Abstract

Snow albedo plays a critical role in the surface energy budget in snow-covered regions and is subject to large uncertainty due to variable physical and optical characteristics of snow. We develop an optically and microphysically consistent snow grain habit mixture (SGHM) model, aiming at an improved representation of bulk snow properties in conjunction with considering the particle size distribution, particle shape, and internally mixed black carbon (BC). Spectral snow albedos computed with two snow layers with the SGHM model implemented in an adding–doubling radiative transfer model agree with observations. Top-snow-layer optical properties essentially determine spectral snow albedo when the top-layer snow water equivalent (SWE) is large. When the top-layer SWE is less than 1 mm, the second-snow-layer optical properties have nonnegligible impacts on the albedo of the snow surface. Snow albedo enhancement with increasing solar zenith angle (SZA) largely depends on snow particle effective radius and also internally mixed BC. Based on the SGHM model and various sensitivity studies, single- and two-layer snow albedos are parameterized for six spectral bands used in NASA Langley Research Center’s modified Fu–Liou broadband radiative transfer model. Parameterized albedo is expressed as a function of snow particle effective radii of the two layers, SWE in the top layer, internally mixed BC mass fraction in both layers, and SZA. Both single-layer and two-layer parameterizations provide band-mean snow albedo consistent with rigorous calculations, achieving correlation coefficients close to 0.99 for all bands.

Abstract

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth’s climate and its variability with time. The Clouds and the Earth’s Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error.

Abstract

Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel-scale variations in cloud-top height (i.e., “cloud bumps”). Monte Carlo simulations comparing 1D model radiances to radiances from overcast cloud fields with 1) cloud-top height variations but constant cloud volume extinction, 2) flat tops but horizontal variations in cloud volume extinction, and 3) variations in both cloud-top height and cloud extinction are performed over a ≈4 km × 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance differences. The reason 3D–1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.

Abstract

The usual procedure for retrieving the optical thickness of liquid water clouds from satellite-measured radiances is based on the assumption of plane-parallel layers composed of liquid water droplets. This study investigates the validity of this assumption from Advanced Earth Orbiting Satellite–Polarization and Directionality of the Earth's Reflectances (ADEOS–POLDER) observations. To do that, the authors take advantage of the multidirectional viewing capability of the POLDER instrument, which functioned nominally aboard ADEOS from November 1996 to June 1997.

The usual plane-parallel cloud model composed of water droplets with an effective radius of 10 μm provides a reasonable approximation of the angular dependence in scattering at visible wavelengths from overcast liquid water clouds for moderate solar zenith angles. However, significant differences between model and observations appear in the rainbow direction and for the smallest observable values of scattering angle (Θ < 90°). A better overall agreement would be obtained for droplets with an effective radius of about 7–8 μm for continental liquid water clouds. On the other hand, changing the water droplet size distribution would not lead to a significant improvement for maritime situations. When horizontal variations in cloud optical thickness are considered by using the independent pixel approximation (IPA), a small improvement is obtained over the whole range of scattering angles but significant discrepancies remain for Θ < 80°, that is for large solar zenith angles in the forward-scattering direction. The remaining differences between various models based on the plane-parallel radiative transfer and POLDER observations are thought to be due to variations in cloud shape.

Abstract

The next generation of earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of Polarization and Directionality of the Earth’s Reflectances 670-nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical angular distribution models (ADMs) for estimating top-of-atmosphere albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types. The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-τ approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-τ approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field.

Albedos based on ADMs that assume cloud properties are unbiased (fixed-τ approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching ≈12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 60° and 70°. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile-τ approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (<1% relative error). When the ADMs are applied separately to populations consisting of only liquid water and ice clouds, significant biases in albedo with viewing geometry are observed (particularly at low sun elevations), highlighting the need to account for cloud phase both in cloud optical depth retrievals and in defining ADM scene types. ADM-derived monthly mean albedos determined for all 5° × 5° lat–long regions over ocean are in good agreement (regional rms relative errors <2%) with those obtained by direct integration when ADM albedos inferred from specific angular bins are averaged together. Albedos inferred from near-nadir and oblique viewing zenith angles are the least accurate, with regional rms errors reaching ∼5%–10% (relative). Compared to an earlier study involving Earth Radiation Budget Experiment ADMs, regional mean albedos based on the 19 scene types considered here show a factor-of-4 reduction in bias error and a factor-of-3 reduction in rms error.

Abstract

Satellite and reanalysis data are used to observe interannual variations in atmospheric diabatic heating and circulation within the ascending and descending branches of the Hadley circulation (HC) during the past 12 yr. The column-integrated divergence of dry static energy (DSE) and kinetic energy is inferred from satellite-based observations of atmospheric radiation, precipitation latent heating, and reanalysis-based surface sensible heat flux for monthly positions of the HC branches, determined from a mass weighted zonal mean meridional streamfunction analysis. Mean surface radiative fluxes inferred from satellite and surface measurements are consistent to 1 W m⁻² (<1%) over land and 4 W m⁻² (2%) over ocean. In the ascending branch, where precipitation latent heating dominates over radiative cooling, discrepancies in latent heating among different precipitation datasets reach 22 W m⁻² (17%), compared to 3–6 W m⁻² in the descending branches. Whereas direct calculations of DSE divergence from two reanalyses show opposite trends, the implied DSE divergence from the satellite observations of atmospheric diabatic heating exhibits no trend in all three HC branches and is strongly correlated (reaching 0.90) with midtropospheric vertical velocity. The implied DSE divergence from satellite observations thus provides a useful independent measure of HC circulation strength variability. The sensitivity to circulation change is 4–5 times larger for precipitation latent heating compared to atmospheric radiative cooling in the descending branches and 20 times larger in the ascending branch. The difference in sensitivity is due to cloud radiative effects, which enhance atmospheric radiative cooling in the descending branches in response to an increase in HC strength but decrease it in the ascending branch.

Abstract

Coincident top-of-atmosphere (TOA) radiative fluxes and cloud optical properties for portions of clouds whose tops are exposed to space within several pressure ranges are used to evaluate how a GCM realizes its all-sky radiative fluxes and vertical structure. In particular, observations of cloud properties and radiative fluxes from the Clouds and the Earth’s Radiant Energy System (CERES) Science Team are used to assess the Canadian Centre for Climate Modeling and Analysis atmospheric global climate model (CanAM4). Through comparison of CanAM4 with CERES observations it was found that, while the July-mean all-sky TOA shortwave and longwave fluxes simulated by CanAM4 agree well with those observed, this agreement rests on compensating biases in simulated cloud properties and radiative fluxes for low, middle, and high clouds. Namely, low and middle cloud albedos simulated by CanAM4 are larger than those observed by CERES attributable to CanAM4 simulating cloud optical depths via large liquid water paths that are too large but are partly compensated by too small cloud fractions. It was also found that CanAM4 produces 2D histograms of cloud fraction and cloud albedo for low, middle, and high clouds that are significantly different than generated using the CERES observations.

Abstract

The Clouds and Earth’s Radiant Energy System (CERES) provides coincident global cloud and aerosol properties together with reflected solar, emitted terrestrial longwave, and infrared window radiative fluxes. These data are needed to improve the understanding and modeling of the interaction between clouds, aerosols, and radiation at the top of the atmosphere, surface, and within the atmosphere. This paper describes the approach used to estimate top-of-atmosphere (TOA) radiative fluxes from instantaneous CERES radiance measurements on the Terra satellite. A key component involves the development of empirical angular distribution models (ADMs) that account for the angular dependence of the earth’s radiation field at the TOA. The CERES Terra ADMs are developed using 24 months of CERES radiances, coincident cloud and aerosol retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from the Global Modeling and Assimilation Office (GMAO)’s Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) V4.0.3 product. Scene information for the ADMs is from MODIS retrievals and GEOS DAS V4.0.3 properties over the ocean, land, desert, and snow for both clear and cloudy conditions. Because the CERES Terra ADMs are global, and far more CERES data are available on Terra than were available from CERES on the Tropical Rainfall Measuring Mission (TRMM), the methodology used to define CERES Terra ADMs is different in many respects from that used to develop CERES TRMM ADMs, particularly over snow/sea ice, under cloudy conditions, and for clear scenes over land and desert.

Abstract

To estimate the earth's radiation budget at the top of the atmosphere (TOA) from satellite-measured radiances, it is necessary to account for the finite geometry of the earth and recognize that the earth is a solid body surrounded by a translucent atmosphere of finite thickness that attenuates solar radiation differently at different heights. As a result, in order to account for all of the reflected solar and emitted thermal radiation from the planet by direct integration of satellite-measured radiances, the measurement viewing geometry must be defined at a reference level well above the earth's surface (e.g., 100 km). This ensures that all radiation contributions, including radiation escaping the planet along slant paths above the earth's tangent point, are accounted for. By using a field-of-view (FOV) reference level that is too low (such as the surface reference level), TOA fluxes for most scene types are systematically underestimated by 1–2 W m⁻². In addition, since TOA flux represents a flow of radiant energy per unit area, and varies with distance from the earth according to the inverse-square law, a reference level is also needed to define satellite-based TOA fluxes. From theoretical radiative transfer calculations using a model that accounts for spherical geometry, the optimal reference level for defining TOA fluxes in radiation budget studies for the earth is estimated to be approximately 20 km. At this reference level, there is no need to explicitly account for horizontal transmission of solar radiation through the atmosphere in the earth radiation budget calculation. In this context, therefore, the 20-km reference level corresponds to the effective radiative “top of atmosphere” for the planet. Although the optimal flux reference level depends slightly on scene type due to differences in effective transmission of solar radiation with cloud height, the difference in flux caused by neglecting the scene-type dependence is less than 0.1%. If an inappropriate TOA flux reference level is used to define satellite TOA fluxes, and horizontal transmission of solar radiation through the planet is not accounted for in the radiation budget equation, systematic errors in net flux of up to 8 W m⁻² can result. Since climate models generally use a plane-parallel model approximation to estimate TOA fluxes and the earth radiation budget, they implicitly assume zero horizontal transmission of solar radiation in the radiation budget equation, and do not need to specify a flux reference level. By defining satellite-based TOA flux estimates at a 20-km flux reference level, comparisons with plane-parallel climate model calculations are simplified since there is no need to explicitly correct plane-parallel climate model fluxes for horizontal transmission of solar radiation through a finite earth.

Abstract

The far-IR spectrum plays an important role in the earth’s radiation budget and remote sensing. The authors compare the near-global (80°S–80°N) outgoing clear-sky far-IR flux inferred from the collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations in 2004 with the counterparts computed from reanalysis datasets subsampled along the same satellite trajectories. The three most recent reanalyses are examined: the ECMWF Interim Re-Analysis (ERA-Interim), NASA Modern-Era Retrospective Analysis for Research and Application (MERRA), and NOAA/NCEP Climate Forecast System Reanalysis (CFSR). Following a previous study by X. Huang et al., clear-sky spectral angular distribution models (ADMs) are developed for five of the CERES land surface scene types as well as for the extratropical oceans. The outgoing longwave radiation (OLR) directly estimated from the AIRS radiances using the authors’ algorithm agrees well with the OLR in the collocated CERES Single Satellite Footprint (SSF) dataset. The daytime difference is 0.96 ±2.02 W m⁻², and the nighttime difference is 0.86 ±1.61 W m⁻². To a large extent, the far-IR flux derived in this way agrees with those directly computed from three reanalyses. The near-global averaged differences between reanalyses and observations tend to be slightly positive (0.66%–1.15%) over 0–400 cm⁻¹ and slightly negative (−0.89% to −0.44%) over 400–600 cm⁻¹. For all three reanalyses, the spatial distributions of such differences show the largest discrepancies over the high-elevation areas during the daytime but not during the nighttime, suggesting discrepancies in the diurnal variation of such areas among different datasets. The composite differences with respect to temperature or precipitable water suggest large discrepancies for cold and humid scenes.