The cloud drop effective radius (R_e) of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products has often taken place under stratiform cloud conditions that favor the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies have noted concerns with respect to significant biases in retrieved R_e arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 “Cloud, Aerosol and Monsoon Processes Philippines Experiment” (CAMP²Ex), which, in part, targeted the objective of providing the first detailed evaluation of R_e retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross-comparisons of R_e between the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the NASA P-3 and Learjet aircraft that are all taken in close spatiotemporal proximity to the same cloud fields. A particular advantage of our approach lies in the capability of the RSP to retrieve R_e using a bi-spectral MODIS approach and a polarimetric approach, which allows for the evaluation of bi-spectral and polarimetric R_e retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP polarimetric method, the in situ method, and the bias-adjusted MODIS method of Fu et al. (2019) show a comparable median (mean ± standard deviation) for the R_e samples of 9.6 (10.2 ± 4.0) µm, 11.0 (13.6 ± 11.3) µm, and 10.4 (10.8 ± 3.8) µm, respectively. These values are far lower than the values of 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when R_e is segregated by cloud-top height and in detailed case studies. The clouds sampled during CAMP²Ex consist of mostly small (mean transect length ∼ 1.4 km) and low clouds (mean cloud-top height ∼ 1 km), which had more numerous small clouds than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). The overestimates of R_e from the RSP bi-spectral technique compared with the polarimetric technique increased as cloud size and cloud optical depth decreased. Drizzle, cloud-top bumpiness, and solar zenith angle, however, are not closely correlated with the overestimate of bi-spectral R_e. For shallow clouds that dominated the liquid cloud cover for the CAMP²Ex region and period, we show that 3-D radiative transfer and cloud heterogeneity, particularly for the optically thin and small clouds, appear to be the leading cause of the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral R_e retrievals in cumulus cloud fields.

Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single‐platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrieved Re covary with the structural characteristics of the cloud and the Sun‐view geometry. Recently, it has been shown that the bias in MODIS Re can be estimated by fusing MODIS data with data from Terra's Multi‐angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional‐scale, bias‐corrected, monthly‐mean Re_1.6, Re_2.1, and Re_3.7 values retrieved from the 1.6, 2.1, and 3.7 μm MODIS spectral channels. Our results reveal that monthly‐mean bias in Re_2.1 exhibits large regional dependency, ranging from at least ~1 to 10 μm (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias‐corrected monthly‐mean Re_2.1 ranges from 4 to 17 μm, compared to 10 to 25 μm for uncorrected Re_2.1, with estimated uncertainties of 0.1 to 1.8 μm. The bias‐corrected monthly‐mean Re_3.7 and Re_2.1 show difference of approximately +0.6 μm in the coastal marine stratocumulus regions and down to approximately −2 μm in the cumuliform cloud regions, compared to uncorrected values of about −1 to −6 μm, respectively. Bias‐corrected Re values compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of global Re distributions from MODIS Re products and may further impact studies, which use the original MODIS Re products to study, for example, aerosol‐cloud interactions and cloud microphysical parameterization.

Cumulus clouds are common over maritime regions. They are important regulators of the global radiative energy budget and global hydrologic cycle, as well as a key contributor to the uncertainty in anthropogenic climate change projections due to uncertainty in aerosol–cloud interactions. These interactions are regionally specific owing to their strong influences on aerosol sources and meteorology. Here, our analysis focuses on the statistical properties of marine boundary layer (MBL) aerosol chemistry and the relationships of MBL aerosol to cumulus cloud properties just above cloud base as sampled in 2019 during the NASA Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP²Ex). The aerosol and clouds were sampled by instruments on the NASA P-3 aircraft over three distinct maritime regions around the Philippines: the West Pacific, the South China Sea, and the Sulu Sea. Our analysis shows three primary sources influenced the aerosol chemical composition: clean marine (ocean source), industrial (Southeast Asia, Manila, and cargo and tanker ship emissions), and biomass burning (Borneo and Indonesia). The clean marine aerosol chemical composition had low values of all sampled chemical signatures, specifically median values of 2.2 µg m⁻³ of organics (ORG), 2.3 µg m⁻³ of SO₄, 0.3 µg m⁻³ of NO₃, 1.4 µg m⁻³ of NH₄, 0.04 µg m⁻³ of Cl, and 0.0074 µg m⁻³ of refractory black carbon (BC). Chemical signatures of the other two aerosol source regions were industrial, with elevated SO₄ having a median value of 6.1 µg m⁻³, and biomass burning, with elevated median concentrations of ORG 21.2 µg m⁻³ and BC 0.1351 µg m⁻³. Based on chemical signatures, the industrial component was primarily from ship emissions, which were sampled within 60 km of ships and within projected ship plumes. Normalized cloud droplet size distributions in clouds sampled near the MBL passes of the P-3 showed that clouds impacted by industrial and biomass burning contained higher concentrations of cloud droplets, by as much as 1.5 orders of magnitude for diameters < 13 µm compared to clean marine clouds, while at size ranges between 13.0–34.5 µm the median concentrations of cloud droplets in all aerosol categories were nearly an order of magnitude less than the clean marine category. In the droplet size bins centered at diameters > 34.5 µm concentrations were equal to, or slightly exceeded, the concentrations of the clean marine clouds. These analyses show that anthropogenic aerosols generated from industrial and biomass burning sources significantly influenced cloud base microphysical structure in the Philippine region enhancing the small droplet concentration and reducing the concentration of mid-sized droplets.

The Multi‐angle Imaging SpectroRadiometer (MISR) provides measurements over a wider scattering‐angle range for a given location than a cross‐track scanning sensor such as the MODerate resolution Imaging Spectroradiometer (MODIS). Based on a full year (2013) of fused MISR‐MODIS datasets, we develop a variable surface roughness model for ice particles with the goal of identifying the optimal degree of roughness in the ice model for a given pixel containing single‐layer ice clouds. For the MISR‐MODIS observations over oceans, severe roughness values are often selected for a pixel when optical thickness (τ) and particle effective radius (R_eff) are large in conjunction with larger cloud heterogeneity index (H_σ) or a warmer cloud top temperature. Furthermore, τ, R_eff, and ice water path are retrieved with the optimal model and compared to operational MODIS Collection 6 (MC6) products that assume a constant roughness. In general, the retrievals based on the present optimal model lead to greater consistency with MISR measurements, and result in larger median τ by 10.1% and smaller median R_eff by 6.5% but almost identical ice water path in comparison with the MC6 counterparts. The higher average τ value is caused by a slightly larger number of large τ cases, but the smaller average R_eff value is due to the shifting of the retrieved R_eff value toward smaller values by approximately 2–4 μm in comparison to the MC6 distribution over all seasons. Both τ retrievals have similar regional and monthly variations, but a larger annual cycle of R_eff is associated with the optimal model.

The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics.

Cumulus clouds are common over maritime regions. They are important regulators of the global radiative energy budget and global hydrologic cycle, and a key contributor to the uncertainty in anthropogenic climate change projections due to uncertainty in aerosol-cloud interactions. These interactions are regionally specific owing to their strong influences on aerosol sources and meteorology. Here, our analysis focuses on the statistical properties of marine boundary layer (MBL) aerosol chemistry and the relationships of MBL aerosol to cumulus cloud properties just above cloud base as sampled in 2019 during the NASA Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP²Ex). The aerosol and clouds were sampled by instruments on the NASA P-3 aircraft over three distinct maritime regions around the Philippines: the West Pacific, the South China Sea, and the Sulu Sea. Our analysis show three primary sources influenced the aerosol chemical composition: marine (ocean source), industrial (Southeast Asia, Manila, and cargo and tanker ship emissions), and biomass burning (Borneo and Indonesia). The marine aerosol chemical composition had low values of all sampled chemical signatures, specifically median values of 2.3 µg/m³ of organics (ORG), 6.1 µg/m³ of SO₄, 0.1 µg/m³ of NO₃, 1.4 µg/m³ of NH₄, 0.04 µg/m³ of Cl, and 0.0074 µg/m³ of refractory black carbon (BC). Chemical signatures of the other two aerosol source regions were: industrial, with elevated SO₄ having a median value of 6.1 µg/m³ and biomass burning, with elevated median concentrations of ORG 21.2 µg/m³ and BC 0.1351 µg/m³. The industrial component was primarily from ship emissions based on chemical signatures. The ship emissions were sampled within 60 km of ships and within projected ship plumes. Normalized cloud-droplet size distributions in clouds sampled near the MBL passes of the P-3 showed that clouds impacted by industrial and biomass burning contained higher concentrations of cloud droplets, by as much as 1.5 orders of magnitude for sizes with diameters < 13 µm compared to marine clouds, while at size ranges between 13.0–34.5 µm the median concentrations of cloud droplets in all aerosol categories were nearly an order of magnitude less than the marine category. In the droplet size bins centered at diameters > 34.5 µm concentrations were equal to, or slightly exceeded, the concentrations of the marine clouds. These analyses show that anthropogenic aerosol generated from industrial and biomass burning sources significantly influence cloud base microphysical structure in the Philippine region enhancing the small droplet concentration and reducing the concentration of mid-sized droplets.