Deepak Singh Bisht
National Centre for Medium Range Weather Forecasting, National Atmospheric Research Laboratory, Malviya national Institute of Technology
Research areas: Physics and Astronomy · Astronomy and Astrophysics · Ionosphere and magnetosphere dynamics · GNSS positioning and interference · Agricultural Productivity and Crop Improvement
0000-0002-3027-0779
About
As a Project Scientist at NCMRWF, my primary responsibility is to develop and implement advanced AI and ML techniques for post-processing weather and climate data to improve forecasting accuracy. I work with the UM global model is a state-of-the-art numerical weather prediction model used for medium-range weather forecasting. I also work closely with the data acquisition and quality control team to ensure data consistency and quality. This involves identifying and addressing data quality issues and ensuring that data is collected and processed in a way that is consistent with best practices.
Employment
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National Centre for Medium Range Weather Forecasting Project Scientist2023 - Present
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National Atmospheric Research Laboratory Senior Research Fellow2017 - Present
Education
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Malviya national Institute of Technology Post Graduation2015 - 2017
Projects & Funding
Projects & funding information is unavailable.
Publications (7)
- A Dual Machine‐Learning Framework for Predicting Phase Stability and Mixing Enthalpy Difference in MSi2‐Type High‐Entropy Refractory Metal Silicides Save
- Deep learning-driven mapping of pre-monsoon features for Indian summer monsoon precipitation forecasting Save
- Deep Learning-Driven Mapping of Pre-Monsoon features for Indian Summer Monsoon Precipitation Forecasting Save
- Bias correction of satellite precipitation estimates using Mumbai-MESONET observations: A Random Forest approach Save
- Prediction of lightning events over Bangladesh: A machine learning perspective Save
- Nowcasting of Storms Using Predicted Integrated Water Vapor With a Machine Learning Technique and Satellite Brightness Temperature Save
- Prediction of Integrated Water Vapor Using a Machine Learning Technique Save