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With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from 21 June to 20 July 2017 and from 15 January to 18 February 2018 in the Azores. The flights were designed to maximize the synergy between in situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

Industry advances have greatly reduced the cost and size of ground-based shortwave (SW) sensors for the ultraviolet, visible, and near-infrared spectral ranges that make up the solar spectrum, while simultaneously increasing their ruggedness, reliability, and calibration accuracy needed for outdoor operation. These sensors and collocated meteorological equipment are an important part of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility, which has supported parallel integrated measurements of atmospheric and surface properties for more than two decades at fixed and mobile sites around the world. The versatile capability of these ground-based measurements includes 1) rich spectral information required for retrieving cloud and aerosol microphysical properties, such as cloud phase, cloud particle size, and aerosol size distributions, and 2) high temporal resolution needed for capturing fast evolution of cloud microphysical properties in response to rapid changes in meteorological conditions. Here we describe examples of how ARM’s spectral radiation measurements are being used to improve understanding of the complex processes governing microphysical, optical, and radiative properties of clouds and aerosol.

Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. The high-spatial resolution, dual-view observations from the advanced baseline imagers onboard the two most recent geostationary operational environmental satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angles throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.019–0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059 ± 0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual-view technique.

Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The Southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically-driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the Southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, which only became apparent when visible reflectance revealed it crossing over an adjacent dark water background. In combining information from numerical modelling, multi-satellite/multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to independent-sensor description of the surface-to-500 mb atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with best performance over land surfaces.

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process., Key Points Machine‐learning trained by in situ data constrains autoconversion and accretion rates with uncertainty of 15% and 5%, respectivelyThere is a surprising relation between autoconversion rate and drizzle number concentration that significantly improves parameterizationsThe exponent of autoconversion rate dependence on cloud number concentration is 0.75, lower than that in existing parameterizations

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.

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

The contribution of cloud to the radiation budget of southern West Africa (SWA) is poorly understood and yet it is important for understanding regional monsoon evolution and for evaluating and improving climate models, which have large biases in this region. Radiative transfer calculations applied to atmospheric profiles obtained from the CERES– CloudSat–CALIPSO–MODIS (CCCM) dataset are used to investigate the effects of 12 different cloud types (defined by their vertical structure) on the regional energy budget of SWA (5°–10°N, 8°W–8°E) during June–September. We show that the large regional mean cloud radiative effect in SWA is due to nonnegligible contributions from many different cloud types; eight cloud types have a cloud fraction larger than 5% and contribute at least 5% of the regional mean shortwave cloud radiative effect at the top of the atmosphere. Low clouds, which are poorly observed by passive satellite measurements, were found to cause net radiative cooling of the atmosphere, which reduces the heating from other cloud types by approximately 10%. The sensitivity of the radiation budget to underestimating low-cloud cover is also investigated. The radiative effect of missing low cloud is found to be up to approximately −25 W m−2 for upwelling shortwave irradiance at the top of the atmosphere and 35 W m−2 for downwelling shortwave irradiance at the surface.

New, viable, and sustainable observation strategies from a constellation of satellites have attracted great attention across many scientific communities. Yet the potential for monitoring global Earth outgoing radiation using such a strategy has not been explored. To evaluate the potential of such a constellation concept and to investigate the configuration requirement for measuring radiation at a time resolution sufficient to resolve the diurnal cycle for weather and climate studies, we have developed a new recovery method and conducted a series of simulation experiments. Using idealized wide field-of-view broadband radiometers as an example, we find that a baseline constellation of 36 satellites can monitor global Earth outgoing radiation reliably to a spatial resolution of 1000 km at an hourly time scale. The error in recovered daily global mean irradiance is 0.16 W m-2 and -0.13 W m-2, and the estimated uncertainty in recovered hourly global mean irradiance from this day is 0.45 W m-2 and 0.15 W m-2, in the shortwave and longwave spectral regions, respectively. Sensitivity tests show that addressing instrument-related issues that lead to systematic measurement error remains of central importance to achieving similar accuracies in reality. The presented error statistics therefore likely represent the lower bounds of what could currently be achieved with the constellation approach, but this study demonstrates the promise of an unprecedented sampling capability for better observing the Earth’s radiation budget.

The spectrum of reflected solar radiation emerging at the top of the atmosphere is rich with Earth system information. To identify spectral signatures in the reflected solar radiation and directly relate them to the underlying physical properties controlling their structure, over 90 000 solar reflectance spectra are computed over West Africa in 2010 using a fast radiation code employing the spectral characteristics of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). Cluster analysis applied to the computed spectra reveals spectral signatures related to distinct surface properties, and cloud regimes distinguished by their spectral shortwave cloud radiative effect (SWCRE). The cloud regimes exhibit a diverse variety of mean broadband SWCREs, and offer an alternative approach to define cloud type for SWCRE applications that does not require any prior assumptions. The direct link between spectral signatures and distinct physical properties extracted from clustering remains robust between spatial scales of 1, 20, and 240 km, and presents an excellent opportunity to understand the underlying properties controlling real spectral reflectance observations. Observed SCIAMACHY spectra are assigned to the calculated spectral clusters, showing that cloud regimes are most frequent during the active West African monsoon season of June–October in 2010, and all cloud regimes have a higher frequency of occurrence during the active monsoon season of 2003 compared with the inactive monsoon season of 2004. Overall, the distinct underlying physical properties controlling spectral signatures show great promise for monitoring evolution of the Earth system directly from solar spectral reflectance observations.

We use the spectrally invariant method to study the variability of cloud optical thickness τ and droplet effective radius reff in transition zones (between the cloudy and clear sky columns) observed from Solar Spectral Flux Radiometer (SSFR) and Shortwave Array Spectroradiometer-Zenith (SASZe) during the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign. The measurements from the SSFR and the SASZe are different, however inter-instrument differences of self-normalized measurements (divided by their own spectra at a fixed time) are small. The spectrally invariant method approximates the spectra in the cloud transition zone as a linear combination of definitely clear and cloudy spectra, where the coefficients, slope and intercept, characterize the spectrally invariant properties of the transition zone. Simulation results from the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model demonstrate that (1) the slope of the visible band is positively correlated with the cloud optical thickness τ while the intercept of the near-infrared band has high negative correlation with the cloud drop effective radius reff even without the exact knowledge of τ; (2) the above relations hold for all Solar Zenith Angle (SZA) and for cloud-contaminated skies. In observations using redundant measurements from SSFR and SASZe, we find that during cloudy-to-clear transitions, (a) the slopes of the visible band decrease, and (b) the intercepts of the near-infrared band remain almost constant near cloud edges. The findings in simulations and observations suggest that, while the optical thickness decreases during the cloudy-to-clear transition, the cloud drop effective radius does not change when cloud edges are approached. These results support the hypothesis that inhomogeneous mixing dominates near cloud edges in the studied cases.

Estimating the impact of radiation transport through cloud sides on the global energy budget is hampered by the lack of a fast radiation scheme suitable for use in global atmospheric models that can represent these effects in both the shortwave and longwave. This two-part paper describes the development of such a scheme, which we refer to as the Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS). The principle of the method is to add extra terms to the two-stream equations to represent lateral transport between clear and cloudy regions, which vary in proportion to the length of cloud edge as a function of height. The present paper describes a robust and accurate method for solving the coupled system of equations in both the shortwave and longwave in terms of matrix exponentials. This solver has been coupled to a correlated-k model for gas absorption. We then confirm the accuracy of SPARTACUS by performing broadband comparisons with fully 3-D radiation calculations by the Monte Carlo model “MYSTIC” for a cumulus cloud field, examining particularly the percentage change in cloud radiative effect (CRE) when 3-D effects are introduced. In the shortwave, SPARTACUS correctly captures this change to CRE, which varies with solar zenith angle between −25% and +120%. In the longwave, SPARTACUS captures well the increase in radiative cooling of the cloud, although it is only able to correctly simulate the 30% increase in surface CRE (around 4 W m−2) if an approximate correction is made for cloud clustering.

Current weather and climate models neglect 3-D radiative transfer through cloud sides, which can change the cloud radiative effect (CRE) significantly. This two-part paper describes the development of the SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS) to capture these effects efficiently in a two-stream radiation scheme for use in global models. The present paper concerns the longwave spectral region, where not much work has been done previously, although the limited previous work has suggested that radiative transfer through cloud sides increases the longwave surface CRE of shallow cumulus by around 30%. To assist the development of a longwave capability for SPARTACUS, we use a reference case of an isolated, isothermal, optically thick, cubic cloud in vacuum, for which 3-D effects increase CRE by exactly 200%. It is shown that for any cloud shape, the 3-D effect can be represented in SPARTACUS provided that correct account is made for (1) the effective zenith angle of diffuse radiation emitted from a cloud, (2) the spatial distribution of fluxes in the cloud, (3) cloud clustering that enhances the interception of emitted radiation by neighboring clouds, and (4) radiative smoothing leading to the effective cloud edge length being less than the measured value. We find empirically that the circumference of an ellipse fitted to a horizontal cross section through a cumulus cloud provides a good estimate of the radiatively effective cloud edge length, which provides some guidance to how cloud observations could be analyzed to extract their most important properties for radiation.

International audience; Unprecedented ground-based and aircraft measurements in June-July 2016 in southern West Africa characterize atmospheric composition and dynamics, low-level cloud properties, the diurnal cycle, and air pollution impacts on health.The EU-funded project DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) investigates the relationship between weather, climate, and air pollution in southern West Africa, an area with rapid population growth, urbanisation, and increase in anthropogenic aerosol emissions. The air over this region contains a unique mixture of natural and anthropogenic gases, liquid droplets and particles, emitted in an environment, in which multi-layer clouds frequently form. These exert a large influence on the local weather and climate, mainly due to their impact on radiation, the surface energy balance, and thus the diurnal cycle of the atmospheric boundary layer.In June and July 2016, DACCIWA organized a major international field campaign in Ivory Coast, Ghana, Togo, Benin, and Nigeria. Three supersites in Kumasi, Savè, and Ile-Ife conducted permanent measurements and 15 Intensive observation periods. Three European aircraft together flew 50 research flights between 27 June and 16 July 2016 for a total of 155 hours. DACCIWA scientists launched weather balloons several times a day across the region (772 in total), measured urban emissions, and evaluated health data. The main objective was to build robust statistics of atmospheric composition, dynamics, and low-level cloud properties in various chemical landscapes to investigate their mutual interactions.This article presents an overview of the DACCIWA field campaign activities as well as some first research highlights. The rich data obtained during the campaign will be made available to the scientific community and help to advance scientific understanding, modeling, and monitoring the atmosphere over southern West Africa.

Capsule: A 21-month deployment to Graciosa Island in the northeastern Atlantic Ocean is providing an unprecedented record of the clouds, aerosols and meteorology in a poorly-sampled remote marine environment The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21 month (April 2009- December 2010) comprehensive dataset documenting clouds, aerosols and precipitation using the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols and precipitation in the marine boundary layer. Graciosa Island straddles the boundary between the subtropics and midlatitudes in the Northeast Atlantic Ocean, and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1- 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging. The data from at Graciosa are being compared with short-range forecasts made a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well, but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.

Ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 were utilized to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration Nd and compute a cloud-aerosol interaction (ACI) metric defined as ACICCN = ∂ ln(Nd)/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN0.4) and 0.3% (CCN0.3) supersaturation. Analysis of CCN0.4, accumulation mode aerosol concentration (Na), and extinction coefficient (σext) indicates that Na and σext can be used as CCN0.4 proxies for estimating ACI. ACICCN derived from 10 min averaged Nd and CCN0.4 and CCN0.3, and CCN0.4 regressions using Na and σext, produce high ACICCN: near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in Nd. ACICCN computed in deep boundary layers was small (ACICCN = 0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACICCN. Satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite Nd and ship-based CCN0.4 and Na yielded a maximum ACICCN = 0.88–0.92, a value slightly less than the ship-based ACICCN, but still consistent with aircraft-based studies in the eastern Pacific.

Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

We have extensively evaluated the response of cloud base drizzle rate (Rcb; mm d−1) in warm clouds to liquid water path (LWP; g m−2) and to cloud condensation nuclei (CCN) number concentration (NCCN; cm−3), an aerosol proxy. This evaluation is based on a 19 month long data set of Doppler radar, lidar, microwave radiometers, and aerosol observing systems from the Atmospheric Radiation Measurement (ARM) Mobile Facility deployments at the Azores and in Germany. Assuming 0.55% supersaturation to calculate NCCN, we found a power law Rcb=0.0015±0.0009⋅LWP1.68±0.05NCCN−0.66±0.08, indicating that Rcb decreases by a factor of 2–3 as NCCN increases from 200 to 1000 cm−3 for fixed LWP. Additionally, the precipitation susceptibility to NCCN ranges between 0.5 and 0.9, in agreement with values from simulations and aircraft measurements. Surprisingly, the susceptibility of the probability of precipitation from our analysis is much higher than that from CloudSat estimates but agrees well with simulations from a multiscale high-resolution aerosol-climate model. Although scale issues are not completely resolved in the intercomparisons, our results are encouraging, suggesting that it is possible for multiscale models to accurately simulate the response of LWP to aerosol perturbations.

[1] 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.

[1] Comprehensive surface-based retrievals of cloud optical and microphysical properties were made at Taihu, a highly polluted site in the central Yangtze Delta region, during a research campaign from May 2008 to December 2009. Cloud optical depth (COD), effective radius (Re), and liquid water path (LWP) were retrieved from measurements made with a suite of ground-based and spaceborne instruments, including an Analytical Spectral Devices spectroradiometer, a multifilter rotating shadowband radiometer, a multichannel microwave radiometer profiler, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. Retrievals from zenith radiance measurements capture better the temporal variation of cloud properties than do retrievals from hemispherical fluxes. Annual mean LWP, COD, and Re are 115.8 ± 90.8 g/m2, 28.5 ± 19.2, and 6.9 ± 4.2 µm. Over 90% of LWP values are less than 250 g/m2. Most of the COD values (>90%) fall between 5 and 60, and ~80% of Re values are less than 10 µm. Maximum (minimum) values of LWP and Re occur in summer (winter); COD is highest in winter and spring. Raining and nonraining clouds have significant differences in LWP, COD, and Re. Rainfall frequency is best correlated with LWP, followed by COD and Re. Cloud properties retrieved from multiple ground-based instruments are also compared with those from satellite retrievals. On average, relative to surface retrievals, mean differences of satellite retrievals in cloud LWP, COD, and Re were −33.6 g/m2 (−26.4%), −5.8 (−31.4%), and 2.9 µm (29.3%) for 11 MODIS-Terra overpasses and −43.3 g/m2 (−22.3%), −3.0 (−10.0%), and −1.3 µm (−12.0%) for 8 MODIS-Aqua overpasses, respectively. These discrepancies indicate that MODIS cloud products still suffer from large uncertainties in this region.

Small boundary-layer clouds are ubiquitous over many parts of the globe and strongly influence the Earths radiative energy balance. However, our understanding of these clouds is insufficient to solve pressing scientific problems. For example, cloud feedback represents the largest uncertainty amongst all climate feedbacks in general circulation models (GCM). Several issues complicate understanding boundary-layer clouds and simulating them in GCMs. The high spatial variability of boundary-layer clouds poses an enormous computational challenge, since their horizontal dimensions and internal variability occur at spatial scales much finer than the computational grids used in GCMs. Aerosol-cloud interactions further complicate boundary-layer cloud measurement and simulation. Additionally, aerosols influence processes such as precipitation and cloud lifetime. An added complication is that at small scales (order meters to 10s of meters) distinguishing cloud from aerosol is increasingly difficult, due to the effects of aerosol humidification, cloud fragments and photon scattering between clouds.

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other spaceborne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so one must first calibrate the reflected solar radiation received by the photon-counting detectors of the GLAS 532-nm channel, the primary channel for atmospheric products. Solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (i) calibration with coincident airborne and GLAS observations, (ii) calibration with coincident Geostationary Operational Environmental Satellite (GOES) and GLAS observations of deep convective clouds, and (iii) calibration from first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retrievals is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern G...

The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21-month (April 2009–December 2010) comprehensive dataset documenting clouds, aerosols, and precipitation using the Atmospheric Radiation Measurement Program (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols, and precipitation in the marine boundary layer.\ud \ud Graciosa Island straddles the boundary between the subtropics and midlatitudes in the northeast Atlantic Ocean and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1 to 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back-trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging.\ud \ud The data from Graciosa are being compared with short-range forecasts made with a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

Southern West Africa (SWA) has a large population that relies on highly variable monsoon rainfall, yet climate models show little consensus over projected precipitation in this region. Understanding of the current and future climate of SWA is further complicated by rapidly increasing anthropogenic emissions and a lack of surface observations. Using multiple satellite observations, the ERA-Interim reanalysis, and four climate models, we document the climatology of cloud, precipitation and radiation over SWA in June-July, highlight discrepancies among satellite products, and identify shortcomings in climate models and ERA-Interim. Large differences exist between monthly mean cloud cover estimates from satellites, which range from 68 to 94 %. In contrast, differences among satellite observations in top of atmosphere outgoing radiation and surface precipitation are smaller, with monthly means of about 230 W m–2 of longwave radiation, 145 W m–2 of shortwave radiation and 5.87 mm day–1 of precipitation. Both ERA-Interim and the climate models show less total cloud cover than observations, mainly due to underestimating low cloud cover. Errors in cloud cover, along with uncertainty in surface albedo, lead to a large spread of outgoing shortwave radiation. Both ERA-Interim and the climate models also show signs of convection developing too early in the diurnal cycle, with associated errors in the diurnal cycles of precipitation and outgoing longwave radiation. Clouds, radiation and precipitation are linked in an analysis of the regional energy budget, which shows that inter-annual variability of precipitation and dry static energy divergence are strongly linked.

We present a novel method for retrieving high-resolution, three-dimensional (3-D) nonprecipitating cloud fields in both overcast and broken-cloud situations. The method uses scanning cloud radar and multiwavelength zenith radiances to obtain gridded 3-D liquid water content (LWC) and effective radius (re) and 2-D column mean droplet number concentration (Nd). By using an adaption of the ensemble Kalman filter, radiances are used to constrain the optical properties of the clouds using a forward model that employs full 3-D radiative transfer while also providing full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from a challenging cumulus cloud field produced by a large-eddy simulation snapshot. Uncertainty due to measurement error in overhead clouds is estimated at 20% in LWC and 6% in re, but the true error can be greater due to uncertainties in the assumed droplet size distribution and radiative transfer. Over the entire domain, LWC and re are retrieved with average error 0.05–0.08 g m-3 and ~2 μm, respectively, depending on the number of radiance channels used. The method is then evaluated using real data from the Atmospheric Radiation Measurement program Mobile Facility at the Azores. Two case studies are considered, one stratocumulus and one cumulus. Where available, the liquid water path retrieved directly above the observation site was found to be in good agreement with independent values obtained from microwave radiometer measurements, with an error of 20 g m-2.

[1] An idealized equilibrium model for the undisturbed partly cloudy boundary layer (BL) is used as a framework to explore the coupling of the energy, water, and carbon cycles over land in midlatitudes and show the sensitivity to the clear-sky shortwave flux, the midtropospheric temperature, moisture, CO2, and subsidence. The changes in the surface fluxes, the BL equilibrium, and cloud cover are shown for a warmer, doubled CO2 climate. Reduced stomatal conductance in a simple vegetation model amplifies the background 2 K ocean temperature rise to an (unrealistically large) 6 K increase in near-surface temperature over land, with a corresponding drop of near-surface relative humidity of about 19%, and a rise of cloud base of about 70 hPa. Cloud changes depend strongly on changes of mean subsidence; but evaporative fraction (EF) decreases. EF is almost uniquely related to mixed layer (ML) depth, independent of background forcing climate. This suggests that it might be possible to infer EF for heterogeneous landscapes from ML depth. The asymmetry of increased evaporation over the oceans and reduced transpiration over land increases in a warmer doubled CO2 climate.

[1] Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program's Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground-based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale.

[1] An assessment of aerosol-cloud interactions (ACI) from ground-based remote sensing under coastal stratiform clouds is presented. The assessment utilizes a long-term, high temporal resolution data set from the Atmospheric Radiation Measurement (ARM) Program deployment at Pt. Reyes, California, United States, in 2005 to provide statistically robust measures of ACI and to characterize the variability of the measures based on variability in environmental conditions and observational approaches. The average ACIN (= dlnNd/dlnα, the change in cloud drop number concentration with aerosol concentration) is 0.48, within a physically plausible range of 0–1.0. Values vary between 0.18 and 0.69 with dependence on (1) the assumption of constant cloud liquid water path (LWP), (2) the relative value of cloud LWP, (3) methods for retrieving Nd, (4) aerosol size distribution, (5) updraft velocity, and (6) the scale and resolution of observations. The sensitivity of the local, diurnally averaged radiative forcing to this variability in ACIN values, assuming an aerosol perturbation of 500 cm−3 relative to a background concentration of 100 cm−3, ranges between −4 and −9 W m−2. Further characterization of ACI and its variability is required to reduce uncertainties in global radiative forcing estimates.

We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon over passes in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals out perform those retrieved from GPROF and MLRS. Over all, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Ac cu rate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

An extensive verification of cloud property retrievals has been conducted for two algorithms using zenith radiances measured by the Atmospheric Radiation Measurement (ARM) Program ground-based passive two-channel (673 and 870 nm) Narrow Field-Of-View Radiometer. The underlying principle of these algorithms is that clouds have nearly identical optical properties at these wavelengths, but corresponding spectral surface reflectances (for vegetated surfaces) differ significantly. The first algorithm, the RED vs. NIR, works for a fully three-dimensional cloud situation. It retrieves not only cloud optical depth, but also an effective radiative cloud fraction. Importantly, due to one-second time resolution of radiance measurements, we are able, for the first time, to capture detailed changes in cloud structure at the natural time scale of cloud evolution. The cloud optical depths tau retrieved by this algorithm are comparable to those inferred from both downward fluxes in overcast situations and microwave brightness temperatures for broken clouds. Moreover, it can retrieve tau for thin patchy clouds, where flux and microwave observations fail to detect them. The second algorithm, referred to as COUPLED, couples zenith radiances with simultaneous fluxes to infer 2. In general, the COUPLED and RED vs. NIR algorithms retrieve consistent values of tau. However, the COUPLED algorithm is more sensitive to the accuracies of measured radiance, flux, and surface reflectance than the RED vs. NIR algorithm. This is especially true for thick overcast clouds where it may substantially overestimate z.