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The Modelling and Simulation Lab (MASLAB) is equipped with state-of-the-art observational instruments and advanced computational infrastructure to support teaching, research, and sponsored projects in atmospheric sciences, climate studies, extreme weather, and planetary atmospheres. The facilities enable data acquisition, numerical modelling, and data-driven analysis, aligning with national research priorities and institutional academic objectives.
The facilities support research scholars, project staff, and collaborators working on weather, climate, extreme events, urban meteorology, tropical cyclones, and planetary atmospheric science.
Atmospheric Observation Facilities
The sun photometer facility is used for continuous monitoring of atmospheric aerosols. It provides high-quality measurements of aerosol optical depth (AOD) and related parameters, enabling studies on aerosol loading, aerosol–radiation interaction, and aerosol–cloud–precipitation processes. The observations support validation of satellite products and numerical model simulations.
The Automatic Weather Station installed under IMD standards provides real-time meteorological observations, including temperature, humidity, wind speed and direction, rainfall, and surface pressure. These data are crucial for:
Local weather analysis
Urban microclimate studies
Model validation and bias assessment
Extreme weather event monitoring
The carbon microsthelometer is used for measuring black carbon and light-absorbing aerosols in the atmosphere. This facility supports research on:
Aerosol–radiation interaction
Urban air quality
Aerosol impacts on cloud microphysics and precipitation
Climate forcing due to absorbing aerosols
The GPS facility supports atmospheric and geospatial applications, including:
Precise location and surveying measurements
Studies related to atmospheric water vapour and boundary layer processes
Integration with meteorological and geospatial datasets
Computational & Modelling Infrastructure
The lab hosts a dedicated rack server for high-resolution numerical simulations and large-scale data processing. It is extensively used for:
Numerical Weather Prediction (NWP)
Climate and regional modelling
Long-duration simulations
Large dataset handling and storage
MASLAB maintains multiple high-end Linux workstations for:
Model development and testing
Data analysis and visualization
Pre- and post-processing of WRF, MPAS, and planetary models
Satellite and reanalysis data analysis
GPU-enabled systems are available to support machine learning and deep learning applications, including:
Rainfall and extreme weather prediction using DL models
Bias correction and downscaling
Hybrid NWP + ML/DL frameworks
Training of neural networks on large atmospheric datasets