MOSDAC

In the aftermath of the two powerful cyclones that struck West Bengal, I embarked on a project to visualize their progression using satellite imagery. The goal was to create a mesmerizing timelapse video that would capture the cyclones' formation and movement across the region.

The source of the data was MOSDAC (Meteorological and Oceanographic Satellite Data Archival Centre), an invaluable resource for satellite imagery and data. MOSDAC publishes live images and data captured by Indian geostationary satellites like INSAT-3D and INSAT-3DR on a daily basis. This wealth of information is a goldmine for researchers, meteorologists, and data enthusiasts alike.

To obtain the necessary data, I developed a Python script to scrape MOSDAC's publicly available image gallery. Over the course of 30 days, the script downloaded and archived over 550 individual satellite images, each providing a snapshot of the cyclones' evolution.

The resulting timelapse offers a unique perspective on the cyclones' progression:

As the timelapse unfolds, you can witness the cyclones taking shape and their paths across the Indian peninsula. The sun's reflection on the ocean, moving from right to left, serves as a natural indicator of time, allowing you to track the passage of days effortlessly.

Here's a glimpse into the code used to scrape the images from MOSDAC's website:

def download_images(sat_id, month):
    ...
    for day in range(1, 32):
        found_image = False
        ...
        for time in time_list:
            link = f"https://mosdac.gov.in/.../3DIMG_{day+month+year}_{time}_{sat_id}.jpg"
            response = requests.get(link, stream=True)
            if response.ok:
                with open(f"images/{day}-{time}.jpg", "wb") as f:
                    f.write(response.content)
                print(f"Image found: {day}-{time}")
                found_image = True
        
        if not found_image:
            print(f"No image on {year}-{month}-{day}")

Once the images were downloaded, I utilized OpenCV to stitch them together into a seamless timelapse video:

def generate_video(path, filename, fps=25):
    ...
    out = cv2.VideoWriter(filename, cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
    for i, img in enumerate(images):
        out.write(img)
    out.release()

Future enhancements

• Daily Wallpaper Generation: One fascinating extension could be to generate daily wallpapers or backgrounds based on the satellite imagery from MOSDAC. Instead of creating a timelapse video, the script could download the latest satellite image each day and process it to create a visually appealing wallpaper. These wallpapers could showcase various meteorological and geographical features captured by the satellites, providing a unique and ever-changing backdrop for users' devices.
• Local Rain Prediction: Leveraging the wealth of data available from MOSDAC, it might be possible to develop a localized rain prediction system. By analyzing patterns in the satellite imagery, cloud formations, temperature gradients, and other relevant data, a machine learning model could be trained to forecast the likelihood of precipitation in specific regions. This could be particularly useful for agricultural purposes, disaster preparedness, or even personal planning.