Drizzle in Stratiform Boundary Layer Clouds. Part I: Vertical and Horizontal Structure

Cloud microphysical property retrievals from the active microwave instrument on a satellite require the cloud droplet size distribution obtained from aircraft observations as a priori data in the iteration procedure. The cloud lognormal size distributions derived from 12 flights over Beijing, China, in 2008–09 were characterized to evaluate and improve regional CloudSat cloud water content retrievals. We present the distribution parameters of stratiform cloud droplet (diameter <500 μm and <1500 μm) and discuss the effect of large particles on distribution parameter fitting. Based on three retrieval schemes with different lognormal size distribution parameters, the vertical distribution of cloud liquid and ice water content were derived and then compared with the aircraft observations. The results showed that the liquid water content (LWC) retrievals from large particle size distributions were more consistent with the vertical distribution of cloud water content profiles derived from in situ data on 25 September 2006. We then applied two schemes with different a priori data derived from flight data to CloudSat overpasses in northern China during April-October in 2008 and 2009. The CloudSat cloud water path (CWP) retrievals were compared with Moderate Resolution Imaging Spectroradiometer (MODIS) liquid water path (LWP) data. The results indicated that considering a priori data including large particle size information can significantly improve the consistency between the CloudSat CWP and MODIS CWP. These results strongly suggest that it is necessary to consider particles with diameters greater than 50 μm in CloudSat LWC retrievals.

Large-eddy simulations of warm, trade wind cumulus clouds are conducted for a range of aerosol conditions with a focus on precipitating clouds. Individual clouds are tracked over the course of their lifetimes. Precipitation rate decreases progressively as aerosol increases. For larger, precipitating clouds, the polluted clouds have longer lifetimes because of precipitation suppression. For clean aerosol conditions, there is good agreement between the average model precipitation rate and that calculated based on observed radar reflectivity Z and precipitation rate R relationships. Precipitation rate can be expressed as a power-law function of liquid water path (LWP) and Nd, to reasonable accuracy. The respective powers for LWP and Nd are of similar magnitude compared to those based on observational studies of stratocumulus clouds. The time-integrated precipitation rate represented by a power-law function of LWP, Nd, and cloud lifetime is much more reliably predicted than is R expressed in terms of LWP and Nd alone. The precipitation susceptibility (So = −dlnR/dlnNd) that quantifies the sensitivity of precipitation to changes in Nd depends strongly on LWP and exhibits nonmonotonic behavior with a maximum at intermediate LWP values. The relationship between So and precipitation efficiency is explored and the importance of including dependence on Nd in the latter is highlighted. The results provide trade cumulus cloud population statistics, as well as relationships between microphysical/macrophysical properties and precipitation, that are amenable for use in larger-scale models.

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

The albedo of marine stratocumuli depends upon cloud liquid water content, droplet effective radius (re), and how these parameters vary with height. Using satellite data and shipborne data from the East Pacific Investigation of Climate (EPIC) Stratocumulus Study, this study investigates the cloud re vertical variation for drizzling and nondrizzling clouds. Visible/near‐infrared retrievals from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the vertical profile of re. MODIS re observations and collocated shipborne scanning C‐band precipitation radar data show that re generally increases with height in nondrizzling clouds, consistent with aircraft observations. It is found that in clouds with precipitation rates greater than a few hundredths of a mm h−1 the vertical gradient of re is significantly less than that in nondrizzling clouds and can become negative when the drizzle is heavier than approximately 0.1 mm h−1. High values of re at drizzling cloud base are consistent with estimates of the ratio of liquid water in the drizzle drops to that in the cloud droplets. C‐band derived cloud base precipitation rates are found to be better correlated with re at cloud base than with re at cloud top, suggesting that passive remote sensing may be useful for drizzle detection.

Ground-based observations at millimeter (mm) and submillimeter (submm) wavelengths were collected at the atmospheric radiation measurement program site at Barrow, AK, during the Arctic winter by a new 25-channel radiometer. A weighting function analysis is presented to demonstrate the enhanced sensitivity of mm- and submm-wave (50-400 GHz) radiometers to low vapor and liquid water contents with respect to conventional instruments such as the ones operating at centimeter (cm) wavelengths (20-30 GHz). In addition, based on measurements, we carried out a quantitative analysis of mm- and submm-wavelength sensitivity, yielding improvement factors from 1.5 to 69 for precipitable water vapor (PWV) and 3 to 4 for liquid water path (LWP) when compared to 20-30 GHz radiometers. Furthermore, using a simulated data set, we evaluate the effect of hydrometeor scattering: given the conditions occurring during the experiment, the scattering contribution is within the instrumental noise for most, but not all, of the considered channels. With the same data set, we demonstrate that in the dry conditions of the Arctic, a simple linear regression yields satisfactory results when applied on selected mm- and submm-wave channels. For a dual-channel combination, the expected accuracy is ~0.23 (0.007) mm for PWV (LWP), when using mm- and submm-wavelengths, whereas it is 0.37 (0.012) mm using cm-wave channels. When the retrieval is applied to real observations, the accuracy is found in agreement with theoretical expectations.

A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm−3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.6 gm−3, was found. This was reduced to 0.5 gm−3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME model (Application of Research to Operations at MEsoscale) showing encouraging results for future improvement of the AROME model initial state during fog conditions.

A new method is proposed to predict the optical thickness, effective radius, and concentration of cloud droplets in water layer clouds by using the spectrum of cloud condensation nuclei (CCN), ascent velocity at cloud base, and liquid water path (LWP). A retrieval method is also proposed to predict CCN number concentration by using independent observational data of ascent velocity at the cloud base, the optical thickness and LWP of clouds. For this purpose, a newly developed cloud microphysical model that relates cloud droplet size distributions to the updraft velocity, and to CCN constituents and size distribution is used. Cloud droplet growth is calculated, with special care being taken to avoid non-physical numerical diffusion of the droplet spectrum. Near the cloud base, CCN activation and subsequent cloud droplet growth are calculated in a Lagrangian framework to model the effect of CCN on growth by condensation more accurately. In the middle and upper parts of the cloud, an Eulerian framework is used to estimate growth by coalescence for cloud droplet size distributions. Simulated vertical profiles of droplet size distributions, and a solu-tion to the radiative transfer equation using the discrete ordinate method, with no parameterizations, yield the optical properties of the cloud for short-wavelength radiation. Using the approximation equation proposed in this study, the maximum value of supersaturation in a cloud (Smax) is predicted by observing cumulative activated CCN number concentration at 0.075% supersaturation Nc(075%)), and the ascent velocity at the cloud base. Using this Smax, we can estimate Nc(Smax), which is the cumulative number concentration of CCN, whose critical supersaturations are lower than Smax. Nc(Smax) is used to predict the concentration of cloud droplets at the middle altitude of layer cloud (Nd). Conventionally Nd is assumed to be Nc(Smax). However, our parameterization can show that Nd is smaller than Nc(Smax), when Nc(Smax) is large. For the fixed liquid water path, optical thickness and effective radius can be expressed as a function of Nd, unless drizzle is falling from the cloud. The parameterizations developed in this paper are based on the U.S. Standard Atmosphere 1976. If necessary, the parameterizations for the extremely different atmosphere from that used here can be developed in the same way.

Abstract. Observed and projected trends in large-scale wind speed over the oceans prompt the question: how do marine stratocumulus clouds and their radiative properties respond to changes in large-scale wind speed? Wind speed drives the surface fluxes of sensible heat, moisture, and momentum and thereby acts on cloud liquid water path (LWP) and cloud radiative properties. We present an investigation of the dynamical response of non-precipitating, overcast marine stratocumulus clouds to different wind speeds over the course of a diurnal cycle, all else equal. In cloud-system resolving simulations, we find that higher wind speed leads to faster boundary layer growth and stronger entrainment. The dynamical driver is enhanced buoyant production of turbulence kinetic energy (TKE) from latent heat release in cloud updrafts. LWP is enhanced during the night and in the morning at higher wind speed, and more strongly suppressed later in the day. Wind speed hence accentuates the diurnal LWP cycle by expanding the morning–afternoon contrast. The higher LWP at higher wind speed does not, however, enhance cloud top cooling because in clouds with LWP ⪆ 50 g m−2, longwave emissions are insensitive to LWP. This leads to the general conclusion that in sufficiently thick stratocumulus clouds, additional boundary layer growth and entrainment due to a boundary layer moistening arises by stronger production of TKE from latent heat release in cloud updrafts, rather than from enhanced longwave cooling. We find that large-scale wind modulates boundary layer decoupling. At nighttime and at low wind speed during daytime, it enhances decoupling in part by faster boundary layer growth and stronger entrainment and in part because shear from large-scale wind in the sub-cloud layer hinders vertical moisture transport between the surface and cloud base. With increasing wind speed, however, in decoupled daytime conditions, shear-driven circulation due to large-scale wind takes over from buoyancy-driven circulation in transporting moisture from the surface to cloud base and thereby reduces decoupling and helps maintain LWP. The total (shortwave + longwave) cloud radiative effect (CRE) responds to changes in LWP and cloud fraction, and higher wind speed translates to a stronger diurnally averaged total CRE. However, the sensitivity of the diurnally averaged total CRE to wind speed decreases with increasing wind speed.

Abstract. This study examines the characteristics of the microphysics and macrophysics of water clouds from East Asia to the North Pacific, using data from active CloudSat radar measurements and passive MODerate-resolution Imaging Spectroradiometer (MODIS) retrievals. Our goals are to clarify differences in microphysics and macrophysics between land and oceanic clouds, seasonal differences unique to the midlatitudes, characteristics of the drizzling process, and cloud vertical structure. In pristine oceanic areas, fractional occurrences of cloud optical thickness (COT) and cloud droplet effective radius (CDR) increase systematically with an increase in drizzle intensity, but these characteristics of the COT and CDR transition are less evident in polluted land areas. In addition, regional and seasonal differences are identified in terms of drizzle intensity as a function of the liquid water path (LWP) and cloud droplet number concentration (Nc). The correlations between drizzle intensity and LWP, and between drizzle intensity and Nc, are both more robust over oceanic areas than over land areas. We also demonstrate regional and seasonal characteristics of the cloud vertical structure. Our results suggest that aerosol–cloud interaction mainly occurs around the cloud base in polluted land areas during the winter season. In addition, a difference between polluted and pristine areas in the efficiency of cloud droplet growth is confirmed. These results suggest that water clouds over the midlatitudes exhibit a different drizzle system to those over the tropics.

Cold-air outbreaks over high-latitude oceans typically include mixed-phase clouds and precipitation, in particular supercooled liquid clouds that support snow and graupel through ice growth processes. The partitioning of the total water into the liquid and ice phases impacts both weather and climate prediction, but accurate measurements on the phase partitioning remain difficult to acquire, especially near–real time. Here, we present a machine learning approach to retrieve liquid water path (LWP) using passive microwave measurements combined with vertically integrated radar reflectivities. The approach is an extension of Cadeddu et al. (2009), with the novel addition of radar reflectivity. The machine learning models are trained using the Passive and Active Microwave Radiative Transfer (PAMTRA) code applied to output from numerical simulations of three independent cold-air outbreaks sampled during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. Brightness temperatures corresponding to the four sidebands of an upward-looking G-band (183 GHz) vapor radiometer, along with the vertically integrated reflectivity from a zenith-pointing 95-GHz Wyoming Cloud Radar, are simulated from the perspective of a near-surface aircraft track. The radar reflectivity helps discriminate the snow contribution to the brightness temperatures. The machine learning models are thereafter tested on a simulation of an independent cold-air outbreak during COMBLE and against measurements from the U.S. Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. This machine learning approach is shown to provide robust, computationally efficient, near-real-time measurements of LWP and water vapor path during the Cold-Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) campaign in February–April 2024. Significance Statement Precipitation from mixed-phase clouds over the high-latitude open waters impacts shipping, fishing, and coastal communities. Mixed-phase clouds are also a modeling challenge, inhibiting weather and climate prediction skill. More comprehensive measurements of the cloud liquid and ice water path occupying the same vertical column improve our understanding and ability to represent this challenging cloud type. Here, we propose a new retrieval to determine the liquid water path within air columns that also contain ice.

Thermodynamical and cloud microphysical response during the transition from southwest to northeast monsoon

Abstract. Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), cloud optical thickness (τ), and cloud droplet effective radius (re) retrievals from 2 years of collocated Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations; estimate the effect of biomass burning smoke on passive imager retrievals; and evaluate the diurnal cycle of South Atlantic marine Sc clouds.The effect of absorbing aerosols from biomass burning on the retrievals was investigated using the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared (VIS/NIR) retrieval biases in τ and re. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6 µm re – was biased low by 14 g m−2 ( ∼ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m−2 ( ∼ 5 %) for the 1.6 µm channel and a high bias of 8 g m−2 ( ∼ 10 %) for the 3.7 µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI–TMI LWP bias considerably. For 2 years of data, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m−2 (5 %–10 %) for overcast cases.The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slow increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the 2-year-mean and seasonal-mean LWP diurnal cycle varied between 35 % and 40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in τ, while re showed only little diurnal variability.

Satellite observations of low-level clouds have challenged the idea that increasing liquid water content with temperature combined with constant physical thickness will lead to a negative cloud optics feedback in a decadal climate change. The reasons for the satellite results are explored using 4 yr of surface remote sensing data from the Atmospheric Radiation Measurement Program Cloud and Radiation Testbed site in the southern Great Plains of the United States. It is found that low-cloud liquid water path is approximately invariant with temperature in winter but decreases strongly with temperature in summer, consistent with satellite inferences at this latitude. This behavior occurs because liquid water content shows no detectable temperature dependence while cloud physical thickness decreases with warming. Thinning of clouds with warming is observed on seasonal, synoptic, and diurnal timescales; it is most obvious in the warm sectors of baroclinic waves. Although cloud top is observed to slight...

The spatial variability of the microphysical fields in stratocumulus clouds is documented in this paper with statistics of droplet number concentration, droplet mean volume radius, and liquid water content for eight cases of the second Aerosol Characterization Experiment. Statistics are calculated in five sublayers, from cloud base to cloud top, and they are utilized for deriving estimates of cloud optical thickness and liquid water path, by assuming either random or maximum overlap. The resulting in situ frequency distributions of optical thickness and liquid water path are validated against distributions of these two parameters retrieved from independent remote sensing measurements of cloud radiances. They are also used for testing parameterizations of optical thickness based on liquid water path and either the droplet effective radius or the cloud droplet number concentration. This unique data set of extensive, concomitant, and independent measurements of cloud microphysical and radiative properties is finally used for assessing the detectability of the aerosol indirect effect through examination of the correlation between cloud optical thickness and droplet effective radius. If only cases of comparable values of geometrical thickness are considered, the correlation between optical thickness and effective radius is negative, as anticipated by Twomey [1977]. However, if the most polluted cases are also accounted for, the trend suggests a positive correlation. In fact, the most polluted cloud systems sampled during ACE‐2 were slightly drier, hence thinner, than the marine and intermediate cases, hence producing a positive correlation between optical thickness and droplet effective radius. This study demonstrates that the monitoring of the aerosol indirect effect with satellite observations requires an independent retrieval of the liquid water path together with the cloud optical thickness and droplet effective radius.

The ability of six scanning cloud radar scan strategies to reconstruct cumulus cloud fields for radiation study is assessed. Utilizing snapshots of clean and polluted cloud fields from large eddy simulations, an analysis is undertaken of error in both the liquid water path and monochromatic downwelling surface irradiance at 870 nm of the reconstructed cloud fields. Error introduced by radar sensitivity, choice of radar scan strategy, retrieval of liquid water content (LWC), and reconstruction scheme is explored. Given an infinitely sensitive radar and perfect LWC retrieval, domain average surface irradiance biases are typically less than 3 W m−2 µm−1, corresponding to 5–10% of the cloud radiative effect (CRE). However, when using a realistic radar sensitivity of −37.5 dBZ at 1 km, optically thin areas and edges of clouds are difficult to detect due to their low radar reflectivity; in clean conditions, overestimates are of order 10 W m−2 µm−1 (~20% of the CRE), but in polluted conditions, where the droplets are smaller, this increases to 10–26 W m−2 µm−1 (~40–100% of the CRE). Drizzle drops are also problematic; if treated as cloud droplets, reconstructions are poor, leading to large underestimates of 20–46 W m−2 µm−1 in domain average surface irradiance (~40–80% of the CRE). Nevertheless, a synergistic retrieval approach combining the detailed cloud structure obtained from scanning radar with the droplet‐size information and location of cloud base gained from other instruments would potentially make accurate solar radiative transfer calculations in broken cloud possible for the first time.