Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/12706
Conceptualizing the impact of dust-contaminated infrared radiances on data assimilation for numerical weather prediction
Title: Conceptualizing the impact of dust-contaminated infrared radiances on data assimilation for numerical weather prediction
Authors: Marquis, Jared W.Oyola, Mayra I.Campbell, James R.Ruston, Benjamin C.Córdoba-Jabonero, CarmenCuevas Agulló, Emilio ORCID RESEARCHERID SCOPUSID Autor AEMETLewis, Jasper R.Toth, Travis D.Zhang, Jianglong
Keywords: Lidars/Lidar observations; Remote sensing; Satellite observations; Soundings; Data assimilation; Aerosols/particulates
Issue Date: 2021
Publisher: American Meteorological Society
Citation: Journal of Atmospheric and Oceanic Technology. 2021, 38(2), p. 209–221
Publisher version: https://dx.doi.org/10.1175/JTECH-D-19-0125.1
Abstract: Numerical 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.
Sponsorship : This 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.
URI: http://hdl.handle.net/20.500.11765/12706
ISSN: 0739-0572
1520-0426
Appears in Collections:Artículos científicos 2019-2021


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