Updated publications

pages/publications.md

@@ -14,9 +14,30 @@ image: waves-band.jpeg
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+<td><strong>2023<BR><BR><BR><BR><BR></strong></td>
+<td><strong>Zero‐Shot Learning of Aerosol Optical Properties Using Graph Neural Networks <a href="https://www.nature.com/articles/s41598-023-45235-8.epdf?sharing_token=CjnJ9KHCzmGUyEPE9sft69RgN0jAjWel9jnR3ZoTv0Nn-a5Srf0dwc4JB6M9HsC9QDzkO7IKihpfOhCCyfhomtYmSpnPUyYza5d9EzKQtEG05zdTvlNap_uxpBvDHO4VdJcSm0CZvMi5rOL6Qu0sk3c_gJ7pXeZQq8sv-A1sWgQ%3D">[preprint]</a></strong><BR>
+K.D. Lamb, P. Gentine.<BR>
+Accepted, Scientific Reports (2023)</td>
+</tr>
+
+ <tr> 
+<td></td>
+<td><strong>Simulating the Air Quality Impacts of Prescribed Fires Using a Graph Neural Network‐Based PM2.5 Forecasting System</strong><br>
+K. Liao, J. Buch, K.D. Lamb, P. Gentine<br>
+Tackling Climate Change with Machine Learning Workshop <br>
+2023 Conference on Neural Information Processing Systems<br></td>
+</tr>
+
+<tr> 
+<td></td>
+<td><strong>Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds with Variational Autoencoders</strong><br>
+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<br>
+Machine Learning and the Physical Sciences Workshop <br>
+2023 Conference on Neural Information Processing Systems<br></td>
+</tr>
 
 <tr>
-    <td><strong>2023<BR><BR><BR><BR><BR></strong></td>
     <td><strong>Pyrocumulonimbus affect average stratospheric aerosol composition<a href="https://www.science.org/doi/10.1126/science.add3101">[link]</a></strong><BR>
     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.<BR>
     <em>Science</em>, 379, 6634 (2023)</td>
@@ -48,10 +69,6 @@ Under Review (2023)<BR></td>
 
  <tr>
 <td><strong>2022<BR><BR><BR><BR><BR></strong></td>
-<td><strong>Zero‐Shot Learning of Aerosol Optical Properties Using Graph Neural Networks <a href="https://arxiv.org/abs/2107.10197">[preprint]</a></strong><BR>
-K.D. Lamb, P. Gentine.<BR>
-Under review (2022)</td>
-</tr>
 <tr>
 <td></td>  
  <td><strong>Identifying the Causes of Pyrocumulonimbus (PyroCb) <a href="https://arxiv.org/abs/2211.08883">[link]</a></strong><br>

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.
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 <p style="text-align: left" align="justify">
-<small> [1] <a href="https://arxiv.org/abs/2107.10197">Lamb and Gentine. Under review (2023) </a> </small><br>
+<small> [1] <a href="https://www.nature.com/articles/s41598-023-45235-8.epdf?sharing_token=CjnJ9KHCzmGUyEPE9sft69RgN0jAjWel9jnR3ZoTv0Nn-a5Srf0dwc4JB6M9HsC9QDzkO7IKihpfOhCCyfhomtYmSpnPUyYza5d9EzKQtEG05zdTvlNap_uxpBvDHO4VdJcSm0CZvMi5rOL6Qu0sk3c_gJ7pXeZQq8sv-A1sWgQ%3D">Lamb and Gentine. Scientific Reports (2023) </a> </small><br>
 <small> [2] <a href="https://www.authorea.com/users/553026/articles/653967-reduced-order-modeling-for-linearized-representations-of-microphysical-process-rates">Lamb, van Lier Walqui, Santos, Morrison. Under review (2023) </a> </small></p> <br>
 
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