The purpose of studying novel fast super resolution convolutional neural network (SRCNN) and earth system model (ESM) is to improve the quality of images and to better understand the Earth's climate system.

SRCNN is a type of deep learning algorithm that is used to enhance the resolution of low-quality images. It does this by using a convolutional neural network to analyze the image and then apply a series of filters to increase the resolution. SRCNN has been used in a variety of applications, including medical imaging, aerial photography, and security surveillance.

ESM is a computer model that is used to simulate and predict the behavior of the Earth's climate system. It includes components such as the atmosphere, oceans, land surface, and ice sheets, and it is used to study issues such as global warming, El Niño, and hurricane formation. ESMs are used by scientists to better understand the climate system and to make predictions about its future behavior.

The areas of application for SRCNN and ESM include:

1. Medical imaging: SRCNN can be used to enhance the resolution of medical images, such as X-rays, CT scans, and MRI scans, to better diagnose and treat diseases.
2.