Self learning environmental physics

In summary, To learn about climate change and its relation to other factors, such as diseases, there are various resources available. Some recommended books include the textbook "Understanding Climate Models" and the historical background of climate models provided by the American Institute of Physics. In terms of programming languages, a combination of Fortran, numerical Python, and Julia may be used for different types of models. Other languages such as MATLAB and Java are also commonly used. Additional resources for learning about climate models can also be found online, including python examples and a website for open source physics modeling in Java.
  • #1
I am really into climate change , and the physical models they use to relate it to other factors such as diseases , however i don't know where to learn about it , i am currently enrolled on the 8th trimester of electronics engineering (out of 12) and in the 4th semester of physics(in a different university ) , i'd like to know about what books i could read , what fields of physics should i focus on , programming languages that may come in handy
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  • #2
Here's a reference to understanding climate models:

Here's some historical background with references to past and current climate models:

I'd imagine that there would be a combination of languages such as Fortran for legacy models, numerical Python and Julia for more recent models. However, some engineers and scientists rely on MATLAB or Java for modeling systems in general (see for opensource physics modeling in Java).

Here's another reference: [Broken]

I found some python examples here: [Broken]
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1. What is self-learning environmental physics?

Self-learning environmental physics is a branch of physics that focuses on understanding the physical processes and interactions within the environment. It involves using data and observations to develop models and theories about how the environment works, and using these models to make predictions and understand how the environment may change in the future.

2. What are the benefits of studying self-learning environmental physics?

Studying self-learning environmental physics allows us to better understand the complex systems and processes that make up our environment. This knowledge can help us make more informed decisions and take more effective action to protect and preserve the environment for future generations.

3. How does self-learning environmental physics differ from traditional environmental science?

While both disciplines focus on the environment, self-learning environmental physics places a greater emphasis on using data and mathematical models to understand and predict environmental processes. It also tends to be more interdisciplinary, drawing from fields such as physics, chemistry, and biology.

4. What are some key topics covered in self-learning environmental physics?

Some key topics in self-learning environmental physics include atmospheric dynamics, oceanography, climate change, biogeochemical cycles, and environmental modeling. Other important areas of study may include environmental risk assessment, pollution control, and sustainable resource management.

5. How can self-learning environmental physics be applied in real-world scenarios?

Self-learning environmental physics has many practical applications, such as predicting and mitigating the impacts of natural disasters, understanding and managing pollution levels, and developing sustainable energy sources. It also plays a crucial role in informing and guiding environmental policies and regulations.

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