Numerical Weather Prediction Puns

Hello,

I am frustrated at the lack of good weather data in this day and age of connected cameras and sensors etc. So I am making projects to assimilate and analyze sources of data that could be used in weather forecasting but are not yet. An obvious one is photos outside.

I'm starting with a simpler target, which is: using labelled photos of the sky, can we train a machine-learning algorithm to do basic classification on the weather conditions on the photo. For example, in a photo of the sky with clouds, and at the bottom trees and roofs, if those items are tagged in the photo then the algorithm can learn about future photos.

A stretch goal is to use all photos taken outside, even if they don't include the sky in them - ground conditions, shadows, etc will all give clues as to the weather outside at the time the photo was taken.

I have asked the machine learning communities what they think and they deem the basic premise of auto-tagging the photos do-able. The main issue, of course, is finding or creating a large enough training data set for this to work.

So that's where I am now. I'm estimated to need maybe a few thousand tagged photos of sky conditions at a minimum to begin work on the training algorithm. To that end and others, I've made an Android app that lets people send in tagged photos of the sky. So far I've only collected around 30 photos - and the labeling is dubious.

All Clear Weather is the app I'm making for this. (US, Android for now, long story, expanding later). The app contains multiple other weather experiments as well not related to photography (barometer data from phone sensors and current conditions corrections).

Closing Thoughts and final points

I'm not sure I made a wise decision making a weather app that contains this small photography feature to use. I think it will be under-utilized. Is there interest in the photography community to contribute something like this? Should I make a separate app for you?

It occurs to me that smartphone cameras are getting much better year over year. What kind of sensory/light details am I missing from smartphone pictures that might resolve features in the sky if a more advanced camera were used? Are smartphone cameras technically capable of more or less than I'm hoping? I'm thinking about things like [this article on cosmic rays](https://gizmodo.com/turn-your-smartphone-into-a-particle-detector-for-cos

• DOI/PMID/ISBN: https://doi.org/10.1029/2019WR025583
• [URL] https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR025583?af=R
• Much appreciated!

I would like to preface with: I have about 7 lectures worth of Fortran Knowledge under my belt.

I am working on a Shallow Water Model for a class project where I need to define a staggered C-grid.

Lx = 6e+06	  ! domain size in x direction
Ly = 2e+06	  ! domain size in y direction

d =  5e+05	  ! Resolution in meters
!d =  2.5e+05
!d =  1.25e+05

Nx = Lx/d	+ 1	! number of grid points in the x direction (13,  25, 49)
Ny = Ly/d + 1	! number of grid points in the y direction (  5,    9, 17)

hs(Nx)		! surface height
hs_t = 2e+03	! Height of topography
! resolution 1
if (d == 5.e+05) then
hs(Nx/2) = hs_t
end if

Now I need to define variables (within 5 different arrays - is my understanding) q, u, v, and h

q is defined at each point from (1:Nx,1:Ny)

u is defined at a half-step between q1 and q2; in x direction

v is defined at a half-step between q1 and q2; in y direction

h is defined within the grid point of q(1,1),q(1,2),q(2,1),q(2,2)

My professor suggested we try to index the variables which are offset using a do loop.

Any suggestions, recommendations?

Please allow me to clarify if you don't understand.

First off, I apologize if this is not the place to ask this.

I am researching and planning on creating a simple numerical weather prediction model. I believe I have found good resources on the equations and the basics on how they should be used. However I can't seem to find an easy way to get current data. I will need to be able to get air pressure, temperature, winds, moisture, etc. For different elevations and locations.

Thanks to anyone who can lead me in the right direction.

I want to dwell more on the role of quantum computing in weather forecasting for a research paper currently in the works.

I've been struggling recently with NWP and it really frustrates me because it should have been fairly easy for me. I came from a strong CS background but NWP makes me stutter. Any tips will help.

I am a statistician interested in learning more about (statistical) weather prediction, but I don't really know where to start...does anyone have any suggestions for introductions to numerical weather modeling?

I’m looking to predict customer churn, I have qualitative data and quantitative data(spend, customer interactions, customer usage , tenure etc). I’ve changed the qualitative data to a binary format to use logistic regression as a predictive tool. Can I use logistic regression for the numerical data? Or should I use another tool?

Great blog post from last night

Key point: >One beef with model maps is they assume a snow:liquid ratio of 10 to 1. That means 10 inches of snow for every one inch of liquid. By all indications 10 to 1 will not be the ratio with Sunday’s snow. This Sunday snow will be a wet snow, and ratios will be lower than 10:1

Recommend reading the whole post though. Lots of good information there.