Abstract

Since the launch of the first Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the Television and Infrared Observational Satellite (TIROS-N), measurements in the 3.7-μm atmospheric window have been exploited for use in cloud detection and screening, cloud thermodynamic phase and surface snow/ice discrimination, and quantitative cloud particle size retrievals. The utility of the band has led to the incorporation of similar channels on a number of existing satellite imagers and future operational imagers. Daytime observations in the band include both reflected solar and thermal emission energy. Since 3.7-μm channels are calibrated to a radiance scale (via onboard blackbodies), knowledge of the top-of-atmosphere solar irradiance in the spectral region is required to infer reflectance. Despite the ubiquity of 3.7-μm channels, absolute solar spectral irradiance data come from either a single measurement campaign (Thekaekara et al.) or synthetic spectra. In the current study, the historical 3.7-μm band spectral irradiance datasets are compared with the recent semiempirical solar model of the quiet sun by Fontenla et al. The model has expected uncertainties of about 2% in the 3.7-μm spectral region. The channel-averaged spectral irradiances using the observations reported by Thekaekara et al. are found to be 3.2%–4.1% greater than those derived from the Fontenla et al. model for Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR instrument bandpasses; the Kurucz spectrum, as included in the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN4) distribution, gives channel-averaged irradiances 1.2%–1.5% smaller than the Fontenla model. For the MODIS instrument, these solar irradiance uncertainties result in cloud microphysical retrieval uncertainties that are comparable to other fundamental reflectance error sources.

Abstract

The authors present the global plane-parallel shortwave albedo bias of liquid clouds for two months, July 2003 and January 2004. The cloud optical properties necessary to perform the bias calculations come from the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and MODIS Aqua level-3 datasets. These data, along with ancillary surface albedo and atmospheric information consistent with the MODIS retrievals, are inserted into a broadband shortwave radiative transfer model to calculate the fluxes at the atmospheric column boundaries. The plane-parallel homogeneous (PPH) calculations are based on the mean cloud properties, while independent column approximation (ICA) calculations are based either on 1D histograms of optical thickness or joint 2D histograms of optical thickness and effective radius. The (positive) PPH albedo bias is simply the difference between PPH and ICA albedo calculations. Two types of biases are therefore examined: 1) the bias due to the horizontal inhomogeneity of optical thickness alone (the effective radius is set to the grid mean value) and 2) the bias due to simultaneous variations of optical thickness and effective radius as derived from their joint histograms. The authors find that the global bias of albedo (liquid cloud portion of the grid boxes only) is ∼+0.03, which corresponds to roughly 8% of the global liquid cloud albedo and is only modestly sensitive to the inclusion of horizontal effective radius variability and time of day, but depends strongly on season and latitude. This albedo bias translates to ∼3–3.5 W m⁻² of bias (stronger negative values) in the diurnally averaged global shortwave cloud radiative forcing, assuming homogeneous conditions for the fraction of the grid box not covered by liquid clouds; zonal values can be as high as 8 W m⁻². Finally, the (positive) broadband atmospheric absorptance bias is about an order of magnitude smaller than the albedo bias. The substantial magnitude of the PPH bias underlines the importance of predicting subgrid variability in GCMs and accounting for its effects on cloud–radiation interactions.

Abstract

The properties of clouds that may be observed by satellite instruments, such as optical thickness and cloud-top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through “instrument simulators,” diagnostic tools that map the model representation to synthetic observations so that differences can be interpreted as model error. But simulators may themselves be restricted by limited information or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essential differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Satellite Cloud Climatology Project (ISCCP), two similar but independent estimates of cloud properties. The authors review the measurements and algorithms underlying these two cloud climatologies, introduce a MODIS simulator, and detail datasets developed for comparison with global models using ISCCP and MODIS simulators. In nature MODIS observes less midlevel cloudiness than ISCCP, consistent with the different methods used to determine cloud-top pressure; aspects of this difference are reproduced by the simulators. Differences in observed distributions of optical thickness, however, are not captured. The largest differences can be traced to different approaches to partly cloudy pixels, which MODIS excludes and ISCCP treats as homogeneous. These cover roughly 15% of the planet and account for most of the optically thinnest clouds. Instrument simulators cannot reproduce these differences because there is no way to synthesize partly cloudy pixels. Nonetheless, MODIS and ISCCP observations are consistent for all but the optically thinnest clouds, and models can be robustly evaluated using instrument simulators by integrating over the robust subset of observations.

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite has been collecting global data since March 2000 and the one on the Aqua satellite since June 2002. In this paper, cirrus cloud properties derived from ground-based remote sensing data are compared with similar cloud properties derived from MODIS data on Terra. To improve the space–time correlation between the satellite and ground-based observations, data from a wind profiler are used to define the cloud advective streamline along which the comparisons are made. In this paper, approximately two dozen cases of cirrus are examined and a statistical approach to the comparison that relaxes the requirement that clouds occur over the ground-based instruments during the overpass instant is explored. The statistical comparison includes 168 cloudy MODIS overpasses of the Southern Great Plains (SGP) region and approximately 300 h of ground-based cirrus observations. The physical and radiative properties of cloud layers are derived from MODIS data separately by the MODIS Atmospheres Team and the Clouds and the Earth’s Radiant Energy System (CERES) Science Team using multiwavelength reflected solar and emitted thermal radiation measurements. Using two ground-based cloud property retrieval algorithms and the two MODIS algorithms, a positive correlation in the effective particle size, the optical thickness, the ice water path, and the cloud-top pressure between the various methods is shown, although sometimes there are significant biases. Classifying the clouds by optical thickness, it is demonstrated that the regionally averaged cloud properties derived from MODIS are similar to those diagnosed from the ground. Because of a conservative approach toward identifying thin cirrus pixels over this region, the area-averaged cloud properties derived from the MODIS Atmospheres MOD06 product tend to be biased slightly toward the optically thicker pixels. This bias tendency has implications for model validation and parameterization development applied to thin cirrus retrieved over SGP-like land surfaces. A persistent bias is also found in the derived cloud tops of thin cirrus with both satellite algorithms reporting cloud top several hundred meters less than that reported by the cloud radar. Overall, however, it is concluded that the MODIS retrieval algorithms characterize with reasonable accuracy the properties of thin cirrus over this region.

Abstract

To understand the radiative impact of tropical thin cirrus clouds, the frequency of occurrence and optical depths of these clouds have been derived. “Thin” cirrus clouds are defined here as being those that are not detected by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask, corresponding to an optical depth value of approximately 0.3 or smaller, but that are detectable in terms of the cirrus reflectance product based on the MODIS 1.375-μm channel. With such a definition, thin cirrus clouds were present in more than 40% of the pixels flagged as “clear sky” by the operational MODIS cloud mask algorithm. It is shown that these thin cirrus clouds are frequently observed in deep convective regions in the western Pacific. Thin cirrus optical depths were derived from the cirrus reflectance product. Regions of significant cloud fraction and large optical depths were observed in the Northern Hemisphere during the boreal spring and summer and moved southward during the boreal autumn and winter. The radiative effects of tropical thin cirrus clouds were studied on the basis of the retrieved cirrus optical depths, the atmospheric profiles derived from the Atmospheric Infrared Sounder (AIRS) observations, and a radiative transfer model in conjunction with a parameterization of ice cloud spectral optical properties. To understand how these clouds regulate the radiation field in the atmosphere, the instantaneous net fluxes at the top of the atmosphere (TOA) and at the surface were calculated. The present study shows positive and negative net forcings at the TOA and at the surface, respectively. The positive (negative) net forcing at the TOA (surface) is due to the dominance of longwave (shortwave) forcing. Both the TOA and surface forcings are in a range of 0–20 W m⁻², depending on the optical depths of thin cirrus clouds.

Abstract

Five years (2000–04) of spatially complete snow-free land surface albedo data have been produced using high-quality-flagged diffuse bihemispherical (white sky) and direct-beam directional hemispherical (black sky) land surface albedo data derived from observations taken by the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Terra satellite platform (MOD43B3, collection 4). In addition, a spatially complete snow-free aggregate albedo climatological product was generated. These spatially complete products were prepared using an ecosystem-dependent temporal interpolation technique that retrieves missing data within 3%–8% error. These datasets have already been integrated into research and operational projects that require snow-free land surface albedo. As such, this paper provides details regarding the spatial and temporal distribution of the filled versus the original MOD43B3 data. The paper also explores the intra- and interannual variation in the 5-yr data record and provides a qualitative comparison of zonal averages and annual cycles of the filled versus the original MOD43B3 data. The analyses emphasize the data’s inter- and intraannual variation and show that the filled data exhibit large- and small-scale phenological behavior that is qualitatively similar to that of the original MOD43B3. These analyses thereby serve to showcase the inherent spectral, spatial, and temporal variability in the MOD43B3 data as well as the ability of the fill technique to preserve these unique regional and pixel-level phenological characteristics.

Abstract

Modern polar-orbiting meteorological satellites provide both imaging and sounding observations simultaneously. Most imagers, however, do not have H₂O and CO₂ absorption bands and therefore struggle to accurately estimate the height of optically thin cirrus clouds. Sounders provide these needed observations, but at a spatial resolution that is too coarse to resolve many important cloud structures. This paper presents a technique to merge sounder and imager observations with the goal of maintaining the details offered by the imager’s high spatial resolution and the accuracy offered by the sounder’s spectral information. The technique involves deriving cloud temperatures from the sounder observations, interpolating the sounder temperatures to the imager pixels, and using the sounder temperatures as an additional constraint in the imager cloud height optimal estimation approach. This technique is demonstrated using collocated VIIRS and Cross-track Infrared Sounder (CrIS) observations with the impact of the sounder observations validated using coincident CALIPSO/CALIOP cloud heights These comparisons show significant improvement in the cloud heights for optically thin cirrus. The technique should be generally applicable to other imager/sounder pairs.

Abstract

A multispectral scanning spectrometer was used to obtain measurements of the bidirectional reflectance and brightness temperature of clouds, sea ice, snow, and tundra surfaces at 50 discrete wavelengths between 0.47 and 14.0 μm. These observations were obtained from the NASA ER-2 aircraft as part of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Arctic Clouds Experiment, conducted over a 1600 km × 500 km region of the north slope of Alaska and surrounding Beaufort and Chukchi Seas between 18 May and 6 June 1998. Multispectral images in eight distinct bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) were used to derive a confidence in clear sky (or alternatively the probability of cloud) over five different ecosystems. Based on the results of individual tests run as part of this cloud mask, an algorithm was developed to estimate the phase of the clouds (liquid water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both water and ice clouds that were detected during one flight line on 4 June.

This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS data in Alaska, is quite capable of distinguishing clouds from bright sea ice surfaces during daytime conditions in the high Arctic. Results of individual tests, however, make it difficult to distinguish ice clouds over snow and sea ice surfaces, so additional tests were added to enhance the confidence in the thermodynamic phase of clouds over the Chukchi Sea. The cloud optical thickness and effective radius retrievals used three distinct bands of the MAS, with a recently developed 1.62- and 2.13-μm-band algorithm being used quite successfully over snow and sea ice surfaces. These results are contrasted with a MODIS-based algorithm that relies on spectral reflectance at 0.87 and 2.13 μm.

Abstract

A computationally efficient radiative transfer model (RTM) is developed for the inference of ice cloud optical thickness and effective particle size from satellite-based infrared (IR) measurements and is aimed at potential use in operational cloud-property retrievals from multispectral satellite imagery. The RTM employs precomputed lookup tables to simulate the top-of-the-atmosphere (TOA) radiances (or brightness temperatures) at 8.5-, 11-, and 12-μm bands. For the clear-sky atmosphere, the optical thickness of each atmospheric layer resulting from gaseous absorption is derived from the correlated-k-distribution method. The cloud reflectance, transmittance, emissivity, and effective temperature are precomputed using the Discrete Ordinate Radiative Transfer model (DISORT). For an atmosphere containing a semitransparent ice cloud layer with a visible optical thickness τ smaller than 5, the TOA brightness temperature differences (BTDs) between the fast model and the more rigorous DISORT results are less than 0.1 K, whereas the BTDs are less than 0.01 K if τ is larger than 10. With the proposed RTM, the cloud optical and microphysical properties are retrieved from collocated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) in conjunction with the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data. Comparisons between the retrieved ice cloud properties (optical thickness and effective particle size) based on the present IR fast model and those from the Aqua/MODIS operational collection-5 cloud products indicate that the IR retrievals are smaller. A comparison between the IR-retrieved ice water path (IWP) and CALIOP-retrieved IWP shows robust agreement over most of the IWP range.

Abstract

Insufficient knowledge of the habit distribution and the degree of surface roughness of ice crystals within ice clouds is a source of uncertainty in the forward light scattering and radiative transfer simulations of ice clouds used in downstream applications. The Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 ice microphysical model presumes a mixture of various ice crystal shapes with smooth facets, except for the compact aggregate of columns for which a severely rough condition is assumed. When compared with Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) polarized reflection data, simulations of polarized reflectance using smooth particles show a poor fit to the measurements, whereas very rough-faceted particles provide an improved fit to the polarized reflectance. In this study a new microphysical model based on a mixture of nine different ice crystal habits with severely roughened facets is developed. Simulated polarized reflectance using the new ice habit distribution is calculated using a vector adding–doubling radiative transfer model, and the simulations closely agree with the polarized reflectance observed by PARASOL. The new general habit mixture is also tested using a spherical albedo differences analysis, and surface roughening is found to improve the consistency of multiangular observations. These results are consistent with previous studies that have used polarized reflection data. It is suggested that an ice model incorporating an ensemble of different habits with severely roughened surfaces would potentially be an adequate choice for global ice cloud retrievals.