Hey,

About Me
I have been improving myself to think like an engineer and observe like an atmospheric scientist, which has led me to develop new ideas to assist Climate Science. One of those ideas was to create a website, 𝐂𝐥𝐢𝐦𝐚𝐭𝐮𝐫𝐤, that reveals the climate conditions of atmospheric variables in Turkey, using comprehensive statistical and thermodynamic methods. I processed the state-of-art climate data utilizing various data types (NETCDF, HDF5, GRIB, CSV) with popular Python libraries (Xarray, Numpy, Pandas, Salem, Matplotlib) and visualized it to the end-user in very engaging ways. 

I developed a Python library called 𝐕𝐢𝐬𝐣𝐨𝐛𝐬 (https://pypi.org/project/visjobs/) (downloaded over 𝟏8.𝟎𝟎𝟎 times according to pepy.tech) that makes it easy for atmospheric scientists to access model and observation data (ERA5, GFS, GEFS, NAM, GHCN) without even having to download them (OpenDAP).

Currently
In my MSc project, I am applying a non-stationary extreme value analysis to high-resolution future projections of extreme precipitation indices. During the research, I become familiar with the concept of downscaling (of COSMO-CLM model) and statistical modeling. Further, I gained extensive practical experience using Xarray, Pandas, and Metpy to analyze various data formats (netCDF, GRIB, HDF) and types (reanalysis, projections, satellite data).

Interested In
- Python | (e.g. Numpy, Pandas, Xarray, Sklearn, Keras, Matplotlib, Metpy)
- Data Science | (Weather/Climate Data Science/Analytics)
- Climate Science | (Climate Modelling using COSMO)
- Machine Learning | (Time Series Modelling)
- WRF Model | (Weather Modelling, Case Studies)
- Visualization | (e.g. Matplotlib, Plotly)
- Renewable Energy | (Wind, Solar Power Data Science/Analytics)
- Dashboards | (e.g. Plotly-Dash, Streamlit)