<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>mtc-m21d.sid.inpe.br 808</site>
		<holdercode>{isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}</holdercode>
		<identifier>QABCDSTQQW/468R742</identifier>
		<repository>urlib.net/www/2022/01.25.13.21</repository>
		<lastupdate>2022:01.25.13.21.46 urlib.net/www/2021/06.04.03.40 simone</lastupdate>
		<metadatarepository>urlib.net/www/2022/01.25.13.21.46</metadatarepository>
		<metadatalastupdate>2023:01.03.16.46.28 urlib.net/www/2021/06.04.03.40 administrator {D 2022}</metadatalastupdate>
		<secondarykey>INPE--PRE/</secondarykey>
		<citationkey>HernándezBanosSapHuGeMay:2022:AsDaAs</citationkey>
		<title>Assessing the Data Assimilation Capability of the Prototype Rapid Refresh Forecast System to Represent an Amazonian Squall Line</title>
		<year>2022</year>
		<secondarytype>PRE CI</secondarytype>
		<author>Hernández Banos, Ivette,</author>
		<author>Sapucci, Luiz Fernando,</author>
		<author>Hu, Ming,</author>
		<author>Ge, G.,</author>
		<author>Mayfield, Will,</author>
		<group>MET-MET-DIPGR-INPE-MCTI-GOV-BR</group>
		<group>DIMNT-CGCT-INPE-MCTI-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>National Oceanic and Atmospheric Administration (NOAA)</affiliation>
		<affiliation>National Oceanic and Atmospheric Administration (NOAA)</affiliation>
		<affiliation>National Center for Atmospheric Research (NCAR)</affiliation>
		<electronicmailaddress>ibanos90@gmail.com</electronicmailaddress>
		<electronicmailaddress>lsapucci@gmail.com</electronicmailaddress>
		<conferencename>American Meteorological Society Annual Meeting, 102</conferencename>
		<conferencelocation>Houston, Texas</conferencelocation>
		<date>23-27 jan. 2022</date>
		<publisher>AMS</publisher>
		<transferableflag>1</transferableflag>
		<versiontype>publisher</versiontype>
		<abstract>Clusters of convective clouds organized in the form of lines which develop along the coastline of northern South America and propagate across the Amazonian basin are known as Amazon coastal squall lines. Amazon coastal squall lines have been studied in many researches by using data such as ar temperature, dew point temperature, pressure, wind velocity and direction from radiosondes; reflectivity and radial wind from radars; precipitable water vapor derived from the ground based Global Positioning System (GPS); and satellite imagery. Some studies have also used numerical models to simulate the development and propagation of these systems, and recently some have advanced to data assimilation applications. However, much of the ground-base available data comes from field campaigns held in the Amazon. Therefore, numerical modeling studies simulating an operational framework are challenging and not many studies are found in the literature. This study aims to investigate the impact of assimilating all available data in a 3 hourly cycling configuration in the representation of Amazon coastal squall lines, by conducting Observing System Experiments using the prototype Rapid Refresh Forecast System coupled to the Gridpoint Statistical Interpolation system. A 3 km grid-length covering northern South America is used and the case of squall line occurred on July 5, 2020 is studied. Results are promising considering the available data for this domain and will be presented during the conference.</abstract>
		<area>MET</area>
		<language>en</language>
		<usergroup>simone</usergroup>
		<visibility>shown</visibility>
		<documentstage>not transferred</documentstage>
		<nexthigherunit>8JMKD3MGPCW/3F35TRS</nexthigherunit>
		<nexthigherunit>8JMKD3MGPCW/46KUATE</nexthigherunit>
		<hostcollection>urlib.net/www/2021/06.04.03.40</hostcollection>
		<username>simone</username>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>urlib.net/www/2021/06.04.03.40</lasthostcollection>
	</metadata>
</metadatalist>