Completed Research Projects
1. Assimilation of Ground Radar Data with WRF in an Information-Theoretic Framework
Funding Agency: Ministry of Earth Sciences (MoES)
Duration: 2020 – 2024
This project focused on integrating ground-based radar observations with the WRF model using an information-theoretic framework to improve rainfall estimation and forecasting. The work bridges numerical weather prediction with data-driven techniques by quantifying information exchange between radar and model outputs.
The project is strongly supported by our research on radar–satellite intercomparison, neural network-based rainfall retrieval, and information-theoretic evaluation methods .
Key Contributions
Development of radar–model assimilation strategies using information theory
Alignment and fusion of ground radar and satellite observations
Machine learning-based enhancement of rainfall retrieval algorithms
Outcomes
Improved rainfall prediction accuracy in high-impact weather events
Framework for integrating multi-source observations into WRF
Publications in radar meteorology and remote sensing
2. Mathematical Modelling for Parameter Estimation and Vibration Analysis
Funding Agency: Defense Research and Development Organization (DRDO)
Duration: 2020
This project involved the development of mathematical models for parameter estimation under varying signal-to-noise ratio (SNR) conditions. The work focused on estimating carrier frequency shifts and slant range using pseudo-noise (PN) sequences, with applications in radar and communication systems.
The modelling and simulation approaches align with our broader expertise in numerical modelling, signal analysis, and data-driven parameter estimation .
Key Contributions
Algorithm development for carrier frequency shift estimation under noisy conditions
Slant range estimation using PN sequence-based signal processing
MATLAB-based simulation framework for model validation
Outcomes
Robust estimation techniques for radar/communication systems
Enhanced understanding of signal behaviour under uncertainty
Foundation for later work in data assimilation and remote sensing
3. Model-Based Decision Support System for Sponge Iron Production Optimization
Funding Agency: Tata Sponge Iron Limited
Duration: 2018 – 2019
This project focused on developing a model-based decision support and control system to optimize sponge iron production processes and achieve target annual capacity. The work involved process modelling, system optimization, and real-time decision support.
This project reflects the application of mathematical modelling and control strategies in industrial systems, complementing later work in energy systems and thermodynamic analysis .
Key Contributions
Development of process models for industrial production systems
Decision-support framework for operational optimization
Integration of modelling with real-time process control
Outcomes
Improved production efficiency and process stability
Scalable framework for industrial optimization problems
Cross-disciplinary application of modelling techniques
4. Vetting and Proof Checking of Refrigeration Systems (3000 MT & 5000 MT)
Funding Agency: Engineering Projects India Ltd.
Duration: 2017
This project involved the verification and validation of cooling load calculations for large-scale cold storage facilities. The work required detailed thermodynamic analysis and evaluation of refrigeration system performance.
The project aligns with research contributions in heat transfer, thermodynamics, and energy systems modelling .
Key Contributions
Validation of cooling load calculations for industrial refrigeration systems
Thermodynamic analysis of large-scale cold storage design
Assessment of system efficiency and operational feasibility
Outcomes
Reliable verification of refrigeration system design
Improved confidence in large-scale cooling infrastructure
Practical application of thermal engineering principles
5. Estimation of Radiation Interaction Parameters Using Numerical Models and Observations
Funding Agency: Indian Institute of Technology Bhubaneswar
Duration: 2015 – 2017
This project focused on estimating radiation interaction parameters through the integration of numerical models and observational data. The work involved radiative transfer modelling and analysis of atmospheric processes.
This research is closely linked to published work on radiative transfer, microwave remote sensing, and neural network-based atmospheric retrieval techniques .
Key Contributions
Application of radiative transfer models for atmospheric simulations and analysis.
Integration of observational data with numerical simulations
Use of machine learning for fast radiative transfer computations
Outcomes
Improved understanding of radiation–atmosphere interactions
Contributions to satellite-based atmospheric retrieval methods
Foundation for subsequent work in remote sensing and climate modelling
Summary of Expertise Developed
Across these projects, the lab has developed strong capabilities in:
Numerical weather prediction and data assimilation
Radar and satellite-based remote sensing
Machine learning applications in atmospheric science
Thermodynamics and energy systems modelling
Industrial process modelling and optimization
These completed projects form the foundation for our ongoing research in AI-driven weather forecasting, urban climate dynamics, and extreme event prediction.