Retrieval Of Cloud Properties Research Articles

CERES S'COOL PROJECT UPDATE: The Evolution and Value of a Long-Running Education Project with a Foundation in NASA Earth Science Missions.

Since 1997, S'COOL has engaged interested participants worldwide in observing clouds and comparing data from ground and satellite sources to inform validation efforts for several NASA Earth science missions.

Bayesian Retrievals of Vertically Resolved Cloud Particle Size Distribution Properties

AbstractRetrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

A 1‐D variational retrieval of temperature, humidity, and liquid cloud properties: Performance under idealized and real conditions

AbstractAn extended version of the Integrated Profiling Technique (IPT) is presented. The IPT combines measurements from cloud radar and microwave radiometer (MWR) with prior information in a 1D‐Var approach in order to retrieve physically consistent atmospheric profiles of temperature, absolute humidity, liquid water content (LWC), and recently also cloud droplet effective radius (REF). Physical consistency implies the reproducibility of the measurements within the uncertainties. Results based on synthetic observations revealed a good retrieval performance with a high convergence rate of 98%. Retrieval uncertainties are typically around 0.06 g m−3 for LWC and 0.6 μm for REF. For the application to real measurements, quality‐controlled, bias‐free observations are crucial. A newly developed MWR spectral consistency check, which was applied to the measurements at the Jülich Observatory for Cloud Evolution (JOYCE), revealed strongly bias‐affected channels. The IPT itself can serve as a further quality check: particularly in clear‐sky cases, nonconvergence or physically inconsistent solutions may hint at measurement offset errors. Based on sensitivity studies, the final set of MWR frequencies was identified and the retrieval applied to 1 year of data. Physically consistent solutions could be found in 62% of all processed cases. A focus was put on the analysis of nondrizzling single‐layer water clouds which typically have small geometrical thicknesses (<300 m), low liquid water paths (<50 g m−2), and small REF (<5 μm). The retrieved data product contains a high potential for the analysis of warm cloud characteristics and, in combination with auxiliary information from the JOYCE instrumentation, of associated boundary layer processes.

Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections

Abstract. Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water–atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly Ts observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source.This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Effects of ice crystal surface roughness and air bubble inclusions on cirrus cloud radiative properties from remote sensing perspective

We study the combined effects of surface roughness and inhomogeneity on the optical scattering properties of ice crystals and explore the consequent implications to remote sensing of cirrus cloud properties. Specifically, surface roughness and inhomogeneity are added to the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 (MC6) cirrus cloud particle habit model. Light scattering properties of the new habit model are simulated using a modified version of the Improved Geometric Optics Method (IGOM). Both inhomogeneity and surface roughness affect the single scattering properties significantly. In visible bands, inhomogeneity and surface roughness both tend to smooth the phase function and eliminate halos and the backscattering peak. The asymmetry parameter varies with the degree of surface roughness following a U shape - decreases and then increases - with a minimum at around 0.15, whereas it decreases monotonically with the air bubble volume fraction. Air bubble inclusions significantly increase phase matrix element -P12 for scattering angles between 20°–120°, whereas surface roughness has a much weaker effect, increasing -P12 slightly from 60°–120°. Radiative transfer simulations and cirrus cloud property retrievals are conducted by including both the factors. In terms of surface roughness and air bubble volume fraction, retrievals of cirrus cloud optical thickness or the asymmetry parameter using solar bands show similar patterns of variation. Polarimetric simulations using the MC6 cirrus cloud particle habit model are shown to be more consistent with observations when both surface roughness and inhomogeneity are simultaneously considered.

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

Abstract. A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTER-specific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in [Zhao and Di Girolamo(2006)]. To validate and evaluate the cloud optical thickness (τ) and cloud effective radius (reff) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τaA and reff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between reff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (1): Cloud Phase Determination

As a key factor in the climate model, cloud phase is an important prerequisite to performing cloud property retrievals from remote sensor measurements. The ability to infer cloud phase using cloud emissivity spectra is investigated by numerical simulations. It is shown that for emissivity below 0.95, several spectral features such as the slopes, the ratios and the differences of the emissivity are consistent with the variation of cloud phase in some spectral regions. Specifically, these features include the slope of the cloud emissivity between 800 and 900 cm(-1), the slope of the cloud emissivity between 900 and 1 000 cm(-1), the difference in the mean emissivity between above-mentioned two regions, the ratio of the emissivity at 862.1 cm(-1) to the emissivity at 989.8 cm(-1), the difference in the emissivity between 862.1 and 989.8 cm(-1), the ratio of the emissivity at 1 900.1 cm-1 to the emissivity at 2 029.3 cm-1, the ratio of the mean emissivity for far-infrared region to the emissivity at 900 cm(-1). A cloud phase classifier is proposed based on support vector machines (SVM). A series of simulations including various cloud patterns are performed. The RBF kernel function parameters and the penalty factor of SVM are selected by using the genetic algorithm. The phase determination algorithm is applied for collecting data from the AERI at the SGP site. The results from the ground-based multisensor cloud phase classifier proposed by Shupe are used to validate the phase determination algorithm. It is found the two results are consistent in general. 30% clouds are indicated as opaque due to its high emissivity. The cloud with small lidar’s depolarization is misclassified as clear sky by the Shupe method. It can be concluded that the proposed algorithm considering the spectral information (spectral slopes, ratios and differences) is efficient for cloud phase determination of thin cloud.

Response of Marine Boundary Layer Cloud Properties to Aerosol Perturbations Associated with Meteorological Conditions from the 19-Month AMF-Azores Campaign

AbstractThis study investigates the response of marine boundary layer (MBL) cloud properties to aerosol loading by accounting for the contributions of large-scale dynamic and thermodynamic conditions and quantifies the first indirect effect (FIE). It makes use of 19-month measurements of aerosols, clouds, and meteorology acquired during the Atmospheric Radiation Measurement Mobile Facility field campaign over the Azores. Cloud droplet number concentrations and cloud optical depth (COD) significantly increased with increasing aerosol number concentration . Cloud droplet effective radius (DER) significantly decreased with increasing . The correlations between cloud microphysical properties [, liquid water path (LWP), and DER] and were stronger under more stable conditions. The correlations between , LWP, DER, and were stronger under ascending-motion conditions, while the correlation between COD and was stronger under descending-motion conditions. The magnitude and corresponding uncertainty of the FIE ranged from 0.060 ± 0.022 to 0.101 ± 0.006 depending on the different LWP values. Under more stable conditions, cloud-base heights were generally lower than those under less stable conditions. This enabled a more effective interaction with aerosols, resulting in a larger value for the FIE. However, the dependence of the response of cloud properties to aerosol perturbations on stability varied according to whether ground- or satellite-based DER retrievals were used. The magnitude of the FIE had a larger variation with changing LWP under ascending-motion conditions and tended to be higher under ascending-motion conditions for clouds with low LWP and under descending-motion conditions for clouds with high LWP. A contrasting dependence of FIE on atmospheric stability estimated from the surface and satellite cloud properties retrievals reported in this study underscores the importance of assessing all-level properties of clouds in aerosol–cloud interaction studies.

A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part 2: Near‐global retrievals and evaluation against A‐Train products

This article presents results of a novel methodology capable of simultaneously retrieving optical and microphysical properties of multi‐level ice and liquid clouds. The method was introduced in Part I, which theoretically demonstrated its capabilities, and its results are here analysed and evaluated against A‐Train operational products. In addition to being robust to multi‐layer conditions, another advantage of the method is that rigorous uncertainties and analysis tools are attached to its retrievals. Also, the combined use of short‐wave and thermal infrared channels provides a wide range of sensitivity from moderately thin to thick ice cloud layers. Finally, the method is also novel in that the ice water path (IWP) is directly retrieved. These new retrievals should therefore be useful in providing new data for evaluating climate model predictions of IWP. In this study, our methodology has been applied to one year of A‐Train measurements, narrowed to daytime conditions over oceanic surfaces. The retrievals and their uncertainties are statistically analysed, after a thorough discussion of the filtering process. It appears that our method is sensitive to IWPs ranging between about 0.5 and 1000 g m−2, with uncertainties better than 25% between 5 and 500 g m−2. Retrievals of the optical depth and effective radius of liquid layers have uncertainties better than 20%. Our retrievals are then compared to five independent operational A‐Train products. Very good agreements, well within a factor of 2, are found by comparisons to products from active and passive instruments. These results overall lead to the validation of our method. Additionally, the robustness of passive operational products to multi‐layer conditions is discussed. Preliminary comparisons show a possible overestimation of retrievals obtained under the single‐layer approximation. A thorough assessment of this problem will be addressed in a following study.

Investigation of ice particle habits to be used for ice cloud remote sensing for the GCOM-C satellite mission

Abstract. In this study, various ice particle habits are investigated in conjunction with inferring the optical properties of ice clouds for use in the Global Change Observation Mission-Climate (GCOM-C) satellite programme. We develop a database of the single-scattering properties of five ice habit models: plates, columns, droxtals, bullet rosettes, and Voronoi. The database is based on the specification of the Second Generation Global Imager (SGLI) sensor on board the GCOM-C satellite, which is scheduled to be launched in 2017 by the Japan Aerospace Exploration Agency. A combination of the finite-difference time-domain method, the geometric optics integral equation technique, and the geometric optics method is applied to compute the single-scattering properties of the selected ice particle habits at 36 wavelengths, from the visible to the infrared spectral regions. This covers the SGLI channels for the size parameter, which is defined as a single-particle radius of an equivalent volume sphere, ranging between 6 and 9000 µm. The database includes the extinction efficiency, absorption efficiency, average geometrical cross section, single-scattering albedo, asymmetry factor, size parameter of a volume-equivalent sphere, maximum distance from the centre of mass, particle volume, and six nonzero elements of the scattering phase matrix. The characteristics of calculated extinction efficiency, single-scattering albedo, and asymmetry factor of the five ice particle habits are compared. Furthermore, size-integrated bulk scattering properties for the five ice particle habit models are calculated from the single-scattering database and microphysical data. Using the five ice particle habit models, the optical thickness and spherical albedo of ice clouds are retrieved from the Polarization and Directionality of the Earth's Reflectances-3 (POLDER-3) measurements, recorded on board the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The optimal ice particle habit for retrieving the SGLI ice cloud properties is investigated by adopting the spherical albedo difference (SAD) method. It is found that the SAD is distributed stably due to the scattering angle increases for bullet rosettes with an effective diameter (Deff) of 10 µm and Voronoi particles with Deff values of 10, 60, and 100 µm. It is confirmed that the SAD of small bullet-rosette particles and all sizes of Voronoi particles has a low angular dependence, indicating that a combination of the bullet-rosette and Voronoi models is sufficient for retrieval of the ice cloud's spherical albedo and optical thickness as effective habit models for the SGLI sensor. Finally, SAD analysis based on the Voronoi habit model with moderate particle size (Deff = 60 µm) is compared with the conventional general habit mixture model, inhomogeneous hexagonal monocrystal model, five-plate aggregate model, and ensemble ice particle model. The Voronoi habit model is found to have an effect similar to that found in some conventional models for the retrieval of ice cloud properties from space-borne radiometric observations.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Insights into Long-Standing Bias in Cloud Property Retrieval

A new framework provides a more comprehensive view of how subpixel variations can create biases in a commonly used method of analyzing cloud properties with satellites.

Properties of marine stratocumulus obtained with partly cloudy pixel retrievals and found in the MODIS MOD06 cloud product

AbstractPartly cloudy pixel retrievals (PCPRs) of cloud properties for marine stratocumulus were compared with those of the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product (MOD06). In addition, the fractional cloud cover obtained from the PCPRs applied to 1 km MODIS radiances was compared with that derived from the 250 m cloud mask (MOD35). The comparisons were made for pixels that were overcast and pixels that were only partially covered by clouds. Accounting for failed retrievals in both the MOD06 cloud properties and those obtained with the PCPRs leads to the suggestion that regional cloud cover be estimated in terms of lower and upper limits. The average could serve as the best estimate of the cloud cover, and the difference between the average and an extreme could serve as the uncertainty. The comparisons reveal that the overcast assumption used in the MODIS cloud property retrievals leads to cloud cover, droplet effective radii, and cloud top temperatures that are overestimated and, shortwave optical depths, liquid water paths that are underestimated. These biases persist when the properties are averaged to form spatial and temporal means. Owing to significant horizontal variations of cloud liquid water within the 1 km MODIS pixels, visible optical depths, droplet effective radii, and liquid water paths derived from the PCPRs show similar biases. The trends of the biases with pixel‐scale and regional‐scale cloud cover suggest that estimates of the aerosol indirect radiative forcing derived from satellites have been overestimated.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content.

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff ), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieving co‐occurring cloud and precipitation properties of warm marine boundary layer clouds with A‐Train data

AbstractIn marine boundary layer (MBL) clouds the formation of precipitation from the cloud droplet distribution in the presence of variable aerosol plays a fundamental role in determining the coupling of these clouds to their environment and ultimately to the climate system. Here the degree to which A‐Train satellite measurements can diagnose simultaneously occurring cloud and precipitation properties in MBL clouds is examined. Beginning with the measurements provided by CloudSat and Moderate Resolution Imaging Spectroradiometer (including a newly available microwave brightness temperature from CloudSat), and a climatology of MBL cloud properties from past field campaigns, an assumption is made that any hydrometeor volume could contain both cloud droplet and precipitation droplet modes. Bayesian optimal estimation is then used to derive atmospheric states by inverting a measurement vector carefully accounting for uncertainties due to instrument noise, forward model error, and assumptions. It is found that in many cases where significant precipitation coexists with cloud, due to forward model error driven by uncertainties in assumptions, the uncertainty in retrieved cloud properties is greater than the variance in the prior climatology. It is often necessary to average several thousand (hundred) precipitating (weakly precipitating) profiles to obtain meaningful information regarding the properties important to microphysical processes. Regardless, if such process level information is deemed necessary for better constraining predictive models of the climate system, measurement systems specifically designed to accomplish such retrievals must be considered for the future.

Observation of aerosol–cloud interaction over New York City using synergetic ground-based remote sensing systems

Using UV Raman Lidar for aerosol extinction (αext), and combining microwave radiometer-derived liquid water path (LWP) with multifilter rotating shadowband radiometer-derived cloud optical depth (τcod) to retrieve cloud droplet effective radius (Reff), we observe clear signatures of the Twomey aerosol indirect effect (IE) under certain specialized conditions. The aerosol–cloud index (ACI) or IE slope relating cloud droplet radius to aerosol loading is calculated and shown to be quantitatively consistent with theoretical constraints. To demonstrate consistency, we use both a neural network multiband (default) approach and a dual-channel (DC) approach for the LWP and observe that the DC approach is generally more robust with more successful retrievals leading to a reduction of error in our regression analysis. We also perform an uncertainty analysis of the IE regression slope taking into account the major sources of error in cloud property retrieval and demonstrate that only sufficiently high values of the IE slope should be observable. Finally, based on the results of multiple cases, we observe the importance of vertical wind uptake on the IE signature.

Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part I: Feature Detection

AbstractA feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.

Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part II: Extinction

AbstractA feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part II of the FEX algorithm: the retrieval of cloud and aerosol extinction profiles. The directly retrieved extinction profiles using the Raman method are supplemented by other retrieval methods developed for elastic backscatter lidars. Portions of features where the extinction-to-backscatter ratios (i.e., lidar ratios) can be obtained are used to infer the lidar ratios for the regions where no such estimate can be made. When neither directly retrieved nor an inferred value can be determined, a climatological lidar ratio is used. This best-estimate approach results in the need to use climatological lidar ratios for less than about 5% of features, except for thin cirrus at the ARM tropical western Pacific Darwin site, where above 12 km, about 20% of clouds use a climatological lidar ratio. A classification of feature type is made, guided by the atmosphere’s thermodynamic state and the feature’s scattering properties: lidar ratio, backscatter, and depolarization. The contribution of multiple scattering is explicitly considered for each of the ARM RL channels. A comparison between aerosol optical depth from FEX and that from collocated sun photometers over multiple years at two ARM sites shows an agreement (in terms of bias error) of about −0.3% to −4.3% (relative to the sun photometer).

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

Marine boundary layer drizzle properties and their impact on cloud property retrieval

Abstract. In this study, we retrieve and document drizzle properties, and investigate the impact of drizzle on cloud property retrieval in Dong et al. (2014a) from ground-based measurements at the ARM Azores facility from June 2009 to December 2010. For the selected cloud and drizzle samples, the drizzle occurrence is 42.6 %, with a maximum of 55.8 % in winter and a minimum of 35.6 % in summer. The annual means of drizzle liquid water path LWPd, effective radius rd, and number concentration Nd for the rain (virga) samples are 4.73 (1.25) g m−2, 61.5 (36.4) μm, and 0.38 (0.79) cm−3. The seasonal mean LWPd values are less than 3 % of the LWP values retrieved by the microwave radiometer (MWR). The annual mean differences in cloud-droplet effective radius with and without drizzle are 0.75 and 2.35 %, respectively, for the virga and rain samples. Therefore, we conclude that the impact of drizzle below the cloud base on cloud property retrieval is insignificant for a solar-transmission-based method, but significant for any retrievals using radar reflectivity.