add people

content/news/2604Gregory.md

@@ -9,4 +9,4 @@ images: ['images/news/2604FloeNet.gif']
 link: 'https://doi.org/10.1029/2026GL122981'
 ---
 
-The sea ice emulator we featured a few months back, **FloeNet**, is now officially published in GRL. Led by **Will Gregory**, the graph neural network emulates GFDL's global sea ice model (SIS2) while conserving mass, reproducing sea ice and snow-on-ice trends and variability with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic across a range of forcing scenarios. [Read the paper](https://doi.org/10.1029/2026GL122981).
+The sea ice emulator we featured a few months back, **FloeNet**, is now officially published in GRL. Led by **Will Gregory**, the graph neural network emulates GFDL's global sea ice model (SIS2) while conserving mass, reproducing sea ice and snow-on-ice trends and variability with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic across a range of forcing scenarios. [Read the paper](https://doi.org/10.1029/2026GL122981).

content/news/2608Chapman.md

@@ -9,4 +9,4 @@ images: ['images/news/2608-Chapman.png']
 link: 'https://doi.org/10.48550/arXiv.2607.18416'
 ---
 
-**Will Chapman** et al. investigate an important challenge in developing **physically consistent AI weather and climate emulators.** The [study](https://doi.org/10.48550/arXiv.2607.18416) shows that enforcing exact water-budget conservation during training can inadvertently allow precipitation biases to grow, even when the final corrected output appears physically perfect. By introducing a revised training strategy that supervises the raw model predictions while penalizing budget imbalances, the authors **restore stable learning** and demonstrate that **exact budget closure alone is not sufficient to ensure physically meaningful AI models.**
+**Will Chapman** et al. investigate an important challenge in developing **physically consistent AI weather and climate emulators.** The [study](https://doi.org/10.48550/arXiv.2607.18416) shows that enforcing exact water-budget conservation during training can inadvertently allow precipitation biases to grow, even when the final corrected output appears physically perfect. By introducing a revised training strategy that supervises the raw model predictions while penalizing budget imbalances, the authors **restore stable learning** and demonstrate that **exact budget closure alone is not sufficient to ensure physically meaningful AI models.**

content/news/2608Shamekh.md

@@ -11,4 +11,4 @@ link: 'https://doi.org/10.1175/JAS-D-25-0178.1'
 
 **Sara Shamekh** and collaborators present a **[new machine learning framework](https://doi.org/10.1175/JAS-D-25-0178.1)** that combines probabilistic modeling with symbolic equation discovery to uncover how **different types of tropical rainfall—shallow convective, deep convective, and stratiform—depend on large-scale atmospheric conditions**. Using satellite observations and reanalysis data, the study derives compact, physically interpretable equations that capture key environmental controls on rain area, providing **new insights into tropical convection** and paving the way for **more realistic, stochastic precipitation parameterizations in climate models.**
 
-Read the paper [here](/files/Shamekh_et_al.2026.pdf)
+Read the paper [here](/files/Shamekh_et_al.2026.pdf)

content/news/2608Zanna.md

@@ -10,4 +10,3 @@ link: 'https://www.simonsfoundation.org/2026/08/05/at-the-simons-science-summit-
 ---
 
 At the 2026 Simons Science Summit, our own **Laure Zanna** joined a panel on how AI is helping tackle complex systems in climate, fusion, and aerospace. Zanna described using AI to connect small-scale ocean processes with large-scale dynamics, helping researchers capture the vast range of scales needed to understand how the ocean works. Read the full recap: **[At the Simons Science Summit, Leaders Explore How AI Is Transforming Scientific Discovery.](https://www.simonsfoundation.org/2026/08/05/at-the-simons-science-summit-leaders-explore-how-ai-is-transforming-scientific-discovery/)**
-

content/team/JinchangLi.md

@@ -0,0 +1,14 @@
+---
+title: "Jinchang 'Thomas' Li"
+draft: false
+image: "/images/team/JinchangLi.png"
+jobtitle: "Affiliate, undergraduate Student"
+promoted: true
+weight: 28
+Website: 'https://github.com/ThomasLi0314'
+Position:
+tags: [Ocean, Atmosphere, Machine Learning]
+---
+
+
+NYU

content/team/YuanpuLi.md

@@ -0,0 +1,14 @@
+---
+title: "Yuanpu Li"
+draft: false
+image: "/images/team/YuanpuLi.jpeg"
+jobtitle: "Postdoc"
+promoted: true
+weight: 20
+Website:
+position:
+tags: [Atmosphere, Machine Learning, Climate Model Development]
+---
+
+
+CU Boulder