From 48c635067c0c2bf05b785da9f24ad7c39fa7817f Mon Sep 17 00:00:00 2001 From: kdlamb Date: Tue, 31 Oct 2023 16:42:16 -0400 Subject: [PATCH] Updated publications --- pages/publications.md | 27 ++++++++++++++++++++++----- pages/research.md | 2 +- 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/pages/publications.md b/pages/publications.md index 827da43..77a7890 100644 --- a/pages/publications.md +++ b/pages/publications.md @@ -14,9 +14,30 @@ image: waves-band.jpeg + +2023




 +Zero‐Shot Learning of Aerosol Optical Properties Using Graph Neural Networks [preprint]
+K.D. Lamb, P. Gentine.
+Accepted, Scientific Reports (2023) + + + + +Simulating the Air Quality Impacts of Prescribed Fires Using a Graph Neural Network‐Based PM2.5 Forecasting System
+K. Liao, J. Buch, K.D. Lamb, P. Gentine
+Tackling Climate Change with Machine Learning Workshop
+2023 Conference on Neural Information Processing Systems
+ + + + +Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds with Variational Autoencoders
+J. Will, A. Jenney, K.D. Lamb, M.S. Pritchard, C. Kaul, P-L Ma, K. Pressel, J. Shpund, M. van Lier Walqui, S. Mandt
+Machine Learning and the Physical Sciences Workshop
+2023 Conference on Neural Information Processing Systems
+ - 2023




 Pyrocumulonimbus affect average stratospheric aerosol composition[link]
J.M Katich, E. Apel, I. Bourgeois, C. Brock, T.P. Bui, P. Campuzano-Jost, R. Commane, B. Daube, M. Dollner, M. Fromm, K.D. Froyd, A.J. Hills, R.S. Hornbrook, J. Jimenez, A. Kupc, K.D. Lamb, K. McKain, F. Moore, D.M. Murphy, B.A. Nault, J. Peischl, D.A. Peterson, E.A. Ray, K.H. Rosenlof, T. Ryerson, G.P. Schill, J.C. Schroder, B. Weinzierl, C. Thompson, C.J. Williamson, S. Wofsy, P. Yu, J.P. Schwarz.
Science, 379, 6634 (2023) @@ -48,10 +69,6 @@ Under Review (2023)
2022




 -Zero‐Shot Learning of Aerosol Optical Properties Using Graph Neural Networks [preprint]
-K.D. Lamb, P. Gentine.
-Under review (2022) - Identifying the Causes of Pyrocumulonimbus (PyroCb) [link]
diff --git a/pages/research.md b/pages/research.md index 9ae7544..4b1dc12 100644 --- a/pages/research.md +++ b/pages/research.md @@ -21,7 +21,7 @@ Clouds and aerosols remain some of the greatest sources of uncertainty for futur Improving representations of aerosol and cloud microphysics in atmospheric models is key to accurately predicting future changes in climate. However current microphysical schemes are limited by both structural and parametric uncertainty in their representation of microphysical processes. Machine learning can be used to emulate more expensive computational models or to develop parameterizations of processes directly from observations and higher resolution models using reduced-order model approaches.

- [1] Lamb and Gentine. Under review (2023)
+ [1] Lamb and Gentine. Scientific Reports (2023)
[2] Lamb, van Lier Walqui, Santos, Morrison. Under review (2023)