<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2664-0880</journal-id>
<journal-title><![CDATA[Revista Cubana de Meteorología]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Cubana Met.]]></abbrev-journal-title>
<issn>2664-0880</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Meteorológica de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2664-08802020000300001</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Corrección del pronóstico cuantitativo de la precipitación mediante el uso de redes neuronales]]></article-title>
<article-title xml:lang="en"><![CDATA[Quantitative precipitation forecast correction using neural network]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fuentes]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sierra]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morfa]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto de Meteorología Centro de Física de la Atmósfera ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de La Habana Instituto Superior de Tecnologías y Ciencias Aplicadas ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Max Planck Institute for Meteorology  ]]></institution>
<addr-line><![CDATA[ Hamburgo]]></addr-line>
<country>Alemania</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>26</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2664-08802020000300001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2664-08802020000300001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2664-08802020000300001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN En el presente trabajo se propone un modelo de redes neuronales como una técnica eficaz para la corrección del pronóstico cuantitativo de precipitación brindado por el modelo WRF. Para ello se emplea un Perceptrón Multi-Capa, con el objetivo de utilizar la salida (observaciones brindadas por las estaciones en superficie) para establecer una relación con los elementos de entrada (salidas del WRF). Se realiza el entrenamiento del modelo con datos reales de acumulado de precipitación correspondientes al año 2017; y se realiza la evaluación con el período comprendido entre el 4 de noviembre de 2018 y el 28 de febrero de 2019. Se logró la corrección del pronóstico cuantitativo de la precipitación en las estaciones analizadas, siendo más significativa la mejoría para la estación de Montaña y en los casos en los que el WRF sobreestima el acumulado de precipitación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT In this paper, a model of neural networks is proposed as an effective technique for the correction of the quantitative precipitation forecast provided by the WRF model. For this, a Multi-Layer Perceptron is used, with the aim of using the output (observations provided by the surface stations) to establish a relationship with the input elements (WRF outputs). Model training is carried out with real rainfall accumulation data corresponding to 2017; and the evaluation is carried out with the period between November 4, 2018 and February 28, 2019. The correction of the quantitative precipitation forecast in the analyzed stations was achieved, the improvement for the mountain station was more significant and, in cases where the WRF overestimates the accumulated rainfall.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[pronóstico cuantitativo de precipitación]]></kwd>
<kwd lng="es"><![CDATA[WRF]]></kwd>
<kwd lng="es"><![CDATA[corrección de sesgos]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[quantitative forecast precipitation]]></kwd>
<kwd lng="en"><![CDATA[WRF]]></kwd>
<kwd lng="en"><![CDATA[bias correction]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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