About

R. Saravanan is a Professor and Department Head at the Texas A&M University College of Arts and Sciences in the Department of Atmospheric Sciences. His research focuses on the variability and predictability of climate on seasonal to millennial timescales, with particular emphasis on coupled ocean-atmosphere interactions and large-scale atmospheric and oceanic dynamics. He employs mathematical and physical approaches to study atmospheric dynamics, climate modeling, and ocean-atmosphere interactions, utilizing global and regional climate models to investigate phenomena such as midlatitude storms, tropical cyclones, and global low-frequency variability modes. A key goal of his work is to improve weather and climate prediction capabilities, especially through understanding how sea surface temperature influences atmospheric flow and how coupled climate models can predict sea surface temperature evolution over months to years. His recent research addresses questions related to the influence of large-scale phenomena like El Niño and the Atlantic Meridional Mode on tropical cyclone activity, the impact of mesoscale ocean eddies on atmospheric storms in mid-latitudes, and the application of statistical and machine learning methods to analyze the relationship between atmospheric states and satellite rainfall measurements. Dr. Saravanan has contributed to the scientific community through his involvement in various service activities, including membership on the Prediction and Research Moored Array in the Atlantic (PIRATA) Science Steering Committee, the American Meteorological Society Committee on Climate Variability and Change, and the National Research Council Committee on Climate Prediction and Predictability. He has also served as an editor for the American Meteorological Society Journal of Climate. His educational background includes a Ph.D. in Atmospheric and Oceanic Sciences from Princeton University and a Master of Science in Physics from the Indian Institute of Technology, Kanpur. He completed postdoctoral research at the University of Cambridge, UK.

Selected publications

• Author Correction: Ocean fronts and eddies force atmospheric rivers and heavy precipitation in western North America

  Nature Communications · 2026-01-30

  articleOpen access
  PublisherOA PDFDOI

• Interconnection of Aerosol‐Cloud Interactions and Cloud Feedback Through Warm Rain Process

  Journal of Geophysical Research Atmospheres · 2026-02-28

  articleOpen accessSenior author

  Abstract Recent research has revealed a correlation within the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations: models exhibiting more pronounced warming due to positive cloud feedback simultaneously show enhanced cooling from aerosol‐cloud interactions (ACI). However, the interplay between ACI and cloud feedback is not well understood in these models. Our study addresses this gap by modifying the autoconversion rate in two Earth system models (ESMs), elucidating how ACI could connect with cloud feedback through the warm rain process. We find that higher autoconversion rates, which are associated with stronger ACI, suppress the increase in cloud liquid water path (LWP) and cloud optical depth with warming by enhancing the precipitation efficiency over the extratropical regions, leading to a larger positive cloud feedback. This study offers new insights into the compensatory mechanism between ACI and cloud feedback through the warm rain process and highlights the importance of constraining the autoconversion parameterization in models.

  PublisherDOI

• Correction to: Impact of Atlantic SST and high frequency atmospheric variability on the 1993 and 2008 Midwest floods: Regional climate model simulations of extreme climate events

  Climatic Change · 2026-02-16

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Author Correction: Central American mountains inhibit eastern North Pacific seasonal tropical cyclone activity

  Nature Communications · 2026-03-16

  articleOpen access
  PublisherOA PDFDOI

• Author Correction: Degree of simulated suppression of Atlantic tropical cyclones modulated by flavour of El Niño

  Nature Geoscience · 2025-12-09

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Weight Optimized Genetic Algorithm Driven Machine Learning Models for Robust Digital Video Watermarking Methods

  Journal of Machine and Computing · 2025-09-15

  article

  Video piracy is increasing due to the standard implementation of online streaming services and storage solutions, posing significant concerns about the security of multimedia content and Intellectual Property Rights (IPR). Digital Watermarking (DW) is a revolutionary technology that enables multimedia IPR by hiding and securing intellectual property from cyberattacks. DW is now recognized as the primary point of study for data verification and IPR security measures. Watermarks are hidden tags used to detect IPR crimes and authenticate data reliability. The Least Significant Bit (LSB) to DVW is proposed to enhance data source verification, thereby increasing the possibility of reducing Mean Square Error (MSE). A Genetic Algorithm (GA) is employed to mitigate the adverse effects of LSB while enhancing the Peak Signal-to-Noise Ratio (PSNR), a crucial metric of watermarking quality. This research work employs statistical methods and experiments to analyze the difficulty of computation, accuracy, resource utilization, speed, and endurance as metrics for performance. With PSNRs exceeding 45.19 dB, the method demonstrates robustness against background noise, filtering, and video encoding. With empirical findings from experiments demonstrating a 75% Normalized Cross-Correlation (NCC), 97.89% training accuracy, and 96.78% validation accuracy, the proposed method outperforms hiding and security methods in terms of accuracy.

  PublisherDOI

• Crop Yield Prediction using Machine Learning

  International Journal of Research Publication and Reviews · 2024-10-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• FACIAL EMOTION RECOGNITION SYSTEM

  INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024-05-21

  articleOpen access1st authorCorresponding

  Face Emotion recognition play a significance role in fields like aid, border management, police work, banking services, and client product. Facial expressions is wide utilized in social communication since they convey heaps of knowledge regarding folks, like moods, emotions, and alternative things. during this paper, we tend to review facial feeling recognition victimisation CNNs and highlight totally different algorithms and their performance impact. Further, we tend to demonstrate that utilizing CNNs during this field - ends up in a considerable performance increase. By forming associate ensemble of recent deep CNNs, we tend to get a FER2013 take a look at accuracy of 91.2%, outperforming previous works while not requiring auxiliary coaching knowledge or face registration. Key Words: Facial Expression, Confusion Matrix, Emotion Optimizer, Haarcascade classifier

  PublisherOA PDFDOI

• Metallo-Porous Organic Polymer as a CO₂ Reduction Catalyst toward Selective Solar Fuel Production

  Chemistry of Materials · 2024-06-14 · 16 citations

  article1st author

  In photocatalytic CO2 reduction for solar fuel production, selectivity and efficiency are crucial. Here, we report the design and synthesis of a donor–acceptor imine-based porous organic polymer (POP) Tpa-Phenda and a metallo-porous organic polymer (M-POP) Tpa-Phenda-Ru, by reacting tris(4-formylphenyl)amine (Tpa) and Phenda/[Ru(Phenda)(bpy)2]2+ (Phenda = 4,4′-(1,10-phenanthroline-3,8-diyl)dianiline; bpy = 2,2′-bipyridine) using acid-catalyzed Schiff base condensation reaction under solvothermal conditions. Here, the donor–acceptor dyads in both polymers harvested the visible light and transferred the photoexcited electrons to the active catalytic center, which is elucidated through in situ UV–vis spectroscopy. Both Tpa-Phenda and Tpa-Phenda-Ru produced CO in the acetonitrile–water medium using 1-benzyl-1,4-dihydronicotinamide (BNAH) and triethylamine (TEA) as sacrificial electron donors. Tpa-Phenda and Tpa-Phenda-Ru produced 0.92 and 9.77 mmol g–1 of CO, respectively. Tpa-Phenda-Ru exhibited a higher rate of CO formation and selectivity compared to bare Tpa-Phenda. This can be attributed to the presence of the coordinated RuII center in Tpa-Phenda-Ru, which acts as a catalytic site. Interestingly, Tpa-Phenda showed a low exciton binding energy (78 meV), which enhances the charge transfer efficiency and minimizes the energy loss. From an in situ diffuse reflectance FTIR spectroscopy (DRIFTS) study together with DFT calculation, a possible catalytic cycle for CO formation was constructed.

  PublisherDOI

• Motor Cycle Chain Sprocket Injuries of the Hand - Patterns, Classification and their Management - A Tertiary Care Experience

  International Journal of Science and Research (IJSR) · 2024-10-05 · 1 citations

  articleOpen access

  Introduction: Motor cycle chain sprocket injuries are common in frequency but has serious impact on the patients work capacity and daily activities. These type of hand injuries occur during cleaning or lubricating the motor cycle chain with the engine turned on. As a result, the fingers that get stuck undergoes serious injuries and most of the time amputation of the fingers occur. Aim: The aim of the present study is to analyse the characteristics of motorcycle chain injuries, & management. Materials and method: All patients who sustained chain sprocket injuries and treated at our institute between June 2022 and May 2023 (1 year) were taken for study. Totally 177 patients were included. All age groups and both the gender included. Data like age, gender, experience, hand dominance, injury patterns, treatment given, time taken to return to work were analysed. Results: Among 177 patients, 174 (98 %) were male. 51% of patients were between 21 to 30 years of age. Right hand injured in 127 patients (72%) and left hand in 50 patients (28 %). In our study, thumb is the most commonly injured finger 37%, followed by index finger 34%. Conclusion: Chain sprocket injuries cause serious trauma to fingers. Awareness among public should be made regarding the danger of chain sprocket injuries and importance of prevention of this type of injuries should be emphasized.

  PublisherOA PDFDOI

Awards & honors

• College of Geosciences Distinguished Research Award, Texas A…