The absorption Ångström exponent (AAE) is an important aerosol optical parameter used for aerosol characterization and apportionment studies. The AAE of black carbon (BC) particles is widely accepted to be 1.0, although observational estimates give quite a wide range of 0.6–1.3. With considerable uncertainties related to observations, a numerical study is a powerful method, if not the only one, to provide a better and more accurate understanding on BC AAE. This study calculates BC AAE using realistic particle geometries based on fractal aggregate and an accurate numerical optical model (namely the multiple-sphere T-matrix method), and considers bulk properties of an ensemble of BC particles following lognormal size distributions. At odds with the expectations, BC AAE is not 1.0, even when BC is assumed to have small sizes and a wavelength-independent refractive index. With a wavelength-independent refractive index, the AAE of fresh BC is approximately 1.05 and relatively insensitive to particle size. For BC with geometric mean diameters larger than 0.12 µm, BC AAE becomes smaller when BC particles are aged (compact structures or coated by other non-absorptive materials). For coated BC, we prescribe the coating fraction variation based on a laboratory study, where smaller BC cores are shown to develop larger coating fractions than those of bigger BC cores. For both compact and coated BC, the AAE is highly sensitive to particle size distribution, ranging from approximately 0.8 to even over 1.4 with wavelength-independent refractive index. When the refractive index is allowed to vary with wavelength, a feature with observational backing, the BC AAE may show an even wider range. For different BC morphologies, we derive simple empirical equations on BC AAE based on our numerical results, which can serve as a guide for the response of BC AAE to BC size and refractive index. Due to its complex influences, the effects of BC geometry is better to be discussed at certain BC properties, i.e., known size and refractive index.

Wildfires emit large amounts of black carbon and light-absorbing organic carbon, known as brown carbon, into the atmosphere. These particles perturb Earth’s radiation budget through absorption of incoming shortwave radiation. It is generally thought that brown carbon loses its absorptivity after emission in the atmosphere due to sunlight-driven photochemical bleaching. Consequently, the atmospheric warming effect exerted by brown carbon remains highly variable and poorly represented in climate models compared with that of the relatively nonreactive black carbon. Given that wildfires are predicted to increase globally in the coming decades, it is increasingly important to quantify these radiative impacts. Here we present measurements of ensemble-scale and particle-scale shortwave absorption in smoke plumes from wildfires in the western United States. We find that a type of dark brown carbon contributes three-quarters of the short visible light absorption and half of the long visible light absorption. This strongly absorbing organic aerosol species is water insoluble, resists daytime photobleaching and increases in absorptivity with night-time atmospheric processing. Our findings suggest that parameterizations of brown carbon in climate models need to be revised to improve the estimation of smoke aerosol radiative forcing and associated warming.

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and K-index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration.

In this study, Gaussian mixture model clustering analysis was carried out to examine characteristics of Global Precipitation Measurement (GPM) Dual‐frequency Precipitation Radar (DPR)‐retrieved mass‐weighted mean diameter (D_m), and normalized intercept parameter (N_w) of the drop size distribution (DSD) for heavy rainfalls (>10 mm h⁻¹) for 6 years (2014–2019). Three objective DSD types – continental, oceanic deep, and oceanic shallow convective types – emerged. The means and standard deviations of D_m and N_w obtained for the three types are in good agreement with various ground‐based observations, indicating that global view of DSD characteristics can be obtained from DPR‐derived DSD parameters. Global distributions of occurrence and contribution of each DSD type to total heavy rainfall are produced for the first time, which will help examine the dominant DSD type, its contribution to total heavy rainfall, and composition of different convective types in the rainfall system at a given location.

Black carbon (BC) plays an important role on the global and regional climate, whereas there are significant uncertainties on its optical properties. Among various optical properties, the Ångström exponent (AE) indicates the spectral variation of the particle‐optic interaction and is widely used to understand the aerosol properties. We consider the influence of BC geometry on its optical properties and assess the sensitivity of the AE to particle geometry and size distribution. The fractal aggregates with different fractal dimensions are used to represent realistic BC particles, and popular equivalent volume spherical and spheroidal models are also considered for comparison. Even if the fractal aggregates become highly compact and spherical, their optical properties are still significantly different from those of equivalent volume spheres or spheroids. Meanwhile, the Rayleigh‐Debye‐Gans approximation can hardly provide accurate results for all optical quantities of aggregates with different dimensions. The extinction Ångström exponent (EAE) and absorption Ångström exponent (AAE) are sensitive to both particle geometry and size distribution. With BC becoming more compact (from fractal aggregate to spheroid and to sphere), the AE becomes more sensitive to particle size distribution. The EAE and AAE of aggregates with different size distributions vary between 1.10–1.63 and 0.87–1.50, respectively, whereas those of the spheres or spheroids have wider ranges. Furthermore, the AE at smaller wavelengths (between 0.35 µm and 0.55 µm) is more sensitive to geometry and size distribution than that given by optical properties at larger wavelengths (between 0.55 µm and 0.88 µm).