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Yes, my dream has come true. I wanted to become a “doctor” and help people, but now I have a “doctorate” and help the country by working on early warning systems for severe weather disasters.

We cannot stop natural disaster, but we can prepare ourselves with knowledge. I am Dr. Siva Saikrishna Tirumani, an Operational Researcher at the Centre for Development of Advanced Computing (C-DAC), I specialize in Urban Modeling. My role involves daily monitoring of operational outputs and delivering critical alerts and warnings to local authorities, including municipalities and disaster management agencies. Earning my Ph.D. has been a life-changing experience, enhanced my problem-solving and analytical skills. It also provided the opportunity to connect with a wide range of researchers, strengthen both personally and professionally.

As a research scholar, I worked on wide range of seasonal prediction models. We have studied the model predictability of the Indian Summer Monsoon and it’s behaviour in the models. I have gained valuable knowledge working with satellite data, hindcast outputs, and significant knowledge in WRF model at seasonal to local-scale.

At C-DAC, my role helped me to learn about the forecasting weather extremes, and engagement with the subject experts. I have made a setup for operational forecast system for short-range forecast system for the pilot cities of India. Additionally, I have mentored interns from various institutes on weather modeling.

Over the past few years, my journey has been one of continuous learning and development, driving my commitment to improving disaster preparedness and response through advanced research and technology.

Saikrishna's Projects

container-wrf icon container-wrf

WRF in containers, related code and data sets for release purposes.

docker-ncl icon docker-ncl

Centos7 with NCL installed for WRF post-processing

era5-reanalysis_plots icon era5-reanalysis_plots

Visualizing ECMWF's ERA5 Reanalysis from both Single and Pressure Levels on hourly basis (These notebooks can be used, accordingly, based on your preference).

hrldas icon hrldas

HRLDAS (High Resolution Land Data Assimilation System)

ncl-scripts-for-wrf icon ncl-scripts-for-wrf

This repository includes NCL scripts that can be used to post-processing WRF outs, including but not limited to spatial plots, write WRF outputs to csv files, and time-height plots. Please feel free to contact Xia Sun ([email protected]) if you have any questions. I would happy to help.

pywrf icon pywrf

pyWRF is designed to read, process, and plot data from the Weather Research and Forecasting model.

rainfall-prediction-for-the-state-of-gujarat-using-deep-learning-technique icon rainfall-prediction-for-the-state-of-gujarat-using-deep-learning-technique

Prediction of rainfall which varies both spatially and temporally is extremely challenging. Infrared and visible spectral data from satellites have been extensively used for rainfall prediction. In this study, two deep learning methods MLP and LSTM are discussed at length for predicting precipitation at a fine spatial (10km × 10km) and temporal (hourly) resolution for the state of Gujarat. These methods are applied by using the multispectral (VIS, SWIR, MIR, WV, TIR1, TIR2) channel data such as cloud top temperature and radiance values of the INSAT-3D satellite (ISRO) as features for the model. Textural features of satellite images are incorporated by considering mean and standard deviation of each pixel’s neighbourhood. Rainfall also heavily depends on the elevation and vegetation of earth’s surface so we have used SRTM DEM and AWIFS NDVI respectively. Measurements of actual rainfall are obtained from AWS (point source stations) and TRMM (10km × 10km resolution). First dataset contains only TIR1 band temperature and AWS rainfall data for training but the second dataset includes multispectral channel data and TRMM rainfall data which brought about great improvement in results. For each data- set, a comparison between MLP and LSTM models is discussed here. We were able to classify the rainfall into nil (0mm), low ( < 2mm), medium ( > = 2mm and < 5mm) and high ( > = 5 mm) with a high accuracy. Metrics like accuracy, precision, recall and fscore have been computed to get better insights about the dataset and its corresponding outcome. Our results show that LSTM performs significantly better than MLP for any given balanced class data-sets.

ssk351 icon ssk351

Config files for my GitHub profile.

wrf icon wrf

will give the namelist files

wrfda_git icon wrfda_git

WRFDA website http://www2.mmm.ucar.edu/wrf/users/wrfda/

wrfda_tools icon wrfda_tools

A repository for graphics and other scripting tools for WRFDA. These are offered AS IS and support may not be available.

wrflib_instsh icon wrflib_instsh

Shell scripts for installing libraries in order to compile the WRF model and additional apps.

wrfv3 icon wrfv3

This is the release repository for the Weather Research and Forecasting Model

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