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Conceptualizing the impact of dust-contaminated infrared radiances on data assimilation for numerical weather prediction
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dc.contributor.authorMarquis, Jared W.es_ES
dc.contributor.authorOyola, Mayra I.es_ES
dc.contributor.authorCampbell, James R.es_ES
dc.contributor.authorRuston, Benjamin C.es_ES
dc.contributor.authorCórdoba-Jabonero, Carmenes_ES
dc.contributor.authorCuevas Agulló, Emilioes_ES
dc.contributor.authorLewis, Jasper R.es_ES
dc.contributor.authorToth, Travis D.es_ES
dc.contributor.authorZhang, Jianglonges_ES
dc.identifier.citationJournal of Atmospheric and Oceanic Technology. 2021, 38(2), p. 209–221es_ES
dc.description.abstractNumerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.es_ES
dc.description.sponsorshipThis study is supported by the NASA ROSES Science of Terra and Aqua program (T. Lee; 80HQTR18T0085). The MPLNET project is funded by the NASA Radiation Sciences Program and Earth Observing System. MPLNET observations at the Santa Cruz de Tenerife site are supported by the INTA Grant IGE03004.es_ES
dc.publisherAmerican Meteorological Societyes_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectLidars/Lidar observationses_ES
dc.subjectRemote sensinges_ES
dc.subjectSatellite observationses_ES
dc.subjectData assimilationes_ES
dc.titleConceptualizing the impact of dust-contaminated infrared radiances on data assimilation for numerical weather predictiones_ES
Appears in Collections:Artículos científicos 2019-2022

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