3D Radiative Effects Research Articles

The impact of coupled 3D shortwave radiative transfer on surface radiation and cumulus clouds over land

Abstract. Radiative transfer is a 3D process, but most atmospheric models consider radiation only in the vertical direction for computational efficiency. This results in inaccurate surface radiation fields, as the horizontal transport of radiation is neglected. Previous work on 3D radiative effects mainly used 3D radiative transfer uncoupled from the flow solver. In contrast, our current work uses 3D radiative transfer coupled to the flow solver to study its impact on the development of clouds and the resulting impact on the domain-averaged surface solar irradiance. To this end, we performed a series of realistic large-eddy simulations with MicroHH. To improve the level of realism of our radiation, we first included the direct effect of aerosols using aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis. Next, we performed simulations with 1D radiative transfer and with a coupled ray tracer for 12 d on which shallow cumulus clouds formed over Cabauw, the Netherlands. In general, simulations with the coupled ray tracer have a higher domain-averaged liquid water path, larger clouds, and similar cloud cover compared to simulations with 1D radiative transfer. Furthermore, the domain-averaged direct radiation is decreased with 3D radiative transfer, and the diffuse radiation is increased. However, the average difference in global radiation is less than 1 W m−2, as the increase in global radiation from uncoupled 3D radiative transfer is counterbalanced by a decrease in global radiation caused by changes in cloud properties.

Correction of cloud optical thickness retrievals from nadir reflectances in the presence of 3D radiative effects. Part I: concept and tests on 3D RT simulations

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.

Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect

Abstract. We investigate how cloud retrieval errors due to the three-dimensional (3D) radiative effects affect broadband shortwave (SW) cloud radiative effects (CREs) in shallow cumulus clouds. A framework based on the combination of large eddy simulations (LESs) and radiative transfer (RT) models was developed to simulate both one-dimensional (1D) and 3D radiance, as well as SW broadband fluxes. Results show that the broadband SW fluxes reflected at top of the domain, transmitted at the surface, and absorbed in the atmosphere, computed from the cloud retrievals using 1D RT (F1D∗), can provide reasonable broadband radiative energy estimates in comparison with those derived from the true cloud fields using 1D RT (F1D). The difference between these 1D-RT-simulated fluxes (F1D∗, F1D) and the benchmark 3D RT simulations computed from the true cloud field (F3D) depends primarily on the horizontal transport of photons in 3D RT, whose characteristics vary with the sun's geometry. When the solar zenith angle (SZA) is 5°, the domain-averaged F1D∗ values are in excellent agreement with the F3D, all within 7 % relative CRE bias. When the SZA is 60°, the CRE differences between calculations from F1D∗ and F3D are determined by how the cloud side-brightening and darkening effects offset each other in the radiance, retrieval, and broadband fluxes. This study suggests that although the cloud property retrievals based on the 1D RT theory may be biased due to the 3D radiative effects, they still provide CRE estimates that are comparable to or better than CREs calculated from the true cloud properties using 1D RT.

Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations

Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations

Twelve Times Faster yet Accurate: A New State‐Of‐The‐Art in Radiation Schemes via Performance and Spectral Optimization

AbstractRadiation schemes are critical components of Earth system models that need to be both efficient and accurate. Despite the use of approximations such as 1D radiative transfer, radiation can account for a large share of the runtime of expensive climate simulations. Here we seek a new state‐of‐the‐art in speed and accuracy by combining code optimization with improved algorithms. To fully benefit from new spectrally reduced gas optics schemes, we restructure code to avoid short vectorized loops where possible by collapsing the spectral and vertical dimensions. Our main focus is the ecRad radiation scheme, where this requires batching of adjacent cloudy layers, trading some simplicity for improved vectorization and instruction‐level parallelism. When combined with common optimization techniques for serial code and porting widely used two‐stream kernels fully to single precision, we find that ecRad with the TripleClouds solver becomes 12 times faster than the operational radiation scheme in ECMWF's Integrated Forecast System (IFS) cycle 47r3, which uses a less accurate gas optics model (RRMTG) and a more noisy solver (McICA). After applying the spectral reduction and extensive optimizations to the more sophisticated SPARTACUS solver, we find that it’s 2.5 times faster than IFS cy47r3 radiation, making cloud 3D radiative effects affordable to compute in large‐scale models. The code optimization itself gave a threefold speedup for both solvers. While SPARTACUS is still under development, preliminary experiments show slightly improved medium‐range forecasts of 2‐m temperature in the tropics, and in year‐long coupled atmosphere‐ocean simulations the 3D effects warm the surface substantially.

Irradiance and cloud optical properties from solar photovoltaic systems

Abstract. Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.

A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds Over Reflecting Surfaces

AbstractIn the Earth Sciences, the 3D radiative transfer equation is often solved for by Monte Carlo (MC) methods. They can, however, be computationally taxing, and that can narrow their range of application and limit their use in explorations of model parameter spaces. A novel family of MC algorithms is investigated here in which single simulations provide estimates of both radiative quantities A for a set of parameters , as usual, as well as the overarching functional (x) that can be evaluated, extremely efficiently, at any x. One such algorithm is developed and demonstrated for horizontally averaged broadband solar radiative fluxes as functions of surface albedo for uniform Lambertian surfaces beneath inhomogeneous cloudy atmospheres. Simulations for a high‐resolution synthetic cloud field, at various solar zenith angles, illustrate the potential of the method to gain insights into the nature of 3D radiative effects for complicated atmosphere‐surface conditions using information specially derived from the MC simulation. For simulations performed with a single surface albedo it is found that as surface albedo increases, 3D radiative effects increase, too, with maxima occurring at middling to large values, and then decrease. By utilizing the derived coefficients that describe it was established that these 3D effects stem from differences in fractions of radiation entrapped at successive orders of internal multiple reflections for 1D and 3D transfer.

Forecasting day-ahead 1-minute irradiance variability from numerical weather predictions

Accurate forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore reliable forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly, missing crucial information on meteorological variability such as clouds. In this work we present a methodology to forecast minute-to-minute irradiance variability in terms of its probability density function (PDF) based on hourly NWP results, by applying statistical postprocessing using machine learning. The algorithm is tested using the 2.5 × 2.5 km2 HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 meteorological stations throughout the Netherlands used as a ground truth. The applicability of the algorithm to 31 × 31 km2 global-scale models is investigated using ERA5 reanalysis data, which yields comparable accuracies. We find that almost half of the inaccuracy of the postprocessed result is due to errors in the radiation forecast of the NWP model used as input. Finally, the proposed post-processing algorithm is compared to the next generation weather models based on high resolution Large Eddy Simulation (LES), at 75 m horizontal grid spacing, on a case study spanning four days. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of cloud and irradiance dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.

An Efficient Parameterization for Surface Shortwave 3D Radiative Effects in Large‐Eddy Simulations of Shallow Cumulus Clouds

AbstractMost atmospheric models consider radiative transfer only in the vertical direction (1D), as 3D radiative transfer calculations are too costly. Thereby, horizontal transfer of radiation is omitted, resulting in incorrect surface radiation fields. The horizontal spreading of diffuse radiation results in darker cloud shadows, whereas it increases the surface radiation in clear sky patches (cloud enhancement). In this study, we developed a simple method to account for the horizontal transfer of diffuse radiation. We spatially filter the surface diffuse radiation field with a Gaussian filter, which is conceptually simple and computationally efficient. We applied the filtering to the results of Large‐Eddy Simulations for two summer days in Cabauw, the Netherlands, on which shallow cumulus clouds formed during the day. We obtained the optimal filter size by matching the simulation results with detailed high‐quality observations (1 Hz). Without the filtering, cloud enhancements are not captured, and the probability distribution of global radiation is unimodal, whereas the observed distribution is bimodal. After filtering, the probability distribution of global radiation is bimodal and cloud enhancements are simulated, in line with the observations. We found that small changes in the filter width do not strongly influence the results. Furthermore, we showed that the width of the filter can be parameterized as a linear function of for example, the cloud cover. Hence, this work presents a proof‐of‐concept for our method to come to more realistic surface irradiances by filtering diffuse radiation at the surface.

Top-of-Atmosphere Albedo Bias from Neglecting Three-Dimensional Cloud Radiative Effects

Abstract Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. We use high-resolution (20–100-m horizontal resolution) large-eddy simulations to produce realistic 3D cloud fields covering three dominant regimes of low-latitude clouds: shallow cumulus, marine stratocumulus, and deep convective cumulonimbus. A Monte Carlo code is used to run 3D and ICA broadband radiative transfer calculations; we calculate the top-of-atmosphere (TOA) reflected flux and surface irradiance biases as functions of solar zenith angle for these three cloud regimes. Finally, we use satellite observations of cloud water path (CWP) climatology, and the robust correlation between CWP and TOA flux bias in our LES sample, to roughly estimate the impact of neglecting 3D cloud radiative effects on a global scale. We find that the flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is most prominent for large zenith angles. In the tropics, the annual-mean shortwave radiative flux bias is estimated to be 3.1 ± 1.6 W m−2, reaching as much as 6.5 W m−2 locally.

A machine learning-based method to account for 3D Short-Wave radiative effects in 1D satellite observation operators

Data assimilation of Short-Wave (SW, ≤ 4μm) satellite radiance in cloudy regions could potentially improve cloud forecasts. Operational SW observation operators neglect cloud 3D radiative effects, which may cause nonnegligible errors in some circumstances, for example, with large solar zenith angles or for broken clouds. This study introduced a machine learning (ML)-based method to include some cloud 3D radiative effects in a 1D observation operator based on liquid water cloud simulations by the Weather Research and Forecasting model and radiative transfer simulations by the Spherical Harmonics Discrete Ordinate Method. The inputs of the ML correction method include the reflectance simulated by a 1D observation operator at the targeting column, cloud top height, cloud water content, and effective particle size at its adjacent columns, and other ancillary information (sun-viewing geometries, water vapor conditions, etc.). Preliminary results were presented for the channel 3 (0.75 ∼ 0.90µm) of the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A. Compared with 1D simulations, the ML correction method could reduce the biases from 6.9% ∼ 11.7% to 2.0% ∼ 4.7% and the root mean square errors from 22.3% ∼ 34.8% to 15.4% ∼ 27.4%. The elapsed CPU time for the ML correction method is approximately the same or two times that of the 1D observation operators, depending on whether atmospheric gases are included or not. In general, the ML correction method is computationally more efficient than traditional 3D radiative transfer solver, and could be used to correct current 1D observation operators’ simulations.

Evidence for the 3D Radiative Effects of Boundary‐Layer Clouds From Observations of Direct and Diffuse Surface Solar Fluxes

AbstractNumerical experiments have revealed the importance of horizontal transport of light in the presence of clouds (“3D effects”), with consequences for climate, weather, and solar resource availability predictions. Yet, analysis of 3D effects from observations remain sparse because of the difficulty to isolate the effect of horizontal transport in radiation measurements. In this study, we provide observational evidence for 3D effects based on the direct‐diffuse partition of surface solar fluxes. It is compared to outputs from the ecRad radiative transfer scheme run on retrieved cloud profiles. The direct‐beam calculation takes careful account of the field‐of‐view of the pyrheliometer to ensure consistency between observed and modeled direct fluxes. Only the solver that accounts for 3D effects is able to reproduce the observed mean direct‐diffuse partition as a function of solar zenith angle and cloud cover, in particular at large solar zenith angles where cloud sides intercept most of the direct beam.

Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects

AbstractProcess‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit simulations of boundary‐layer dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm−2 with LES‐derived parameters to ∼5 Wm−2 with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm−2.

A biased sampling approach to accelerate backward Monte Carlo atmospheric radiative transfer simulations and its application to Arctic heterogeneous cloud and surface conditions

Electromagnetic radiation within the Earth-atmosphere system is inherently of three dimensional (3D) nature. To quantitatively investigate the various 3D radiative effects in arbitrary spatial resolution, the backward Monte Carlo approach provides an efficient and flexible technique. In this paper, a new importance sampling scheme of 3D backward Monte Carlo radiative transfer is introduced as Light Estimator Including Polarization, Surface Inhomogeneities, and Clouds (LEIPSIC) code (https://gitee.com/BinLeipzig/LEIPSIC). The new code is described, and its accuracy and efficiency are quantified. Two test cases of typical Arctic surface and atmosphere scenarios are investigated. Applying the new code it is shown that the neglect of 3D effects introduces a negative (cooling) net irradiance error of around 13Wm−2 integrated over the solar spectrum.

Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer

Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space–time averaging method, and the 3D radiative effect.

Entrapment: An Important Mechanism to Explain the Shortwave 3D Radiative Effect of Clouds

AbstractSeveral mechanisms have previously been proposed to explain differences between the shortwave reflectance of realistic cloud scenes computed using the 1D independent column approximation (ICA) and 3D solutions of the radiative transfer equation. When the sun is low in the sky, interception of sunlight by cloud sides tends to increase reflectance relative to ICA estimates that neglect this effect. When the sun is high, 3D radiative transfer tends to make clouds less reflective, which we argue is explained by the mechanism of “entrapment” whereby horizontal transport of radiation beneath a cloud layer increases the chances, relative to the ICA, of light being absorbed by cloud or the surface. It is especially important for multilayered cloud scenes. We describe modifications to the previously described Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS) to represent different entrapment assumptions, and test their impact on 65 contrasting scenes from a cloud-resolving model. When entrapment is represented explicitly via a calculation of the mean horizontal distance traveled by reflected light, SPARTACUS predicts a mean “3D radiative effect” (the difference in top-of-atmosphere irradiances between 3D and ICA calculations) of 8.1 W m−2 for overhead sun. This is within 2% of broadband Monte Carlo calculations on the same scenes. The importance of entrapment is highlighted by the finding that the extreme assumptions in SPARTACUS of “zero entrapment” and “maximum entrapment” lead to corresponding mean 3D radiative effects of 1.7 and 19.6 W m−2, respectively.

Retrieval of aerosol properties from ground-based polarimetric sky-radiance measurements under cloudy conditions

To study aerosol-cloud-interactions, observations in the vicinity of clouds are necessary. We have developed a new algorithm for reconstructing aerosol microphysical parameters from ground-based polarized sky radiance measurements. To enable retrievals in partly cloudy scenes, we also have implemented a new method of cloud screening. Synthetic observations have been generated with 3D Monte-Carlo radiative transfer simulations, including cuboid clouds, as well as more realistic Large-Eddy-Simulation (LES) generated cloud fields. By applying our retrieval to these synthetic datasets, we assess the accuracy of the retrieval algorithm. The 3D radiative effects of clouds on the cloudless radiance between them are investigated as a function of the distance from clouds using cuboid cloud street simulations. The LES cloud fields were used to get an estimate for the expected effects in real world scenarios, and validate the cloud-screening.For most cases, effective radius and optical thickness of the aerosol can be retrieved well, even if a significant portion (up to 70%) of the sky is covered by clouds. We find that the aerosol optical thickness is typically slightly overestimated (not more than 0.03 or 10%). The retrieval of fine mode particle effective radius is accurate to within 0.04 µm regardless of the cloud contamination. For the retrieved coarse mode effective radius the error becomes larger towards bigger particles. A positive bias in the retrieved index of refraction has been observed in most cases.Being a potential reason for the retrieval inaccuracies, the radiative effects of clouds on the adjacent clear-sky measurements have been investigated from the simulations. Total radiance is enhanced by up to 55% on average close to clouds at 550 nm, while linear polarized radiance is reduced, but only by about 25% in the same case.

Cirrus Cloud Optical Thickness and Effective Diameter Retrieved by MODIS: Impacts of Single Habit Assumption, 3‐D Radiative Effects, and Cloud Inhomogeneity

AbstractFor inhomogeneous cirrus clouds, cloud optical thickness (COT) and effective diameter (De) provided by the Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 cloud products are associated with errors due to the single habit assumption (SHA), independent pixel assumption (IPA), photon absorption effect (PAE), and plane‐parallel assumption (PPA). SHA means that every cirrus cloud is assumed to have the same shape habit of ice crystals. IPA errors are caused by three‐dimensional (3D) radiative effects. PPA and PAE errors are caused by cloud inhomogeneity. We proposed a method to single out these different errors. These errors were examined using the Spherical Harmonics Discrete Ordinate Method simulations done for the MODIS 0.86 μm and 2.13 μm bands. Four midlatitude and tropical cirrus cases were studied. For the COT retrieval, the impacts of SHA and IPA were especially large for optically thick cirrus cases. SHA errors in COT varied distinctly with scattering angles. For the De retrieval, SHA decreased De under most circumstances. PAE decreased De for optically thick cirrus cases. For the COT and De retrievals, the dominant error source was SHA for overhead sun whereas for oblique sun, it could be any of SHA, IPA, and PAE, varying with cirrus cases and sun‐satellite viewing geometries. On the domain average, the SHA errors in COT (De) were within −16.1%–42.6% (−38.7%–2.0%), whereas the 3‐D radiative effects‐ and cloud inhomogeneity‐induced errors in COT (De) were within −5.6%–19.6% (−2.9%–8.0%) and −2.6%–0% (−3.7%–9.8%), respectively.

Impact of reflecting land surface on radiation environment over Hornsund, Spitsbergen – a model study for cloudless skies

Abstract This paper addresses the influence of land topography and cover on 3D radiative effects under cloudless skies in the Hornsund area, Spitsbergen, Svalbard. The authors used Monte Carlo simulations of solar radiation transfer over a heterogeneous surface to study the impact of a non-uniform surface on: (1) the spatial distribution of irradiance transmittance at the fjord surface under cloudless skies; (2) the spectral shortwave aerosol radiative forcing at the fjord surface; (3) normalized nadir radiance at the Top Of the Atmosphere (TOA) over the fjord. The modelled transmittances and radiances over the fjord are compared to the transmittances and radiances over the open ocean under the same conditions. The dependence of the 3D radiative effects on aerosol optical thickness, aerosol type, surface albedo distribution, solar azimuth and zenith angle and spectral channel is discussed. The analysis was done for channels 3 (459-479 nm) and 2 (841-876 nm) of the MODIS radiometer. In the simulations a flat water surface was assumed. The study shows that snow-covered land surrounding the fjord strongly modifies the radiation environment over the fjord surface. The enhancement of the mean irradiance transmittance over the fjord with respect to the open ocean is up to 0.06 for channel 3. The enhancement exceeds 0.11 in the vicinity of sunlit cliffs. The influence of the snow-covered land on the TOA radiance over the fjord in channel 3 is comparable to the impact of an increase in aerosol optical thickness of over 100%, and in lateral fjords of up to several hundred percent. The increase in TOA radiance is wavelength dependent. These effects may affect retrievals of aerosol optical thickness.

Improving the modelling of short‐wave radiation through the use of a 3D scene construction algorithm

Active satellite sensors, such as Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat, provide cloud properties that are not available from passive sensors, such as MODerate‐resolution Imaging Spectroradiometer (MODIS). While active sensors provide vertical profiles of clouds, their spatial coverage is limited to their narrow, nadir ground‐track. As a result, estimation of radiation by combining active sensors and broadband instrument has limitations due to their different spatial coverages. This study uses a scene construction algorithm (SCA) and MODIS data to extend two‐dimensional (2D) nadir cloud profiles into the cross‐track direction, and examines how the resulting constructed 3D cloud fields improve simulation of solar radiative transfer. Clouds and the Earth's Radiant Energy System (CERES) radiances are used as references to assess the improvements.While use of constructed 3D cloud fields only slightly impacts mean‐bias errors for instantaneous 20 km CERES footprint‐averaged top‐of‐atmosphere (TOA) radiances, reductions in random errors are about 40%. The largest improvements in TOA radiance simulation are for clouds with small‐scale horizontal inhomogeneity such as stratocumulus and cumulus. In contrast, uniform clouds such as nimbostratus, and deep convective clouds (Dc) show little response to the SCA. The impact of using the SCA on instantaneous surface irradiances is significant for stratocumulus and cumulus, but weak for nimbostratus and Dc. Conversely, SCA significantly influences atmospheric absorption and heating rates for nimbostratus and Dc.Differences in TOA radiances simulated by 1D and 3D transfer models are smaller than differences due to use of only the 2D nadir cross‐sections and the 3D constructed fields. This is because of smoothing of 3D radiative effects when averaged up to CERES footprints. For surface irradiance and atmospheric absorption, however, differences simulated by 1D and 3D transfer models are more comparable to differences that stem from use of 2D and 3D cloud information.