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Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Increased aerosol concentrations can brighten low‐level clouds and extend their lifetimes, but aerosol–cloud interactions (ACI) remain highly uncertain and difficult to quantify. We show that part of this uncertainty is caused by topographical influences on clouds, that is, those arising from land–water contrasts. This is demonstrated using satellite retrievals in regions with extensive river networks, such as the Amazon Basin. 15 years of MODerate resolution Imaging Spectroradiometer (MODIS) satellite data show cloud formation over the Amazon River basin is suppressed by 26% with warm low clouds above the river exhibiting a 22% smaller droplet effective radius and 18% higher droplet concentration (NdN_(d) ) compared to adjacent land clouds. Thus, clouds above the river may appear polluted but are actually influenced by river‐breeze circulations driven by the thermal contrast between the river and the surrounding land. These responses are robust in both wet and dry seasons, and tests using an improved MODIS retrieval product show cloud differences are unlikely due to retrieval artifacts. In situ measurements from the Green Ocean Amazon Experiment (GoAmazon) confirm that NdN_(d)is elevated above rivers and are also higher when carbon monoxide concentrations are elevated near the large city of Manaus. Lagrangian airmass tracking over Manaus shows that regional‐scale river‐breeze circulations impact NdN_(d)as much as the urban aerosol plume, complicating ACI attribution and highlighting the need to isolate land‐surface effects to assess ACI in continental regions. Plain Language Summary Aerosols, tiny particles suspended in the air, play an important role in cloud formation by acting as seeds for cloud droplets to grow, thus affecting cloud brightness. Cloud brightness and size regulate how clouds cool the atmosphere by reflecting sunlight, but isolating how aerosols influence clouds over land is challenging because surface properties also affect cloud properties. Using 15 years of satellite data from the Amazon river basin, it was found that river‐breeze circulations, caused by temperature differences between cooler river surfaces and warmer surrounding land, significantly affect cloud properties. Clouds over rivers have smaller droplet sizes and more numerous droplets compared to clouds over nearby land. These clouds can look “polluted” by high aerosol concentrations even when their characteristics are shaped by natural processes rather than human activity. Data from the GoAmazon experiment supports these findings, and tests with improved satellite retrieval products confirm that these cloud differences are not due to satellite data errors. This research emphasizes the importance of separating the natural effects of rivers on cloud properties from aerosol impacts to accurately attribute human‐related influences, such as those from cities and fires, and improve our understanding of how aerosols affect clouds and the energy budget. Key Points Daytime low‐cloud fraction is strongly suppressed over Amazon and other major rivers compared to adjacent land Urban aerosols from Manaus significantly increase cloud droplet concentrations, locally, and up to 200 km from the city River‐breeze circulations influence low‐cloud properties as much as the aerosol sources, supporting greater drop concentrations over rivers

In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields – specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport (H) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε, which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12–19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections.

Coordinated flight legs of two aircraft above and below extended ice clouds played an important role in the Tropical Composition, Cloud and Climate Coupling Experiment (Costa Rica, 2007). The Solar Spectral Flux Radiometer measured up‐ and downward irradiance on the high‐altitude (ER‐2) and the low‐altitude (DC‐8) aircraft, which allowed deriving apparent absorption on a point‐by‐point basis along the flight track. Apparent absorption is the vertical divergence of irradiance, calculated from the difference of net flux at the top and bottom of a cloud. While this is the only practical method of deriving absorption from aircraft radiation measurements, it differs from true absorption when horizontal flux divergence is nonzero. Differences between true and apparent absorption are inevitable in any inhomogeneous atmosphere, especially clouds. We show, for the first time, the spectral shape of measured apparent absorption and compare with results from a three‐dimensional radiative transfer model. The model cloud field is created from optical thickness and effective radius retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator and from reflectivity profiles from the Cloud Radar System, both on board the ER‐2. Although the spectral shape is reproduced by the model calculations, the measured apparent absorption in the visible spectral range is higher than the model results along extended parts of the flight leg. This is possibly due to a net loss of photons into neighboring cirrus‐free areas that are not contained within the model domain.

The MODIS Cloud Optical and Microphysical Product (MOD_PR060D) for Data Collection 6 has entered full scale production. Aqua reprocessing is almost completed and Terra reprocessing will begin shortly. Unlike previous collections, the CHIMAERA code base allows for simultaneous processing for multiple sensors and the operational CHIMAERA 6.0.76 stream is also available for VIIRS and SEVIRI sensors and for our E-MAS airborne platform.

This presentation covered OSSEs, nature run simulators, and the error characterization of OSSEs, as well as OSSE related activities performed at NASA-GMAO.