Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately -2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

EPIC Spectral Observations of Variability in Earth's Global Reflectance

NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun-Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth. In particular, the paper shows that (a) diurnal variations of the Earth's reflectance are determined mostly by periodic changes in the land-ocean fraction of its the sunlit side; (b) the daily reflectance displays clear seasonal variations that are significant even without including the contributions from snow and ice in the polar regions (which can enhance daily mean reflectances by up to 2 to 6% in winter and up to 1 to 4% in summer); (c) the seasonal variations of the sunlit Earth reflectance are mostly determined by the latitudinal distribution of oceanic clouds.

Observation-based study on aerosol optical depth and particle size in partly cloudy regions

This study seeks to help better understand aerosol-cloud interactions by examining statistical relationships between aerosol properties and nearby low-altitude cloudiness using satellite data. The analysis of a global dataset of MODIS (Moderate Resolution Imaging Spectroradiometer) observations reveals that the positive correlation between cloudiness and aerosol optical depth (AOD) reported in earlier studies is strong throughout the globe and during both winter and summer. Typically, AOD is 30-50% higher on cloudier-than-average days than on less cloudy days. A combination of satellite observations and MERRA-2 global reanalysis data reveals that the correlation between cloud cover and AOD is strong for all aerosol types considered: sulfate, dust, carbon, and sea salt. The observations also indicate that in the presence of nearby clouds, aerosol size distributions tend to shift toward smaller particles over large regions of the Earth. This is consistent with a greater cloud-related increase in the AOD of fine mode than of coarse mode particles. The greater increase in fine mode AOD implies that the cloudiness-AOD correlation does not come predominantly from cloud detection uncertainties. Additionally, the results show that aerosol particle size increases near clouds even in regions where it decreases with increasing cloudiness. This suggests that the decrease with cloudiness comes mainly from changes in large-scale environment, rather than from clouds increasing the number or the size of fine mode aerosols. Finally, combining different aerosol retrieval algorithms demonstrated that quality assessment flags based on local variability can help identifying when the observed aerosol populations are affected by surrounding clouds.

Testing the Two-Layer Model for Correcting Near Cloud Reflectance Enhancement Using LES/SHDOM Simulated Radiances

A transition zone exists between cloudy skies and clear sky, such that clouds scatter solar radiation into clear sky regions. From a satellite perspective, it appears that clouds enhance the radiation nearby. We seek a simple method to estimate this enhancement, since it is so computationally expensive to account for all 3-dimensional (3D) scattering processes. In previous studies, we developed a simple two-layer model (2LM) that estimated the radiation scattered via cloud-molecular interactions. Here we have developed a new model to accounts for cloud-surface interaction (CSI). We test the models by comparing to calculations provided by full 3D radiative transfer simulations of realistic cloud scenes. For these scenes, the MODIS-like radiance fields were computed from the Spherical Harmonic Discrete Ordinate Method (SHDOM), based on a large number of cumulus fields simulated by the UCLA Large Eddy Simulation (LES) model. We find that the original 2LM model that estimates cloud-air molecule interactions accounts for 64% of the total reflectance enhancement, and the new model (2LM+CSI) that also includes cloud-surface interactions accounts for nearly 80%. We discuss the possibility of accounting for cloud-aerosol radiative interactions in 3D cloud induced reflectance enhancement, which may explain the remaining 20% of enhancements. Because these are simple models, these corrections can be applied to global satellite observations (e.g. MODIS) and help to reduce biases in aerosol and other clear-sky retrievals.