# Weather Forecast dataset

Citation Author(s):
NOAA
dataset
Submitted by:
Arshi Gupta
Last updated:
Tue, 12/19/2023 - 14:38
DOI:
10.21227/czq4-bm60
Data Format:
1101 Views
Categories:
Keywords:

#### Abstract

The provided dataset appears to contain weather-related information for New Delhi Safdarjung, India, spanning from January 1, 2023, to July 21, 2023. The dataset includes the following columns: Station ID, Station Name, Date, Precipitation (PRCP), Average Temperature (TAVG), Maximum Temperature (TMThe dataset includes daily observations with information on precipitation and temperature. It seems that some values are missing (NULL values), and there are variations in the units used for precipitation AX), and Minimum Temperature (TMIN).

Instructions:

Here are detailed instructions to help users understand and analyze this dataset:

1. Column Descriptions:

• `STATION`: Station code or identifier.
• `NAME`: Name of the weather station (New Delhi Safdarjung, India, in this case).
• `DATE`: Date of the recorded weather data.
• `PRCP`: Precipitation in inches. If the value is 0, there was no precipitation. Non-zero values indicate the amount of precipitation.
• `TAVG`: Average temperature in Fahrenheit.
• `TMAX`: Maximum temperature in Fahrenheit.
• `TMIN`: Minimum temperature in Fahrenheit.
2. Missing Data:

• Look for missing values in any of the columns. Handle them appropriately based on the analysis (e.g., interpolation, removal, or filling with mean/median).
3. Data Types:

• Ensure that the data types of each column are appropriate (e.g., date as a datetime object, temperatures as floats, etc.).
4. Descriptive Statistics:

• Calculate descriptive statistics for each column, such as mean, median, standard deviation, and quartiles, to get an overview of the data distribution.
5. Temperature Trends:

• Analyze temperature trends over time. Look for patterns, seasonality, and any significant deviations.
6. Precipitation Analysis:

• Examine the precipitation data. Identify days with significant precipitation, and check if there are patterns or trends.
7. Temperature Extremes:

• Identify days with unusually high or low temperatures. Check for any extreme weather events.
8. Visualizations:

• Create visualizations such as line charts for temperature trends, bar charts for precipitation, and scatter plots for relationships between variables.
9. Monthly and Seasonal Analysis:

• Aggregate data on a monthly or seasonal basis to observe broader patterns.
10. Correlation Analysis:

• Check for correlations between different weather variables (e.g., temperature and precipitation).
11. Outlier Detection:

• Identify and investigate any outliers in the dataset that may affect the analysis.
12. Data Range:

• Check if the dataset covers a specific time range, and ensure it's suitable for the intended analysis.

These instructions provide a comprehensive guide to understanding and exploring the given weather dataset for New Delhi Safdarjung, India. Users can use statistical and visual analysis techniques to derive insights from the data.

good

Submitted by Mrudul pr on Wed, 12/20/2023 - 03:45

Hi! Thanks for sharing! When will the dataset be uploaded?

Submitted by Pengrui Quan on Fri, 01/26/2024 - 02:12

#### Dataset Files

Files have not been uploaded for this dataset