBride, Brent; Remer, Lorraine
Small satellites are efficient at performing Earth science from space due to their limited cost and size. Small satellites (cubesats) achieve much with limited power production/storage, heat dissipation, data storage, and ground contact points/bandwidth. As such it is beneficial to offload as much as possible to ground support systems. Consider the HyperAngular Rainbow Polarimeter (HARP) Cubesat. Its goals were to serve as a technical demonstration prior to the development of HARP2 aboard the NASA Plankton Aerosol Cloud and ocean Ecosystem (PACE) mission and to serve as an Earth viewing remote sensing platform which measured the characteristics of clouds and aerosols. HARP cubesat was limited to taking 5-minute capture sequences once every 24 h. It took approximately 10 such captures before it needed to perform data downlink and have its memory cleared for continued use. A ground station at NASA Wallops supported HARP with approximately three points of contact each day. To maximize the value of each capture, ground support software was developed leveraging public data to inform the schedule of each capture. In this paper, we review the algorithms and data sources that allowed us to: 1; predict the HARP orbital track a week in advance, 2; predict also the location of other remote sensing satellites and ground stations relative to HARP, 3; predict the ground view geometry of the instrument along its orbital track, 4; compare global climatological data products of clouds and aerosols along the predicted orbital tracks, and 5; identify and integrate important ground target locations based on remote sensing literature and ongoing natural phenomena. This HARP Orbital Prediction System (HOPS) made HARP into a successful technical demonstration which also offered significant science value. The HOPS system presents a valuable methodology for small satellites to operate efficiently despite their limited capabilities. HOPS is also a useful testbed for studying the sensitivity of scene geometry. Using HOPS, we show that for a wide field-of-view (FOV) instrument, like HARP, latitude/longitude geolocation varies by approximately 0.1° at a height of 8–10 km. Scattering angles vary less than 0.01° at similar heights, with the worst performance near direct backscatter (180°).
Atmospheric Gravity Wave Detection Using Transfer Learning Techniques
(IEEE, 2022-12) González, Jorge López; Chapman, Theodore; Chen, Kathryn; Nguyen, Hannah; Chambers, Logan; Mostafa, Seraj Al Mahmud; Wang, Jianwu; Purushotham, Sanjay; Wang, Chenxi; Yue, Jia
Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy.
Effect of Dust Morphology on Aerosol Optics in the GEOS-Chem Chemical Transport Model, on UV-Vis Trace Gas Retrievals, and on Surface Area Available for Reactive Uptake
(AGU, 2024-09-26) Singh, Inderjeet; Martin, Randall V.; Bindle, Liam; Chatterjee, Deepangsu; Li, Chi; Oxford, Christopher; Xu, Xiaoguang; Wang, Jun
Many chemical transport models treat mineral dust as spherical. Solar backscatter retrievals of trace gases (e.g., OMI and TROPOMI) implicitly treat mineral dust as spherical. The impact of the morphology of mineral dust particles is studied to assess its implications for global chemical transport model (GEOS-Chem) simulations and solar backscatter trace gas retrievals at ultraviolet and visible (UV-Vis) wavelengths. We investigate how the morphology of mineral dust particles affects the simulated dust aerosol optical depth; surface area, reaction, and diffusion parameters for heterogeneous chemistry; phase function, and scattering weights for air mass factor (AMF) calculations used in solar backscatter retrievals. We use a mixture of various aspect ratios of spheroids to model the dust optical properties and a combination of shape and porosity to model the surface area, reaction, and diffusion parameters. We find that assuming spherical particles can introduce size-dependent and wavelength-dependent errors of up to 14% in simulated dust extinction efficiency with corresponding error in simulated dust optical depth typically within 5%. We find that use of spheroids rather than spheres increases forward scattered radiance and decreases backward scattering that in turn decrease the sensitivity of solar backscatter retrievals of NO₂ to aerosols by factors of 2.0–2.5. We develop and apply a theoretical framework based on porosity and surface fractal dimension with corresponding increase in the reactive uptake coefficient driven by increased surface area and species reactivity. Differences are large enough to warrant consideration of dust non-sphericity for chemical transport models and UV-Vis trace gas retrievals.
Integrating Fourier Transform and Residual Learning for Arctic Sea Ice Forecasting
(IEEE, 2023-12) Lapp, Louis; Ali, Sahara; Wang, Jianwu
Arctic sea ice plays integral roles in both polar and global environmental systems, notably ecosystems, commu-nities, and economies. As sea ice continues to decline due to climate change, it has become imperative to accurately predict the future of sea ice extent (SIE). Using datasets of Arctic meteorological and SIE variables spanning 1979 to 2021, we propose architectures capable of processing multivariate time series and spatiotemporal data. Our proposed framework consists of ensembled stacked Fourier Transform signals (FFTstack) and Gradient Boosting models. In FFTstack, grid search iteratively detects the optimal combination of representative FFT signals, a process that improves upon current FFT implementations and deseasonalizers. An optimized Gradient Boosting Regressor is then trained on the residual of the FFTstack output. Through ex-periment, we found that the models trained on both multivariate and spatiotemporal time series data performed either similar to or better than models in existing research. In addition, we found that integration of FFTstack improves the performance of current multivariate time series deep learning models. We conclude that the high flexibility and performance of this methodology have promising applications in guiding future adaptation, resilience, and mitigation efforts in response to Arctic sea ice retreat.
Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
(2024-09-19) Nji, Francis Ndikum; Faruque, Omar; Cham, Mostafa; Janeja, Vandana; Wang, Jianwu
Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability. Since no single clustering algorithm ensures optimal results, researchers have increasingly explored the effectiveness of ensemble approaches. Ensemble clustering has attracted much attention due to increased diversity, better generalization, and overall improved clustering performance. While ensemble clustering may yield promising results on simple datasets, it has not been fully explored on complex multivariate spatiotemporal data. For our contribution to this field, we propose a novel hybrid ensemble deep graph temporal clustering (HEDGTC) method for multivariate spatiotemporal data. HEDGTC integrates homogeneous and heterogeneous ensemble methods and adopts a dual consensus approach to address noise and misclassification from traditional clustering. It further applies a graph attention autoencoder network to improve clustering performance and stability. When evaluated on three real-world multivariate spatiotemporal data, HEDGTC outperforms state-of-the-art ensemble clustering models by showing improved performance and stability with consistent results. This indicates that HEDGTC can effectively capture implicit temporal patterns in complex spatiotemporal data.
Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application
(2024-09-23) Ekpetere, Kenneth Okechukwu; Mehta, Amita V.; Coll, James Matthew; Liang, Chen; Onochie, Sandra Ogugua; Ekpetere, Michael Chinedu
A web application - IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.
The 2021-2024 Winter Precipitation Ground Validation Field Campaign at The University of Connecticut
(IEEE, 2024-09-05) Cerrai, Diego; Filipiak, Brian; Spaulding, Aaron; Wolff, David B.; Tokay, Ali; Helms, Charles N.; Loftus, Adrian M.; Chibisov, Alexey V.; Schirtzinger, Carl; Bliven, Larry; Pabla, Charanjit S.; Boulanger, Mick J.; Kim, EunYeol; Thant, Hein; Junyent, Francesc; Chandrasekar, Venkatachalam; Notaros, Branislav; De Azevedo, Gustavo B. H.
During three consecutive winter seasons, between December 2021 and April 2024, several ground-based wintry precipitation measurement instruments were deployed at the University of Connecticut’s main campus. The instruments included an assortment of K-band and W-band profiling radars and Ka-Ku band scanning radars, weighing, and tipping bucket pluviometers, laser disdrometers, high-speed and high-resolution cameras for quantitative precipitation measurement, weather stations, and an unmanned aircraft system for environmental variables. The goal of this field campaign is to provide a dataset for validating NASA Global Precipitation Measurement (GPM) products, and to examine the error characteristics of co-located ground-based instruments. In this manuscript, we present the instrument suite and discuss possible uses of this unique set of measurements for remote sensing applications.
From SuperTIGER to TIGERISS
(MDPI, 2024-01-11) Rauch, B. F.; Zober, W. V.; Abarr, Q.; Akaike, Y.; Binns, W. R.; Fernandez-Borda, Roberto; Bose, R. G.; Brandt, T. J.; Braun, D. L.; Buckley, J. H.; Cannady, Nicholas; Coutu, S.; Crabill, R. M.; Dowkontt, P. F.; Israel, M. H.; Kandula, M.; Krizmanic, J. F.; Labrador, A. W.; Labrador, W.; Lisalda, L.; Martins, J. Vanderlei; McPherson, M. P.; Mewaldt, R. A.; Mitchell, J. G.; Mitchell, J. W.; Mognet, S. a I.; Murphy, R. P.; de Nolfo, G. A.; Nutter, S.; Olevitch, M. A.; Osborn, N. E.; Pastrana, I. M.; Sakai, K.; Sasaki, M.; Smith, S.; Tolentino, H. A.; Walsh, N. E.; Ward, J. E.; Washington, D.; West, A. T.; Williams, L. P.
The Trans-Iron Galactic Element Recorder (TIGER) family of instruments is optimized to measure the relative abundances of the rare, ultra-heavy galactic cosmic rays (UHGCRs) with atomic number (Z) Z ≥ 30. Observing the UHGCRs places a premium on exposure that the balloon-borne SuperTIGER achieved with a large area detector (5.6 m²) and two Antarctic flights totaling 87 days, while the smaller (∼1 m²) TIGER for the International Space Station (TIGERISS) aims to achieve this with a longer observation time from one to several years. SuperTIGER uses a combination of scintillator and Cherenkov detectors to determine charge and energy. TIGERISS will use silicon strip detectors (SSDs) instead of scintillators, with improved charge resolution, signal linearity, and dynamic range. Extended single-element resolution UHGCR measurements through ₈₂Pb will cover elements produced in s-process and r-process neutron capture nucleosynthesis, adding to the multi-messenger effort to determine the relative contributions of supernovae (SNe) and Neutron Star Merger (NSM) events to the r-process nucleosynthesis product content of the galaxy.
Flood-ResNet50: Optimized Deep Learning Model for Efficient Flood Detection on Edge Device
(IEEE, 2024-03-19) Khan, Md Azim; Ahmed, Nadeem; Padela, Joyce; Raza, Muhammad Shehrose; Gangopadhyay, Aryya; Wang, Jianwu; Foulds, James; Busart, Carl; Erbacher, Robert F.
Floods are highly destructive natural disasters that result in significant economic losses and endanger human and wildlife lives. Efficiently monitoring Flooded areas through the utilization of deep learning models can contribute to mitigating these risks. This study focuses on the deployment of deep learning models specifically designed for classifying flooded and non-flooded in UAV images. In consideration of computational costs, we propose modified version of ResNet50 called Flood-ResNet50. By incorporating additional layers and leveraging transfer learning techniques, Flood-ResNet50 achieves comparable performance to larger models like VGG16/19, AlexNet, DenseNet161, EfficientNetB7, Swin(small), and vision transformer. Experimental results demonstrate that the proposed modification of ResNet50, incorporating additional layers, achieves a classification accuracy of 96.43%, F1 score of 86.36%, Recall of 81.11%, Precision of 92.41 %, model size 98MB and FLOPs 4.3 billions for the FloodNet dataset. When deployed on edge devices such as the Jetson Nano, our model demonstrates faster inference speed (820 ms), higher throughput (39.02 fps), and lower average power consumption (6.9 W) compared to larger ResNet101 and ResNet152 models.
Variability of Eastern North Atlantic Summertime Marine Boundary Layer Clouds and Aerosols Across Different Synoptic Regimes Identified with Multiple Conditions
(2024-08-22) Zheng, Xue; Qiu, Shaoyue; Zhang, Damao; Adebiyi, Adeyemi A.; Zheng, Xiaojian; Faruque, Omar; Tao, Cheng; Wang, Jianwu
This study estimates the meteorological covariations of aerosol and marine boundary layer (MBL) cloud properties in the Eastern North Atlantic (ENA) region, characterized by diverse synoptic conditions. Using a deep-learning-based clustering model with mid-level and surface daily meteorological data, we identify seven distinct synoptic regimes during the summer from 2016 to 2021. Our analysis, incorporating reanalysis data and satellite retrievals, shows that surface aerosols and MBL clouds exhibit clear regime-dependent characteristics, while lower tropospheric aerosols do not. This discrepancy likely arises synoptic regimes determined by daily large-scale conditions may overlook air mass histories that predominantly dictate lower tropospheric aerosol conditions. Focusing on three regimes dominated by northerly winds, we analyze the Atmospheric Radiation Measurement Program (ARM) ENA observations on Graciosa Island in the Azores. In the subtropical anticyclone regime, fewer cumulus clouds and more single-layer stratocumulus clouds with light drizzles are observed, along with the highest cloud droplet number concentration (Nd), surface Cloud Condensation Nuclei (CCN) and surface aerosol levels. The post-trough regime features more broken or multi-layer stratocumulus clouds with slightly higher surface rain rate, and lower Nd and surface CCN levels. The weak trough regime is characterized by the deepest MBL clouds, primarily cumulus and broken stratocumulus clouds, with the strongest surface rain rate and the lowest Nd, surface CCN and surface aerosol levels, indicating strong wet scavenging. These findings highlight the importance of considering the covariation of cloud and aerosol properties driven by large-scale regimes when assessing aerosol indirect effects using observations.
Stratospheric Hydration Processes in Tropopause-Overshooting Convection Revealed by Tracer-Tracer Correlations From the DCOTSS Field Campaign
(AGU, 2024-08-20) Homeyer, Cameron R.; Gordon, Andrea E.; Smith, Jessica B.; Ueyama, Rei; Wilmouth, David M.; Sayres, David S.; Hare, Jennifer; Pandey, Apoorva; Hanisco, Thomas F.; Dean-Day, Jonathan M.; Hannun, Reem; St. Clair, Jason
Hydration of the stratosphere by tropopause-overshooting convection has received increasing interest due to the extreme concentrations of water vapor that can result and, ultimately, the climate warming potential such hydration provides. Previous work has recognized the importance of numerous dynamic and physical processes that control stratospheric water vapor delivery by convection. This study leverages recent comprehensive observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign to determine the frequency at which each process operates during real events. Specifically, a well-established analysis technique known as tracer-tracer correlation is applied to DCOTSS observations of ozone, water vapor, and potential temperature to identify the occurrence of known processes. It is found that approximately half of convectively-driven stratospheric hydration samples show no indication of significant air mass transport and mixing, emphasizing the importance of ice sublimation to stratospheric water vapor delivery. Furthermore, the temperature of the upper troposphere and lower stratosphere environment and/or overshoot appears to be a commonly active constraint, since the approximate maximum possible water vapor concentration that can be reached in an air mass is limited to the saturation mixing ratio when ice is present. Finally, little evidence of relationships between dynamic and physical processes and their spatial distribution was found, implying that stratospheric water vapor delivery by convection is likely facilitated by a complex collection of processes in each overshooting event.
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method
(2024-08-26) Mostafa, Seraj Al Mahmud; Faruque, Omar; Wang, Chenxi; Yue, Jia; Purushotham, Sanjay; Wang, Jianwu
Atmospheric gravity waves occur in the Earths atmosphere caused by an interplay between gravity and buoyancy forces. These waves have profound impacts on various aspects of the atmosphere, including the patterns of precipitation, cloud formation, ozone distribution, aerosols, and pollutant dispersion. Therefore, understanding gravity waves is essential to comprehend and monitor changes in a wide range of atmospheric behaviors. Limited studies have been conducted to identify gravity waves from satellite data using machine learning techniques. Particularly, without applying noise removal techniques, it remains an underexplored area of research. This study presents a novel kernel design aimed at identifying gravity waves within satellite images. The proposed kernel is seamlessly integrated into a deep convolutional neural network, denoted as gWaveNet. Our proposed model exhibits impressive proficiency in detecting images containing gravity waves from noisy satellite data without any feature engineering. The empirical results show our model outperforms related approaches by achieving over 98% training accuracy and over 94% test accuracy which is known to be the best result for gravity waves detection up to the time of this work. We open sourced our code at https://rb.gy/qn68ku.

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