<p>I come from a background in research in pure Mathematics and, after a period of self-learning, I am currently looking for a career in Deep Learning. I recently applied for a job in Deep Learning and Meteorology in the agriculture sector.</p>
<p>They actually showed interest in my application, but asked me to propose a concrete plan for increasing my competence in the domain of meteorology. By any means, I don't want you to do the work for me, but I would like some experts to point me in the right direction to come up with this plan.</p>
<ol>
<li>What are the fundamental papers/books on meteorology that are
relevant in agriculture?</li>
<li>What are some important papers on the state-of-the-art of Deep Learning applications in meteorology?</li>
</ol>
<p>If you think that there are more appropriate platforms where I could ask this questions or have other suggestions in general, please let me know. Thank you.</p>

CC BY-SA 4.0
2022-10-31T17:41:55.027Z
Given that you've mentioned meteorology & agriculture I'm assuming that is your intended focus, as opposed to formation of hurricanes & predicting their paths & strengths, amongst other meteorological events. Regarding agriculture, are you interested in agricultural meteorology for a particular part of the world, or global? Different weather systems affect different parts of the world: El Nino LaNina Southern Oscillation Index in the Pacific Ocean, the India Ocean Dipole, the Southern Annual Mode (proximity of wind to Antarctica, the jet streams, just to name a small selection.
Fred
CC BY-SA 4.0
2022-10-31T23:48:09.593Z
How much do you already know about meteorology, chemistry, physics, applied mathematics, artificial intelligence, and of course deep learning? It's a big jump from pure mathematics to not only applied mathematics, but also a field that uses applied mathematics (and doesn't care much about pure mathematics). It would help to know your background.
David Hammen
CC BY-SA 4.0
2022-11-01T09:21:00.037Z
@Fred Your assumption is correct. It is my understanding that the company works mainly in Europe.
Pgatti
CC BY-SA 4.0
2022-11-01T09:38:13.980Z
@DavidHammen At the moment I know nothing about meteorology and my chemistry knowledge is high school level. I studied quite some physics in university, so I'd say I have a comprehensive but not specialized knowledge of the subject. I have studied some applied math and I am confident I can tackle new topics in that domain. For what concerns Deep Learning, I understand how neural networks work and some fundamental architectures: CNNs, LSTMs, Autoencoders...
Pgatti
CC BY-SA 4.0
2022-11-01T09:55:01.643Z
You'll have to learn some basics regarding chemistry, particularly with respect to water. With regard to physics, you can forget quantum mechanics. What's much more important is to learn about continuum mechanics and (classical) thermodynamics. With regard to meteorology, that's a tough one. You know nothing (your own words), so start with a look at intro level course notes. You'll need to learn about teleconnections.
David Hammen
CC BY-SA 4.0
2022-11-01T09:59:20.003Z
With regard to agriculture, I'm trying to understand how deep learning can help. Maybe diagnosing diseased / under-watered / over-watered portions of a field, maybe path planning for where to next (and then again, after that) send a fleet of harvesters that cost multiple hundreds of thousands of dollars apiece (those fleets go from Mexico to Canada in North America), maybe detecting a rock that could send one of those very expensive harvesters into the repair shop (this will require drone data).
David Hammen
CC BY-SA 4.0
2022-11-01T10:45:09.883Z
@DavidHammen Thank you for your feedback. As I understand, the company collects basic meteorological data from the fields and uses deep learning and meteorology to predict weather hazards, plants diseases, correct irrigation... So the link between DL and agriculture is always through meteorology. I understand that it is a good idea to start studying from the foundations, but I was also looking for some more advanced and domain specific resources that I can start to look into. This way I can go back to the necessary basic concepts when they come up and work my way "backwards" in a sense.
Pgatti
CC BY-SA 4.0
2022-11-01T12:08:36.480Z
Look into teleconnections, ENSO, etc. But since you know nothing (your own words), starting with a look at intro level meteorology courses is a good idea.
David Hammen
<meteorology><reference-request>
Meteorology and Deep Learning, where to start?
Pgatti
# Meteorology and Deep Learning, where to start?

(Asked by: username_0 on 2022-10-31)

I come from a background in research in pure Mathematics and, after a period of self-learning, I am currently looking for a career in Deep Learning. I recently applied for a job in Deep Learning and Meteorology in the agriculture sector.

They actually showed interest in my application, but asked me to propose a concrete plan for increasing my competence in the domain of meteorology. By any means, I don't want you to do the work for me, but I would like some experts to point me in the right direction to come up with this plan.

1. What are the fundamental papers/books on meteorology that are
   relevant in agriculture?
2. What are some important papers on the state-of-the-art of Deep Learning applications in meteorology?

If you think that there are more appropriate platforms where I could ask this questions or have other suggestions in general, please let me know. Thank you.

**Tags:** meteorology, reference-request

> **Comments:**
> **username_1**: Given that you've mentioned meteorology & agriculture I'm assuming that is your intended focus, as opposed to formation of hurricanes & predicting their paths & strengths, amongst other meteorological events. Regarding agriculture, are you interested in agricultural meteorology for a particular part of the world, or global? Different weather systems affect different parts of the world: El Nino LaNina Southern Oscillation Index in the Pacific Ocean, the India Ocean Dipole, the Southern Annual Mode (proximity of wind to Antarctica, the jet streams, just to name a small selection.  
> **username_2**: How much do you already know about meteorology, chemistry, physics, applied mathematics, artificial intelligence, and of course deep learning? It's a big jump from pure mathematics to not only applied mathematics, but also a field that uses applied mathematics (and doesn't care much about pure mathematics). It would help to know your background.  
> **username_0**: @username_1 Your assumption is correct. It is my understanding that the company works mainly in Europe.  
> **username_0**: @username_2 At the moment I know nothing about meteorology and my chemistry knowledge is high school level. I studied quite some physics in university, so I'd say I have a comprehensive but not specialized knowledge of the subject. I have studied some applied math and I am confident I can tackle new topics in that domain. For what concerns Deep Learning, I understand how neural networks work and some fundamental architectures: CNNs, LSTMs, Autoencoders...  
> **username_2**: You'll have to learn some basics regarding chemistry, particularly with respect to water. With regard to physics, you can forget quantum mechanics. What's much more important is to learn about continuum mechanics and (classical) thermodynamics. With regard to meteorology, that's a tough one. You know nothing (your own words), so start with a look at intro level course notes. You'll need to learn about teleconnections.  
> **username_2**: With regard to agriculture, I'm trying to understand how deep learning can help. Maybe diagnosing diseased / under-watered / over-watered portions of a field, maybe path planning for where to next (and then again, after that) send a fleet of harvesters that cost multiple hundreds of thousands of dollars apiece (those fleets go from Mexico to Canada in North America), maybe detecting a rock that could send one of those very expensive harvesters into the repair shop (this will require drone data).  
> **username_0**: @username_2 Thank you for your feedback. As I understand, the company collects basic meteorological data from the fields and uses deep learning and meteorology to predict weather hazards, plants diseases, correct irrigation... So the link between DL and agriculture is always through meteorology. I understand that it is a good idea to start studying from the foundations, but I was also looking for some more advanced and domain specific resources that I can start to look into. This way I can go back to the necessary basic concepts when they come up and work my way "backwards" in a sense.  
> **username_2**: Look into teleconnections, ENSO, etc. But since you know nothing (your own words), starting with a look at intro level meteorology courses is a good idea.  

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## 0 answers

