INTRODUCTION

Dr. Benjamin Marchant Research Scientist SUMMARY I have 20 years of experience in atmospheric remote sensing, with 12 years at NASA Goddard and 1 year at NOAA Center for Weather and Climate Prediction (NCWCP). While working at NASA, I spearheaded the development of the cloud thermodynamic phase product for both MODIS and VIIRS. This particular product plays a crucial role in the NASA cloud optical products. In 2022, I became a part of NOAA, where I actively contribute to the development and enhancement of fire-related products. My responsibilities involve evaluating the effectiveness of existing products and constantly exploring innovative ways to improve them. Throughout my years at NOAA and NASA, I gained valuable experience collaborating within a team, constantly learning new skills in data analysis and visualization to enhance our product. ORCID

EDUCATION

• 2020: Postgraduate Diploma in Machine Learning and Artificial Intelligence from Columbia University (Emeritus Program).
• 2009: PhD Thesis in Atmospheric Science at the "Laboratoire d’Optique Atmosphérique" (LOA), Université Lille 1, France: Exploring the optical characteristics of cirrus ice crystals and investigating the impact of vertical variations in ice crystal size distribution on the radiative properties of cirrus clouds.

PROFESSIONAL EXPERIENCE

2022-present: IMSG Research Scientist at NOAA/NESDIS/STAR, College Park, MD. I am responsible for evaluating and enhancing the NOAA VIIRS Active Fire product, a crucial component in the improvement of NOAA emissions products, specifically the Global Biomass Burning Emissions Product (GBBEPx). This year, I have developed an algorithm that effectively detects persistent fire anomalies on solar panels using VIIRS. At the Eumetsat conference in Sweden, September 2023, I shared compelling evidence showcasing the substantial influence it has on accurately estimating fire radiative power. I have also contributed to the evaluation of the NOAA Active Fire product by comparing it with airborne campaigns like FIREX-AQ MASTER. 2010-2022: USRA Research Scientist at NASA Goddard. I have overseen the development of NASA MODIS Collection 6 Cloud Thermodynamic Phase, a crucial component of the Cloud Optical Product. I have evaluated the product by comparing it with CALIOP and CLOUDSAT. Consequently, the cloud phase algorithm has been implemented as an operational product.

TECHNICAL EXPERTISE

I have developed and continue to refine my technical skills in areas such as data analysis and data visualization. To accomplish my tasks, I have become proficient in the Python programming language and various Python libraries such as Matplotlib, Cartopy, Pandas, and Geopandas. Additionally, I have gained expertise in machine learning libraries like TensorFlow and Scikit-Learn.

CERTIFICATIONS

• Big-O Time Complexity in Python Code
• Convolutional Neural Networks
• Deep Learning Specialization
• Sequence Models
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Neural Networks and Deep Learning
• Structuring Machine Learning Projects

HONORS, AWARDS & RECOGNITIONS

• 2017: Best First-Authored Paper: "For his paper documenting significant improvement to the MODIS phase algorithm, an essential first step in obtaining useful cloud optical property retrievals.”
• 2014: Outstanding technical support/achievement: For sustained effort leading to successful delivery of the MODIS Collection 6 algorithms and codes producing improved Level 2 and Level 3 aerosol and cloud products.
• 2017: Co-I of two NASA proposals (Program element A.37, the Science of TERRA, AQUA, and SUOMI NPP)

PUBLICATIONS/PRESENTATIONS/REPORTS

• Platnick S, Meyer K, Wind G, Holz RE, Amarasinghe N, Hubanks PA, Marchant B, Dutcher S, Veglio P. The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products. Remote Sensing. 2021; 13(1):2.
• Marchant, B., Platnick, S., Meyer, K., & Wind, G. (2020). Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products. Atmospheric Measurement Techniques, 13(6), 3263–3275.
• Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., & Riedi, J. (2017). The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua. IEEE Transactions on Geoscience and Remote Sensing, 55(1), 502–525.
• Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., & Riedi, J. (2016). MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP. Atmospheric Measurement Techniques, 9(4), 1587–1599. https://doi.org/10.5194/amt-9-1587-2016