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leap-stc · Nov 12, 2024 · 0847d8f · 0847d8f
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diff --git a/README.html b/README.html
@@ -392,6 +392,9 @@ <h1>ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seas
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 <p>ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) <strong>double sensitivities</strong> to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) <strong>butterfly effect</strong>, and our (3) <strong>inherent lack of understanding</strong> of physical processes operating at this scale. Thus, given the <strong>high socioeconomic stakes</strong> for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.</p>
+<blockquote>
+<div><p>💡 View our interactive <strong>leaderboard</strong> <a class="reference external" href="https://leap-stc.github.io/ChaosBench/leaderboard.html">here</a></p>
+</div></blockquote>
 <section id="features">
 <h2>Features<a class="headerlink" href="#features" title="Link to this heading">#</a></h2>
 <p><img alt="Overview of ChaosBench" src="_images/chaosbench_scheme-scheme.jpg" /></p>

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diff --git a/_sources/README.md b/_sources/README.md
@@ -10,6 +10,7 @@
 
 ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) __double sensitivities__ to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) __butterfly effect__, and our (3) __inherent lack of understanding__ of physical processes operating at this scale. Thus, given the __high socioeconomic stakes__ for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.
 
+> 💡 View our interactive __leaderboard__ [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)
 
 
 ## Features
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		ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) __double sensitivities__ to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) __butterfly effect__, and our (3) __inherent lack of understanding__ of physical processes operating at this scale. Thus, given the __high socioeconomic stakes__ for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.

		> 💡 View our interactive __leaderboard__ [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)


		## Features
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