Fetching the paper…

Reading the bibliography…

2019

Deep learning for Aerosol Forecasting

Hoyne, Caleb, Mukkavilli, S. Karthik, Meger, David

Original
PDF
Post on X

Understand

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth.

Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally.
This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites.
The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.

Deep learning for Aerosol Forecasting · Around

Built on

Dropout: A simple way to prevent neural networks from overfitting
Original
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
Aerosol optical depth retrieval by neural network ensembles with adaptive cost function
Radosavljevic, V., Vucetic, S., and Obradovic, Z. (2007) · 2007
Earlier work this paper cites.

Similar

Estimation and bias correction of aerosol abundance using data-driven machine learning and remote sensing
Malakar, N. K., Lary, D. J., Moore, A., Gencaga, D., Roscoe, B., Albayrak, A., and Wei, J. (2012) · 2012
Cited alongside, same era.
Predicting top-of-atmosphere thermal radiance using merra-2 atmospheric data with deep learning
Kleynhans, T., Montanaro, M., Gerace, A., and Kanan, C. (2017) · 2017
Cited alongside, same era.

Then

Tackling climate change with machine learning
Original
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., and Bengio, Y. (2018) · 2018
Later among the works it cites.
Assessment of atmospheric aerosols from two reanalysis products over australia
Mukkavilli, S. K., Prasad, A., Taylor, R. A., Huang, J., Mitchell, R. M., and Kay, M. (2019) · 2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…