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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model">
		<rdfs:label><![CDATA[Towards a Dynamic Data Driven AI Regional  Weather Forecast Model]]></rdfs:label>
		<pub:title><![CDATA[Towards a Dynamic Data Driven AI Regional  Weather Forecast Model]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2024-11-08T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[The advent of long-term reanalysis datasets such as ECMWF
ERA 4/5 has enabled the development of AI-driven machine learning
models for weather forecasting. The major benefit of AI as an approach
is its ability to reduce computational forecast time from tens of hours

to tens of seconds, thereby enabling a variety of new applications rang-
ing from extreme regional weather event forecasting to first responder

aid for wildfires, severe storms, floods, oil spills, tornadoes, and other
extreme events in real time. Today, several operational weather forecast
centers are evaluating these models as complements or alternatives to

their existing models. However, similar efforts in applying AI/ML ap-
proaches to mesoscale weather forecasting have lagged behind due to

the lack of a reanalysis of current operational regional weather forecast

models. Recently, the ECMWF made publicly available Copernicus Eu-
ropean Regional ReAnalysis (CERRA) at spatial resolutions of 11km

(0.10) and 5.5km (0.050) from 1984 to the present. We present the first

demonstration of a successful AI regional forecast at 5.5 km spatial reso-
lution employing the Nvidia FourCastNet (FCN) model with its Adaptive

Fourier Neural Operator (AFNO) and transformer self-attention model-
ing approach. We describe the training of a regional FourCastNet model

in the NASA Center for Climate Studies (NCCS) Adapt cluster at the
Goddard Space Flight Center using five years of CERRA reanalysis data
at 3-hour intervals for five variables at four pressure levels. We show the
RMSE forecast errors of a 5.5km implementation trained on five years of
data improved for all variables but one over a forecast trained on three.
We also devise a nesting scheme wherein our regional model is boundary

forced by a global forecast. We find that our model improves on the per-
formance]]></pub:abstract>
		<pub:counter>1688</pub:counter>
		<pub:booktitle><![CDATA[DDDAS (Dynamic Data Driven Applications Systems) 2024 to Appear in Springer]]></pub:booktitle>
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					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Jennifer/Sleeman"><person:name><![CDATA[Jennifer Sleeman]]></person:name><rdfs:label><![CDATA[Jennifer Sleeman]]></rdfs:label></person:Person>
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							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Milton/Halem"><person:name><![CDATA[Milton Halem]]></person:name><rdfs:label><![CDATA[Milton Halem]]></rdfs:label></person:Person>
						</rdf:first>
						<rdf:rest>							<rdf:List>
								<rdf:first>
									<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Sophia/Hamer"><person:name><![CDATA[Sophia Hamer]]></person:name><rdfs:label><![CDATA[Sophia Hamer]]></rdfs:label></person:Person>
								</rdf:first>
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		<pub:firstAuthor>
<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Jennifer/Sleeman"><person:name><![CDATA[Jennifer Sleeman]]></person:name><rdfs:label><![CDATA[Jennifer Sleeman]]></rdfs:label></person:Person>
		</pub:firstAuthor>
		<pub:softCopy><pub:SoftCopy>
			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/1453.pdf]]></pub:softCopyURI>
			<pub:softCopySize>2290261</pub:softCopySize>
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	</pub:InProceedings>

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