Conceptualizing the Impact of Dust Contaminated Infrared Radiances on Data Assimilation for Numerical Weather Prediction

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 sys...

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Bibliographic Details
Published in:Journal of atmospheric and oceanic technology 2021-02, Vol.38 (2), p.209-221
Main Authors: Marquis, Jared W., Campbell, James R, Ruston, Benjamin C., Córdoba-Jabonero, Carmen, Lewis, Jasper R, Toth, Travis D., Zhang, Jianglong
Format: Article
Language:English
Subjects:
Aerosol effects
Aerosol observations
Aerosol optical depth
Aerosol Robotic Network
Aerosols
Armed forces
Atmospheric particulates
Clouds
Contamination
Data assimilation
Data collection
Dust
Dust storms
Earth Resources And Remote Sensing
Environment Pollution
Exploitation
Infrared radiation
Innovations
Lidar
Mathematical models
Meteorological satellites
Moisture
Numerical weather forecasting
Oceanography
Optical analysis
Optical thickness
Predictions
Radiance
Radiative transfer
Rawinsondes
Sensors
Temperature
Weather forecasting
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Summary: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.
ISSN:0739-0572
1520-0426
DOI:10.1175/JTECH-D-19-0125.1