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Jiulong is located on the east side of the Qinghai-Tibet Plateau (QTP) and is a region prone to southwest vortex. Cloud detection with new-type detection equipment in this region helps enhance the knowledge of cloud characteristics in the southwest vortex-prone region. In this study, based on the ground-based microwave radiometer data from June to August of 2018-2019 in Jiulong, the observational characteristics of cloud occurrence frequency (COF), liquid water path (LWP), and supercooled liquid water path (SLWP) for non-precipitating clouds during the summer seasons are investigated. The results are as follows. The monthly average COF of summer non-precipitating clouds in Jiulong is between 67%-82%, with low and middle clouds being the main types, and high clouds being less common. For low clouds, the COF is low in daytime and high in nighttime, while it is the opposite for middle and high clouds. The vertical distribution of COF presents an unimodal pattern, with a peak of 8.1% at a height of about 2 km. Due to the diurnal variation of atmospheric thermal stratification, the unimodal pattern of COF shows diurnal differences. Moreover, the average LWP of summer non-precipitating clouds in Jiulong is 0.433 kg·m-2, with the average LWPs of low, middle, and high clouds being 0.665, 0.240, and 0.102 kg·m-2, respectively. The diurnal variation of LWP in low clouds is similar to their COF, while the diurnal variations of LWP in middle and high clouds are not significant. Additionally, the average SLWP of cold clouds among summer non-precipitating clouds in Jiulong is 0.154 kg·m-2, with the average SLWPs of low, middle, and high clouds being 0.065, 0.166, and 0.102 kg·m-2, respectively. On the whole, the proportion of SLWP in LWP is about 34.3%-38.8%. The proportion of SLWP increases with the height of the cloud, which makes the diurnal variations of SLWP in middle and high clouds similar to that of LWP. Compared with central China, the characteristics of summer non-precipitating clouds in Jiulong are significantly different, and this is closely related to the different characteristics of atmospheric water vapor between the two regions.

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean‐state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean‐state LWP on effective radiative forcing due to ACI (ERFACI). Plain Language Summary: Since the preindustrial era, emissions of greenhouse gases (GHGs) and aerosols have both increased substantially. Planetary warming from the elevated GHGs causes changes in cloud distribution and properties, thereby imposing feedbacks on the climate system. At the same time, aerosols from air pollution exert a cooling effect by modifying cloud properties and lifetime. Cloud feedback and aerosol‐cloud interactions (ACI) are two critical factors for understanding the past and projecting the future climate change. In this study, we examine the relationships of ACI and cloud feedback with the mean‐state cloud liquid water amount predicted from simulations with two different Earth system models. We find that both the effective radiative forcing due to aerosol‐cloud interactions (ERFACI) and the cloud feedback are modulated by mean‐state liquid water path (LWP). The warming induced by cloud feedback and cooling by ACI counteracts each other. Our study suggests that the mean‐state LWP is an important factor influencing both ERFACI and the cloud feedback, and is a useful index for the future climate projection. Key Points: Effective radiative forcing due to aerosol‐cloud interactions strengthens with the increase of mean‐state LWP in two Earth System ModelsCloud radiative feedback increases monotonically with the increase of mean‐state LWPMean‐state LWP is a good predictor for both ERFACI and cloud radiative feedback [ABSTRACT FROM AUTHOR]

The identification of supercooled liquid water (SLW) in clouds, is important for artificially influencing weather and preventing aircraft from freezing. However, it has always been a difficult area of meteorological sounding. Millimeter-wave radar is an effective tool for the continuous detection of cloud structures and physical features. Based on the observations from a Ka-band millimeter radar and a microwave radiometer in Mêdog, which is located at the entrance of the water vapor passage in southeastern Tibetan Plateau, a threshold method and a fuzzy logic algorithm were used to identify hydrometer phase in clouds, including ice, snow, SLW, and mixed phase. Two stratocumulus cloud cases were selected to analyze the effect of SLW identification. The SLW identification results were analyzed and initially assessed by comparing their liquid water paths (LWPs) with that from collocated microwave radiometer. Results showed that the SLW areas identified by both the fuzzy logic method and the threshold method are basically reasonable, and the fuzzy logic method can identify more SLW areas. From In terms of quantitative evaluation, the LWP of SLW from fuzzy logic method is closer to that from microwave radiometer than that form threshold method. The effective radius of SLW in stratocumulus clouds in Mêdog ranges from 7 to 15 μm and the values of liquid water content are between 0. 01 and 0. 3 g / m³, which are relatively consistent with those in other regions. However, the distribution of SLW in Mêdog is usually located in cloud tops, cloud bottoms, and midclouds at the same time, which is more abundant than that in other areas. [ABSTRACT FROM AUTHOR]

Abstract Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean‐state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean‐state LWP on effective radiative forcing due to ACI (ERFACI).

Clouds and precipitation over mountainous terrain are a challenge for models and observations alike. In this study, we exploit a unique, nearly one decade long dataset of collocated microwave radiometer, radar, ceilometer, and auxiliary observations collected at the Environmental Research Station Schneefernerhaus (UFS). Located at 2650 m a.s.l. just 300 m below the summit of Zugspitze, Germany's highest mountain, this dataset allows a combined view on water vapor, clouds, and precipitation. Annual and diurnal cycles of water vapor, cloud liquid water, cloud ice, rainfall, and snowfall rate are investigated. Strong diurnal cycles during summer in several observables indicate a strong coupling with the surface and convective transport of air from the surrounding valleys to the level of UFS resulting in maximum amounts in integrated water vapor (IWV), cloud liquid water path (LWP) and rain during the afternoon. In contrast, no diurnal cycle is found during winter, which points to the predominance of advection of cloud systems associated with large scale dynamics during winter. Daily precipitation estimates for snowfall and rainfall derived from a vertically pointing, low-cost micro rain radar (MRR) are found to be in good agreement with manual observations from the German Weather Service at the summit. Exploiting the synergy of MRR and microwave radiometer measurements revealed that almost 90 % of the snow clouds contained significant amounts of super-cooled LWP but only a weak correlation between snowfall rate and LWP is found. The still growing data set at this very particular location, also in combination with further observations, such as trace gases and aerosols, has a unique potential for many applications, e.g. to investigate cloud processes, evaluate high resolution models, and to validate satellite products.

Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the Nd, we have also considered adiabatic Nd (NAd) from the LES model for comparison. The joint histogram analysis shows that the NAd-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower NAd and LWP; the corresponding mean LWP (LWP) shows a weak relation with NAd. The CP shows a larger spread at higher NAd (>50 cm–3), and the LWP increases non-monotonically with increasing NAd in both cases. Nevertheless, both lack the negative NAd-LWP relationship at higher NAd, the entrainment effect on cloud droplets. In contrast, the model simulated Nd-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing Nd and a decrease in LWP at higher Nd) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the NAd-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear NAd-LWP relationship. Hence a regime-based Nd-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals.

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration ( N d , a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and N d are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and N d is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: N d –LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs. [ABSTRACT FROM AUTHOR]

Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the Nd, we have also considered adiabatic Nd (NAd) from the LES model for comparison. The joint histogram analysis shows that the NAd-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower NAd and LWP; the corresponding mean LWP () shows a weak relation with NAd. The CP shows a larger spread at higher NAd (>50 cm-3), and the LWP increases non-monotonically with Ad in both cases. Nevertheless, both lack the negative NAd-LWP relationship Ad, the entrainment effect on cloud droplets. In contrast, the model d-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing Nd and a decrease in LWP at higher Nd) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the NAd-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear NAd-LWP relationship. Hence a regime-based Nd-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals. [ABSTRACT FROM AUTHOR]

Although great progress has been made in the study of aerosol‐cloud interactions (ACIs), representation of ACI is still the largest uncertainty in current weather and climate models. In this study, the ACI over the North China Plain and north of the Yangtze Plain is investigated using multi‐source data including satellite, ground‐based, reanalysis, and fusion data. The effects of aerosol characteristics (PM2.5, PM10, and PM2.5/PM10 ratio), atmospheric conditions including liquid water path (LWP) and lower tropospheric stability (LTS), and drizzle on ACI are analyzed. Results suggest that both aerosol characteristics and atmospheric conditions can affect cloud effective radius (re), but drizzle in clouds plays a decisive role on ACI. In the case of fixed LTS and LWP, the average cloud droplet re increases first and then fluctuates for all clouds examined with the increase of PM2.5 concentration. The PM2.5 concentration corresponding to the turning point of re increases with the increasing LWP due to the increased chance of collision and coalescence. In contrast, the average cloud droplet re and PM2.5 concentration show a negative correlation for clouds without drizzle, which is mainly caused by competition of water vapor. The quantitative ACI results show that the values of the aerosol first indirect effect are negative for most cases when considering all clouds examined, but positive when only considering clouds without drizzle. Plain Language Summary: Aerosols can affect the generation, development, and dissipation of clouds by acting as cloud condensation nuclei or ice nuclei. Representation of aerosol‐cloud interaction (ACI) is the largest source of uncertainties in current weather and climate models. Here, the influence of aerosols on clouds over the North China Plain and north of the Yangtze plain is investigated using multi‐source data obtained simultaneously. The results show that both aerosol properties and atmospheric conditions can affect cloud effective radius, while drizzle plays a more important role on ACI process. For all clouds including those with and without drizzle, when the lower tropospheric stability is small, the average cloud droplet effective radius increases and then fluctuates as the PM2.5 concentration increases, and the PM2.5 concentration corresponding to the turning point of cloud droplet effective radius increases as the liquid water path increases. For clouds without drizzle, the average cloud droplet effective radius gradually decreases as the PM2.5 concentration increases. The first indirect effect of aerosol results also verify our above findings. Key Points: The influence of aerosol on clouds is investigated using multi‐source data over the North China Plain and north of the Yangtze PlainBoth aerosol properties and atmospheric conditions affect cloud droplet re, while drizzle has a greater impact on aerosol‐cloud interactionThe cloud droplet re and PM2.5 concentration show a positive (negative) correlation for all clouds (clouds without drizzle) [ABSTRACT FROM AUTHOR]

Aerosols are the crucial element that influence water cycle and radiation budget. They are responsible for cloud formation to take place and they also absorbs solar radiation coming towards earth surface and by these processes aerosols also effect climate on earth. As the aerosols are responsible for cloud formation, research is being performed how these aerosol results can be extended towards precipitation and to generate an aerosol cloud precipitation relationship. However, the problem faced is lack of understanding of the different mechanism responsible for precipitation. Research work is being performed by atmospheric and climatic communities in order to understand mechanism and to remove uncertainties related to the prediction of climate. In this article, past efforts are reviewed and current understanding on the effect of aerosols on precipitation are summarized. Aerosols effect on precipitation can be analyzed by (a) theoretical approaches that helps in understanding cloud micro physics, (b) observation data and (c) numerical models simulations. There are few differences between results obtained by ground based observational data (the data obtained through satellites) and numerical data (obtained through cloud models) that are also discussed. The article discusses the following topics: theories explaining the effect of aerosol over cloud micro physics and precipitation, evidence of aerosols effecting the precipitation obtained from observations, aerosols effecting cloud formation and cloud dynamics through large scale analysis, cloud resolving models and simulations, results from numerical models over global scale. Lastly future research areas for understanding aerosol cloud precipitation relationship has also suggested. [ABSTRACT FROM AUTHOR]

To develop the method of retrieving cloud liquid water content by using both radar and microwave radiometer, the present study analyzes the data from the dual band cloud radar and microwave radiometer in the comprehensive observation test that was carried out in Longmen by the Chinese Academy of Meteorological Sciences from April to September 2019. Firstly, the temperature (T), relative humidity (RH), liquid water content (LWC) and liquid water path (LWP) retrieved by microwave radiometer zenith observation and off-zenith observation during precipitation events is tested, and then the difference between LWC and LWP retrieved by two kinds of detection equipment is analyzed. The results are as follows: (1) The products retrieved by microwave radiometer in off-zenith observation mode are less affected by precipitation, and the retrieval results are obviously better than those of zenith observation mode. (2) The LWP retrieved by the two detection devices has good correlation and is consistent over time. However, the LWP retrieved by cloud radar is significantly correlated with average echo intensity. With the increase of radar echo, the ratio of LWP retrieved by cloud radar to microwave radiometer increases. (3) The correlation of LWC retrieved by the two detection devices is poor and there is a significant deviation. When the melting layer is not considered, the LWC retrieved by single band cloud radar and microwave radiometer has a similar change trend with height, and the retrieval results of dualband cloud radar and microwave radiometer are significantly different in the range of 1km and above. [ABSTRACT FROM AUTHOR]

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.

In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content (LWC) and liquid water path (LWP), particle parameter scheme is the main factor affecting retrieval performance. In this paper, synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50µm and the particle parameter scheme with diameter less than 500µm and 1500µm (scheme 1, scheme 2, scheme 3, and scheme 4, respectively). The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP. When the reflectivity factor is less than - 30dBZ, the retrieval error of scheme 1 is the minimum. When the reflectivity factor is greater than -30dBZ, the retrieval error of scheme 4 is the minimum. Based on the reflectance factor value, the LWP retrievals of scheme 4 are closer to the measurements, the average relative bias is 5.2%, and the minimum relative bias is 4.4%. Compared with other schemes, scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China. [ABSTRACT FROM AUTHOR]

Climate models generally simulate a unidirectional, positive liquid water path (LWP) response to increasing aerosol number concentration. However, satellite observations and large‐eddy simulations show that the LWP may either increase or decrease with increasing aerosol concentration, influencing the overall magnitude of the aerosol indirect effect (AIE). We use large‐eddy simulation to investigate the LWP response of a marine stratocumulus cloud and its dependence on different parameterizations for obtaining cloud droplet number concentration (CDNC). The simulations confirm that the LWP response is not always positive—regardless of CDNC treatment. However, the AIE simulated with the model version with prescribed CDNC is almost 3 times larger compared to the version with prognostic CDNC. The reason is that the CDNC in the prognostic scheme varies in time due to supersaturation fluctuations, collection, and other microphysical processes. A substantial spread in simulated AIE may thus arise simply due to the CDNC treatment. Plain Language Summary: Our poor understanding of aerosol‐cloud‐radiation interactions (aerosol indirect effects) results in a major uncertainty in estimates of anthropogenic aerosol forcing. In climate models, the cloud water response to an increased aerosol number concentration may be especially uncertain as models simplify, or do not account for, processes that affect the cloud droplet number concentration and the total amount of cloud water. In this study, we employ large‐eddy simulation to explore how different model descriptions for obtaining the number concentration of cloud droplets influences the cloud water response of a marine stratocumulus cloud and thus the simulated aerosol indirect effect. Our simulations show a qualitatively similar cloud water response regardless of model description: the total amount of cloud water increases first and then decreases with increasing aerosol concentration. However, the simulated aerosol indirect effect is almost 3 times as large when the number concentration of cloud droplets is prescribed compared to when it is dependent on the calculated supersaturation and other microphysical processes such as collisions between cloud droplets. Our findings show that a relatively simple difference in the treatment of the number concentration of cloud droplets in climate models may result in a significant spread in the simulated aerosol indirect effect. Key Points: We use large‐eddy simulation to investigate the response of a marine stratocumulus cloud to increasing aerosol concentrationThe aerosol indirect effect is almost 3 times as large when the cloud droplet concentration is prescribed instead of prognosticTime variations in supersaturation and collection influence the overall cloud droplet number and aerosol indirect effect [ABSTRACT FROM AUTHOR]

Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments.

Cloud fraction (CF) is known as the dominant modulator of Earth’s radiative fluxes. Ground-based CF observations are useful to characterize the cloudiness of a specific site and are valuable for comparison with satellite observations and numerical models. We present for the first time CF statistics (relative to liquid clouds only) for Bern, Switzerland, derived from the observations of a ground-based microwave radiometer. CF is derived with a new method involving the analysis of the integrated liquid water distribution measured by the radiometer. The 10-year analyzed period (2004–2013) allowed us to compute a CF climatology for Bern, showing a maximum CF of 60.9% in winter and a minimum CF of 42.0% in summer. The CF monthly anomalies are identified with respect to the climatological mean values, and they are confirmed through MeteoSwiss yearly climatological bulletins. The CF monthly mean variations are similar to the observations taken at another Swiss location, Payerne, suggesting a large-scale correlation between different sites on the Swiss Plateau. A CF diurnal cycle is also computed, and large intraseasonal variations are found. The overall mean CF diurnal cycle, however, shows a typical sinusoidal cycle, with higher values in the morning and lower values in the afternoon.

Earth's albedo is the primary determinant of the amount of energy absorbed by the Earth–atmosphere system. It is a function of the fractional cloud cover and the cloudy- and clear-sky albedos, and thereby of the aerosol loading of the atmosphere. Here, we introduce a method by which we can examine the spatial distribution of the albedo variability that is independent of variations in the two dominant factors of albedo: cloud fraction and liquid water path (LWP). The analysis is based on data simultaneously retrieved from the CERES and MODIS instruments carried on board the Aqua satellite. We analysed the daily overpass data between July 2002 and June 2014 and showed that perturbations in albedo, accounting for variations induced by cloud fraction and LWP, display a coherent geographical pattern. Positive deviations occur in proximity to known anthropogenic aerosol sources, and negative deviations coincide with areas of intense precipitation, acting as aerosol sinks. A simple multiplication of the observed positive perturbations in albedo with the solar flux of 340 W m−2 yields a magnitude of that effect of several watts per square meter locally. While the location and scale of the geographical pattern might suggest an anthropogenic contribution to the positive albedo perturbations, it is imperative to first carefully examine all other possible causal factors behind the perturbations. Finally, although we have not attempted a full calculation of detection limits, the analysis is capable of sensing very small changes in average albedo of the order of 0.003 out of a total albedo of the order of 0.3. Hence, the applied method might find utilisation in a variety of situations where there is a need to quantify small perturbations of a dependent variable in noisy global data sets.

ABSTRACT: Cloud microphysical variables are needed to evaluate and improve cloud parameterization and data assimilation in a numerical weather model. In order to obtain high spatiotemporal resolution data for the cloud, data from the Ka‐band cloud radar (KaCR), an instrument specialized for cloud observation, were used. In this study, the liquid water content (LWC) was estimated, and the reflectivity–liquid water content (Z–LWC) relationships were derived using the vertical profiles of radar reflectivity from the KaCR and the liquid water path (LWP) from a microwave radiometer. The data were collected at the Boseong National Center for Intensive Observation of Severe Weather in the Republic of Korea during an intensive observation period in 2014 (2014‐IOP, 16 June to 15 July 2014). First, the KaCR reflectivity was corrected using the linear depolarization ratio from the KaCR. The process also involved removing reflectivity profiles that have signal attenuation, compared with the cloud‐top heights retrieved from a satellite. The LWC profiles were calculated from the LWP by dividing using the weight of the KaCR reflectivity. Different constants were derived from the Z–LWC relationship according to each respective echo type. Therefore, the KaCR echoes were categorized into non‐precipitating clouds, precipitating clouds, and raindrops, using a micro rain radar and a ceilometer. The Z–LWC relationships were finally derived for each categorized echo. The results of this study suggest that the new method should be useful for retrieving cloud microphysical variables. These relationships allowed the estimation of 3D LWC in high resolution using a single KaCR platform. [ABSTRACT FROM AUTHOR]

Abstract: Cloud droplet number concentration (Nc) is retrieved from remotely observed marine stratocumulus cloud liquid water path (LWP), cloud optical depth (τc), and cloud thickness, using an optical model that assumes liquid water content (LWC) increases linearly from cloud base. Assuming that LWC is vertically uniform would underestimate τc by 5% and Nc by 14%. Individual retrievals of Nc from 10‐min averages vary by orders of magnitude from long‐term averages. Surface cloud condensation nuclei (CCN) number concentration NCCN is weakly but significantly correlated with Nc (R = 0.3) for the day leading and 6 hr following Nc. Consistent with coalescence and drizzle scavenging cloud droplets, lag correlations show that Nc decreases for 1 hr after the peak area‐average rain rate. Greater observed LWP for lower Nc [d(log Nc)/d(log LWP) = −2.3] is consistent with enhanced entrainment drying of clouds with greater Nc and consistent with removal of Nc by thicker clouds with more coalescence and drizzle. Stronger precipitation in clouds with greater Nc is the opposite sensitivity as expected were LWP to be controlled by the “cloud lifetime” indirect aerosol effect. The strong sensitivity of Nc to LWP suggests that cloud dynamic and thermodynamic forcings drive macrophysical variability that controls Nc in southeastern tropical Pacific stratocumulus clouds. Regressions are relatively insensitive to assumptions about the covariance of errors among the sensors. [ABSTRACT FROM AUTHOR]