deploy: 3d88adb

Author: nikudarafshi

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@@ -262,25 +262,25 @@ <h1 class="flex-none">
 
 
         <div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
-          <a href="http://datawaveproject.github.io/publications/papers/lott_comparison_strateole/" class="db grow">
-            <img src="http://datawaveproject.github.io/images/Data_Images/Lott_Comparison.png" class="img" alt="image from Comparison between non-orographic gravity-wave parameterizations used in QBOi models and Strateole 2 constant-level balloons">
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_ml/" class="db grow">
+            <img src="http://datawaveproject.github.io/images/Data_Images/Gupta_ml.png" class="img" alt="image from Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation">
           </a>
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         <h1 class="f3 fw1 athelas mt0 lh-title">
-          <a href="http://datawaveproject.github.io/publications/papers/lott_comparison_strateole/" class="color-inherit dim link">
-            Comparison between non-orographic gravity-wave parameterizations used in QBOi models and Strateole 2 constant-level balloons
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_ml/" class="color-inherit dim link">
+            Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
             </a>
         </h1>
         <div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
           <h2 id="authors">Authors:</h2>
-<p><em><strong>F. Lott</strong></em>, <em><strong>R. Rani</strong></em>, C. McLandress, A. Podglajen, A. Bushell, M. Bramberger, H.-K. Lee, <em><strong>J. Alexander</strong></em>, J. Anstey, H.-Y. Chun, A. Hertzog, N. Butchart, Y.-H. Kim, Y. Kawatani, B. Legras, E. Manzini, H. Naoe, S. Osprey, <em><strong>R. Plougonven</strong></em>, H. Pohlmann, J. H. Richter, J. Scinocca, J. García-Serrano, F. Serva, T. Stockdale, S. Versick, S. Watanabe, K. Yoshida</p>
-<p><a href="https://doi.org/10.1002/qj.4793">Read the full paper here</a></p>
+<p><em><strong>Aman Gupta</strong></em>, <em><strong>Aditi Sheshadri</strong></em>, Sujit Roy, Vishal Gaur, Manil Maskey, and Rahul Ramachandran</p>
+<p><a href="https://doi.org/10.48550/arXiv.2406.14775">Read the full paper here</a></p>
 <h2 id="abstract">Abstract:</h2>
-<p>Gravity-wave (GW) parameterizations from 12 general circulation models (GCMs) participating in the Quasi-Biennial Oscillation initiative (QBOi) are compared with Strateole 2 balloon observations made in the tropical lower stratosphere from November 2019–February 2020 (phase 1) and from October 2021–January 2022 (phase 2). The parameterizations employ the three standard techniques used in GCMs to represent subgrid-scale non-orographic GWs, namely the two globally spectral techniques developed by Warner and McIntyre (1999) and Hines (1997), as well as the “multiwaves” approaches following the work of Lindzen (1981).</p>
+<p>Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy.</p>
         </div>
-          <a href="http://datawaveproject.github.io/publications/papers/lott_comparison_strateole/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_ml/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
 
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@@ -298,25 +298,25 @@ <h2 id="abstract">Abstract:</h2>
 
 
         <div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
-          <a href="http://datawaveproject.github.io/publications/papers/achatz_gw_review/" class="db grow">
-            <img src="http://datawaveproject.github.io/images/Data_Images/achatz_gw_r.png" class="img" alt="image from Atmospheric Gravity Waves: Processes and Parameterization">
+          <a href="http://datawaveproject.github.io/publications/papers/connelly_forest/" class="db grow">
+            <img src="http://datawaveproject.github.io/images/Data_Images/Connelly_forest.png" class="img" alt="image from Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection ">
           </a>
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         <h1 class="f3 fw1 athelas mt0 lh-title">
-          <a href="http://datawaveproject.github.io/publications/papers/achatz_gw_review/" class="color-inherit dim link">
-            Atmospheric Gravity Waves: Processes and Parameterization
+          <a href="http://datawaveproject.github.io/publications/papers/connelly_forest/" class="color-inherit dim link">
+            Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection 
             </a>
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           <h2 id="authors">Authors:</h2>
-<p><em><strong>Ulrich Achatz</strong></em>, <strong>Joan Alexander</strong>, Erich Becker, Hye-Yeong Chun, Andreas Dörnbrack, Laura Holt, <em><strong>Riwal Plougonven</strong></em>, Inna Polichtchouk, Kaoru Sato, <em><strong>Aditi Sheshadri</strong></em>, <em><strong>Claudia Christine Stephan</strong></em>, Annelize van Niekerk, and Corwin J. Wright</p>
-<p><a href="https://doi.org/10.1175/JAS-D-23-0210.1">Read the full paper here</a></p>
+<p><em><strong>David S. Connelly</strong></em> and <em><strong>Edwin P. Gerber</strong></em></p>
+<p><a href="https://doi.org/10.1029/2023MS004184">Read the full paper here</a></p>
 <h2 id="abstract">Abstract:</h2>
-<p>Atmospheric predictability from subseasonal to seasonal time scales and climate variability are both influenced critically by gravity waves (GW). The quality of regional and global numerical models relies on thorough understanding of GW dynamics and its interplay with chemistry, precipitation, clouds, and climate across many scales. For the foreseeable future, GWs and many other relevant processes will remain partly unresolved, and models will continue to rely on parameterizations.</p>
+<p>We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network.</p>
         </div>
-          <a href="http://datawaveproject.github.io/publications/papers/achatz_gw_review/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
+          <a href="http://datawaveproject.github.io/publications/papers/connelly_forest/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
 
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@@ -334,25 +334,25 @@ <h2 id="abstract">Abstract:</h2>
 
 
         <div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
-          <a href="http://datawaveproject.github.io/publications/papers/ml_nonorographic_hardiman/" class="db grow">
-            <img src="http://datawaveproject.github.io/images/Data_Images/Hardiman_ML.png" class="img" alt="image from Machine Learning for Nonorographic Gravity Waves in a Climate Model">
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_mom_flux/" class="db grow">
+            <img src="http://datawaveproject.github.io/images/Data_Images/Gupta_momflux.png" class="img" alt="image from Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System">
           </a>
         </div>
 
       <div class="blah w-100 w-60-ns pl3-ns">
         <h1 class="f3 fw1 athelas mt0 lh-title">
-          <a href="http://datawaveproject.github.io/publications/papers/ml_nonorographic_hardiman/" class="color-inherit dim link">
-            Machine Learning for Nonorographic Gravity Waves in a Climate Model
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_mom_flux/" class="color-inherit dim link">
+            Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System
             </a>
         </h1>
         <div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
           <h2 id="authors">Authors:</h2>
-<p><em><strong>Steven Hardiman</strong></em>, <em><strong>Adam Scaife</strong></em>, Annelize Niekerk, Rachel Prudden, Aled Owen, Samantha Adams, Tom Dunstan, Nick Dunstone, and Sam Madge</p>
-<p><a href="https://doi.org/10.1175/AIES-D-22-0081.1">Read the full paper here</a></p>
+<p><em><strong>Aman Gupta</strong></em>, <em><strong>Aditi Sheshadri</strong></em>, and Valentine Anantharaj</p>
+<p><a href="https://doi.org/10.1038/s41597-024-03699-x">Read the full paper here</a></p>
 <h2 id="abstract">Abstract:</h2>
-<p>There is growing use of machine learning algorithms to replicate subgrid parameterization schemes in global climate models. Parameterizations rely on approximations; thus, there is potential for machine learning to aid improvements. In this study, a neural network is used to mimic the behavior of the nonorographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability.</p>
+<p>Progress in understanding the impact of mesoscale variability, including gravity waves (GWs), on atmospheric circulation is often limited by the availability of global fine-resolution observations and simulated data. This study presents momentum fluxes due to atmospheric GWs extracted from four months of an experimental “nature run&quot;, integrated at a 1 km resolution (XNR1K) using the Integrated Forecast System (IFS) model. Helmholtz decomposition is used to compute zonal and meridional flux of vertical momentum from ~1.5 petabytes of data; quantities often emulated by climate model parameterization of GWs.</p>
         </div>
-          <a href="http://datawaveproject.github.io/publications/papers/ml_nonorographic_hardiman/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
+          <a href="http://datawaveproject.github.io/publications/papers/gupta_mom_flux/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
 
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@@ -369,26 +369,26 @@ <h1 class="f3">More</h1>
 
 
           <h2 class="f5 fw4 mb4 dib mr3">
-            <a href="http://datawaveproject.github.io/publications/papers/multi_scale_achatz/" class="link black dim">
-              Multi-scale dynamics of the interaction between waves and mean flows: From nonlinear WKB theory to gravity-wave parameterizations in weather and climate models
+            <a href="http://datawaveproject.github.io/publications/papers/gupta_extratropical/" class="link black dim">
+              Insights on Lateral Gravity Wave Propagation in the Extratropical Stratosphere From 44 Years of ERA5 Data
             </a>
           </h2>
 
           <h2 class="f5 fw4 mb4 dib mr3">
-            <a href="http://datawaveproject.github.io/publications/papers/coriolis_acceleration_chew/" class="link black dim">
-              An unstable mode of the stratified atmosphere under the non-traditional Coriolis acceleration
+            <a href="http://datawaveproject.github.io/publications/papers/jochum_transience-copy/" class="link black dim">
+              The impact of transience in the interaction between orographic gravity waves and mean flow
             </a>
           </h2>
 
           <h2 class="f5 fw4 mb4 dib mr3">
-            <a href="http://datawaveproject.github.io/publications/papers/comparing_balloon_kohler/" class="link black dim">
-              Comparing Loon Superpressure Balloon Observations of Gravity Waves in the Tropics With Global Storm-Resolving Models
+            <a href="http://datawaveproject.github.io/publications/papers/chew_unstructured_grids/" class="link black dim">
+              A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids
             </a>
           </h2>
 
           <h2 class="f5 fw4 mb4 dib mr3">
-            <a href="http://datawaveproject.github.io/publications/papers/quantifying_drag_qiang/" class="link black dim">
-              Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-Permitting Simulations for Data-Driven Parameterizations
+            <a href="http://datawaveproject.github.io/publications/papers/sun_waccm/" class="link black dim">
+              Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM
             </a>
           </h2>