Atmospheric & Climate Data Specialist | Geospatial Analytics | Hazard & Risk Modeling
I’m an atmospheric and climate data specialist with 4+ years of experience transforming complex environmental datasets into actionable insights for natural hazard forecasting, climate resilience, and infrastructure risk modeling. My work spans reanalysis data pipelines, numerical modeling, geospatial analytics, and automation, with applications across wind energy, extreme weather, and climate risk assessment.
- 🌪️ Natural Hazard Modeling – Cyclone & extreme wind modeling, hazard-specific simulations
- 🌍 Climate & Reanalysis Data – ERA5, ECMWF, GFS, NetCDF/GRIB pipelines
- 🗺️ Geospatial & Remote Sensing – GIS workflows, satellite & radar interpretation
- ⚙️ Automation & APIs – Scalable data ingestion, processing, and delivery systems
- 📊 Forecast & Risk Analysis – Bias correction, downscaling, and predictive insights
Programming & Data
- Python, R (Advanced), MATLAB (Intermediate), SQL
Climate & Atmospheric Systems
- NetCDF, HDF5, GRIB, Zarr
- METAR, BUFKIT, GFS, ECMWF
- Numerical Weather Prediction (WRF), HYSPLIT
GIS & Remote Sensing
- ArcGIS, QGIS, GDAL, CDO, NCO
Modeling & Analytics
- Statistical modeling & downscaling
- ML-based bias correction (LSTM, CNN)
- Windographer
Visualization
- Plotly, Leaflet, ggplot2
DevOps & APIs
- Git, Docker, Google Cloud CI/CD
- Swagger, Postman, REST APIs, Plumber
Automated reanalysis data acquisition and preprocessing for wind energy projects, reducing processing time by 75% and eliminating 95% manual effort.
Numerical model for cyclone/typhoon-induced wind profiles to support wind farm risk assessment.
R Shiny application converting raw CSV sensor data into standardized NetCDF formats for improved interoperability.
Improved forecast precision by 18% using deep learning–based bias correction and spatial downscaling techniques.
- M.Sc. Atmosphere & Ocean Sciences – IIT Bhubaneswar
- B.Sc. Geology – St. Xavier’s College, Ranchi
- Extreme rainfall variability & intraseasonal dynamics
- Kalman filtering (HPC project)
- Field work at IMD, NARL, and INCOIS
Focused on building scientifically sound, production-ready climate intelligence systems for real-world decision-making.