<?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>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-62362024000100034</article-id>
<article-id pub-id-type="doi">10.20937/atm.53349</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An index for precipitation on the north coast of Peru using logistic regression]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivas Quispe]]></surname>
<given-names><![CDATA[Piero Rodrigo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Anderson-Frey]]></surname>
<given-names><![CDATA[Alexandra]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mcmurdie]]></surname>
<given-names><![CDATA[Lynn A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,University of Washington Department of Atmospheric Sciences ]]></institution>
<addr-line><![CDATA[Seattle WA]]></addr-line>
<country>USA</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>38</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362024000100034&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362024000100034&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362024000100034&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The northern coast of Peru has a desert-like climate. Since precipitation is so scarce, convective rainfall events have a major impact. However, little is known about these events, and their prediction is complex. To date, anomalous convective activity has mainly been associated with warm sea surface temperature anomalies near the Peruvian coast. However, a more comprehensive analysis of atmospheric variables could shed light on how these precipitation events are triggered. To address this need, this study presents a new diagnostic index of precipitation using logistic regression. Satellite radar data are used as predictands, and ERA5 reanalysis parameters are used as predictors. The new index includes the mixing ratio and divergence at different levels (950, 700, and 250 hPa) and the Gàlvez-Davison Index. This combination yields a logistic regression equation that ultimately takes the form of a new index, which we call RAMI (Rivas, Anderson-Frey, McMurdie Index). The RAMI is useful for diagnosing rainfall on the northern coast of Peru and could be useful for forecasting in this region, which is devoid of surface radars or other severe weather instruments.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La costa norte del Perú tiene un clima desértico. Dado que las precipitaciones son tan escasas, los fuertes eventos convectivos tienen un gran impacto en esta región. Sin embargo, poco se sabe de ellos y su predicción es muy compleja. A la fecha, la actividad convectiva en esta región ha sido relacionada con anomalías positivas de temperatura superficial del mar. No obstante, un estudio más completo de variables atmosféricas puede dilucidar cómo se desatan estos eventos convectivos. Para atender esta necesidad, este estudio presenta un nuevo índice para diagnosticar e identificar precipitaciones usando regresión logística. Los datos de radar basados en satélites se utilizan como predictandos, mientras que los parámetros de reanálisis ERA5 se utilizan como predictores. El nuevo índice consta de la relación de mezcla a 700 hPa, la divergencia a 950 y 250 hPa, y el índice Gálvez-Davison. Esta combinación produce una ecuación de regresión logística que finalmente toma la forma de un nuevo índice propuesto para el diagnóstico y predicción de las precipitaciones en la costa norte del Perú llamado RAMI (Rivas, Anderson-Frey, McMurdie Index). RAMI es útil para diagnosticar precipitaciones y puede ser útil para pronosticar precipitaciones en la costa norte del Perú, región que no cuenta con radares o instrumentos para análisis de tiempo severo.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[weather forecasting]]></kwd>
<kwd lng="en"><![CDATA[index]]></kwd>
<kwd lng="en"><![CDATA[precipitation]]></kwd>
<kwd lng="en"><![CDATA[rainfall]]></kwd>
<kwd lng="en"><![CDATA[logistic regression]]></kwd>
<kwd lng="en"><![CDATA[north coast of Peru]]></kwd>
</kwd-group>
</article-meta>
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