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

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  • 1 University of North Dakota, Department of Atmospheric Sciences, Grand Forks, North Dakota, USA
  • 2 Jet Propulsion Laboratory, Pasadena, California, USA
  • 3 Naval Research Laboratory, Monterey, California, USA
  • 4 Instituo Nacional de Técnica Aeroespacial, Torrejón de Ardoz, Madrid, Spain
  • 5 Agencia Estatal de Meteorología, Santa Cruz de Tenerife, Canary Islands, Spain
  • 6 NASA Goddard Space Flight Center / University of Maryland Baltimore County, USA
  • 7 Atmospheric Composition Branch, NASA Langley Research Center, USA
  • 8 University of North Dakota, Department of Atmospheric Sciences, Grand Forks, North Dakota, USA
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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 dew point, 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.

Corresponding Author Contact Address: Jared W. Marquis, Clifford Hall Room 400, 4149 University Ave Stop 9006, Grand Forks, ND, 58202, USA. (jared.marquis@und.edu)

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 dew point, 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.

Corresponding Author Contact Address: Jared W. Marquis, Clifford Hall Room 400, 4149 University Ave Stop 9006, Grand Forks, ND, 58202, USA. (jared.marquis@und.edu)
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