The code in this repository formed part of an exercise in exploring meteorological daa. I cleaned and explored a large dataset, millions of observations across five variables, from the Indian Meteorological Department. The individual datasets making up the larger dataset contained grid-point data on daily rainfall/temperature from 2009-2013. Each grid point came from a 1◦(latitude) by 1◦(longitude) grid covering the Indian subcontinent.
The primary sub-task of the data cleaning step involved collapsing the grid-level dataset into a district-level dataset.
I implemented an algorithim to match each grid point to a district. The algorithm to match each grid point to a district was as follows:
- Take a weighted average of daily mean temperature, daily mean rainfall, and daily total rainfall for all grid points within 100 KM of each district’s geographic center.
- The weights are the inverse of the squared distance from the district's center.
- A district crosswalk dataset had data on the district centroids for the five Indian states I was expected to focus on.
To determine which grid points lay within 100 KM of each district center, I also had to calculate the distance between every grid point and every centroid.
The primary sub-task of the data exploration step involved using the district-level dataset from the previous step to document the monsoon season in various districts.
To that end for each district and state, I
- Calculated average and total daily rainfall over the five-year period
- Calculated average daily temperature over the five-year period
- Estimated the monsoon season's start and end dates
- Concisely presented my findings from (1), (2), and (3) by creating publication-quality tables and graphs