1. Introduction
The microphysical and radiative properties of cloud and aerosol are the major sources of uncertainties in the atmospheric sciences (Stocker et al. 2013). During the first years of the twenty-first century, various state-of-the-art airborne and spaceborne instruments were deployed to improve the understanding of cloud and atmospheric aerosol. With observations by active sensors, such as the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) (Winker et al. 2010) and CloudSat (Stephens et al. 2002), researchers were able to construct a global-scale, three-dimensional map of cloud and aerosol distributions. In addition, it is found that the averaged occurrence of multiple cloud layers is about 50% and may be as large as 60% in the tropical western Pacific, equatorial South America, and Africa (Kato et al. 2010; Liu et al. 2012; Mace et al. 2009). Devasthale and Thomas (2011) reported that in some regions, such as the northern Pacific Ocean, western Africa, and western South America, the occurrence frequencies of aerosol–water overlap approached 20%. Despite the high frequency of overlap between cloud and aerosol (hereafter referred to as scattering layers) in real atmospheres, some widely used cloud–aerosol retrieval algorithms remain restricted to conditions with only one scattering layer (Hsu et al. 2006; Levy 2009). For a single-scattering-layer scene, a forward radiative transfer model (RTM) is unnecessary in the operational retrieval algorithm because satellite observations can be efficiently simulated using precomputed single-layer cloud–aerosol lookup tables (LUTs) for reflectivity, transmissivity, and emissivity in conjunction with some simplifications of the atmosphere and surface correction (Kaufman et al. 1997; Wang and King 1997; Platnick et al. 2003). For scenes with multiple scattering layers, an RTM is indispensable for accurate modeling and retrieval implementation. A significant number of computations cannot be avoided if an accurate RTM is employed to map the properties of atmospheric components to observational space (Kokhanovsky et al. 2010). For this reason, Chang and Li (2005) developed a simplified method to efficiently infer multilayered cloud optical thickness values using a combination of several solar reflection bands and thermal infrared (IR) bands. In their retrieval algorithm, relatively large uncertainties may result from not using a rigorous RTM and ignoring the effects of multiple scattering in thermal IR bands and cloud particle size.
RTMs encompass the most important physical processes related to atmospheric constituents [e.g., clouds and gases (Kokhanovsky et al. 2010)] and are, therefore, widely used in cloud and aerosol retrieval algorithms (e.g., Doutriaux-Boucher and Dubuisson 2009; Garnier et al. 2012; Poulsen et al. 2011; Francis et al. 2012; Kahn et al. 2014; Dubuisson et al. 2014; Iwabuchi et al. 2014; Yi et al. 2014) and sensitivity studies (e.g., Kahn et al. 2004; Jin and Nasiri 2014). With increasing effort dedicated to quantifying radiative forcings and climate feedbacks of different atmospheric constituents and detecting climate change signals, a pressing need arises as to how to efficiently facilitate the forward simulations of spectrally resolved outgoing longwave radiation (OLR) at the top of the atmosphere (TOA), of the surface downwelling longwave radiation (DLR), and of fluxes at arbitrary atmospheric levels (Leroy et al. 2008; Huang et al. 2010; Chen et al. 2013; Huang 2013). RTMs used in these studies, for example, the moderate resolution atmospheric transmission (MODTRAN), version 5 (Anderson et al. 2007), are shown to have approximately a 1-K root-mean-square error (RMSE) in the longwave region under overcast-sky conditions (Chen et al. 2013). Some rigorous RTMs, such as the Discrete Ordinate Method Radiative Transfer model (DISORT) code (Stamnes et al. 1988; Dubuisson et al. 1996), the adding–doubling RTM (Twomey et al. 1966; Hansen and Hovenier 1971), and the successive order of scattering method (Heidinger et al. 2006; Lenoble et al. 2007), consider scattering, absorption, and thermal emission in plane-parallel atmospheres. These RTMs are usually referred to as benchmarks for precise simulations; however, tremendous demands on computation restrict the applications of these models if large temporal and spatial scales are involved. To alleviate the computational burden, various fast RTMs have been developed for diverse atmospheres. For example, Saunders et al. (1999) developed a fast RTM [Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) (RTTOV)], which simulates outgoing radiances at the TOA in cloudy atmospheres. To reduce computational time, the improved RTTOV does not consider cloud scattering effects, leading to relatively large errors in the brightness temperature (BT) difference between the 8.7- and 12-μm bands. Liou et al. (1988) developed a radiative transfer formulation based on the delta-four-stream (D4S) approximation. The speed of D4S is approximately 150 times faster than the doubling method (Liou et al. 2005), while the simulation error in terms of radiance is limited to 0.4% (Yue and Liou 2009). Dubuisson et al. (2005) developed three RTMs that are applicable to purely absorbing media, partially absorbing media without scattering effects, and scattering media. The three RTMs are designed to simulate TOA radiances and brightness temperatures measured by the three IR channels (8.7, 10.6, and 12 μm) of the Infrared Imaging Radiometer (IIR). Wei et al. (2004) and Wang et al. (2011, 2013b) developed fast IR RTMs for single-layer cloudy atmospheres using precomputed LUTs for both water and ice clouds. The computational efficiencies of these fast IR RTMs are three orders of magnitude higher than the DISORT, and the TOA BTs simulation errors are smaller than 0.2 K. Niu et al. (2007) developed a fast IR RTM to simulate the TOA radiances under conditions with two cloud layers. However, the previous IR RTM neglects the zenith-angle dependence of the incident radiance and integrates cloud bidirectional reflectivity and transmissivity over all incident angles, formalizing an oversimplified scheme and incurring an increase in simulation errors. Fast RTMs based on the adding–doubling principle were developed by Zhang et al. (2007) for IR regions and by Wang et al. (2013a) for visible (VIS) and shortwave infrared (SWIR) regions. The two RTMs can be applied to multiple scattering-layer scenes and fully consider the zenith-angle dependence of the incident radiance field, which provides high accuracy but with low computational efficiency compared to other fast RTMs.
This study is aimed at filling the gap between the rigorous and oversimplified fast RTMs and providing a flexible and reliable modeling capability for various research purposes. To increase the computational efficiency without significantly reducing accuracy, we use a hybrid algorithm to consider cloud–aerosol reflectivity and transmissivity in the IR region. Instead of solely using either local albedo and transmittance or bidirectional reflectivity and transmissivity, we fully consider the zenith-angle dependence of the incident field associated with cloud bidirectional transmissivity and, for simplicity, assume a fully isotropic incident field when calculating scattering-layer reflection. Although the assumption causes errors in the simulation of reflected radiation and the errors could be amplified once the scattering layer has a large albedo, the present RTM is quite accurate for a wide variety of atmospheric remote sensing applications. Moreover, the model simulations, including spectrally resolved radiances and fluxes at arbitrary atmospheric levels, facilitate theoretical studies of radiative forcing and climate change.
The paper is organized into six sections. The methodology details are described in section 2. Section 3 introduces the single-scattering and microphysical properties of water and ice cloud particles and of dust aerosols. In section 4, we compare simulations of the present RTM with the 32-stream DISORT. A case study is given in section 5 to demonstrate the potential applications of the RTM to cloud property retrieval implementations and in cloud radiative forcing studies. Section 6 presents a summary with the conclusions.
2. Methodology
Global albedos for (top) water cloud, (middle) ice cloud, and (bottom) mineral dust aerosol. The cloud particles satisfy the gamma distribution with an effective variance of 0.1. The effective particle size values for water and ice clouds are 24 and 50 μm, respectively. A bilognormal size distribution is used to specify dust aerosol. The fine and coarse modes (specified in terms of diameter) are 0.4 and 3 μm, respectively. The standard deviations for the two modes are 1 μm.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
Optical thickness and particle size for water cloud, ice cloud, and mineral dust aerosol selected in the LUTs.
Figure 2 is a schematic illustration of an atmosphere with M discretized clear and scattering layers. Three scattering layers are located at layers i, j, and k. The terms Ii−1 and Fi−1 indicate the radiance and flux at the top of the ith layer. The upward and downward arrows indicate the radiance or flux directions. For example, 
Illustrative diagram for a discretized atmosphere, including three scattering layers.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
3. Absorption properties of atmosphere and scattering properties of cloud and aerosol particles
A precomputed database is used to simulate clear-sky optical thickness (Wang et al. 2013a). The database stores gas transmittances at a 0.1-cm−1 spectral resolution, which are integrations of monochromatic transmittances calculated with the rigorous line-by-line radiative transfer model (LBLRTM) (Clough et al. 2005) for major absorptive gases such as H2O, CO2, and O3. Specifically, transmittances of H2O and O3 are functions of gas concentration, air temperature, and pressure, whereas O2 and CO2 are considered well-mixed gases with fixed concentrations of 0.209 × 106 and 385 ppmv, respectively. We wish to emphasize that calculations of transmission, scattering, and emission of multiple layers are quite accurate at monochromatic wavelengths. However, equations and formulations shown in section 2 are based on a 0.1-cm−1 spectral resolution. Within a narrow spectral interval, an implicit assumption is that the gas absorption, the optical properties of atmospheric particles, and the Planck function have little variations. The assumption is true for the Planck function and the atmospheric particle optical properties, which smoothly vary in the thermal infrared region. For absorptive gases, however, the assumption becomes problematic. Wang et al. (2013b) compared narrowband clear-sky transmittance values that are integrals of transmittance with three different spectral intervals: quasi monochromatic (0.001 cm−1), 0.1 cm−1, and 1.0 cm−1. The 0.1-cm−1 spectral interval was found to have only a 0.1% relative error, while the 1.0-cm−1 interval had a 4% relative error. Therefore, the 0.1-cm−1 spectral interval, which maintains reasonable accuracy in the simulations, can be applied to current high-spectral-resolution sensors, such as the Atmospheric Infrared Sounder (AIRS) (Aumann et al. 2003) with an averaged 1.5-cm−1 full width at half maximum (FWHM) near 1000 cm−1 and the Infrared Atmospheric Sounding Interferometer (IASI) (Blumstein et al. 2004) with a fixed 0.5-cm−1 FWHM between 645 and 1210 cm−1. In addition, the model can be applied to various narrowband imagers by considering instrument spectral response functions (SRF). However, limitations in the current clear-sky database model are obvious. For example, calculations at thousands of 0.1-cm−1 spectral grids are required in order to simulate observations in a single narrow band, because IR narrow bands always span several hundred wavenumbers. Furthermore, the 0.1-cm−1 spectral interval may lead to larger errors in strongly absorptive bands, such as the CO2 vibrational band (600–700 cm−1) and the water vapor vibrational–rotational band (1300–2000 cm−1).
Meng et al. (2010) generated a single-scattering property database for triaxial ellipsoidal mineral dust aerosols. Four numerical methods—the Lorenz–Mie theory (Bohren and Huffman 2008), the 
4. Model comparisons
In one run, the present fast RTM simulates radiances/fluxes with a high-spectral resolution at arbitrary altitudes and directions (for radiances). The fast RTM is evaluated in comparison with the reference 32-stream DISORT in terms of numerical accuracy and computational time. A typical midlatitude summer atmosphere (McClatchey et al. 1972) with a surface temperature of 299 K is used to conduct the comparisons. The atmosphere is separated into 80 layers with a 0.5-km thickness from the surface to 30 km and a 1-km thickness for the upper atmosphere. The clear-sky optical thickness values for each clear layer are precalculated by using the LBLRTM (Clough et al. 2005) and are used as input for both the fast RTM and DISORT.
Figure 3 compares the simulations associated with multiple ice cloud layers from the fast model and the 32-stream DISORT. An upper ice cloud consisting of small ice particles (Deff = 30 μm) with optical thickness 1.0 and cloud-top height (CTH) of 12.5 km and a lower ice cloud consisting of large ice crystals (Deff = 50 μm) with optical thickness of 2.0 and CTH of 7.5 km are used. A Lambertian surface with an albedo of 0.05 is assumed in the calculation. The left column of Fig. 3 shows the two model simulations of TOA BT at a 20° viewing zenith angle, the TOA upward flux, the downward flux at the surface, and the upward flux at the higher ice cloud bottom, respectively. The right column of Fig. 3 shows simulation errors of the fast RTM in terms of BT difference and relative flux errors. Under multilayered ice cloud conditions, the TOA BT errors of the fast RTM are generally smaller than 0.1 K, and the relative errors of the simulated flux on different levels are limited to 0.3%.
Comparisons between the fast RTM and the 32-stream DISORT for a typical midlatitude summer atmosphere with two ice cloud layers. The upper ice cloud (CTH = 12.5 km, τ = 1.0) consists of small ice crystals with Deff = 30 μm. The lower cloud (CTH = 7.5 km, τ = 2.0) has more large particles with Deff = 50 μm. The surface temperature is 299 K and the surface albedo is 0.05 throughout the IR region. The viewing zenith angle is 20.0°. (left) The fast model (black solid line) and 32-stream DISORT (red dashed line) (a) simulated TOA BTs, (c) upward flux at TOA, (e) downward flux at the surface, and (g) upward flux at the bottom of the upper ice cloud. (right) Simulation errors of the fast model and the DISORT in terms of (b) TOA BT errors, and (d),(f),(h) relative flux errors.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
Figure 4 is similar to Fig. 3 but shows comparisons between the two models for a scene consisting of a high cirrus cloud (τ = 0.5, Deff = 30 μm, CTH = 12.5 km) above an optically thick water cloud (τ = 5, Deff = 24 μm, CTH = 5 km). Note that in the first case, the lower cloud consists of relatively large ice crystals with a Deff of 50 μm, and in the second case, Deff of the lower water cloud layer is 24 μm, which results in a higher cloud albedo and larger reflection impact. Therefore, the fast model simulated upward fluxes (Figs. 4b,d) have some biases, and the relative errors reach 0.5% at wavenumbers larger than 1100 cm−1. A typical case with a high cirrus cloud (τ = 0.5, Deff = 30 μm, CTH = 12.5 km) existing above a lower mineral dust aerosol layer with an optical thickness of 2.0, and an aerosol layer top height (ATH) of 2.5 km is tested (Fig. 5). The assumed full aerosol particle size distribution is contributed by two lognormal distributions including a fine-mode diameter of 0.4 μm and a coarse-mode diameter of 3.0 μm. The standard deviations for the two distributions are 1 μm. For small aerosol particles in comparison with the thermal IR wavelength, the aerosol optical thickness values are generally smaller than 0.2 in the spectral region between 1100 and 1300 cm−1, when the 0.64-μm optical thickness is 2. In this spectral region, more emitted or reflected energy by the upper ice cloud layer can reach the surface, leading to a slight decrease in model accuracy. In conclusion, we test a more complicated scene with optically thin ice and aerosol layers and an optically thick water cloud simultaneously existing at three levels (Fig. 6). With the increase in transparent layers, the accuracy of the upward radiance simulation decreases, which may be caused by simplifications associated with cloud/aerosol-layer reflection. For this case, the TOA BT errors exceed 0.1 K in some regions and the overall BT RMSE is 0.05 K, higher than in the two cases without an aerosol layer. We also investigate the model performance when the surface is more reflective. Figure 7 shows the comparisons between the two models with a surface albedo of 0.1. The other model inputs are the same as the ones used to plot Fig. 4. Figure 7g shows the comparison between the upwelling fluxes at the bottom of the lower water cloud. Increasing the surface albedos seems to have little impact on the model accuracy.
Comparisons between the fast RTM and the 32-stream DISORT. An upper ice cloud (CTH = 12.5 km, Deff = 30 μm, τ = 1.0) and an underlying water cloud (CTH = 5 km, Deff = 24 μm, τ = 5.0) are assumed in the model atmosphere. The other conditions are as in Fig. 3.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
Comparisons between the fast RTM and the 32-stream DISORT. An upper ice cloud (CTH = 12.5 km, Deff = 30 μm, τ = 1.0) and an underlying mineral dust aerosol layer [ATH = 2.5 km, fine mode (specified in terms of diameter) = 0.4 μm, coarse mode (specified in terms of diameter) = 3.0 μm, standard deviation = 1.0 μm, τ = 2.0] are defined in the model atmosphere. The other conditions are as in Fig. 3.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
Comparisons between the fast RTM and the 32-stream DISORT. Three scattering layers are specified in the model atmosphere. The upper ice cloud (CTH = 12.5 km) and lower water cloud (CTH = 2.0 km) have the same properties as the ice and water clouds in Fig. 4. The aerosol layer (ATH = 3.5 km) has the same properties as in the aerosol layer in Fig. 5. The other conditions are as in Fig. 3.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
Comparisons between the fast RTM and the 32-stream DISORT. The surface albedo is 0.1. The other conditions are as in Fig. 4. (g) Upwelling fluxes at the bottom of the lower water cloud simulated by the fast RTM and the DISORT and (h) the corresponding fast RTM errors.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
We also compare the computational time for 100 runs by the two models using a single 2.93-GHz CPU on a Linux sever. For the single-cloud-layer case, the DISORT takes 28 522 s, while the fast RTM takes only 2.7 s, more than 10 000 times faster than the DISORT. With an increase in the number of cloud and aerosol layers, the computational time of the DISORT does not significantly increase: however, the computational time of the fast RTM is almost doubled in the two-layer case (4.6 s) and tripled in the three-layer case (6.6 s), suggesting that the fast RTM spends most of the computational time in conjunction with scattering layers. Generally speaking, the fast RTM is four orders of magnitude faster than the DISORT under single-scattering-layer conditions and three orders of magnitude faster under multiple-scattering-layer conditions.
Note that simulations related to a specific sensor can be performed by integrating SRF-weighted results. More importantly, the current model can be combined with various clear-sky models designed for specific sensors, such as the correlated-k distribution (CKD) method (e.g., Kratz 1995; Shi et al. 2009), the principal component method (Liu et al. 2006), and the clear-sky module of the community radiative transfer model (Han et al. 2006). For example, instead of conducting simulation at a 0.1-cm−1 spectral resolution, the CKD method reduces computational burden by providing several gas transmittance profiles with different weightings for a specific band. With a CKD clear-sky module, only a small number of forward computations are required to calculate radiances by using different weighted transmittance profiles and cloud–aerosol LUTs. Note, SRF can be incorporated into the CKD method (Edwards and Francis 2000). The band-based simulation can be averaged with these radiances and CKD weightings, which significantly reduces computational time for applications to remote sensing with observations by high-spectral sensors such as the AIRS, and by narrowband sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). With these advantages, the present cloud–aerosol module can be applied to various remote sensing implementations.
5. Model applications
With the highly accurate and computationally efficient RTM, various remote sensing applications in realistic atmospheres are expected. In this section, we demonstrate the present RTM’s capability for cloud property retrieval and cloud radiative forcing studies involving a multiple-cloud-layer atmosphere.
A cloudy case off the western coast of South America on 10 August 2008 (around 0710 UTC, local nighttime) is presented. Collocated Aqua MODIS observations (MYD02 Level 1B), CloudSat 2B-GEOPROF-lidar product (version 004), and CloudSat 2B-CLDCLASS-lidar product (version 1.0) are used to conduct the case study (both CloudSat products are available from http://www.cloudsat.cira.colostate.edu/dataHome.php). Lidar is a powerful, active instrument with inherent advantages in optically thin cloud detection. However, the lidar backscattering signals attenuate rapidly with the increase of τ and cannot penetrate optically thick clouds with τ larger than 3 (Sassen and Cho 1992; Protat et al. 2006). In contrast, radar signals always miss very thin cirrus clouds but are able to penetrate optically thick clouds that are opaque to lidar (Comstock et al. 2004). Therefore, the Cloudsat 2B-GEOPROF-lidar product incorporated both the CALIPSO lidar and CloudSat radar-detected cloud-layer boundary information to provide full vertical atmospheric column structures. The CloudSat 2B-CLDCLASS-lidar product provides the thermodynamic phase of each layer. Figures 8a and 8b show cloud boundary altitudes and temperatures along the CALISPO/CloudSat track, respectively. In the region of interest, the CALIPSO track ranges from 5°N, 80°W to 4°S, 83°W. The cloud scenes are complicated: warm and broken lower-level water clouds (red) are covered by a cold, high-level ice cloud layer (blue) located between 10 and 16 km. The clear-sky profiles are extracted from a 3-h instantaneous National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective Analysis for Research and Applications (MERRA) product (Rienecker et al. 2008). The ocean surface emissivity values throughout the spectral region are assumed to be a constant: 0.98.
A multiple-cloud-layer case study using the collocated MODIS, CALIPSO, and CloudSat data on 10 Aug 2008. (a) The lidar- and radar-detected cloud boundaries (top and base) of the upper ice cloud (blue) and lower water cloud (red). (b) The cloud boundary temperatures and the MODIS-observed 11-μm BTs. (c) Comparisons between CALIPSO τ and RTM-retrieved τ. (d) The RTM-simulated TOA ice CRF (integrated from 700 to 1300 cm−1) using retrieved cloud properties.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
First, we apply a simple retrieval method to estimate cloud optical thickness values with the assumed effective diameter values of a lower-level water cloud; specifically, the lower water cloud has droplets with a constant effective diameter of 20 μm. We need to emphasize that the present study is focused on the RTM, rather than on the retrieval technique. In the future, sophisticated retrieval methods can be developed, based on the present RTM, to simultaneously retrieve the microphysical and optical properties of both ice and water clouds. By using different ice cloud τice (varying between 0.1 and 10), Deff,ice (10–150 μm), and water cloud τwat (1.0–5.0) combinations, the RTM calculates TOA BTs at a 0.1-cm−1 spectral resolution and subsequently simulates MODIS observations at bands 29, 31, and 32 (8.5, 11, and 12 μm) with the MODIS SRFs. The appropriate τice–Deff,ice–τwat combination, in which the simulations are closest to the MODIS observations, is recorded. Figure 8c shows the CALIPSO optical thickness values for both ice (blue) and water (red) clouds, as well as the RTM-based retrievals for ice (black) and water (green) clouds. Although the RTM-based retrievals differ from their CALIPSO counterparts in some regions if cloud boundaries have obvious variations (e.g., latitudes 4°–5°N, 0°–1°N, and 1°–2°S), the two cloud τ products are consistent when the cloud boundaries are at approximately the same height.
(a)–(c) Spectral breakdown of TOA upwelling flux with and without the ice cloud layer and (d)–(f) corresponding TOA ice CRF of three typical profiles near 4.6°, 2.2°, and 1.0°N. The spectrally integrated TOA ice CRFs are 82.5, 28.6, and 9.2 W m−2, respectively.
Citation: Journal of the Atmospheric Sciences 72, 2; 10.1175/JAS-D-14-0046.1
6. Summary
Based on a hybrid method, we develop an IR RTM to consider scattering-layer reflection and transmission. Since the reflection effect of a scattering layer is much smaller than the transmission and emission in the thermal IR spectral region, we generate local albedo LUTs for different scattering layers. The use of local albedo LUTs assumes an isotropic incident field rather than fully considering the directional variance of the incident radiance. Furthermore, higher orders of scattering-layer reflection are neglected to increase the computational efficiency without significantly reducing accuracy. Radiance that transmits diffusely through a scattering layer is calculated using the bidirectional transmissivity LUT, which, for higher accuracy, fully considers the zenith dependence of the incident field.
Comparison against a reference model, the 32-stream DISORT, shows that the fast model RMSE of TOA BTs are generally smaller than 0.05 K, and the relative flux errors for atmospheres with multiple scattering layers are less than 1%. Furthermore, the fast model is more than 10 000 times faster than the DISORT for single-layer scenes and over 6000 and 4000 times faster than the DISORT for two- and three-layer scenes.
The present fast RTM is much more flexible than its counterpart described in Wang et al. (2013b). In the previous RTM, TOA radiance is the only output applicable to satellite-based remote sensing. The new version calculates radiances and fluxes at arbitrary levels and in various directions, resulting in a wider variety of applications. For example, this model can be applied to the simulations of measurements from space, the surface, or any location within the atmosphere. Furthermore, the model capability is significantly enhanced, allowing the simultaneous occurrence of three different types of scattering layers (ice cloud, water cloud, and mineral dust aerosols) in an atmospheric column. Atmospheres consisting of more complicated scattering layers can be simulated and retrieved. By replacing the current clear-sky transmittance module with other clear-sky modules designed for specific sensors or broad bands, the computing time of the RTM can be reduced by a factor of 10 for high-spectral sensors, such as the AIRS, and by a factor of several hundred for narrowband sensors, such as the MODIS and the VIIRS. With its flexibility, accuracy, and computational efficiency, the present IR RTM has the potential to be applied not only to atmospheric radiative transfer simulation and remote sensing implementations, but also to the data assimilation in operational NWP systems and cloud–aerosol–gas radiative forcing studies. A limitation of the present model is the fixed 0.1-cm−1 spectral resolution, which leads to relatively large errors in strongly absorptive bands, such as the CO2 band (600–700 cm−1) and the H2O band (1300–1600 cm−1). However, as described in section 4, it is convenient to replace the present clear-sky module with other models that perform better for spectral bands with strong absorption.
This study was partly supported by NASA grants (NNX12AL90G and NNX11AK37G) and the endowment funds related to the David Bullock Harris Chair in Geosciences at the College of Geosciences, Texas A&M University.
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