Abstract
Abstract. The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from satellite infrared sensors. In particular, the case study shows that assimilating aerosol-affected BTs significantly affects analyzed temperatures in the lower atmosphere across several regions of the globe. Consequently, a fully cycled aerosol-aware experiment improves 1–5 d forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials, and code repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed.
Highlights
An accurate and computationally efficient radiative transfer model is essential in radiance assimilation for supporting weather prediction, physical retrievals for satellite environmental data records, and inter-comparison between different remote sensing instruments
The aerosol-aware option reduces such errors by enabling aerosols to influence Gridpoint Statistical Interpolation (GSI)’s radiance observation operator, Community Radiative Transfer Model (CRTM), which calculates the brightness temperature (BT) and Jacobians. This option, may fluctuate the amount of observations assimilated in GSI because the quality control (QC) algorithm screens out observations based on measured BTs and aerosol-free simulated BTs
To illustrate how an aerosol transmittance correction is required within satellite radiances assimilated into meteorological data assimilation systems, we present a detailed analysis of a single-cycle GSI experiment using GOCART fields from MERRA-2 at 12:00 Z on 22 June 2020
Summary
An accurate and computationally efficient radiative transfer model is essential in radiance assimilation for supporting weather prediction, physical retrievals for satellite environmental data records, and inter-comparison between different remote sensing instruments. The offline experiment uses identical observations and first guesses as the control experiment, and the response of atmospheric analysis to aerosol-aware radiance calculations can be clearly demonstrated. The studies by Kim et al (2018) and Wei et al (2021) reported that (i) there is a considerable cooling effect on simulated BT when aerosols are considered, (ii) including aerosol transmittance effects in the BT calculation improves the fit to observations over the dust-laden regions, and (iii) the offline aerosol-aware experiment produces warmer analyzed SST (0.3–0.5 K) over the Atlantic Ocean. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed
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