| DELA CRUZ NOFEL LAGROSAS | Last modified date：2024.04.20 |
Associate Professor /
Faculty of Engineering
Undergraduate School
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Homepage
https://kyushu-u.elsevierpure.com/en/persons/nofel-lagrosas
Reseacher Profiling Tool Kyushu University Pure
Academic Degree
Ph.D. in Artificial Science and Systems (Electronics and Photonics Systems) Chiba University, Japan
Country of degree conferring institution (Overseas)
Yes Bachelor Master
Field of Specialization
Remote sensing, Physics, Aerosols, Clouds
ORCID(Open Researcher and Contributor ID)
https://orcid.org/0000-0002-8672-4717
Total Priod of education and research career in the foreign country
18years00months
Research
Research Interests
Membership in Academic Society
- Advancing Understanding of Aerosols and Cloud Dynamics through Remote Sensing: A Multifaceted Approach
In recent decades, the interplay between aerosols and clouds has emerged as a critical yet complex aspect of Earth's climate system. Aerosols, comprising particles suspended in the atmosphere, influence cloud formation, microphysical properties, and radiative processes. Understanding these interactions is crucial for accurate climate modeling, weather forecasting, and assessing climate change impacts. Remote sensing technologies offer a unique vantage point to investigate these intricate relationships, providing comprehensive data on aerosol properties, cloud dynamics, and their spatiotemporal variability.
The research theme aims to explore and advance our understanding of aerosol-cloud interactions through ground and spaced-based remote sensing techniques, involving various disciplines and methodologies. The key research avenues include:
- Aerosol characterization: Utilizing remote sensing instruments such as lidar, satellite-based imagers (e.g., MODIS, VIIRS, Himawari), and ground-based sensors to characterize aerosol properties such as composition, size distribution, vertical distribution, and optical properties.
- Cloud detection, temporal properties and formation: Investigating how aerosols impact cloud formation, structure, and evolution using remote sensing observations. Analyzing cloud microphysical properties (e.g., cloud droplet size, concentration) and macro-physical characteristics (e.g., cloud fraction, altitude) to understand aerosol-cloud interactions across different spatial and temporal scales.
- Radiative forcing and climate implications: Assessing the radiative effects of aerosols on clouds and the Earth's energy budget using remote sensing-derived data. Quantifying aerosol indirect effects on cloud albedo, precipitation patterns, and atmospheric heating/cooling processes. Understanding the implications of aerosol-cloud interactions for regional climate variability, extreme weather events, and climate change projections.
- Model-data Integration and validation: This is a long-term main goal. Integrating remote sensing observations into numerical models to improve the representation of aerosol-cloud processes and constrain model uncertainties. Employing data assimilation techniques to assimilate remote sensing data into model simulations for real-time monitoring and forecasting of aerosol-cloud interactions.
- Long-term Trends and climate Feedbacks: This is also another long-term main goal. Investigating long-term trends in aerosol and cloud properties using remote sensing archives to assess their role in climate variability and change. Identifying feedback mechanisms between aerosols, clouds, and the broader climate system, including feedback loops amplifying or mitigating climate warming.
- Cross-cutting research themes: Exploring interdisciplinary research themes such as air quality, human health impacts, ecosystem dynamics, and socioeconomic implications of aerosol-cloud interactions, integrating remote sensing with ground-based measurements, modeling, and socio-economic data.
By addressing these research themes and using an interdisciplinary research approach, I hope to advance our understanding of aerosol-cloud interactions, enhance the accuracy of climate models, and inform climate mitigation and adaptation strategies in a changing world.
keyword : aerosols, clouds, remote sensing, cameras, satellites
2005.04～2034.04.
- Japan Society of Applied Physics
- Remote Sensing Society of Japan
- American Geophysical Union (AGU)
Educational
Educational Activities
Courses:
Complex function theory, ordinary differential equations (ODEs), and Fourier analysis:
Some educational activities:
Complex Function Theory
Visual Demonstrations: Use graphing software or interactive tools to demonstrate the behavior of complex functions, including mapping, singularities, and contour integrals.
Mapping Exercises: Have students work on mapping exercises to understand the geometric transformations of complex functions, emphasizing concepts like conformal mapping.
Applications in Engineering: Explore applications of complex analysis in engineering fields, such as fluid dynamics, electrical engineering, and signal processing.
Ordinary Differential Equations (ODEs):
Modeling Projects: Assign problems where students must model real-world phenomena using ODEs, such as population growth, chemical reactions, or mechanical systems.
Numerical Solutions: Introduce numerical methods for solving ODEs and have students implement these methods in programming languages like MATLAB or Python.
Phase Plane Analysis: Engage students with phase plane analysis to visualize and analyze the behavior of ODEs, including stability and bifurcations.
Fourier Analysis:
Signal Processing Simulations: Use software tools to simulate signal processing applications of Fourier analysis, such as filtering, compression, and modulation/demodulation.
Interactive Demonstrations: Conduct interactive demonstrations to show the Fourier series representation of different functions and the concept of frequency analysis.
Applications in Image Processing: Explore how Fourier analysis is used in image processing for tasks like image filtering and compression.
Complex function theory, ordinary differential equations (ODEs), and Fourier analysis:
Some educational activities:
Complex Function Theory
Visual Demonstrations: Use graphing software or interactive tools to demonstrate the behavior of complex functions, including mapping, singularities, and contour integrals.
Mapping Exercises: Have students work on mapping exercises to understand the geometric transformations of complex functions, emphasizing concepts like conformal mapping.
Applications in Engineering: Explore applications of complex analysis in engineering fields, such as fluid dynamics, electrical engineering, and signal processing.
Ordinary Differential Equations (ODEs):
Modeling Projects: Assign problems where students must model real-world phenomena using ODEs, such as population growth, chemical reactions, or mechanical systems.
Numerical Solutions: Introduce numerical methods for solving ODEs and have students implement these methods in programming languages like MATLAB or Python.
Phase Plane Analysis: Engage students with phase plane analysis to visualize and analyze the behavior of ODEs, including stability and bifurcations.
Fourier Analysis:
Signal Processing Simulations: Use software tools to simulate signal processing applications of Fourier analysis, such as filtering, compression, and modulation/demodulation.
Interactive Demonstrations: Conduct interactive demonstrations to show the Fourier series representation of different functions and the concept of frequency analysis.
Applications in Image Processing: Explore how Fourier analysis is used in image processing for tasks like image filtering and compression.
Social
Professional and Outreach Activities
The collaborative research on the detection of nighttime clouds using cameras is led by me, with the participation of scientists and research institutions. The collaboration aims to gather data on nighttime clouds and develop a robust method for detecting nighttime clouds with the collaboration of other collaborators. The framework of the collaboration includes the identification of collaborators with expertise in atmospheric science, remote sensing, and image processing. Collaborators are requested to gather data at their sites and share the data to Kyushu University for storage and analysis. Data sharing and integration are central to the collaboration. Methodologies for camera deployment, image acquisition, and data processing are standardized across collaborators to ensure consistency. Collaborators can develop bettter detection algorithms and sharing codes and methodologies to improve detection capabilities. The collaborative analysis of results includes comparing outcomes from different sites and algorithms, interpreting findings, and preparing joint publications and presentations. Future directions for the collaboration include continuing collaborative efforts, capacity building, and training opportunities to enhance research capabilities. The collaborative research, initiated by me, aims to make significant contributions to atmospheric science and remote sensing, with potential applications in weather forecasting, climate studies, and environmental monitoring..
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