Radiative Transfer Model Research Articles

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

Preflight Spectral Calibration of the Ozone Monitoring Suite-Nadir on FengYun 3F Satellite

The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China’s Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the “asymmetric central peak” and “secondary peak”. The analysis results show that if the influences of the instrument spectral response function “asymmetric central peak” and “secondary peak” in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line.

A reduced-order model for nonlinear radiative transfer problems based on moment equations and POD-Petrov-Galerkin projection of the normalized Boltzmann transport equation

A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low-order quasidiffusion (aka variable Eddington factor) equations for moments of the radiation intensity and (ii) the normalized Boltzmann transport equation (BTE). The multilevel system of moment equations is derived by projection of the BTE onto a sequence of subspaces which represent elements of the phase space of the problem. Exact closure for the moment equations is provided by the Eddington tensor. A Petrov-Galerkin (PG) projection of the normalized BTE is formulated using a proper orthogonal decomposition (POD) basis representing the normalized radiation intensity over the whole phase space and time. The Eddington tensor linearly depends on the solution of the normalized BTE. By linear superposition of the POD basis functions, a low-rank expansion of the Eddington tensor is constructed with coefficients defined by the PG projected normalized BTE. The material energy balance (MEB) equation is coupled with the effective gray low-order equations which exist on the same dimensional scale as the MEB equation. The resulting TRT ROM is structure and asymptotic preserving. A detailed analysis of the ROM is performed on the classical Fleck-Cummings (F-C) TRT multigroup test problem in 2D geometry. Numerical results are presented to demonstrate the ROM's effectiveness in the simulation of radiation wave phenomena. The ROM is shown to produce solutions with sufficiently high accuracy while using low-rank approximation of the normalized BTE solution. Essential physical characteristics of supersonic radiation wave are preserved in the ROM solutions.

Polarized Bidirectional Reflectance Distribution Function Matrix Derived from Two-Scale Roughness Theory and Its Applications in Active Remote Sensing

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals.

INTEGRATION OF STUMPF'S RATIO MODEL AND RANDOM FOREST FOR SATELLITE-DERIVED BATHYMETRY ESTIMATION

Abstract. The development of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation strategies against the impacts of human activities. However, remote sensing must consider the complex interplay of the atmosphere and water column. Ongoing research focuses on refining water column correction techniques, including Depth Invariant Indices (DII), Radiative Transfer models, and bathymetry models. This study specifically aims to enhance the Stumpf's Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which incorporates visible bands from a Sentinel-2 MSI image, and their Stumpf's ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively. This was followed by the multivariate SRM with RMSE and R2 values of 2.196 m and 0.554 respectively These findings demonstrate the promising potential of using RF machine learning regression with SRM for bathymetry modelling.

PDS 70 unveiled by star-hopping: Total intensity, polarimetry, and millimeter imaging modeled in concert

Most ground-based direct-imaging planet search campaigns use angular differential imaging that distorts the signal from extended sources, such as protoplanetary disks. In the case of the young system PDS 70, for which two planets were detected within the cavity of a protoplanetary disk, obtaining a reliable image of both planets and the disk is essential to understanding planet-disk interactions. Our goals are to reveal the true intensity of the planets and disk without self-subtraction effects for the first time, search for new giant planets beyond separations of 0.1$''$, and to study the morphology of the disk shaped by two massive planets. We present $YJHK$-band imaging, polarimetry, and spatially resolved spectroscopy of PDS 70 using near-simultaneous reference star differential imaging, also known as star-hopping. We created a radiative transfer model of the system to try to match the near-infrared imaging and polarimetric data within measurement errors. Sub-millimeter imaging data from ALMA were also modeled. Furthermore, we extracted the spectra of the planets and the disk and compared them. With strong constraints, we find that the disk is quite flared, with a scale height of sim 15<!PCT!> at the outer edge of the disk at sim 90 au, similar to some disks in the literature. The gap inside sim 50 au is estimated to have sim 1<!PCT!> of the dust density of the outer disk. The northeast outer disk arc seen in previous observations is likely the outer lip of the flared disk. Abundance ratios of sub-micron, micron, and grains estimated by the modeling indicate a shallow grain-size index greater than $-2.7$, instead of the canonical -3.5. There is both vertical and radial segregation of grains. Planet $c$ is well separated from the disk and has a spectrum similar to planet $b$, and it is clearly redder than the disk spectra. Planet $c$ is possibly associated with the sudden flaring of the disk starting at sim 50 au. We found no new planets in the system. If we assume DUSTY models and an age of 5 Myrs, this indicates no new planets more massive than 5 outside a 12 au separation.

Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration

The remote sensing of turbidity and suspended particulate matter (SPM) relies on atmospheric corrections and bio-optical algorithms, but there is no one method that has better accuracy than the others for all satellites, bands, study areas, and purposes. Here, we evaluated different combinations of satellites (Landsat-8, Sentinel-2, and Sentinel-3), atmospheric corrections (ACOLITE and POLYMER), algorithms (single- and multiband; empirical and semi-analytical), and bands (665 and 865 nm) to estimate turbidity and SPM in Patos Lagoon (Brazil). The region is suitable for a case study of the regionality of remote-sensing algorithms, which we addressed by regionally recalibrating the coefficients of the algorithms using a method for geophysical observation models (GeoCalVal). Additionally, we examined the results associated with the use of different statistical parameters for classifying algorithms and introduced a new metric (GoF) that reflects performance. The best performance was achieved via POLYMER atmospheric correction and the use of single-band algorithms. Regarding SPM, the recalibrated coefficients yielded a better performance, but, for turbidity, a tradeoff between two statistical parameters occurred. Therefore, the uncertainties in the atmospheric corrections and algorithms used were analyzed based on previous studies. In the future, we suggest the use of in situ radiometric data to better evaluate atmospheric corrections, radiative transfer modeling to bridge data gaps, and multisensor data merging for compiling climate records.

Astrochemistry of the Molecular Gas in Dusty Star-Forming Galaxies at the Cosmic Noon

Far-infrared and submillimeter observations have established the fundamental role of dust-obscured star formation in the assembly of stellar mass over the past ∼12 billion years. At z = 2–4, the so-called “cosmic noon”, the bulk of star formation is enshrouded in dust, and dusty star-forming galaxies (DSFGs) contain ∼50% of the total stellar mass density. Star formation occurs in dense molecular clouds, and is regulated by a complex interplay between all the ISM components that contribute to the energy budget of a galaxy: gas, dust, cosmic rays, interstellar electromagnetic fields, gravitational field, and dark matter. Molecular gas is the actual link between star-forming gas and its complex environment: much of what we know about star formation comes from observations of molecular line emissions. They provide by far the richest information about the star formation process. However, their interpretation requires complex modeling of the astrochemical networks which regulate molecular formation and establish molecular abundances in a cloud, and a modeling of the physical conditions of the gas in which molecular energy levels become populated. This paper critically reviews the main astrochemical parameters needed to obtain predictions about molecular signals in DSFGs. Molecular lines can be very bright compared to the continuum emission, but radiative transfer models are required to properly interpret the observed brightness. We review the current knowledge and the open questions about the interstellar medium of DSFGs, outlining the key role of molecular gas as a tracer and shaper of the star formation process.

Impact of a New Radiation Scheme on Simulated Climate in the Global–Regional Integrated SysTem Model under Varying Physical Parameterization Schemes

In this study, the radiation scheme BCC-RAD (Beijing Climate Center RADiative transfer model) developed for global climate models is implemented into the Global–Regional Integrated SysTem (GRIST) model as an alternative to the default RRTMG (general circulation model (GCM) version of the Rapid Radiative Transfer Model) scheme. Its impact on the simulated climate is comprehensively evaluated under different physics parametrization packages, in comparison with both the CERES (partly from ERA5 reanalysis) observations and multi-model results from CMIP6. The results indicate that under the default physics parameterization package of GRIST (PhysC), BCC-RAD improved the simulated global mean cloud cover by ~3% and the clear-sky outgoing longwave radiation by ~5.6 W/m2. Upon the inclusion of the PhysCN parameterization package, BCC-RAD exhibited further improvement in simulated cloud cover and radiative forcing (particularly longwave radiative forcing, the bias of which decreases from −9.2 W/m2 to −1.8 W/m2), leading it to be closer to observations than RRTMG. Additionally, BCC-RAD improved the simulation of atmospheric temperature and hence notably diminished the apparent overestimation of atmospheric humidity seen in RRTMG. This study demonstrates the advantages of BCC-RAD over RRTMG in certain aspects of the GRIST-simulated climate, verifying its capability for the climate-oriented configuration of GRIST.

Greenhouse gas retrievals for the CO2M mission using the FOCAL method: first performance estimates

Abstract. The Anthropogenic Carbon Dioxide Monitoring (CO2M) mission is a constellation of satellites currently planned to be launched in 2026. CO2M is planned to be a core component of a Monitoring and Verification Support (MVS) service capacity under development as part of the Copernicus Atmosphere Monitoring Service (CAMS). The CO2M radiance measurements will be used to retrieve column-averaged dry-air mole fractions of atmospheric carbon dioxide (XCO2), methane (XCH4) and total columns of nitrogen dioxide (NO2). Using appropriate inverse modelling, the atmospheric greenhouse gas (GHG) observations will be used to derive United Nations Framework Convention on Climate Change (UNFCCC) COP 21 Paris Agreement relevant information on GHG sources and sinks. This challenging application requires highly accurate XCO2 and XCH4 retrievals. Three different retrieval algorithms to derive XCO2 and XCH4 are currently under development for the operational processing system at EUMETSAT. One of these algorithms uses the heritage of the FOCAL (Fast atmOspheric traCe gAs retrievaL) method, which has already successfully been applied to measurements from other satellites. Here, we show recent results generated using the CO2M version of FOCAL, called FOCAL-CO2M. To assess the quality of the FOCAL-CO2M retrievals, a large set of representative simulated radiance spectra has been generated using the radiative transfer model SCIATRAN. These simulations consider the planned viewing geometry of the CO2 instrument and corresponding geophysical scene data (including different types of aerosols and varying surface properties), which were taken from model data for the year 2015. We consider instrument noise and systematic errors caused by the retrieval method but have not considered additional error sources due to, for example, instrumental issues, spectroscopy or meteorology. On the other hand, we have also not taken advantage in this study of CO2M's MAP (multi-angle polarimeter) instrument, which will provide additional information on aerosols and cirrus clouds. By application of the FOCAL retrieval to these simulated data, confidence is gained that the FOCAL method is able to fulfil the challenging requirements for systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).

Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument

Abstract. The filtered radiances measured by the Clouds and the Earth's Radiant Energy System (CERES) instruments are converted to shortwave (SW), longwave (LW), and window unfiltered radiances based on regressions developed from theoretical radiative transfer simulations to relate filtered and unfiltered radiances. This paper describes an update to the existing Edition 4 CERES unfiltering algorithm (Loeb et al., 2001), incorporating the most recent developments in radiative transfer modeling, ancillary input datasets, and increased computational and storage capabilities during the past 20 years. Simulations are performed with the updated Moderate Resolution Atmospheric Transmission (MODTRAN) 5.4 version. Over land and snow, the surface bidirectional reflectance distribution function (BRDF) is characterized by a kernel-based representation in the simulations, instead of the Lambertian surface used in the Edition 4 unfiltering process. Radiance unfiltering is explicitly separated into four seasonally dependent land surface groups based on the spectral radiation similarities of different surface types (defined by the International Geosphere-Biosphere Programme); over snow, it is separated into fresh snow, permanent snow, and sea ice. This differs from the Edition 4 unfiltering process where only one set of regressions was used for land and snow, respectively. The instantaneous unfiltering errors are estimated with independent test cases generated from radiative transfer simulations in which the “true” unfiltered radiances from radiative transfer simulations are compared with the unfiltered radiances calculated from the regressions. Overall, the relative errors are mostly within ±0.5 % for SW, within ±0.2 % for daytime LW, and within ±0.1 % for nighttime LW for both CERES Terra Flight Model 1 (FM1) and Aqua FM3 instruments. The unfiltered radiances are converted to fluxes and compared to CERES Edition 4 fluxes. The global mean instantaneous fluxes for Aqua FM3 are reduced by 0.34 to 0.45 W m−2 for SW and increased by 0.25 to 0.46 W m−2 for daytime LW; for Terra FM1, they are reduced by 0.24 to 0.34 W m−2 for SW and increased by 0.08 to 0.28 W m−2 for daytime LW. Nighttime LW flux differences are negligible for both instruments.

Into the thick of it: ALMA 0.45 mm observations of HL Tau at a resolution of 2 au

To comprehend the efficiency of dust evolution within protoplanetary disks, it is crucial to conduct studies of these disks using high-resolution observations at multiple wavelengths with the Atacama Large Millimeter/submillimeter Array (ALMA). In this work, we present high-frequency ALMA observations of the HL Tau disk using its Band 9 centered at a wavelength of 0.45 mm. These observations achieve the highest angular resolution in a protoplanetary disk to date, 12 milliarcseconds (mas), allowing the study of the dust emission at scales of 2 au. We used these data to extend the previously published multiwavelength analysis of the HL Tau disk, constraining the dust temperature, dust surface density, and maximum grain size throughout the disk. We performed this modeling for compact solid dust particles as well as for porous particles. Our new 0.45 mm data mainly trace optically thick emission, providing a tight constraint to the dust temperature profile. We derive maximum particle sizes of $ cm from the inner disk to $ au. Beyond this radius, we find particles between 300 mu m and 1 mm. The total dust mass of the disk is 2.1 J $ with compact grains, and it increases to 6.3 M$_ J $ assuming porous particles. Moreover, an intriguing asymmetry is observed at 32 au in the northeast inner part of the HL Tau disk at 0.45 mm. We propose that this asymmetry is the outcome of a combination of factors, including the optically thick nature of the emission, the orientation of the disk, and a relatively large dust scale height of the grains that is preferentially traced at 0.45 mm. To validate this, we conducted a series of radiative transfer models using the software RADMC-3D. Our models varying dust masses and scale heights successfully replicate the observed asymmetry in the HL Tau disk. If this scenario is correct, our measured dust mass within 32 au would suggest a dust scale height H/R$>0.08$ for the inner disk. Finally, the unprecedented resolution allowed us to probe the dust emission down to scales of a few au for the first time. We observed an increase in brightness temperature inside the estimated water snowline, and we speculate whether this might indicate a traffic-jam effect in the inner disk. Our results show that 0.45 mm observations of protoplanetary disks can be used to robustly constrain the radial profile of their dust temperature. Additionally, the higher optical depths at this wavelength can be used to constrain the vertical scale height of the dust. Finally, these higher frequencies allow us to reach higher spatial resolutions, which have the potential to resolve the region within the water snowline in disks.

Retrieval of Total Suspended Matter Concentration Based on the Iterative Analysis of Multiple Equations: A Case Study of a Lake Taihu Image from the First Sustainable Development Goals Science Satellite’s Multispectral Imager for Inshore

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (Ctsm). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate Ctsm. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations B6/3 and B6/5 for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving Ctsm was formed, which was successfully verified by using the modeling data (R2 = 0.97, RMSE = 4.89 g/m3, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R2 = 0.87, RMSE = 3.92 g/m3, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring Ctsm was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of Ctsm, as well as monitoring and improving the water environment.

Assessment of remote-sensed vegetation indices for estimating forest chlorophyll concentration

Remote-sensed vegetation indices (VIs) have emerged as essential tools for retrieving forest chlorophyll concentration. Although VIs are widely used, some concerns regarding VIs for estimating chlorophyll remain to be addressed, such as saturation effect, leaf area index (LAI) disturbance, and soil brightness influence. Currently, a systematic study on such performance evaluation of chlorophyll-related VIs considering these issues is still insufficient. This study coupled two radiative transfer models, the PROSPECT model and the LESS model, to simulate Eucalyptus monocultures with different chlorophyll content and systematically evaluated the 18 broad-band VIs’ ability in chlorophyll estimation at different scales. Our results indicate that most VIs designed for chlorophyll estimation were relatively resistant to saturation, except for SIPI and some classical VIs (e.g., NDVI and DVI), which were insensitive to chlorophyll decreases and tended to reach saturation quickly (when leaf chlorophyll content (LCC) exceeded 40 ug/cm2). The relationships between NDVI, SR, DVI, and LCC were easily influenced by soil brightness and LAI. S2REP, MTCI, TGI, TCARI, and EVI were insensitive to soil brightness when estimating LCC. Overall, S2REP was best at quantitatively retrieving chlorophyll and resisting interference from other factors. For practical applications, our study suggests that it is preferable to use S2REP for LCC estimation when the red-edge band is available; otherwise, CVI can be used instead. The judicious utilization of VI can effectively depict chlorophyll levels and improve the understanding of vegetation response to climate change. Our findings provide the necessary information for the selection of specific VIs tailored to specific vegetation parameters.

Robust gas species and concentration monitoring via cross-talk transformer with snapshot infrared spectral imager

With the rising industrialization, monitoring of chemical gases such as carbon dioxide, methane, and sulfur dioxide has become critical due to their profound impact on the global environment, human health, and industrial development. Recently, the Uncooled Snapshot Infrared Spectrometer (USIRS) has been employed for rapid, long-distance identification and concentration sensing of various gases. However, these uncooled instruments face limitations in gas detection accuracy due to weak signals and a low signal-to-noise ratio. To address these issues, we introduce the Cross-talk Transformer, which leverages the attention mechanism to learn deep correlations between the target gas and the gas spectral library. Furthermore, we have developed a comprehensive radiation transfer model for USIRS to generate substantial data, thereby adequately training the Cross-talk Transformer. Upon validation, our method achieves a recognition accuracy of 98.63% on experimental data for 11 types of chemical gases. In terms of concentration prediction, our approach attains a mean error of 330 ppm for chemical gases with concentrations up to 30,000 ppm. These results highlight the high accuracy and robustness of our method in monitoring gas types and concentrations, demonstrating its potential for industrial monitoring and other applications.

Impact of Channel Selection with Different Bandwidths on Retrieval at 50–60 GHz

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.

Direct radiative forcing of light-absorbing carbonaceous aerosols in China

China is an important emitter of light-absorbing carbonaceous aerosols (LACs), including black carbon (BC) and brown carbon (BrC). Currently, there are large uncertainties in model-estimated direct radiative forcing (DRF) of LACs, partially due to the poor understanding of the emissions and optical properties of LACs. In this study, we estimated the DRF of LACs over China during the implementation of the Air Pollution Prevention and Control Action Plan (APPCAP) using the global chemical transport model (GEOS-Chem) coupled with the Rapid Radiative Transfer Model of GCMs (RRTMG). We updated the refractive index of BC, includedbiomass burning (BB) sources, biofuel (BF) and coal combustion (CC) sources in the residential sector as BrC emission sources and the optical properties were updated, which were not fully considered in the previous model studies. Our results showed that model could reasonably capture the spatial and temporal variations of LACs in China with the correlation coefficients between model simulated and Aerosol Robotic Network (AERONET) observed daily absorption aerosol optical depth (AAOD) of LACs at 440 nm above 0.63 and the corresponding values of the normalized mean bias within ±30%. The simulated annual mean LACs AAOD at 440 nm in China was 0.016 (0.021) in 2017 (2014) and BrC contributed about 20% (21%). The estimated annual mean clear-sky LACs DRF at the top of the atmosphere in China was 1.02 W m−2 in 2017 and 1.38 W m−2 in 2014, and the contribution of BrC was about 10% and 11%, respectively, which was dominated by the BF sources (46% in 2017 and 44% in 2014) and the BB sources (38% in 2017 and 43% in 2014), with CC sources being low (16% in 2017 and 13% in 2014). The annual mean AAOD and DRF of LACs in China decreased by 0.005 and 0.36 W m−2 from 2014 to 2017, which were largely attributed to the reductions of anthropogenic emissions during the implementation of APPCAP. Our results would improve the understanding of the light absorption capacity and climate effects of LACs in China.

An improved BRDF hotspot model and its use in VLIDORT for studying the impact of atmospheric scattering on hotspot directional signatures in the atmosphere

Abstract. The term “hotspot” refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature.

The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM

AbstractA significant portion of surface melt on the Greenland Ice Sheet (GrIS) is due to dark ice regions in the ablation zone, where solar absorption is influenced by the physical properties of the ice, light absorbing constituents (LACs), and the overlying crustal surface or melt ponds. Earth system models (ESMs) typically prescribe the albedo of ice surfaces as a constant value in the visible and near‐infrared spectral regions. This work advances ESM ice radiative transfer modeling by (a) incorporating a physically based radiative transfer model (SNow, ICe and Aerosol Radiation model Adding‐Doubling Version 4; SNICAR‐ADv4) into the Energy Exascale Earth System Model (E3SM), (b) determining spatially and temporally varying bare ice physical properties over the GrIS ablation zone from satellite observations to inform SNICAR‐ADv4, and (c) assessing the impacts on simulated GrIS albedo and surface mass balance associated with modeling of more realistic bare ice albedo. GrIS‐wide bare ice albedo in E3SMv2 is overestimated by ∼4% in the visible and ∼7% in the near‐infrared wavelengths compared to the Moderate Resolution Imaging Spectroradiometer. Our bare ice physical property retrieval method found that LACs, ice crustal surfaces, and melt ponds reduce visible albedo by 30% in the bare ice region of the GrIS ablation zone. The realistic bare ice albedo reduces surface mass balance by ∼145 Gt, or 0.4 mm of sea‐level equivalent between 2000 and 2021 compared to the default E3SM. This work highlights the importance of simulating bare ice albedo accurately and realistically to improve our ability to quantify changes in the GrIS surface mass and radiative energy budgets.

RSUIGM: Realistic Synthetic Underwater Image Generation with Image Formation Model

In this paper, we propose to synthesize realistic underwater images with a novel image formation model, considering both downwelling depth and line of sight (LOS) distance as cue and call it as Realistic Synthetic Underwater Image Generation Model, RSUIGM. The light interaction in the ocean is a complex process and demands specific modeling of direct and backscattering phenomenon to capture the degradations. Most of the image formation models rely on complex radiative transfer models and in-situ measurements for synthesizing and restoration of underwater images. Typical image formation models consider only line of sight distance z and ignore downwelling depth d in the estimation of effect of direct light scattering. We derive the dependencies of downwelling irradiance in direct light estimation for generation of synthetic underwater images unlike state-of-the-art image formation models. We propose to incorporate the derived downwelling irradiance in estimation of direct light scattering for modeling the image formation process and generate realistic synthetic underwater images with the proposed RSUIGM, and name it as RSUIGM dataset . We demonstrate the effectiveness of the proposed RSUIGM by using RSUIGM dataset in training deep learning based restoration methods. We compare the quality of restored images with state-of-the-art methods using benchmark real underwater image datasets and achieve improved results. In addition, we validate the distribution of realistic synthetic underwater images versus real underwater images both qualitatively and quantitatively. The proposed RSUIGM dataset is available here.