UMBC Center for Accelerated Real Time Analysis

Permanent URI for this collectionhttp://hdl.handle.net/11603/26415

Real time analytics is the leading edge of a smart data revolution, pushed by Internet advances in sensor hardware on one side and AI/ML streaming acceleration on the other. Center for Accelerated Real Time Analytics (CARTA) explores the realm streaming applications of Magna Analytics. The center works with next-generation hardware technologies, like the IBM Minsky with onboard GPU accelerated processors and Flash RAM, a Smart Cyber Physical Sensor Systems to build Cognitive Analytics systems and Active storage devices for real time analytics. This will lead to the automated ingestion and simultaneous analytics of Big Datasets generated in various domains including Cyberspace, Healthcare, Internet of Things (IoT) and the Scientific arena, and the creation of self learning, self correcting “smart” systems.

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Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
(2025-03-08) Khan, Md Azim; Gangopadhyay, Aryya; Wang, Jianwu; Erbacher, Robert F.
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially when required to perform efficiently in real environments. This research presents a novel vision language model (VLM) framework that leverages frequency domain transformations and low-rank adaptation (LoRA) to enhance feature extraction, scalability, and efficiency. Unlike traditional VLMs, which rely solely on spatial-domain representations, our approach incorporates Discrete Fourier Transform (DFT) based low-rank features while retaining pretrained spatial weights, enabling robust performance in noisy or low visibility scenarios. We evaluated the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise. Quantitative results demonstrate that our model achieves evaluation metrics comparable to state-of-the-art VLMs, such as CLIP ViT-L/14 and SigLIP. Qualitative analysis further reveals that our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV).
Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction
(Cambridge University Press, 2025-03-10) Chakraborty, Sudip; Devnath, Maloy Kumar; Jabeli, Atefeh; Kulkarni, Chhaya; Boteju, Gehan; Wang, Jianwu; Janeja, Vandana
This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (~ 33,000 km²) during the presence of BC AARs as compared to ~13,000 km² during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.
Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram
(Cambridge University Press, 2024-11-28) Maibam, Pooya Chanu; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 ±± \pm .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses
Electroencephalogram based Control of Prosthetic Hand using Optimizable Support Vector Machine
(ACM, 2023-11-02) Pooya Chanu, Maibam; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Research on electromyogram (EMG) controlled prosthetic hands has advanced significantly, enriching the social and professional lives of people with hand amputation. Even so, the non-functionality of motor neurons in the remnant muscles impedes the generation of EMG as a control signal. However, such people have the same ability as healthy individuals to generate motor cortical activity. The work presented in this paper investigates electroencephalogram (EEG)-based control of a prosthetic hand. EEG of 10 healthy subjects performing the grasping operations were acquired for classification of hand movements. 15 EEG channels were selected to classify hand open and close operations. Hand movement-class-specific time-domain features were extracted from the filtered EEG. A support vector machine (SVM) was employed with 24-fold cross-validation for classification using extracted features. SVM hyper-parameters for the classification model were optimized with a Bayesian optimizer with a minimum prediction error as an objective function. During training and testing of the classifier model, an average accuracy of 96.8 ± 0.98% and 93.4 ± 1.16% respectively, were achieved across the subjects. The trained classifier model was employed to control prosthetic hand open and close operations. This study demonstrates that EEG can be used to control a prosthetic hand by amputees with motor neuron disabilities.
Decoding motor execution and motor imagery from EEG with deep learning and source localization
(Elsevier, 2025-06-01) Kaviri, Sina Makhdoomi; Vinjamuri, Ramana
The use of noninvasive imaging techniques has become pivotal in understanding human brain functionality. While modalities like MEG and fMRI offer excellent spatial resolution, their limited temporal resolution, often measured in seconds, restricts their application in real-time brain activity monitoring. In contrast, EEG provides superior temporal resolution, making it ideal for real-time applications in brain–computer interface systems. In this study, we combined deep learning with source localization to classify two motor task types: motor execution and motor imagery. For motor imagery tasks—left hand, right hand, both feet, and tongue—we transformed EEG signals into cortical activity maps using Minimum Norm Estimation (MNE), dipole fitting, and beamforming. These were analyzed with a custom ResNet CNN, where beamforming achieved the highest accuracy of 99.15%, outperforming most traditional methods. For motor execution involving six types of reach-and-grasp tasks, beamforming achieved 90.83% accuracy compared to 56.39% from a sensor domain approach (ICA + PSD + TSCR-Net). These results underscore the significant advantages of integrating source localization with deep learning for EEG-based motor task classification, demonstrating that source localization techniques greatly enhance classification accuracy compared to sensor domain approaches.
Decoding and generating synergy-based hand movements using electroencephalography during motor execution and motor imagery
(Elsevier, 2025-06-01) Pei, Dingyi; Vinjamuri, Ramana
Brain-machine interfaces (BMIs) have proven valuable in motor control and rehabilitation. Motor imagery (MI) is a key tool for developing BMIs, particularly for individuals with impaired limb function. Motor planning and internal programming are hypothesized to be similar during motor execution (ME) and motor imagination. The anatomical and functional similarity between motor execution and motor imagery suggests that synergy-based movement generation can be achieved by extracting neural correlates of synergies or movement primitives from motor imagery. This study explored the feasibility of synergy-based hand movement generation using electroencephalogram (EEG) from imagined hand movements. Ten subjects participated in an experiment to imagine and execute hand movement tasks while their hand kinematics and neural activity were recorded. Hand kinematic synergies derived from executed movements were correlated with EEG spectral features to create a neural decoding model. This model was used to decode the weights of kinematic synergies from motor imagery EEG. These decoded weights were then combined with kinematic synergies to generate hand movements. As a result, the decoding model successfully predicted hand joint angular velocity patterns associated with grasping different objects. This adaptability demonstrates the model's ability to capture the motor control characteristics of ME and MI, advancing our understanding of MI-based neural decoding. The results hold promise for potential applications in noninvasive synergy-based neuromotor control and rehabilitation for populations with upper limb motor disabilities.
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
(2025-03-03) Hossain, Emam; Ferdous, Muhammad Hasan; Wang, Jianwu; Subramanian, Aneesh; Gani, Md Osman
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ causal discovery algorithms with a hybrid deep learning architecture. Using 43 years (1979-2021) of daily and monthly Arctic Sea Ice Extent (SIE) and ocean-atmospheric datasets, our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency. Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times. Beyond SIE prediction, the proposed framework offers a scalable solution for dynamic, high-dimensional systems, advancing both theoretical understanding and practical applications in predictive modeling.
Performance Characteristics of EMG Controlled Prosthetic Hand
(ACM, 2023-11-02) Kalita, Amlan Jyoti; Chanu, Maibam Pooya; Kakoty, Nayan M.; Vinjamuri, Ramana; Borah, Satyajit
Much development has been seen in commercial and laboratory prototypes of prosthetic hands during last two decades. However, prosthetic hands emulating human hand characteristics are very limited. In order to emulate human hand, performance characteristics evaluation of prosthetic hands is of paramount importance. This paper explains the performance characteristics of an EMG CoNtrolled PRosthetIC Hand called ENRICH involving end users’ feedback from clinical testing. ENRICH is a real time EMG controlled prosthetic hand that can perform grasping operations in 250 ± 1.1 milliseconds satisfying the neuromuscular constraint of human hand. The performance characteristics of ENRICH vis-à-vis commercial and laboratory prototypes are evaluated in terms of weight, size, degrees of freedom, finger joint range of motion, control strategy, operation time and clinical testing. This evaluation establishes ENRICH as one of the promising prosthetic hands with tangible benefits to amputees.
Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression
(IEEE, 2025-01-16) Zhang, Olivia; Grissom, Brianna; Pulido, Julian; Munoz-Ordaz, Kenia; He, Jonathan; Cham, Mostafa; Jing, Haotong; Qian, Weikang; Wen, Yixin; Wang, Jianwu
Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods
A Framework for Empirical Fourier Decomposition based Gesture Classification for Stroke Rehabilitation
(IEEE, 2024-11-11) Chen, Ke; Wang, Honggang; Catlin, Andrew; Satyanarayana, Ashwin; Vinjamuri, Ramana; Kadiyala, Sai Praveen
The demand for surface electromyography (sEMG) based exoskeletons is rapidly increasing due to their non-invasive nature and ease of use. With increase in use of Internet-of-Things (IoT) based devices in daily life, there is a greater acceptance of exoskeleton based rehab. As a result, there is a need for highly accurate and generalizable gesture classification mechanisms based on sEMG data. In this work, we present a framework which pre-processes raw sEMG signals with Empirical Fourier Decomposition (EFD) based approach followed by dimension reduction. This resulted in improved performance of the hand gesture classification. EFD decomposition’s efficacy of handling mode mixing problem on non-stationary signals, resulted in less number of decomposed components. In the next step, a thorough analysis of decomposed components as well as inter-channel analysis is performed to identify the key components and channels that contribute towards the improved gesture classification accuracy. As a third step, we conducted ablation studies on time-domain features to observe the variations in accuracy on different models. Finally, we present a case study of comparison of automated feature extraction based gesture classification vs. manual feature extraction based methods. Experimental results show that manual feature based gesture classification method thoroughly outperformed automated feature extraction based methods, thus emphasizing a need for rigorous fine tuning of automated models.
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.
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
Identifying neurophysiological correlates of stress
(frontiers, 2024-10-24) Pei, Dingyi; Tirumala, Shravika; Tun, Kyaw T.; Ajendla, Akshara; Vinjamuri, Ramana
Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.
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.
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.
Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation
(MDPI, 2024-09-27) Kaviri, Sina Makhdoomi; Vinjamuri, Ramana
Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain–computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.
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.
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.

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