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	<title>severe convective weather &#8211; BIOENGINEER.ORG</title>
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		<title>AI Rebuilds Missing Radar Echoes Without Knowing Where the Gaps Are</title>
		<link>https://bioengineer.org/ai-rebuilds-missing-radar-echoes-without-knowing-where-the-gaps-are/</link>
		
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		<pubDate>Thu, 08 Oct 2026 13:02:22 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Atmospheric Measurement Techniques]]></category>
		<category><![CDATA[BiConvLSTM-UNet]]></category>
		<category><![CDATA[ConvLSTM]]></category>
		<category><![CDATA[data reconstruction]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[nowcasting]]></category>
		<category><![CDATA[optical flow]]></category>
		<category><![CDATA[precipitation estimation]]></category>
		<category><![CDATA[radar mosaic]]></category>
		<category><![CDATA[severe convective weather]]></category>
		<category><![CDATA[U-Net]]></category>
		<category><![CDATA[weather radar]]></category>
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					<description><![CDATA[A new deep learning model called BiConvLSTM-UNet can restore missing regions in weather radar mosaic data without requiring any map of where the data gaps are, outperforming optical flow and other deep learning approaches across multiple missing-data scenarios.]]></description>
		
		
		
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