View the final report: https://buoy-hw.static.domains/hw4
The goal of this assignment was to explore and analyze meteorological data collected by a buoy off the coast of Boston (Buoy 44013) from 1985 to 2023, and rainfall data for the Boston area from 1985 to 2013. The focus was on identifying patterns between rainfall and various weather factors like water temperature (WTMP), air temperature (ATMP), and barometric pressure (BAR). The assignment was structured into several steps:
-
Data Acquisition and Cleaning: We retrieved and processed meteorological data from the buoy and rainfall data, handling missing values and ensuring consistency in the date and time formats between the two datasets.
-
Descriptive Statistics and Visualizations: We calculated key summary statistics and visualized the data, including yearly trends in rainfall and water temperature. Scatter plots were used to examine the relationship between rainfall and the buoy variables (WTMP, ATMP, BAR).
-
Statistical Modeling: A simple linear regression model was built to predict rainfall based on the buoy variables. Despite initial observations, the model showed no significant relationship between water temperature, air temperature, or barometric pressure and rainfall.
-
Conclusions: Through visualizations and modeling, it was determined that the weather buoy variables had weak or no correlation with rainfall. This highlighted the complexity of weather prediction and the challenge of forecasting rainfall accurately using a limited set of variables.
Overall, this assignment emphasized the importance of data cleaning, visualization, and statistical modeling in exploring environmental data, as well as the challenges associated with interpreting such data in real-world contexts like weather forecasting.