Cloud Precipitation Interactions Research Articles

Distinctive aerosol–cloud–precipitation interactions in marine boundary layer clouds from the ACE-ENA and SOCRATES aircraft field campaigns

Abstract. The aerosol–cloud–precipitation interactions within the cloud-topped marine boundary layer (MBL) are examined using aircraft in situ measurements from Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) field campaigns. SOCRATES clouds exhibit a larger number concentration and smaller cloud droplet effective radius (148.3 cm−3 and 8.0 µm) compared to ACE-ENA summertime (89.4 cm−3 and 9.0 µm) and wintertime clouds (70.6 cm−3 and 9.8 µm). The ACE-ENA clouds, especially during the winter, feature stronger drizzle formation via droplet growth through enhanced collision–coalescence that is attributed to a relatively cleaner environment and deeper cloud layer. Furthermore, the aerosol–cloud interaction (ACI) indices from the two aircraft field campaigns exhibit distinct sensitivities, indicating different cloud microphysical responses to aerosols. The ACE-ENA winter season features relatively fewer aerosols, which are more likely activated into cloud droplets under the conditions of sufficient water vapor availability and strong turbulence. The enriched aerosol loading during ACE-ENA summer and SOCRATES generally leads to smaller cloud droplets competing for the limited water vapor and exhibiting a stronger ACI. Notably, the precipitation susceptibilities are stronger during the ACE-ENA than during the SOCRATES campaigns. The in-cloud drizzle behavior significantly alters sub-cloud cloud condensation nuclei (CCN) budgets through the coalescence-scavenging effect and, in turn, impacts the ACI assessments. The results of this study can enhance understanding and aid in future model simulation and assessment of the aerosol–cloud interaction.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

How Cloud Droplet Number Concentration Impacts Liquid Water Path and Precipitation in Marine Stratocumulus Clouds—A Satellite-Based Analysis Using Explainable Machine Learning

Aerosol–cloud–precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds, which constitute the most common cloud type globally. In this work, a dataset composed of satellite observations and reanalysis data is used in explainable machine learning models to analyze the relationship between the cloud droplet number concentration (Nd), cloud liquid water path (LWP), and the fraction of precipitating clouds (PF) in five distinct marine stratocumulus regions. This framework makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of Nd from other confounding factors, which proved to be very difficult in previous satellite-based studies. All regions display a decrease of PF and an increase in LWP with increasing Nd, despite marked inter-regional differences in the distribution of Nd. Polluted (high Nd) conditions are characterized by an increase of 12 gm−2 in LWP and a decrease of 0.13 in PF on average when compared to pristine (low Nd) conditions. The negative Nd–PF relationship is stronger in high LWP conditions, while the positive Nd–LWP relationship is amplified in precipitating clouds. These findings indicate that precipitation suppression plays an important role in MSC adjusting to aerosol-driven perturbations in Nd.

Predicting and Reconstructing Aerosol–Cloud–Precipitation Interactions with Physics-Informed Neural Networks

Interactions between clouds, aerosol, and precipitation are crucial aspects of weather and climate. The simple Koren–Feingold conceptual model is important for providing deeper insight into the complex aerosol–cloud–precipitation system. Recently, artificial neural networks (ANNs) and physics-informed neural networks (PINNs) have been used to study multiple dynamic systems. However, the Koren–Feingold model for aerosol–cloud–precipitation interactions has not yet been studied with either ANNs or PINNs. It is challenging for pure data-driven models, such as ANNs, to accurately predict and reconstruct time series in a small data regime. The pure data-driven approach results in the ANN becoming a “black box” that limits physical interpretability. We demonstrate how these challenges can be overcome by combining a simple ANN with physical laws into a PINN model (not purely data-driven, good for the small data regime, and interpretable). This paper is the first to use PINNs to learn about the original and modified Koren–Feingold models in a small data regime, including external forcings such as wildfire-induced aerosols or the diurnal cycle of clouds. By adding external forcing, we investigate the effects of environmental phenomena on the aerosol–cloud–precipitation system. In addition to predicting the system’s future, we also use PINN to reconstruct the system’s past: a nontrivial task because of time delay. So far, most research has focused on using PINNs to predict the future of dynamic systems. We demonstrate the PINN’s ability to reconstruct the past with limited data for a dynamic system with nonlinear delayed differential equations, such as the Koren–Feingold model, which remains underexplored in the literature. The main reason that this is possible is that the model is non-diffusive. We also demonstrate for the first time that PINNs have significant advantages over traditional ANNs in predicting the future and reconstructing the past of the original and modified Koren–Feingold models containing external forcings in the small data regime. We also show that the accuracy of the PINN is not sensitive to the value of the regularization factor (λ), a key parameter for the PINN that controls the weight for the physics loss relative to the data loss, for a broad range (from λ=1×103 to λ=1×105).

Investigating the Applicability of a Global Average Calibration Line for Ambient Size-Resolved Cloud Condensation Nuclei (CCN) Measurements: A Technical Note

Abstract Aerosol–cloud–precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlation was observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high-altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high-altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.

Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe – modeling and observations

Abstract. The Terrestrial Systems Modeling Platform (TSMP) was extended with a chemical transport model and polarimetric radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. The model was used at kilometer-scale (convection-permitting) resolution to simulate a deep convective storm event over Germany which produced large hail, high precipitation, and severe damaging winds. The ensemble model simulation was, in general, able to capture the storm structure, its evolution, and the spatial pattern of accumulated precipitation. However, the model was found to underestimate regions of high accumulated precipitation (&gt; 35 mm) and convective area fraction in the early period of the storm. While the model tends to simulate too high reflectivity in the downdraft region of the storm above the melting layer (mostly contributed by graupel), the model also simulates very weak polarimetric signatures in this region, when compared to the radar observations. The above findings remained almost unchanged when using a narrower cloud drop size distribution (CDSD) acknowledging the missing feedback between aerosol physical and chemical properties and CDSD shape parameters. The kilometer-scale simulation showed that the strong updraft in the convective core produces aerosol-tower-like features, increasing the aerosol number concentrations and hence increasing the cloud droplet number concentration and reducing the mean cloud drop size. This could also be a source of discrepancy between the simulated polarimetric features like differential reflectivity (ZDR) and specific differential-phase (KDP) columns along the vicinity of the convective core compared to the X-band radar observations. However, the use of narrow CDSD did improve the simulation of ZDR columns. Besides, the evaluation of simulated trace gases and aerosols was encouraging; however, a low bias was observed for aerosol optical depth (AOD), which could be partly linked to an underestimation of dust mass in the forcing data associated with a Saharan dust event. This study illustrates the importance and the additional complexity associated with the inclusion of chemistry transport model when studying aerosol–cloud–precipitation interactions. But, along with polarimetric radar data for model evaluation, it allows us to identify and better constrain the traditional two-moment bulk cloud microphysical schemes used in the numerical weather prediction models for weather and climate.

Furthering Understanding of Aerosol–Cloud–Precipitation Interactions in the Arctic

Abstract Conference Title: 2nd QuIESCENT (Quantifying the Indirect Effect: from Sources to Climate Effects of Natural and Transported aerosol in the Arctic) Workshop What: Atmospheric scientists, shared and discussed recent work to understand the complex interactions between aerosols, clouds, precipitation, radiation and dynamics at northern high latitudes, as well as recent and upcoming field campaigns to improve that understanding. When: 30 March – 1 April, 2022 Where: Tromsø, Norway

Observation of bioaerosol transport using wideband integrated bioaerosol sensor and coherent Doppler lidar

Abstract. Bioaerosols are usually defined as aerosols arising from biological systems such as bacteria, fungi, and viruses. They play an important role in atmospheric physical and chemical processes including ice nucleation and cloud condensation. As such, their dispersion affects not only public health but also regional climate. Lidar is an effective technique for aerosol detection and pollution monitoring. It is also used to profile the vertical distribution of wind vectors. In this paper, a coherent Doppler wind lidar (CDWL) is deployed for aerosol and wind detection in Hefei, China, from 11 to 20 March in 2020. A wideband integrated bioaerosol sensor (WIBS) is used to monitor variations in local fluorescent bioaerosols. Three aerosol transport events are captured. The WIBS data show that, during these transport events, several types of fluorescent aerosol particles exhibit abnormal increases in their concentration, number fractions to total particles, and number fractions to whole fluorescent aerosols. These increases are attributed to external fluorescent bioaerosols instead of local bioaerosols. Based on the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model and the characteristics of external aerosols in WIBS, their possible sources, transport paths, and components are discussed. The results prove the influence of external aerosol transport on local high particulate matter (PM) pollution and fluorescent aerosol particle composition. The combination of WIBS and CDWL expands the aerosol monitoring parameters and provides a potential method for real-time monitoring of fluorescent biological aerosol transport events. In addition, it also helps to understand the relationships between atmospheric phenomena at high altitudes like virga and the variation of surface bioaerosol. It contributes to the further understanding of long-range bioaerosol transport, the roles of bioaerosols in atmospheric processes, and in aerosol–cloud–precipitation interactions.

Factors affecting precipitation formation and precipitation susceptibility of marine stratocumulus with variable above- and below-cloud aerosol concentrations over the Southeast Atlantic

Abstract. Aerosol–cloud–precipitation interactions (ACIs) provide the greatest source of uncertainties in predicting changes in Earth's energy budget due to poor representation of marine stratocumulus and the associated ACIs in climate models. Using in situ data from 329 cloud profiles across 24 research flights from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign in September 2016, August 2017, and October 2018, it is shown that contact between above-cloud biomass burning aerosols and marine stratocumulus over the Southeast Atlantic Ocean was associated with precipitation suppression and a decrease in the precipitation susceptibility (So) to aerosols. The 173 “contact” profiles with aerosol concentration (Na) greater than 500 cm−3 within 100 m above cloud tops had a 50 % lower precipitation rate (Rp) and a 20 % lower So, on average, compared to 156 “separated” profiles with Na less than 500 cm−3 up to at least 100 m above cloud tops. Contact and separated profiles had statistically significant differences in droplet concentration (Nc) and effective radius (Re) (95 % confidence intervals from a two-sample t test are reported). Contact profiles had 84 to 90 cm−3 higher Nc and 1.4 to 1.6 µm lower Re compared to separated profiles. In clean boundary layers (below-cloud Na less than 350 cm−3), contact profiles had 25 to 31 cm−3 higher Nc and 0.2 to 0.5 µm lower Re. In polluted boundary layers (below-cloud Na exceeding 350 cm−3), contact profiles had 98 to 108 cm−3 higher Nc and 1.6 to 1.8 µm lower Re. On the other hand, contact and separated profiles had statistically insignificant differences between the average liquid water path, cloud thickness, and meteorological parameters like surface temperature, lower tropospheric stability, and estimated inversion strength. These results suggest the changes in cloud microphysical properties were driven by ACIs rather than meteorological effects, and adjustments to existing relationships between Rp and Nc in model parameterizations should be considered to account for the role of ACIs.

Aerosol–cloud–precipitation interactions during a Saharan dust event – A summertime case‐study from the Alps

AbstractChanges in the ambient aerosol concentration are known to affect the microphysical properties of clouds. Especially regarding precipitation formation, increasing aerosol concentrations are assumed to delay the precipitation onset, but may increase precipitation rates via convective invigoration and orographic spillover further downstream. In this study, we analyse the effect of increased aerosol concentrations on a heavy precipitation event observed in summer 2017 over northeastern Switzerland, an event which was considerably underestimated by the operational weather forecast model. Preceding the precipitation event, Saharan dust was advected towards the Alps, which could have contributed to increased precipitation rates north of the Alpine ridge. To investigate the potential impact of the increased ambient aerosol concentrations on surface precipitation, we perform a series of sensitivity simulations using the Consortium for Small‐scale Modeling (COSMO) model with different microphysical parametrizations and prognostic aerosol perturbations. The results show that the choice of the microphysical parametrization scheme in terms of a one‐ or two‐moment scheme has the relatively largest impact on surface precipitation rates. In the one‐moment scheme, surface precipitation is strongly reduced over the Alpine ridge and increased further downstream. Simulated changes in surface precipitation in response to aerosol perturbations remain smaller in contrast to the impact of the microphysics scheme. Elevated cloud condensation nuclei (CCN) concentrations lead to increased cloud water and decreased cloud ice mass, especially in regions of high convective activity south of the Alps. These altered cloud properties indeed increase surface precipitation further downstream, but the simulated change is too small to explain the observed heavy precipitation event. Additional ice‐nucleating particles (INPs) increase cloud ice mass, but only trigger local changes in downstream surface precipitation. Thus, increased aerosol number concentrations during the Saharan dust outbreak are unlikely to have caused the heavy precipitation event in summer 2017.

Global evidence of aerosol-induced invigoration in marine cumulus clouds

Abstract. Aerosol–cloud–precipitation interactions can lead to a myriad of responses within shallow cumulus clouds, including an invigoration response, whereby aerosol loading results in a higher rain rate, more turbulence, and deepening of the cloud layer. However few global studies have found direct evidence that invigoration occurs. The few satellite-based studies that report evidence for such effects generally focus on only the deepening response. Here, we show evidence of invigoration beyond a deepening response by investigating the effects of aerosol loading on the latent heating and vertical motion profiles of warm rain. Using latent heating and vertical motion profiles derived from CloudSat radar observations, we show precipitating cumulus clouds in unstable, polluted environments exhibit a marked increase in precipitation formation rates and cloud top entrainment rates. However, invigoration is only discernible when the stability of the boundary layer is explicitly accounted for in the analysis. Without this environmental constraint, the mean polluted and pristine cloud responses are indiscernible from each other due to offsetting cloud responses in stable and unstable environments. Invigoration, or suppression depending on the environment, may induce possible feedbacks in both stable and unstable conditions that could subdue or enhance these effects, respectively. The strength of the invigoration response is found to additionally depend on cloud organization defined here by the size of the warm rain system. These results suggest that warm cloud parameterizations must account for not only the possibility of aerosol-induced cloud invigoration, but also the dependence of this invigorated state on the environment and the organization of the rain system.

Impact of the variability in vertical separation between biomass burning aerosols and marine stratocumulus on cloud microphysical properties over the Southeast Atlantic

Abstract. Marine stratocumulus cloud properties over the Southeast Atlantic Ocean are impacted by contact between above-cloud biomass burning aerosols and cloud tops. Different vertical separations (0 to 2000 m) between the aerosol layer and cloud tops were observed on six research flights in September 2016 during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. There were 30 contact profiles, where an aerosol layer with aerosol concentration (Na) &gt; 500 cm−3 was within 100 m of cloud tops, and 41 separated profiles, where the aerosol layer with Na &gt; 500 cm−3 was located more than 100 m above cloud tops. For contact profiles, the average cloud droplet concentration (Nc) in the cloud layer was up to 68 cm−3 higher, the effective radius (Re) up to 1.3 µm lower, and the liquid water content (LWC) within 0.01 g m−3 compared to separated profiles. Free-tropospheric humidity was higher in the presence of biomass burning aerosols, and contact profiles had a smaller decrease in humidity (and positive buoyancy) across cloud tops with higher median above-cloud Na (895 cm−3) compared to separated profiles (30 cm−3). Due to droplet evaporation from entrainment mixing of warm, dry free-tropospheric air into the clouds, the median Nc and LWC for contact profiles decreased with height by 21 and 9 % in the top 20 % of the cloud layer. The impact of droplet evaporation was stronger during separated profiles as a greater decrease in humidity (and negative buoyancy) across cloud tops led to greater decreases in median Nc (30 %) and LWC (16 %) near cloud tops. Below-cloud Na was sampled during 61 profiles, and most contact profiles (20 out of 28) were within high-Na (&gt; 350 cm−3) boundary layers, while most separated profiles (22 out of 33) were within low-Na (&lt; 350 cm−3) boundary layers. Although the differences in below-cloud Na were statistically insignificant, contact profiles within low-Na boundary layers had up to 34.9 cm−3 higher Nc compared to separated profiles. This is consistent with a weaker impact of droplet evaporation in the presence of biomass burning aerosols within 100 m above cloud tops. For contact profiles within high-Na boundary layers, the presence of biomass burning aerosols led to higher below-cloud Na (up to 70.5 cm−3) and additional droplet nucleation above the cloud base along with weaker droplet evaporation. Consequently, the contact profiles in high-Na boundary layers had up to 88.4 cm−3 higher Nc compared to separated profiles. These results motivate investigations of aerosol–cloud–precipitation interactions over the Southeast Atlantic since the changes in Nc and Re induced by the presence of above-cloud biomass burning aerosols are likely to impact precipitation rates, liquid water path, and cloud fraction, and modulate closed-to-open-cell transitions.

Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment

Abstract. Climate is critically affected by aerosols, which alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud property datasets from MODIS (Moderate Resolution Imaging Spectroradiometer), onboard the Aqua satellite, and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on low-level warm-cloud microphysics and drizzle over northern Taiwan during multiple fall seasons (from 15 October to 30 November of 2005–2017). Our results indicated that northwestern Taiwan, which has several densely populated cities, is dominated by low-level clouds (e.g., warm, thin, and broken clouds) during the fall season. The observed effects of aerosols on warm clouds indicated aerosol indirect effects (i.e., increased aerosol loading caused a decrease in cloud effective radius (CER)), an increase in cloud optical thickness, an increase in cloud fraction, and a decrease in cloud-top temperature under a fixed cloud water path. Quantitatively, aerosol–cloud interactions (ACI=-∂ln⁡CER∂ln⁡α|CWP, changes in CER relative to changes in aerosol amounts) were 0.07 for our research domain and varied between 0.09 and 0.06 in the surrounding remote (i.e., ocean) and polluted (i.e., land) areas, respectively, indicating aerosol indirect effects were stronger in the remote area. From the raindrop size distribution analysis, high aerosol loading resulted in a decreased frequency of drizzle events, redistribution of cloud water to more numerous and smaller droplets, and reduced collision–coalescence rates. However, during light rain (≤1 mm h−1), high aerosol concentrations drove raindrops towards smaller droplet sizes and increased the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on clouds and precipitation, and observational strategies for future research on aerosol–cloud–precipitation interactions.

Snow-induced buffering in aerosol–cloud interactions

Abstract. Complex aerosol–cloud–precipitation interactions lead to large differences in estimates of aerosol impacts on climate among general circulation models (GCMs) and satellite retrievals. Typically, precipitating hydrometeors are treated diagnostically in most GCMs, and their radiative effects are ignored. Here, we quantify how the treatment of precipitation influences the simulated effective radiative forcing due to aerosol–cloud interactions (ERFaci) using a state-of-the-art GCM with a two-moment prognostic precipitation scheme that incorporates the radiative effect of precipitating particles, and we investigate how microphysical process representations are related to macroscopic climate effects. Prognostic precipitation substantially weakens the magnitude of ERFaci (by approximately 54 %) compared with the traditional diagnostic scheme, and this is the result of the increased longwave (warming) and weakened shortwave (cooling) components of ERFaci. The former is attributed to additional adjustment processes induced by falling snow, and the latter stems largely from riming of snow by collection of cloud droplets. The significant reduction in ERFaci does not occur without prognostic snow, which contributes mainly by buffering the cloud response to aerosol perturbations through depleting cloud water via collection. Prognostic precipitation also alters the regional pattern of ERFaci, particularly over northern midlatitudes where snow is abundant. The treatment of precipitation is thus a highly influential controlling factor of ERFaci, contributing more than other uncertain “tunable” processes related to aerosol–cloud–precipitation interactions. This change in ERFaci caused by the treatment of precipitation is large enough to explain the existing difference in ERFaci between GCMs and observations.

Source Apportionment of Aerosol at a Coastal Site and Relationships with Precipitation Chemistry: A Case Study over the Southeast United States.

This study focuses on the long-term aerosol and precipitation chemistry measurements from colocated monitoring sites in Southern Florida between 2013 and 2018. A positive matrix factorization (PMF) model identified six potential emission sources impacting the study area. The PMF model solution yielded the following source concentration profiles: (i) combustion; (ii) fresh sea salt; (iii) aged sea salt; (iv) secondary sulfate; (v) shipping emissions; and (vi) dust. Based on these results, concentration-weighted trajectory maps were developed to identify sources contributing to the PMF factors. Monthly mean precipitation pH values ranged from 4.98 to 5.58, being positively related to crustal species and negatively related to SO42−. Sea salt dominated wet deposition volume-weighted concentrations year-round without much variability in its mass fraction in contrast to stronger seasonal changes in PM2.5 composition where fresh sea salt was far less influential. The highest mean annual deposition fluxes were attributed to Cl−, NO3−, SO42−, and Na+ between April and October. Nitrate is strongly correlated with dust constituents (unlike sea salt) in precipitation samples, indicative of efficient partitioning to dust. Interrelationships between precipitation chemistry and aerosol species based on long-term surface data provide insight into aerosol–cloud–precipitation interactions.

Cloud, Aerosol, and Boundary Layer Structure across the Northeast Pacific Stratocumulus–Cumulus Transition as Observed during CSET

AbstractDuring the Cloud System Evolution in the Trades (CSET) field study, 14 research flights of the National Science Foundation G-V sampled the stratocumulus–cumulus transition between Northern California and Hawaii and its synoptic variability. The G-V made vertically resolved measurements of turbulence, cloud microphysics, aerosol characteristics, and trace gases. It also carried dropsondes and a vertically pointing W-band radar and lidar. This paper summarizes these observations with the goals of fostering novel comparisons with theory, models and reanalyses, and satellite-derived products. A longitude–height binning and compositing strategy mitigates limitations of sparse sampling and spatiotemporal variability. Typically, a 1-km-deep decoupled stratocumulus-capped boundary layer near California evolved into 2-km-deep precipitating cumulus clusters surrounded by patches of thin stratus that dissipated toward Hawaii. Low cloud cover was correlated with estimated inversion strength more than with cloud droplet number, even though the thickest clouds were generally precipitating and ultraclean layers indicative of aerosol–cloud–precipitation interaction were common west of 140°W. Accumulation-mode aerosol concentration correlated well with collocated cloud droplet number concentration and was typically largest near the surface. Aitken mode aerosol concentration was typically larger in the free troposphere. Wildfire smoke produced spikes of aerosol and trace gases on some flights. CSET data are compared with space–time collocated output from MERRA-2 reanalysis and from the CAM6 climate model run with winds and temperature nudged toward this reanalysis. The reanalysis compares better with the observed relative humidity than does nudged CAM6. Both vertically diffuse the stratocumulus cloud layer versus observations. MERRA-2 slightly underestimates in situ carbon monoxide measurements and underestimates ozone depletion within the boundary layer.

A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing

AbstractLight shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol–cloud–precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Jülich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

Spatio‐temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

This article presents the spatio‐temporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low‐level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of space‐borne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations.The resulting 12 years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land–sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8‐day yet high‐spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed observations of warm rain create opportunities to further investigate the diurnal cycle of warm rain, study aerosol–cloud–precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond.

TRMM-Based Optical and Microphysical Features of Precipitating Clouds in Summer Over the Yangtze–Huaihe River Valley, China

The optical and microphysical features of precipitating clouds are key information for studying the satellite-based precipitation estimation, cloud radiative effects, aerosol–cloud–precipitation interactions, cloud and precipitation parameterization in weather and climate models. In this study, 15-year synchronous spectral and radar observations from the TRMM satellite were used to statistically explore the optical and microphysical features of precipitating clouds (PCs), including cloud effective radius (CER), cloud optical thickness (COT), cloud water path (CWP), thermal infrared brightness temperature at channel 4 (TB4) of cloud top, and storm top height (STH) and their relationships with surface rain rates in summer over Yangtze–Huaihe River Valley (YHRV). Results show that the optical and microphysical features of PCs/stratiform PCs/convective PCs vary with geographical locations in summer over YHRV, due to the different ambient meteorological and topographical conditions. Higher CER/COT/CWP/STH and lower TB4 mainly locate at areas of bigger rain rates. For PCs, their spatial distribution of CER is mainly dominated by stratiform PCs, while their spatial distribution of COT/CWP is mainly dominated by convective PCs. Moreover, stratiform precipitation is the dominant form in summer over YHRV and, thus, most PCs present vertical structures of optical and microphysical features as stratiform PCs. Stratiform PCs are usually thicker and contain more water vapor with bigger cloud particles than convective PCs (including deep and shallow convective PCs). In addition, existing shallow convective PCs are associated with lower storm heights and warmer cloud tops. Finally, surface rain rates of PCs (convective/stratiform PCs) increase gradually with the increment of CER/COT/CWP/STH, especially under 5 (15/5) mm/h. Similar relationship between surface rain rates and COT/CWP for shallow convective PCs is also found under 0.75 mm/h. Surface rain rate of PCs (convective/stratiform PCs) with cold cloud tops (TB4 264 K), surface rain rate usually increases as CER decreases, which suggests that aerosol indirect effects are dominant in lower PCs, because over pollution regions abundant aerosols enter into lower clouds more easily and then suppress the development of shallow convective PCs.