Cloud Optical Depth Retrievals Research Articles

Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors

One major source of uncertainty in the cloud-mediated aerosol forcing arises from the magnitude of the cloud liquid water path (LWP) adjustment to aerosol-cloud interactions, which is poorly constrained by observations. Many of the recent satellite-based studies have observed a decreasing LWP as a function of cloud droplet number concentration (CDNC) as the dominating behavior. Estimating the LWP response to the CDNC changes is a complex task since various confounding factors need to be isolated. However, an important aspect has not been sufficiently considered: the propagation of natural spatial variability and errors in satellite retrievals of cloud optical depth and cloud effective radius to estimates of CDNC and LWP. Here we use satellite and simulated measurements to demonstrate that, because of this propagation, even a positive LWP adjustment is likely to be misinterpreted as negative. This biasing effect therefore leads to an underestimate of the aerosol-cloud-climate cooling and must be properly considered in future studies.

Surface shortwave cloud radiative effect of cumulus and stratocumulus-cumulus cloud types in the Caribbean area (Camagüey Cuba, 2010-2016)

The effects of cumulus (Cu) clouds and the combination of stratocumulus-cumulus (Sc-Cu) clouds on solar radiation at the Earth’s surface were evaluated at Camagüey, Cuba, during a 6-yr period (from June 2010 to May 2016). Two methods to calculate the cloud radiative effect (CRE) were employed. The first method (CREm) uses solar irradiances in cloudy conditions from actinometric observations, where cloud information was also reported by visual observation. In the second method (CRE0) surface solar irradiances were estimated for both cloudy and clear sky conditions using a 1-D radiative transfer model, and cloud optical depth (COD) retrieved from an AERONET sun-photometer as the main input. A temporal correspondence criterion between COD retrievals and actinometric observations was performed in order to classify the COD of each cloud type. After the application of this criterion, the COD belonging to the optically thin clouds was removed. Finally, 255 and 732 COD observations for Cu and Sc-Cu, respectively, were found. Results show a statistically significant difference at the 95% confidence level between CRE calculated for Sc-Cu and Cu, using both methods. Mean values of CREm and CRE0 for Cu (Sc-Cu) were −442 (−390) and −460 (−417) Wm–2, respectively. CRE0 shows a linear relation with ln(COD), with stronger correlation at a lower solar zenith angle. The shortwave cloud effect efficiency (CEE) for the two cloud types sharply decreases with the increase of the COD value up to 20. For larger COD, the CEE is less sensitive to the increase of COD.

Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign

Abstract. Satellite retrievals of cloud droplet effective radius (re) and optical depth (τ) from the Thirteenth Geostationary Operational Environmental Satellite (GOES-13) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra, based on the Clouds and the Earth's Radiant Energy System (CERES) project algorithms, are evaluated with airborne data collected over the midlatitude boundary layer during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne dataset comprises in situ re from the Cloud Droplet Probe (CDP) and remotely sensed re and τ from the airborne Research Scanning Polarimeter (RSP). GOES-13 and MODIS (Aqua and Terra) re values are systematically greater than those from the CDP and RSP by at least 4.8 (GOES-13) and 1.7 µm (MODIS) despite relatively high linear correlation coefficients (r=0.52–0.68). In contrast, the satellite τ underestimates its RSP counterpart by −3.0, with r=0.76–0.77. Overall, MODIS yields better agreement with airborne data than GOES-13, with biases consistent with those reported for subtropical stratocumulus clouds. While the negative bias in satellite τ is mostly due to the retrievals having been collected in highly heterogeneous cloud scenes, the causes for the positive bias in satellite re, especially for GOES-13, are more complex. Although the high viewing zenith angle (∼65∘) and coarser pixel resolution for GOES-13 could explain a re bias of at least 0.7 µm, the higher GOES-13 re bias relative to that from MODIS is likely rooted in other factors. In this regard, a near-monotonic increase was also observed in GOES-13 re up to 1.0 µm with the satellite scattering angle (Θ) over the angular range 116–165∘; that is, re increases toward the backscattering direction. Understanding the variations of re with Θ will require the combined use of theoretical computations along with intercomparisons of satellite retrievals derived from sensors with dissimilar viewing geometry.

Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples

Abstract. The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1 km2 compared to the standard 3×3 km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6 µm, 0.8 µm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6 µm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6 µm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains.

A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations

Abstract. Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world. Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid. This assumption has the potential to introduce errors into long-term statistics of retrieved optical depth for clouds that also contain ice. Using a set of idealised cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence of ice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55 % to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2–4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 % and surface flux to about 15 %.

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties

Abstract. Improvements to climate model results in polar regions require improved knowledge of cloud properties. Surface-based infrared (IR) radiance spectrometers have been used to retrieve cloud properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effects of errors and instrument resolution on cloud property retrievals from downwelling IR radiances for resolutions of 0.1 to 20 cm−1. Retrievals are tested on 336 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, and liquid-topped clouds and a variety of ice habits. Retrieval accuracy is found to be unaffected by resolution from 0.1 to 4 cm−1, after which it decreases slightly. When cloud heights are retrieved, errors in retrieved cloud optical depth (COD) and ice fraction are considerably smaller for clouds with bases below 2 km than for higher clouds. For example, at a resolution of 4 cm−1, with errors imposed (noise and radiation bias of 0.2 mW/(m2 sr cm−1) and biases in temperature of 0.2 K and in water vapor of −3 %), using retrieved cloud heights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to 0.18 for ice fraction (fice), and 10 to 7 µm for ice effective radius (errors remain at 2 µm for liquid effective radius). These results indicate that a moderately low-resolution, surface-based IR spectrometer could provide cloud property retrievals with accuracy comparable to existing higher-resolution instruments and that such an instrument would be particularly useful for low-level clouds.

Local time variation of water ice clouds on Mars as observed by THEMIS

The move of the Odyssey spacecraft during Mars Years 31 and 32 to an orbit with local time near 7:00 AM and PM has enabled the systematic retrieval of water ice cloud optical depth using THEMIS thermal infrared images at a time of day not accessible from Mars Global Surveyor, Mars Reconnaissance Orbiter, or previous Odyssey observations. Because water ice clouds form by condensation, relatively small changes in atmospheric temperature can cause clouds to form or sublimate quickly, and there can be large changes in water ice cloud optical depth over the course of a day. Retrievals of water ice cloud optical depth using THEMIS observations show significant differences in cloud locations and optical depth as a function of local time and season. Cloud optical depth generally increases from the earliest (14:30) to latest (19:30) observations. During the aphelion season the increase from afternoon to evening is primarily associated with the thickening of existing clouds, while during the equinoctial and perihelion seasons there is a proportionally greater increase associated with the formation of clouds in the evening at locations where clouds were not present during the afternoon.

Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer

Abstract. This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager

This paper presents a cloud masking, classification and optical depth retrieval algorithm using visible, near-infrared and thermal-infrared bands. A time-series-based approach was developed for cloud masking with quantitative validation against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). An overall hit rate (the proportion of pixels identified by both sensors as either clear or cloudy) of 87% was found. However, analysis revealed that, under partially cloudy conditions, the small footprint of CALIOP had a major impact on the hit rate. When partially cloudy pixels are excluded a hit rate of ~98% was found, even for thin clouds with optical depth 0.98 at all sites. Further assessment was conducted by comparing seasonal and annual cloud fraction with that of ISCCP (International Satellite Cloud Climatology Project) over Australia and surrounding region. It showed high degree of resemblance between the two datasets in their total cloud fraction. The geographical distribution of cloud classes also showed broad resemblance, though detailed differences exist, especially for high clouds, probably due to the use of different cloud classification systems in the two datasets. The products generated from this study are being used in several applications including ocean colour remote sensing, solar energy, vegetation monitoring and detection of smoke for the study of their health impacts, and aerosol and land surface bidirectional reflectance distribution function (BRDF) retrieval. The method developed herein can be applied to other geostationary sensors.

Mechanisms Behind the Extratropical Stratiform Low‐Cloud Optical Depth Response to Temperature in ARM Site Observations

AbstractGround‐based observations from three middle‐ and high‐latitude sites managed by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program are used to determine the sensitivity of the low‐cloud optical depth to temperature and to test whether observations support mechanisms previously proposed to affect the optical depth feedback. Analysis of cloud optical depth retrievals support previous satellite findings that the optical depth decreases or stays constant with increases in temperature when the cloud is warm but increases when the cloud is cold. The cloud liquid water path sensitivity to warming largely explains the optical depth sensitivity at all sites. Mechanisms examined in this study involve the temperature dependence of (a) the moist‐adiabatic lapse rate, (b) cloud phase partitioning, (c) drying efficiency of cloud top mixing, (d) cloud top inversion strength, and (e) boundary layer decoupling. Mechanism (a) is present across all clouds and explains 30% to 50% of the increase in liquid water path with warming at temperatures below 0 °C. However, the cloud's adiabaticity, the ratio between the liquid water path and its theoretical maximum, is at least as important and determines how the liquid water path sensitivity to temperature varies with temperature. At temperatures below 0 °C, the adiabaticity increases with warming, and the data support mechanism (b). At warmer temperatures, the adiabaticity decreases with warming, overwhelming mechanism (a) and resulting in the liquid water path decreasing with warming. This adiabaticity decrease arises primarily because of mechanism (d), and to a lesser degree because of mechanism (e). No evidence is found supporting mechanism (c).

Characteristics of Optically Thin Coastal Florida Cumuli Derived From Surface‐Based Lidar Measurements

AbstractThe optical depths and sizes of optically thin passive shallow cumuli are characterized with a micropulse lidar. The measurements are from a coastal south Florida location spanning a 10‐week synoptically suppressed winter time period with almost no upper‐level clouds present. Clouds are distinguished from aerosol through visual comparison to zenith‐viewing time‐lapse camera images, establishing an instrument‐specific particle backscatter coefficient threshold. Sun photometers provide a continuous independent aerosol optical depth estimate applied within the lidar retrieval of cloud optical depth. The focus is on clouds that do not attenuate the lidar signal. Clouds with optical depths less than one constitute more than 13% of the sampled data set, with decreasing optical depths occurring more frequently. Most of the clouds are less than 60 m thick and are located below 1 km. The optically thin regions occur most frequently in individual 15‐s samples, corresponding to a horizontal width of less than 100 m, with occasional stratiform segments extending up to 2 km. Many segments border optically thicker regions, but some are self‐contained, reflecting small‐scale humidity variations near‐saturation. To our knowledge, this study is the first to apply micropulse lidar data toward characterizing these low, shallow, broken clouds, too small for robust characterization from space but whose ubiquitiousness renders them radiatively important.

Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations

We present a neural network (NN) based algorithm for the retrieval of liquid low-level marine stratocumulus cloud microphysical property parameters (cloud optical depth, cloud droplet size effective radius and variance) from airborne multi-angle polarimetric measurements. We establish our retrieval method for the Research Scanning Polarimeter (RSP) airborne instrument, which measures both polarized and total reflectance in the spectral range of 410–2260 nm, scanning along the flight track at ∼150 viewing zenith angles spanning the angular range between −60° and 60°. In this study, we present the development of the algorithm, including the optimization and selection of input parameters and the network architecture. We perform a sensitivity study to test the effect of random and correlated instrument noise on the retrieval performance, and to assess which of the measured radiometric quantities (i.e., total reflectance, polarized reflectance, degree of linear polarization and combinations thereof) are best suited for marine stratocumulus liquid cloud property retrievals using simulated RSP data. Finally, we show the application of this method to airborne observations from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) 2016 field campaign, which primarily encountered low altitude marine clouds. Retrieved cloud optical depth compares favorably (r2 = 0.96) to standard algorithms, but cloud droplet size effective radius less so (r2 = 0.45), providing an assessment of the NN approach strengths and limitations. Specifically, the latter seemed to be affected by the cloud macro-structure and the liquid cloud droplet vertical distribution.

Parameterizing cloud top effective radii from satellite retrieved values, accounting for vertical photon transport: quantification and correction of the resulting bias in droplet concentration and liquid water path retrievals

Abstract. Droplet concentration (Nd) and liquid water path (LWP) retrievals from passive satellite retrievals of cloud optical depth (τ) and effective radius (re) usually assume the model of an idealized cloud in which the liquid water content (LWC) increases linearly between cloud base and cloud top (i.e. at a fixed fraction of the adiabatic LWC). Generally it is assumed that the retrieved re value is that at the top of the cloud. In reality, barring re retrieval biases due to cloud heterogeneity, the retrieved re is representative of smaller values that occur lower down in the cloud due to the vertical penetration of photons at the shortwave-infrared wavelengths used to retrieve re. This inconsistency will cause an overestimate of Nd and an underestimate of LWP (referred to here as the “penetration depth bias”), which this paper quantifies via a parameterization of the cloud top re as a function of the retrieved re and τ. Here we estimate the relative re underestimate for a range of idealized modelled adiabatic clouds using bispectral retrievals and plane-parallel radiative transfer. We find a tight relationship between gre=recloud top/reretrieved and τ and that a 1-D relationship approximates the modelled data well. Using this relationship we find that gre values and hence Nd and LWP biases are higher for the 2.1 µm channel re retrieval (re2.1) compared to the 3.7 µm one (re3.7). The theoretical bias in the retrieved Nd is very large for optically thin clouds, but rapidly reduces as cloud thickness increases. However, it remains above 20 % for τ<19.8 and τ<7.7 for re2.1 and re3.7, respectively. We also provide a parameterization of penetration depth in terms of the optical depth below cloud top (dτ) for which the retrieved re is likely to be representative. The magnitude of the Nd and LWP biases for climatological data sets is estimated globally using 1 year of daily MODIS (MODerate Imaging Spectroradiometer) data. Screening criteria are applied that are consistent with those required to help ensure accurate Nd and LWP retrievals. The results show that the SE Atlantic, SE Pacific and Californian stratocumulus regions produce fairly large overestimates due to the penetration depth bias with mean biases of 32–35 % for re2.1 and 15–17 % for re3.7. For the other stratocumulus regions examined the errors are smaller (24–28 % for re2.1 and 10–12 % for re3.7). Significant time variability in the percentage errors is also found with regional mean standard deviations of 19–37 % of the regional mean percentage error for re2.1 and 32–56 % for re3.7. This shows that it is important to apply a daily correction to Nd for the penetration depth error rather than a time–mean correction when examining daily data. We also examine the seasonal variation of the bias and find that the biases in the SE Atlantic, SE Pacific and Californian stratocumulus regions exhibit the most seasonality, with the largest errors occurring in the December, January and February (DJF) season. LWP biases are smaller in magnitude than those for Nd (−8 to −11 % for re2.1 and −3.6 to −6.1 % for re3.7). In reality, and especially for more heterogeneous clouds, the vertical penetration error will be combined with a number of other errors that affect both the re and τ, which are potentially larger and may compensate or enhance the bias due to vertical penetration depth. Therefore caution is required when applying the bias corrections; we suggest that they are only used for more homogeneous clouds.

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.

Coupled Retrieval of Liquid Water Cloud and Above‐Cloud Aerosol Properties Using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)

AbstractAn optimization algorithm is developed to retrieve liquid water cloud properties including cloud optical depth (COD), droplet size distribution and cloud top height (CTH), and above‐cloud aerosol properties including aerosol optical depth (AOD), single‐scattering albedo, and microphysical properties from sweep‐mode observations by Jet Propulsion Laboratory's Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) instrument. The retrieval is composed of three major steps: (1) initial estimate of the mean droplet size distribution across the entire image of 80–100 km along track by 10–25 km across track from polarimetric cloudbow observations, (2) coupled retrieval of image‐scale cloud and above‐cloud aerosol properties by fitting the polarimetric data at all observation angles, and (3) iterative retrieval of 1‐D radiative transfer‐based COD and droplet size distribution at pixel scale (25 m) by establishing relationships between COD and droplet size and fitting the total radiance measurements. Our retrieval is tested using 134 AirMSPI data sets acquired during the National Aeronautics and Space Administration (NASA) field campaign ObseRvations of Aerosols above CLouds and their intEractionS. The retrieved above‐cloud AOD and CTH are compared to coincident HSRL‐2 (HSRL‐2, NASA Langley Research Center) data, and COD and droplet size distribution parameters (effective radius reff and effective variance veff) are compared to coincident Research Scanning Polarimeter (RSP) (NASA Goddard Institute for Space Studies) data. Mean absolute differences between AirMSPI and HSRL‐2 retrievals of above‐cloud AOD at 532 nm and CTH are 0.03 and <0.5 km, respectively. At RSP's footprint scale (~ 323 m), mean absolute differences between RSP and AirMSPI retrievals of COD, reff, and veff in the cloudbow area are 2.33, 0.69 μm, and 0.020, respectively. Neglect of smoke aerosols above cloud leads to an underestimate of image‐averaged COD by ~15%.

Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations

Ice cloud particles exhibit a range of shapes and sizes affecting a cloud’s single-scattering properties. Because they cannot be inferred from passive visible/infrared imager measurements, assumptions about the bulk single-scattering properties of ice clouds are fundamental to satellite cloud retrievals and broadband radiative flux calculations. To examine the sensitivity to ice particle model assumptions, three sets of models are used in satellite imager retrievals of ice cloud fraction, thermodynamic phase, optical depth, effective height, and particle size, and in top-of-atmosphere (TOA) and surface broadband radiative flux calculations. The three ice particle models include smooth hexagonal ice columns (SMOOTH), roughened hexagonal ice columns, and a two-habit model (THM) comprising an ensemble of hexagonal columns and 20-element aggregates. While the choice of ice particle model has a negligible impact on daytime cloud fraction and thermodynamic phase, the global mean ice cloud optical depth retrieved from THM is smaller than from SMOOTH by 2.3 (28%), and the regional root-mean-square difference (RMSD) is 2.8 (32%). Effective radii derived from THM are 3.9 μm (16%) smaller than SMOOTH values and the RMSD is 5.2 μm (21%). In contrast, the regional RMSD in TOA and surface flux between THM and SMOOTH is only 1% in the shortwave and 0.3% in the longwave when a consistent ice particle model is assumed in the cloud property retrievals and forward radiative transfer model calculations. Consequently, radiative fluxes derived using a consistent ice particle model assumption throughout provide a more robust reference for climate model evaluation compared to ice cloud property retrievals.

The effects of different footprint sizes and cloud algorithms on the top-of-atmosphere radiative flux calculation from the Clouds and Earth's Radiant Energy System (CERES) instrument on Suomi National Polar-orbiting Partnership (NPP)

Abstract. Only one Clouds and Earth's Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W m−2 for SW (shortwave) and less than 1.0 W m−2 for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W m−2 when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W m−2 between them; the biases of daytime and nighttime LW flux are close to zero with RMS errors of 0.8 and 0.2 W m−2. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W m−2 in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W m−2 and the RMS error increases to 5.2 W m−2. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W m−2 in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record.

The OCO‐2 oxygen A‐band response to liquid marine cloud properties from CALIPSO and MODIS

AbstractSpectra of reflected sunlight in the oxygen A‐band contain information about cloud properties such as cloud top pressure, optical depth, and pressure thickness. Here we show, for the first time, that high‐spectral‐resolution A‐band Orbiting Carbon Observatory‐2 (OCO‐2) spectra respond largely as simulated to the optical properties of water clouds over ocean during November 2015 (N = 184,318) using input cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Cloud‐Aerosol Lidar with Orthogonal Polarization on Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). In A‐band continuum channels the standard deviation of simulated minus observed radiance is ±37%. Selecting horizontally homogeneous clouds to mitigate three‐dimensional cloud effects and collocation error with the other satellites, the standard deviation of the residuals is reduced to ±18%. Using a look‐up table developed from simulations, OCO‐2's estimated cloud top pressure for low clouds (Ptop > 680 hPa) has a standard deviation of ±61 hPa relative to CALIPSO retrievals, and bias is dependent on assumed cloud pressure thickness, with our smallest value being −5 hPa. Versus MODIS optical depth, the standard deviation is ±9.0 and the bias is −2.0, although these shrink for clouds of τ < 30. These values include collocation error between the different satellites, meaning that they place an upper bound on the OCO‐2 retrieval uncertainty. The theoretical precision limit from OCO‐2's instrumental uncertainty is shown to be ±2.4 hPa in above‐cloud path and ±0.2% in optical depth for a two‐channel retrieval. Options for retrieving cloud optical depth, cloud top pressure, and pressure thickness are discussed in the context of a formal OCO‐2 cloud property retrieval.

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Abstract. Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21 µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.