This mini project delves into an innovative application of Digital Elevation Models (SRTM data) and ERA5 land data to explore temperatures above 3000 meters in the diverse terrains of Jammu, Kashmir, and Ladakh. Contrary to correlating elevation with temperature, our focus is on pinpointing and visualizing temperatures at elevations above 3000 meters, showcasing how DEM data complements surface-level climate data for targeted geospatial analysis.
matplotlib
, geopandas
), we plot temperatures for areas above 3000 meters elevation, providing clear, insightful visual representations of our findings.Participants will learn how to:
This project is a practical guide for students, data enthusiasts, and professionals looking to enhance their skills in environmental data analysis and geospatial visualization.
Join us in this analytical journey to unveil the climatic conditions of high-altitude terrains through the lens of data science.
Warm regards,
Hamid Ali Syed
Our adventure began in the quaint town of West Lafayette, Indiana. Known for its charming streets and friendly locals, it was the perfect starting point for our journey. With our bags packed and spirits high, we set off in our trusty car, eager to see what the road had in store for us.
Our first major stop was St. Louis, Missouri, where we marveled at the iconic Gateway Arch. Standing beneath this colossal monument, we felt a sense of awe at the sheer scale of human ingenuity. The city’s vibrant energy was infectious, and we found ourselves immersed in its rich history and culture.
As we continued our journey, we made an impromptu stop in Springfield, Missouri. Here, we stumbled upon a unique find - a theatre we mistook for a shrine! This unexpected discovery reminded us of the joys of road trips, where even ‘mistakes’ can lead to memorable experiences.
Driving through the heartland, we reached Oklahoma City, where we spent a night recharging before heading to the quirky Cadillac Ranch in Amarillo, Texas. But it was Albuquerque, New Mexico, that truly captivated us. Visiting the University of New Mexico and meeting my cousin, an associate professor there, gave us a deeper appreciation for the city’s academic and cultural vibrancy.
In Santa Fe, New Mexico, we were enchanted by the Christmas celebrations. The historic Church of Santa Fe, adorned with lights and decorations, was a sight to behold. The festive atmosphere was a heartwarming reminder of the season’s joy and togetherness.
No road trip in the West is complete without witnessing the grandeur of the Grand Canyon. As we explored Mather Point and other breathtaking vistas, the canyon’s majestic beauty left us speechless. Our journey also took us to the Hoover Dam and the mesmerizing landscapes of Canyons and Arches National Parks.
Our visit to Mesa Verde National Park and the Aztec Ruins National Monument was a journey back in time. Learning about the ancient cultures that once thrived in these lands was a humbling and educational experience.
As all good things must come to an end, so did our road trip. We concluded our adventure in Denver, Colorado, before making our way back to West Lafayette. The journey home was a time for reflection on the incredible experiences and the bonds of friendship that had grown stronger with each mile.
This road trip was more than just a journey through different states; it was a journey through diverse cultures, stunning landscapes, and shared experiences. It reminded us of the beauty of exploration and the joy of discovering the unknown. Until our next adventure, we’ll hold these memories close to our hearts.
Checkout our route
]]>Welcome to this interactive tutorial on creating custom colormaps. By the end of this post, you’ll be able to design your own colormaps effortlessly by manipulating RGB lines according to your preferences.
Run the following code while the copied matrix is still in your clipboard:
import pandas as pd
import matplotlib as mpl
import numpy as np
def create_colorbar_from_clipboard():
df = pd.read_clipboard(header=None)
df.columns = ['R', 'G', 'B']
c = np.array(df/255.0)
cm = mpl.colors.ListedColormap(c)
return cm
cmap = create_colorbar_from_clipboard()
cmap
Congratulations! You’ve successfully created your custom colormap. Feel free to reach out if you need further assistance or have any questions.
Best,
Hamid
PERiLS 2022 | PERiLS 2023 |
I hope this helps you get started with your introduction notebook. Let me know if you need any further assistance.
Best,
H. A. Syed
]]>The India Meteorological Department (IMD) operates a network of weather radars across India to monitor and predict weather patterns. This network of radars plays a crucial role in providing real-time weather information and forecasting services to various sectors such as agriculture, aviation, and disaster management.
In this notebook, we will explore the IMD Radar Network and create a dataset containing sites with geographic coordinates and their band.
To plot the IMD Radar Network, we will first gather data from the IMD website using web scraping techniques. We will scrape the IMD website to obtain information about the radar sites, including their names, locations, and bands.
After scraping the IMD website, we will clean the data and create a dataset containing the radar site names, their geographic coordinates, and their bands. We will use Python libraries such as pandas and geopandas to perform this task.
Finally, we will plot the IMD Radar Network using matplotlib and geopandas libraries. We will use the dataset that we created earlier to plot the radar sites on a map of India. We will also add additional features such as state boundaries and rivers to provide context to the plot.
By the end of this notebook, you will have a better understanding of the IMD Radar Network and the process of collecting, cleaning, and visualizing geospatial data. You will also have a dataset that you can use for further analysis and exploration of the IMD Radar Network.
I hope this helps you get started with your introduction notebook. Let me know if you need any further assistance.
Best,
H. A. Syed
]]>This notebook will introduce you to DrPy Library, developed by Dr. Randy Chase. It basically helps to read the data through the most popular python package called xarray, which gives us multi-dimensional labelled data.