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naufaljouhary

Naufal Jouhary

@naufaljouhary

Climate Data Analyst and Risk Assessment

Indonésie
Indonésien, Anglais, Japonais
Certaines informations sont présentées en anglais.
À propos de moi
Climate Data Analyst; M.Sc. Applied Climatology. Specializing in high-resolution renewable energy projections and climate risk assessment using RegCM, CMIP6, ERA5, Python, CDO & HPC. Key expertise: • High-Resolution Wind Energy Projections (Q1 publication): Hybrid CMIP6 downscaling for Southern Indonesia. • Wind-Solar Hybrid Systems & Intermittency (Thesis): Energy drought & complementarity analysis for grid resilience. • SOLIS: Climate data based financial modeling for household solar investments. Open to collaborations with climate-tech & renewable energy practitioners.... Plus d’infos

Compétences

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naufaljouhary
Naufal Jouhary
hors ligne • 

Voir mes services

Graphiques & Diagrammes
I will translate climate data into actionable insights
De25 $US / projetPlus de détails

Portfolio

Expérience professionnelle

BRIN_RI

BRIN RI

Temps partiel • 1 yr 8 mos

Research Assistant

Jun 2025 - May 2026 • 11 mos

1. Executed high-resolution (25 km) RegCM dynamical downscaling simulations at an intensive 1-hour temporal frequency across Western Indonesia via High-Performance Computing (HPC) environments to project future wind and wave energy potential. 2. Managed a high-throughput data generation pipeline, orchestrating over 7 Terabytes of raw SRF output data (spanning 168 simulated years) into optimized, actionable datasets using Python and CDO for a Kemenristekdikti funded project. 3. Delivered data-driven insights to formulate a comprehensive marine renewable energy atlas, directly guiding strategic investments in Indonesia's green energy sector.

Research Intern

Oct 2024 - Jul 2025 • 9 mos

1. Developed a high-resolution (25 km, 6-hourly) wind energy projection framework for the Java to Nusa Tenggara region by downscaling EC-Earth3 global historic data. 2. Engineered an efficient data reduction pipeline, successfully compressing and optimizing massive raw GCM inputs into lightweight, JupyterLab-ready datasets for advanced geospatial visualizations. 3. Translated complex atmospheric data into actionable renewable energy metrics, building the foundational data architecture that culminated in a Q1 peer-reviewed publication.