The Aerosol Module in the Community Radiative Transfer Model

18 The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the 19 Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We 20 conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI 21 analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature 22 (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how 23 failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from 24 satellite IR sensors. In particular, the case study shows that assimilating aerosol-affected BTs significantly affects analyzed 25 temperatures in the lower atmosphere across several regions of the globe. Consequently, a fully-cycled aerosol-aware 26 experiment improves 1-5 day forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern 27 Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials and code 28 repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in 29 the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware 30 radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed. 31


Introduction 32
An accurate and computationally efficient radiative transfer model is essential in radiance assimilation for supporting weather 33 prediction, physical retrievals for satellite environmental data records, and inter-comparison between differentamong remote 34 sensing instrumentssensors. The Community Radiative Transfer Model (CRTM) is a sensor-based radiative transfer model 35 used extensively within satellite and remote sensing systems (Weng, 2007;Han et al., 2007). It was primarily designed for 36 computing satellite radiances and has been widely used within the Gridpoint Statistical Interpolation (GSI, Wu et al., 2002;37 Kleist et al., 2009) system for directly assimilating radiances from infrared (IR) and microwave (MW) sensors. Specifically, 38 clear-sky radiance calculations are carried out within the CRTM given the atmospheric scattering and absorption profile, 39 surface emissivity and reflectivity, and source functions. For cloudy radiance simulations (Stegmann et al., 2018), vertical 40 profiles of hydrometeor variables (e.g., cloud liquid water path and ice water path) are also required. Note that the CRTM 41 wasis not designed to enact composition-radiation interaction effects within describe spectral longwave and shortwave 42 broadband radiative transfer calculations infor general circulation models applications. Instead, the CRTM wasit is developed 43 to support monochromatic satellite radiance data assimilation from longwave and microwave sensors, and for satellite retrieval 44 algorithm development. 45

46
Past studies have demonstrated that aerosols significantly impact the simulation of brightness temperature (BT) in the IR 47 channels. BT is "a descriptive measure of radiation in terms of the temperature of a hypothetical blackbody emitting an 48 identical amount of radiation at the same wavelength" (American Meteorological Society, 2012). A reduction in retrieved BT 49 of 2-4 K in the atmospheric window region due to a strong dust outbreak was reported during the Saharan Dust Experiment 50 (SHADE) campaign (Highwood et al., 2003). Pierangelo et al. (2004) and Peyridieu et al. (2009) showed that the dust cooling 51 effects may reach 3 K in tropical atmospheric conditions depending on the dust burden. Diaz et al. (2001) found that there is 52 a significant increase in the errors of sea surface temperature (SST) retrievals in the presence of enhanced aerosol loading in 53 the atmosphere. The dust effects on satellite derived SST are constrained by accounting for dust absorption (Weaver et al.,54 that is based on the aerosol types of the mass-based Goddard Chemistry Aerosol Radiation and Transport (GOCART, Chin et Operationally, given aerosol types, radius, concentration and ambient relative humidity, CRTM generates aerosol optical 131 profiles that the radiative transfer solver requires for multi-scattering simulations and radiance calculations. The effect of 132 aerosols on MW sensors is not considered yet because the impact of aerosols on MW radiance is usually very small, given 133 aerosols size is generally much smaller than MW wavelengths (Petty, 2006 When GOCART was selected as the aerosol module within WRF-Chem, it was configured with fourteen GOCART aerosol 148 species (Liu et al., 2011): sulfate; hydrophobic and hydrophilic OC and BC; sea salt in four particle size bins (with radii of 149 0.1-0.5, 0.5-1.5, 1.5-5, and 5-10 µm) and dust particles in five particle size bins (with radii of 0.1-1.0, 1.0-1.8, 1.8-3, 3-6, and 150 6-10 µm). A default CRTM lookup-table has been used for pre-calculated aerosol optical property parameters for the fourteen 151 GOCART aerosol species (Liu et al., 2007;Liu and Lu, 2016). We assume that the particles are spherical and externally mixed. 152 We also assume lognormal size distributions for sulfate and carbonaceous aerosols as well as for each sea salt and dust bin. 153 The lognormal size distribution for N particles can be expressed as follows (d' Almeida et al., 1991), 154 where r is a radius, rg the geometric median radius, and σg the geometric mean standard deviation. The k th moment of the 156 distribution can be expressed as follows (Binkowski and  . (2) 158 (3) 161 162 Table 1 lists the GOCART size parameters (particle density, effective radius, and geometric standard deviation) and refractive 163 indices at 550 nm used in CRTM version 2. The optical properties of each aerosol species are computed based on Mie scattering 164 theory. Hydrophilic aerosol particle size increases as relative humidity (RH) of the ambient atmosphere increases. Therefore, 165 the water content in aerosol needs to be considered when calculating the refractive index. The effective radius growth factor 166 for hygroscopic aerosols may be theoretically calculated or obtained from a pre-calculated look-up table (d' Almeida et al., 167 1991). In this study, the hygroscopic growth factor used for the GOCART model (Chin et al., 2002) is adopted and given in 168 Table 2. Once the growth factor ag is evaluated, the refractive index nr for the hygroscopic aerosol can be calculated using a 169 volume mixing method as: 170 (4) 171 where no and nw are the refractive indices for dry aerosols and water, respectively. We adopt the refractive index no from the 172 Optical Properties of Aerosols and Clouds (OPAC) dataset (Hess et al. 1998), while the water refractive index is given by 173 (Hale and Querry, 1973 The GOCART model used by GMAO and NCEP for aerosol forecast and reanalysis has evolved to use 5 sea salt size bins 181 (with radii of 0.03-0.1, 0.1-0.5, 0.5-1.5, 1.5-5, and 5-10 µm). The first sub-micron sea salt bin was added to facilitate optical 182 properties and aerosol-cloud interaction studies (Colarco et al., 2010), but was excluded from the previous GOCART versions 183 as well as the WRF-Chem GOCART model. While GMAO's GEOS and NCEP's GFS contain fifteen GOCART aerosol 184 species, the CRTM aerosol module has also not yet been modified to include the new added sub-micron sea salt bin (see Table  185 1). To overcome this discrepancy, the latest GSI/CRTM release (i.e., GSI 3.7 and CRTM 2.3) combines the mixing ratios from 186 the two sub-micron sea salt bins in order to use the aerosol optical property parameters from the original GOCART model. 187 This limitation is acknowledged in this article and will be addressed in a future CRTM release (see section 4). 188 189 While the CRTM is primarily designed for computing satellite radiances, an additional module was added to CRTM by Liu 190 and Lu (2016) to compute aerosol optical depth (AOD). This CRTM-AOD module enables the GSI system to assimilate AOD All fifteen GOCART aerosol species are passed along to the CRTM. In addition to the background file, a user needs to modify 208 the configuration files, anavinfo and satinfo, in the "fix" directory. The anavinfo file is the information file to set control and 209 analysis variables. The satinfo file is the information file to specify satellite channels to be assimilated and associated 210 parameters. For an aerosol-aware experiment where aerosol absorption and scattering are included in BT calculations, aerosol 211 species are specified in the "chem_guess" section of anavinfo and sensors and channels are set to 1 in the "iaerosol" column 212 of satinfo. The reader can refer to the fv3aerorad_satinfo.txt and anavinfo_fv3aerorad for the aerosol-aware configuration. The 213 corresponding namelist (gsiparm.anl) can be found at the "global_C96_fv3aerorad" section (line 2931-3046) in 214 regression_namelists.sh under the "regression" directory. It should be noted that the namelist variable, "lread_ext_aerosol", 215 determines how GSI ingests the aerosol information from background files or external files. 216

Aerosol impacts on BT calculations 218
To illustrate how an aerosol transmittance correction is required within satellite radiances assimilated into meteorological data 219 assimilation systems, we present a detailed analysis of a single-cycle GSI experiment (the AER experiment) using GOCART 220 fields from MERRA-2 on 12Z June 22, 2020. This time is chosen because it captures a strong Saharan dust event that covers 221 the trans-Atlantic region. A baseline GSI experiment (the CTL experiment) with the anavinfo and satinfo resource files reverted 222 back to the default aerosol-blind configuration was also conducted. Both experiments used the same first-guess fields and 223 assimilated identical conventional and satellite observations within a ±3-hour assimilation window. In AER, the aerosol 224 transmittance effects were only considered in the CRTM simulation for IR sensors.  Table 3 describes the range and the average of total aerosol column mass density 250 over the regions with different dominant aerosol species. It shows that the total loading of aerosols is similar over the dust and 251 carbonaceous aerosols dominated regions. This indicates that the stronger cooling effects by dust aerosol on BT in the IR 252 window region is not due to stronger loading. Note that in the northern hemisphere, the high-latitude region is characterized 253 as dust-dominant except for the Russian Far East in MERRA-2 (Figure 2b). While anomalous or erroneous modeled aerosol 254 loading may bias the results, the finding that dust has the largest impact on the BTs simulations, reported in this study and 255 previous studies, remains unchanged. Therefore, we focus our remaining analysis on dust over Tropical Africa and the Mid-   the trans-Atlantic region, where a large dust plume spans the region. Significant aerosol cooling (~4 K) in BT was found in 270 the aerosol-aware experiment (Fig. 3a) due to the large plume. Comparing the first guess departures from CTL and AER 271 experiments ( Fig. 3b and 3c) shows that OMFs for AER are warmer than CTL (cf. 0.27 K vs. -0.09 K). Note that some 272 observations assimilated in CTL were rejected in AER (near 55° W and 15° N) and vice versa (near 65° W and 15° N, and 273 over Africa). This feature suggests that the quality control has been influenced by including aerosol transmittance effects in 274 CRTM. Over the trans-Atlantic region, the aerosol-aware experiment assimilated several observations with larger first-guess 275 departures located in the strong dust plume (Fig. 3d). Figure 4 presents the scatter plot of dust column mass density versus 276 OMF differences (AER -CTL) for these data points assimilated in AER on 12Z June 22, 2020. The data points with large 277 OMF differences are corresponding to the areas with higher dust loading. Nevertheless, when considering aerosol information, 278 the root-mean-square first-guess departures decreased 0.08 K globally and 0.42 K over the trans-Atlantic region at this channel 279 (not shown here). This implies that simulated BTs in the aerosol aware run are in better agreement with the observations.

Conclusions and Future Outlook 354
This article described aerosol absorption and scattering calculations of the CRTM version 2 in the GSI analysis. We also 355 conducted sensitivity experiments to investigate the aerosol-affected GSI analysis in both single-cycle and fully-cycled runs. 356 Both GSI and CRTM are well documented with user guides, tutorials and code repositories available online. This article is 357 primarily a joined-up documentation for aerosol absorption and scattering calculations in the CRTM version 2 and GSI. It also 358 provides guidance for prospective users of the CRTM aerosol option. Scientific aspects of aerosol-affected BT in atmospheric 359 data assimilation are briefly discussed. Specifically, numerical experiments were conducted to illustrate how including aerosol 360 radiative effects in CRTM changes the GSI analysis. We found that taking the aerosols into account reduces simulated BT in 361 thermal window channels over dust-dominant regions. Assimilating aerosol-affected BTs produces a warmer analyzed lower 362 atmosphere. From the verification scorecard, neutral to positive results are found in the fully-cycled, aerosol aware experiment. 363

364
The CRTM team, in coordination with its partners and collaborators, is building a robust capability to accurately and 365 consistently simulate the emission, absorption, and scattering properties of all (radiatively important) atmospheric constituents. 366 There are several ongoing and planned efforts to enhance the CRTM aerosol module. For example, more aerosol optical look-367 up tables have been added and the calculations of aerosol optical properties are being evaluated. In addition, the CRTM is 368 being refactored toward a more flexible aerosol interface to handle aerosol optical look-up-tables as well as to support aerosol 369 specifications from other operational aerosol models, such as Community Multiscale Air Quality (CMAQ). Other aerosol-370 related efforts include, but are not limited to, improving the physical representation of aerosols and including active sensors 371 such as aerosol lidar. These developments, once implemented and tested, will be reported in future manuscripts. 372 Code and Data Availability. 373 Various software packages are referred to throughout the paper. The following list contain links to the main software 374 documentations or repositories discussed: 375 The GSI webpage: https://dtcenter.ucar.edu/com-GSI/users/index.php 376 The GSI v3.7 user guide: https://dtcenter.ucar.edu/com-GSI/users/docs/users_guide/html_v3.7/index.html 377 The GSI v3.7 online tutorial: https://dtcenter.ucar.edu/com-GSI/users/tutorial/online_tutorial/index_v3.7.php 378 The