CUNY CREST Doctoral student in Civil Engineering at the City College of New York, Mr. Said A Mejia Manrique successfully defended his dissertation on December 6, 2024. Mr. Manrique conducted research on Deep Learning Architectures for Improving Weather Research and Forecasting (WRF) Precipitation Estimates and Landslide Susceptibility Mapping: A Case Study of Puerto Rico under the mentorship of CREST Executive Director, Dr. Reza Khanbilvardi. Elated on the achievement, Mr. Manrique said, “Completing my Ph.D. has been a transformative journey, filled with challenges, resilience, and personal growth. Successfully defending my dissertation is not only an academic achievement but also a significant personal milestone. This experience has strengthened my belief in the importance of perseverance and the pursuit of knowledge to tackle real-world challenges in weather prediction and hazard mitigation.”
Mr. Manrique’s research aimed to enhance the accuracy of precipitation estimates from the WRF model using deep learning techniques and develop a machine learning-based approach for landslide susceptibility mapping, integrating improved precipitation predictions with environmental and soil conditions, and landscape information. He said, “My research contributes to the broader effort of improving hazard prediction models, directly impacting communities vulnerable to extreme weather events and the deep learning model developed in this research has the potential to be adapted for other regions beyond Puerto Rico, expanding its applicability in global climate resilience efforts.”
Mr. Manrique carries valuable lessons from his Ph.D. journey. He said, “I learnt that research is an iterative process; failure and setbacks are part of the journey of discovery, and persistence is essential. And it is also crucial to balance academic work with personal well-being.”
CREST has played a vital role in Mr. Manrique’s Ph.D. journey by providing him with a supportive research environment, mentorship, funding, and several opportunities to excel. He said, “CREST-led projects allowed me to apply research in real-world applications, particularly in hazard risk assessment for Puerto Rico and guidance from Dr. Reza Khanbvilardi and other faculty members were instrumental in refining my research objectives and methodology.”
During his Ph.D., Mr. Manrique actively engaged in mentoring and teaching across various programs that helped him grow. As part of the CUNY CREST HIRES program, led by Dr. Shakila Merchant, Mr. Manrique taught Python programming to high school students, focusing on data analysis, visualization, and solving real-world problems. Additionally, he served as a teaching assistant (TA) for multiple courses in the Civil Engineering Department, including a Data Analysis course, where he guided undergraduate and graduate students in statistical methods, programming, and data-driven decision-making. Mr. Manrique also assisted students in hydrology and environmental modeling courses, helping them understand complex concepts and apply analytical techniques effectively. These experiences allowed Mr. Manrique to develop his teaching skills while fostering the next generation of researchers in the field.
After successfully defending his dissertation, Mr. Manrique embarked on a new journey by joining CCNY as a postdoctoral fellow on January 2, 2025. He will conduct research on GSX Science Collaboration and Establishing a GXS Baseline Retrieval Algorithm. CREST Research Scientist and NOAA EPP/MSI CESSRST II Distinguished Research Scientist, Dr. Mitch Goldberg serves as Mr. Manrique’s primary supervisor while Dr. Reza Khanbilvardi and NOAA GEOXO program Scientists including Dr. Andrew Heidinger are co-collaborators.
Mr. Manrique’s research aims to develop a baseline retrieval algorithm for the Geostationary Extended Observations (GeoXO) Hyperspectral Infrared Sounder (GXS), which is expected to launch in the 2030s. It will focus on improving atmospheric temperature, water vapor, and wind retrievals, which will enhance nowcasting, short-term forecasting, and climate monitoring applications. The project will leverage advanced radiative transfer models to refine atmospheric data retrieval techniques and the research will contribute to disaster preparedness by improving real-time weather monitoring. He said, “The study includes mentorship opportunities for graduate students in atmospheric retrieval and precipitation analysis.”
Excited about the journey, Mr. Manrique commented, “I am looking forward to contributing to the next generation of atmospheric remote sensing technology. Working on the GXS retrieval algorithm will allow me to engage with cutting-edge AI techniques and collaborate with NOAA and CUNY scientists. Additionally, the mentorship aspect of my role is particularly exciting, as it will help train the next wave of researchers in atmospheric science and remote sensing.” Mr. Manrique wants to strengthen CCNY’s role in atmospheric remote sensing by developing algorithms that make weather predictions more accurate.
In his postdoctoral journey, Mr. Manrique will continue contributing to CREST’s mission of advancing Earth system science through innovative research and capacity building by training graduate students in remote sensing applications, in addition to hydraulic and hydrologic applications, participating in interdisciplinary research groups, and collaborating with researchers on atmospheric retrieval and AI model development.
CREST wishes Mr. Manrique success in his academic and professional growth!
