Cloud Precipitation Research Articles

Evaluation of a Flexible Single Ice Microphysics and a Gaussian Probability-Density-Function Macrophysics Scheme in a Single Column Model

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) and the Atmospheric Radiation Measurement Southern Great Plains Experiment in 1997 (ARM97). Their performance was evaluated against observations and other reference schemes. The new schemes simulated reasonable precipitation with proper fluctuations and peaks, ice, and liquid water contents, especially in lower levels below 650 hPa during the wet period in the TWP-ICE. The root mean square error (RMSE) of the simulated cloud fraction was below 200 hPa was 0.10/0.08 in the wet/dry period, which showed an obvious improvement when compared to that, i.e., 0.11/0.11 of original scheme. Accumulated ice water content below the melting level decreased by 21.57% in the SI. The well-matched average liquid water content displayed between the new scheme and observations, which was two times larger than those with the referencing scheme. In the ARM97 simulations, the SI scheme produced considerable ice water content, especially when convection was active. Low-level cloud fraction and precipitation extremes were improved using the Gauss-PDF scheme, which displayed the RMSE of cloud fraction of 0.02, being only half of the original schemes. The study indicates that the SI and Gauss-PDF schemes are promising approaches to simplify the microphysics process and improve the low-level cloud modeling.

Cloud vertical structure measurements from a ground-based cloud radar over the southeastern Tibetan Plateau

The southeastern Tibetan Plateau (TP) is the critical region of water vapor transport over the TP. Ground-based Ka-band cloud radar (KaCR) measurements collected at the Motuo National Climate Observatory in 2019 were used to analyze the physical properties of clouds at the front of the water vapor channel over the TP. The cloud top heights (CTHs) measured by the KaCR were verified through a comparison with observations from the Fengyun-4A (FY-4A) satellite at the same location. The KaCR-obtained CTHs were generally consistent with the FY-4A-obtained CTHs, although the KaCR slightly overestimated the CTHs of nonprecipitation clouds and underestimated the CTHs of precipitation clouds. Negative deviations between the KaCR- and FY-4A-obtained CTHs increased with rainfall rate. Cloud cover frequently occurred over Motuo, with an annual mean occurrence frequency of 65.3%. Clouds tended to form frequently at night and dissipate gradually in the daytime. Two peaks in the cloud base height (CBH) distribution were observed in Motuo, located at 0–1 km and 2–3 km, corresponding to precipitation and nonprecipitation clouds, respectively. The CTHs reached a peak at 6–7 km, which might be related to precipitation clouds. Moreover, a maximum CTH probability was observed in the midnight-early morning hours during the rainy season, while the CTH frequency peaked in the evening during the dry season. In addition, low and middle clouds were prevalent in Motuo for the period of observation in 2019. Understanding the characteristics of clouds over the TP will provide key validations of satellite measurements and promote research on cloud forces over the TP.

Assessing the Effect of Riming on Snow Microphysics: The First Observational Study in East China

AbstractThrough the collision and freezing of supercooled droplets on the surface of snow particles, riming can change the density, fall velocity and aspect ratio of snow particles. Riming plays an important role in the formation of precipitation in cold clouds. To our knowledge, the impact of riming on snow microphysics is yet well understood, and there is a lack of observations obtained in China addressing this issue. For the first time in East China, the connection between riming and snow microphysical properties was investigated quantitatively during snow events in two winters. To quantify the degree of riming, the rime mass fraction (FR) is derived using the combination of a 2‐D video disdrometer (2DVD) and a weighing gauge. FR is added to the density‐diameter and fall velocity‐diameter relations, and a quantitative relation between riming and shapes of snowflakes is established based on the in situ observation of the 2DVD. The results show that riming can well explain the variability in the density and fall velocity of snowflakes. The changes in the shape of snowflakes can be divided into two distinct stages with increasing riming: in the initial stage (FR < 0.5), the aspect ratio increases very slowly, while in the later stage (FR > 0.5), the aspect ratio increases rapidly. Direct observations of the continuous changes in the shapes of snowflakes with riming are in good agreement with the retrieval results of radar.

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) > 500 cm−3 was within 100 m of cloud tops, and 41 separated profiles, where the aerosol layer with Na > 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 (> 350 cm−3) boundary layers, while most separated profiles (22 out of 33) were within low-Na (< 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.

A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1

Abstract. The evaluation of models in general is a nontrivial task and can, due to epistemological and practical reasons, never be considered complete. Due to this incompleteness, a model may yield correct results for the wrong reasons, i.e., via a different chain of processes than found in observations. While guidelines and strategies exist in the atmospheric sciences to maximize the chances that models are correct for the right reasons, these are mostly applicable to full physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field, atmospheric quantities such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt–Väisälä frequencies are calculated in accordance with linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary that are different to the standard zero-gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations and the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates that the precipitation maximum calculated by the ICAR simulation employing the developed recommendations is spatially shifted upwind in comparison to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such an analysis may not reflect the skill of the model in capturing atmospheric processes like gravity waves and cloud formation.

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.

Impacts of Indian Ocean Dipole–Like SST on Rice Yield Anomalies in Jiangsu Province

One of the main water vapor sources of eastern China is the Bay of Bengal, over which the circulation is influenced by the Tropical Indian Ocean Dipole (TIOD). The TIOD has a long-lasting effect on weather patterns, which in turn influence the rice yield and quality in eastern China, such as in Jiangsu Province. To identify the main mechanism involved, we perform a detailed investigation of the connections between the TIOD-like sea surface temperature (SST) and the climatic suitability for growing rice, and the subsequent rice yield anomalies, in Jiangsu Province. In particular, we compare the relationships, and identify the underlying mechanisms, of TIOD SST with suitable sunshine duration, temperature and precipitation during the period of rice culture in the province. Singular Value Decomposition (SVD) results show that the TIOD-like SST has a close correlation with the rice yield anomalies, with a temporal correlation coefficient of 0.43 for 53 years, passing the 99% significance level. Furthermore, in the negative TIOD-like SST years, the background circulation weakens the transport effect of the atmospheric river through which water vapor is transported from the Bay of Bengal to eastern China. This decreased amount of transported water vapor decreases the precipitation and total cloud cover in the province. In turn, this significantly increases the sunshine duration, which plays a key role in rice yield anomalies. The increased sunshine duration and higher temperatures lead to positive rice yield anomalies, and vice versa. Our findings highlight that climatic factors, such as TIOD-like SST, have a far-reaching influence on agricultural production (in this case, rice yield), and thus special attention should be paid to interdisciplinary research in the fields of climate and agriculture.

Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1–3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April–June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions.

Sea surface temperature variability and ischemic heart disease outcomes among older adults

Ischemic heart disease (IHD) is one of the leading causes of death worldwide. While extreme summer surface air temperatures are thought to be a risk factor for IHD, it is unclear whether large-scale climate patterns also influence this risk. This multi-national population-based study investigated the association between summer Pacific and Atlantic sea surface temperature (SST) variability and annual acute myocardial infarction (AMI) or IHD event rates among older adults residing in North America and the United Kingdom. Overall, a shift from cool to warm phase of the El Niño Southern Oscillation (ENSO) was associated with reduced AMI admissions in western Canada (adjusted rate ratio [RR] 0.89; 95% CI, 0.80–0.99), where this climate pattern predominatly forces below-normal cloud cover and precipitation during summertime, and increased AMI deaths in western United States (RR 1.09; 95% CI, 1.04–1.15), where it forces increased cloud cover and precipitation. Whereas, the Atlantic Multidecadal Oscillation (AMO) during a strong positive phase was associated with reduced AMI admissions in eastern Canada (RR 0.93; 95% CI, 0.87–0.98) and increased IHD mortality during summer months in the United Kingdom (RR 1.08; 95% CI, 1.03–1.14). These findings suggest that SST variability can be used to predict changes in cardiovascular event rates in regions that are susceptible.

Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest

Mountain ranges generate clouds, precipitation, and perennial streamflow for water supplies, but the role of forest cover in mountain hydrometeorology and cloud formation is not well understood. In the Luquillo Experimental Forest of Puerto Rico, mountains are immersed in clouds nightly, providing a steady precipitation source to support the tropical forest ecosystems and human uses. A severe drought in 2015 and the removal of forest canopy (defoliation) by Hurricane Maria in 2017 created natural experiments to examine interactions between the living forest and hydroclimatic processes. These unprecedented land-based observations over 4.5 y revealed that the orographic cloud system was highly responsive to local land-surface moisture and energy balances moderated by the forest. Cloud layer thickness and immersion frequency on the mountain slope correlated with antecedent rainfall, linking recycled terrestrial moisture to the formation of mountain clouds; and cloud-base altitude rose during drought stress and posthurricane defoliation. Changes in diurnal cycles of temperature and vapor-pressure deficit and an increase in sensible versus latent heat flux quantified local meteorological response to forest disturbances. Temperature and water vapor anomalies along the mountain slope persisted for at least 12 mo posthurricane, showing that understory recovery did not replace intact forest canopy function. In many similar settings around the world, prolonged drought, increasing temperatures, and deforestation could affect orographic cloud precipitation and the humans and ecosystems that depend on it.

Precipitation cloud identification based on faster-RCNN for Doppler weather radar

Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

A Graphical Interpretation of Circumgalactic Precipitation

Abstract Both observations and recent numerical simulations of the circumgalactic medium (CGM) support the hypothesis that a self-regulating feedback loop suspends the gas density of the ambient CGM close to the galaxy in a state with a ratio of cooling time to freefall time ≳10. This limiting ratio is thought to arise because circumgalactic gas becomes increasingly susceptible to multiphase condensation as the ratio declines. If the timescale ratio gets too small, then cold clouds precipitate out of the CGM, rain into the galaxy, and fuel energetic feedback that raises the ambient cooling time. The astrophysical origin of this so-called precipitation limit is not simple but is critical to understanding the CGM and its role in galaxy evolution. This paper therefore attempts to interpret its origin as simply as possible, relying mainly on conceptual reasoning and schematic diagrams. It illustrates how the precipitation limit can depend on both the global configuration of a galactic atmosphere and the degree to which dynamical disturbances drive CGM perturbations. It also frames some tests of the precipitation hypothesis that can be applied to both CGM observations and numerical simulations of galaxy evolution.

Research on recognition method of cloud precipitation particle shape based on BP neural network

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

Estimating cloud base height from Himawari-8 based on a random forest algorithm

ABSTRACT Estimating cloud-base height (CBH) from satellite-based passive imaging radiometers is very challenging because the conventional visible and infrared radiance measured by satellite cannot be employed to profile clouds from top to base. The Advanced Himawari Imager (AHI) aboard Himawari-8 is the first of Japanese new-generation visible-infrared imaging spectroradiometer with an improved temporal and spatial resolution. A CBH estimation algorithm for Himawari-8 is imminently needed for aviation safety and climate research. This study presents a machine learning methodology for CBH estimation from Himawari-8 data using a random forest (RF) algorithm, which was conducted for the daytime focusing on single-layer clouds. About 2,876,230 matchups in the period from August 2015 to July 2016 are used in both RF training and validation processes for the purpose of learning the relationship between inputs from Himawari-8 Level-2 cloud products together with geolocation information and target CBH from CloudSat/CALIPSO combined measurements. With the trained RF model, the CBH prediction can be independently generated using Himawari-8 upstream products. Data of ground-based Ka radar in Beijing during 2017 and 2019 are used for independent validation. The results indicate that the Himawari-8 CBH estimation agrees well with those of the Ka radar. The CBH estimations for single-layer nonprecipitation clouds show the best performance with a mean difference of −0.2 ± 1.7 km. The presence of multi-layer clouds mostly leads to overestimated CBHs relative to the radar values, particularly for situations that cirrus clouds overlapping clouds at low-level. Since the ability of the Ka radar is limited to retrieve CBH in precipitation cases, significant overestimation also occurs for precipitation clouds with mean difference of 1.7 ± 2.4 km.

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.

Linkage among ice crystal microphysics, mesoscale dynamics, and cloud and precipitation structures revealed by collocated microwave radiometer and multifrequency radar observations

Abstract. Ice clouds and falling snow are ubiquitous globally and play important roles in the Earth's radiation budget and precipitation processes. Ice particle microphysical properties (e.g., size, habit and orientation) are not only influenced by the ambient environment's dynamic and thermodynamic conditions, but are also intimately connected to the cloud radiative effects and particle fall speeds, which therefore have an impact on future climate projection as well as on the details of the surface precipitation (e.g., onset time, location, type and strength). Our previous work revealed that high-frequency (> 150 GHz) polarimetric radiance difference (PD) from passive microwave sensors is a good indicator of the bulk aspect ratio of horizontally oriented ice particles that often occur inside anvil clouds and/or stratiform precipitation. In this current work, we further investigate the dynamic and thermodynamic mechanisms and cloud–precipitation structures associated with ice-phase microphysics corresponding to different PD signals. In order to do so, collocated CloudSat radar (W-band) and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR, Ku–Ka-bands) observations as well as European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere background profiles are grouped according to the magnitude of PD for only stratiform precipitation and/or anvil cloud scenes. We found that horizontally oriented snow aggregates or large snow particles are likely the major contributor to the high-PD signals at 166 GHz, while low-PD magnitudes can be attributed to small cloud ice, randomly oriented snow aggregates, riming snow or supercooled water. Further, high-PD (low-PD) scenes are found to be associated with stronger (weaker) wind shear and higher (lower) ambient humidity, both of which help promote (prohibit) the growth of frozen particles and the organization of convective systems. An ensemble of squall line cases is studied at the end to demonstrate that the PD asymmetry in the leading and trailing edges of the deep convection line is closely tied to the anvil cloud and stratiform precipitation layers, respectively, suggesting the potential usefulness of PD as a proxy of stratiform–convective precipitation flag, as well as a proxy of convection life stage.

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.

Comparative Study of Cloud Liquid Water and Rain Liquid Water Obtained From Microwave Radiometer and Micro Rain Radar Observations Over Central China During the Monsoon

AbstractWe investigated the cloud liquid water (CLW) and rain liquid water (RLW) during weak precipitations (rain rate below 12 mm/h) using microwave radiometer and microrain radar measurements collected by the Integrative Monsoon Frontal Rainfall Experiment over central China in 2018. The CLW path increased sharply from 0.6 to 4.1 mm for precipitation clouds. RLW path presented a similar trend, although it had a larger correlation coefficient with rain rate. Precipitation efficiency reached up to ~50% and then clearly decreased as precipitation weakened. Because weak precipitation is mostly formed in stable nimbostratus, CLW content (CLWC) during precipitation tends to has a quasi‐normal distribution with mode at 0.38 g/m3, whereas RLW content (RLWC) shows a positively skewed distribution with mode at 0.06 g/m3. Normalized CLWC initially increases then decreases with height in nonprecipitation clouds but varies slightly in precipitation clouds due to relatively monodispersed droplets in the weaker convective motion. CLWC derived from millimeter‐wave cloud radar (MMCR) shows similar vertical distribution but with larger values. The mean normalized CLWCs are 0.06 and 0.38 g/m3 for nonprecipitation and precipitation clouds, respectively. RLWC varies slightly with height with a mean of 0.22 g/m3 because both the collision and breakup of raindrops are weak. A case study showed different distributions and vertical structures of CLWC and RLWC in various stages of precipitation. Thicker clouds result in larger CLWC and RLWC, which will cause greater rain rate. This qualitatively explains relationships among cloud thickness, CLW, RLW, and rain rate in precipitation during the monsoon.

Neural Network Classification of Ice-Crystal Images Observed by an Airborne Cloud Imaging Probe

ABSTRACT In the atmosphere, cloud particles have different shapes. The study of cloud particle shapes plays an important role in understanding cloud precipitation processes, radiative transfer, and weather modification. The image resolution and data quality of cloud probes affect the accuracy of the classification of particle shapes. To solve the occlusion of the photosensitive edge of the particle image and achieve automatic, high-precision ice-crystal classification of airborne Cloud Imaging Probe (CIP) ice-crystal images, this study uses a traditional image processing algorithm for data quality control and applies artificial intelligence algorithms to classify ice-crystal images. At present, there are mainly two types of ice-crystal classification methods, one classifies the shape of ice crystals using a pattern parameterization scheme, and the other uses an artificial intelligence network model to classify the shape. Combined with data quality control, the dataset was tested on eight models, and the TL-EfficientNet-b6 model was found to be the most accurate. Therefore, the TL-EfficientNet-b6 classifier model was used in this study, which is a newly developed convolutional neural network (CNN) based on a transfer learning method. Experimental results show that the TL-EfficientNet-b6 model can reach 100% in the single-class precision of tiny and hexagonal ice crystals, and the average precision can reach 98%. These results are more accurate than those using traditional classification methods. This method could be valuable in cloud microphysics research and weather modification.

Long-Term Statistics of Riming in Nonconvective Clouds Derived from Ground-Based Doppler Cloud Radar Observations

AbstractRiming is an efficient process of converting liquid cloud water into ice and plays an important role in the formation of precipitation in cold clouds. Due to the rapid increase in ice particle mass, riming enhances the particle’s terminal velocity, which can be detected by ground-based vertically pointing cloud radars if the effect of vertical air motions can be sufficiently mitigated. In our study, we first revisit a previously published approach to relate the radar mean Doppler velocity (MDV) to rime mass fraction (FR) using a large ground-based in situ dataset. This relation is then applied to multiyear datasets of cloud radar observations collected at four European sites covering polluted central European, clean maritime, and Arctic climatic conditions. We find that riming occurs in 1%–8% of the nonconvective ice containing clouds with median FR between 0.5 and 0.6. Both the frequency of riming and FR reveal relatively small variations for different seasons. In contrast to previous studies, which suggested enhanced riming for clean environments, our statistics indicate the opposite; however, the differences between the locations are overall small. We find a very strong relation between the frequency of riming and temperature. While riming is rare at temperatures lower than −12°C, it strongly increases toward 0°C. Previous studies found a very similar temperature dependence for the amount and droplet size of supercooled liquid water, which might be closely connected to the riming signature found in this study. In contrast to riming frequency, we find almost no temperature dependence for FR.