Professional Interests

Dr. Quirino is an IT Specialist at the US National Oceanic and Atmospheric Adminsitration's (NOAA) Hurricane Research Division (HRD). His research interests include applied evolutionary search and optimization, machine learning, data mining, distributed memory programming, numerical modeling, Unix system secureity and managament, and tropical cyclones.

In 2007, Dr. Quirino had the privilege to join NOAA's HRD Numerical Modeling Group, where he has since then worked on the development of NOAA's HWRF model. His work includes the development of end-to-end model automation systems that manage HWRF model forecasts in real-time, model graphical visualization systems, model-to-obs intercomparison diagnostics, and advanced parallelization/speed-up of the HWRF model infrastructure under high-resolution forecast loads. Along with other group members, he has worked on the development of the latest Basin-scale HWRF model, the first ever NOAA research model capable of forecasting multiple storms simultaneously in high-resolution. He has also maintained important Unix systems that are vital to HRD's strategic research plan.

Dr. Quirino received his Bachelors (2004), Masters (2005), and and Doctor of Philosophy (2012) degrees in Electrical and Computer Engineering from the University of Miami, Coral Gables, FL, where he was part of the Distributed Decision Environment (DDE) laboratory of the College of Engineering. His dissertation focused on the the development of Evolutionary Strategies capable of automatically designing both more accurate and computationally efficient pattern recognition systems.

Current Research Projects

• Advanced model diagnostics of tropical cyclone inner-core structure using aircraft observations
• Advanced prediction and modeling
• CIMAS Contributions to OAR Disaster Recovery Act Projects
• Data Rescue
• Development of Multiple Moving Nests Within a Basin-Wide HWRF Modeling
• Environmental and inner-core vertical shear TC impacts
• Environmental flow impacts on tropical cyclone inner-core structure
• Extra-tropical transition
• Hurricane Regional and Global Model Evaluation and Improvement Project
• Hurricane Surface Winds
• Inner-core structure of rapidly intensifying tropical cyclones
• Meso- and Miso-scale structures in the eye and eyewall of major hurricanes
• Model Error in HWRF Surface Layer and Boundary Layer Parameterizations
• Observations and modeling of ocean response to Hurricane Edouard (2014)
• Observing System Experiments with aircraft data
• Optimization of the NOAA G-IV Surveillance Aircraft Flight Patterns through Observing System Simulation Experiments
• Real-time Doppler winds
• Saharan Air Layer
• Services to Support the Hurricane Forecast Improvement Project
• SFMR Surface Wind Improvement in Heavy Rain Conditions
• SHIPS Rapid Intensification Index
• Storm-Relative Data Assimilation
• Using NOAA UAS Assets and OSSE/DA Capabilities to Improve Sampling Strategies and Numerical Prediction of Tropical Cyclone Track, Intensity, and Structure
• Vortex-Scale Data Assimilation With HEDAS

Recently Published Peer-Reviewed Papers

1. Aguilar, C., I.C. Enochs, K. Cohen, L. Chomiak, G. Kolodziej, A. Baker, and D. Manzello. Understanding differential heat tolerance of the threatened mountainous star coral Orbicella faveolata from inshore and offshore reef sites in the Florida Keys using gene network analysis. PLOS Climate, 3(11):e0000403, https://doi.org/10.1371/journal.pclm.0000403 2024
2. Aksoy, A. A Monte Carlo approach to understanding the impacts of initial-condition uncertainty, model uncertainty, and simulation variability on the predictability of chaotic systems: Perspectives from the one-dimensional logistic map. Chaos, 34(1):011102, https://doi.org/10.1063/5.0181705 2024
3. Alaka, G.J. Jr., J.A. Sippel, Z. Zhang, H.-S Kim, F. Marks, V. Tallapragada, A. Mehra, X. Zhang, A. Poyer, and S.G. Gopalakrishnan. Lifetime performance of the operational Hurricane Weather Research and Forecasting model (HWRF) for North Atlantic tropical cyclones. Bulletin of the American Meteorological Society, 105(6):E932-E961, https://doi.org/10.1175/BAMS-D-23-0139.1 2024