This study reports a characterization of the real part of dry particle refractive index (n) at 532 nm based on airborne measurements over the United States, Canada, the Pacific Ocean, and the Gulf of Mexico from the 2012 Deep Convective Clouds and Chemistry (DC3) and 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) campaigns. Effective n values are reported, with the limitations and uncertainties discussed. Eight air mass types were identified based on criteria related to gas-phase tracer concentrations, location, and altitude. Average values of n for these air types ranged from 1.50 to 1.53. Values of n for the organic aerosol (OA) fraction (n OA ) were calculated using a linear mixing rule for each air mass type, with 1.52 shown to be a good approximation for all OA. Case studies detailing vertical structure revealed that n and n OA increased with altitude, simultaneous with enhancements in the mass fraction of OA. Values of n OA were positively (negatively) correlated with the O:C (H:C) ratio in the absence of biomass burning influence; in contrast, the cumulative data set revealed a slight decrease in n OA as a function of the O:C ratio. The performance of parametric (multiple linear regression) and nonparametric (Gaussian process regression) methods in predicting n based on aerosol composition data is discussed. It is shown that even small perturbations in n values significantly impact aerosol optical depth retrievals, radiative forcing, and optical sizing instruments, emphasizing the importance of further improving the understanding of this important aerosol property.

Abstract. This study provides a detailed characterization of stratocumulus clearings off the US West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009–2018) of Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify the monthly frequency, growth rate of total area (GRArea), and dimensional characteristics of 306 total clearings. While there is interannual variability, the summer (winter) months experienced the most (least) clearing events, with the lowest cloud fractions being in close proximity to coastal topographical features along the central to northern coast of California, including especially just south of Cape Mendocino and Cape Blanco. From 09:00 to 18:00 (PST), the median length, width, and area of clearings increased from 680 to 1231, 193 to 443, and ∼67 000 to ∼250 000 km2, respectively. Machine learning was applied to identify the most influential factors governing the GRArea of clearings between 09:00 and 12:00 PST, which is the time frame of most rapid clearing expansion. The results from gradient-boosted regression tree (GBRT) modeling revealed that air temperature at 850 hPa (T850), specific humidity at 950 hPa (q950), sea surface temperature (SST), and anomaly in mean sea level pressure (MSLPanom) were probably most impactful in enhancing GRArea using two scoring schemes. Clearings have distinguishing features such as an enhanced Pacific high shifted more towards northern California, offshore air that is warm and dry, stronger coastal surface winds, enhanced lower-tropospheric static stability, and increased subsidence. Although clearings are associated obviously with reduced cloud fraction where they reside, the domain-averaged cloud albedo was actually slightly higher on clearing days as compared to non-clearing days. To validate speculated processes linking environmental parameters to clearing growth rates based on satellite and reanalysis data, airborne data from three case flights were examined. Measurements were compared on both sides of the clear–cloudy border of clearings at multiple altitudes in the boundary layer and free troposphere, with results helping to support links suggested by this study's model simulations. More specifically, airborne data revealed the influence of the coastal low-level jet and extensive horizontal shear at cloud-relevant altitudes that promoted mixing between clear and cloudy air. Vertical profile data provide support for warm and dry air in the free troposphere, additionally promoting expansion of clearings. Airborne data revealed greater evidence of sea salt in clouds on clearing days, pointing to a possible role for, or simply the presence of, this aerosol type in clearing areas coincident with stronger coastal winds.

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

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We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.