<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Artificial Intelligence in Climate Science | Josiah K. Kimani</title><link>https://josiahkim29.github.io/tags/artificial-intelligence-in-climate-science/</link><atom:link href="https://josiahkim29.github.io/tags/artificial-intelligence-in-climate-science/index.xml" rel="self" type="application/rss+xml"/><description>Artificial Intelligence in Climate Science</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 09 Jul 2025 00:00:00 +0000</lastBuildDate><image><url>https://josiahkim29.github.io/media/icon_hu7729264130191091259.png</url><title>Artificial Intelligence in Climate Science</title><link>https://josiahkim29.github.io/tags/artificial-intelligence-in-climate-science/</link></image><item><title>PiggyCast: Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach</title><link>https://josiahkim29.github.io/publication/preprint/</link><pubDate>Wed, 09 Jul 2025 00:00:00 +0000</pubDate><guid>https://josiahkim29.github.io/publication/preprint/</guid><description>&lt;!-- This work is driven by the results in my [previous paper](/publication/conference-paper/) on LLMs.
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