Choosing a colormap to display atmospheric lidar data is challenging in several aspects. First, light backscattered by aerosol and cloud layers generates signals that spread over many orders of magnitude. Atmospheric scientists require images of this data to have high contrast in many portions of this extensive scale. Second, some lidar images can be very noisy, such as daytime images that are contaminated by solar background or spaceborne lidar for which the signal-to-noise ratio can be quite low due to power limitations and the great distance to the targets. Finally, the images need to be accessible to people with color vision deficiency, which represents a sizable portion of the population-approximately 4%-8% of men and 0.4%-1.7% of women, depending on ethnicity. We define a set of colormaps that address these challenges that were developed using lidar measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) space lidar and the high-spectral-resolution lidar (HSRL) airborne lidar (backscatter, depolarization, and color ratio). They are distributed in a package called cmlidar and released for Python. SIGNIFICANCE STATEMENT: Choosing the right colormap for atmospheric lidar data is crucial for clear and accurate visual interpretation. Lidar signals vary over a wide intensity range and can be noisy, making it difficult to display fine details effectively. Additionally, colormaps must be accessible to those with color vision deficiencies. This study presents a set of colormaps designed to enhance contrast across different signal levels, reduce noise impact, and improve accessibility. Developed using spaceborne and airborne lidar measurements, these colormaps are available in the Python package cmlidar. This work enables more effective data visualization in atmospheric science, helping researchers analyze and communicate complex lidar data more clearly.

In this study, we describe an improved Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite retrieval which uses the CALIPSO Imaging Infrared Radiometer (IIR) and the CALIPSO lidar for retrievals of ice particle number concentration Ni, effective diameter De and ice water content (IWC). By exploiting two IIR channels, this approach is fundamentally different from another satellite retrieval based on cloud radar and lidar that retrieves all three properties. A global retrieval scheme was developed using in situ observations from several field campaigns. The Ni retrieval is formulated in terms of Ni/APSD ratios, where APSD is the directly measured area concentration of the ice particle size distribution (PSD), along with the absorption optical depth in two IIR channels and the equivalent cloud thickness seen by IIR. It is sensitive to the shape of the PSD, which is accounted for, and uses a more accurate mass-dimension relationship relative to earlier work. The new retrieval is tested against corresponding cloud properties from the field campaigns used to develop this retrieval, as well as a recent cirrus cloud property climatology based on numerous field campaigns from around the world. In all cases, favorable agreement was found. This analysis indicated that Ni varies as a function of tau. By providing near closure to the ice PSD, the natural atmosphere may be used more like a laboratory for studying key processes responsible for the evolution and life cycle of cirrus clouds and their impact on climate.

Cirrus clouds can form through two ice nucleation pathways (homo- and heterogeneous ice nucleation; henceforth hom and het, respectively) that result in very different cloud physical and radiative properties. While important to the climate system, they are poorly understood due to lack of knowledge on the relative roles of hom and het. This study differs from earlier relevant studies by estimating the relative radiative contribution of hom-affected cirrus clouds. Here, we employ new global retrievals (described in Part 1: Mitchell et al., 2025; henceforth M2025) of cirrus cloud ice particle number concentration, effective diameter (De), ice water content (IWC), shortwave extinction coefficient (αext), optical depth (τ) and cloud radiative temperature based on Imaging Infrared Radiometer (IIR) and Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) co-located observations onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Transition from het dominated to hom-affected regimes are identified using αext and De. Over oceans outside the tropics in winter, the zonal fraction of hom-affected cirrus generally ranges between 20 % and 35 %, with comparable contributions from in situ and warm base cirrus. Using τ distributions to establish a proxy for net cloud radiative effect (CRE), the τ-weighted fraction for hom-affected cirrus over oceans outside the tropics during winter was > 50 %, indicating that hom cirrus play an important role in climate. Using these retrievals (including those relating to the cloud geometric thickness), a conceptual model of cirrus cloud characterization is proposed.

After 17 years of highly successful operations, the CALIPSO satellite mission, a partnership between NASA and CNES, ended in August 2023, leaving behind a long and very stable measurement record that has helped to transform our understanding of climate, weather, and air quality. The CALIPSO instrument suite included a two-wavelength (532 and 1064 nm), depolarization-sensitive profiling lidar for clouds and aerosols, an imaging infrared radiometer optimized for better understanding cirrus optical and microphysical properties, and a wide field of view camera to provide meteorological context. Thus far, over 4300 peer-reviewed publications have been produced from the CALIPSO data set. CALIPSO was truly a pathfinder mission, not only with its unprecedented ability to probe the vertical structure of the Earth’s atmosphere, but also in the development of sophisticated new algorithms for retrieving the spatial and optical properties of aerosols and clouds. Throughout the mission development and operational phases, the CALIPSO team relied heavily on aerosol and cloud measurements from ground-based and airborne sensors to aid in the design and revision of fully automated retrieval algorithms, the assessment of data products, as well as with the evaluation of instrument performance and calibration. This presentation highlights the impressive breadth of validation support the CALIPSO team received from the European field measurements community across all data processing levels. CALIPSO Level 1 (L1) data report geolocated and fully calibrated lidar backscatter profiles. Because L1 is the wellspring for all subsequent data products, rigorous calibration validation is absolutely essential. While NASA’s high spectral resolution lidar validated the CALIPSO calibration over North America (Rogers et al., 2011), earlier studies conducted by European research institutes (e.g., Mamouri et al., 2009; Mona et al., 2009; Pappalardo et al., 2010) were critically important in establishing the global reliability of the CALIPSO L1 data. Similarly, European investi­gators were at the forefront in assessing the geophysical variables reported in the CALIPSO Level 2 (L2) products. Extensive Raman lidar measurements from SAMUN-2 and SALTRACE (Wandinger et al., 2010; Tesche et al., 2013; Haarig et al., 2017) sparked years of vigorous, highly rewarding discussion about regional variations in CALIPSO’s dust lidar ratio assignments. For clouds, combined in-situ and airborne lidar measurements of cirrus provided key insights into the effects of ice crystal morphology on CALIPSO retrievals of cirrus extinction coefficients (Mioche et al., 2010). The literature is replete with additional, highly compelling L2 assessments of aerosols, clouds, and the algorithms used to derive their properties. Finally, Level 3 (L3) delivers a climatological rather than instantaneous data representation by aggregating L2 variables onto uniform space-time grids. Leveraging years of EARLINET observations, Papagiannopoulos et al. (2016) conducted a comprehensive assessment of the monthly mean vertical distributions of aerosol occurrence frequency and optical properties reported in CALIPSO’s L3 tropospheric aerosol product.

Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Since the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

Abstract Super‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR.

Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe Version 1.0 of a monthly gridded ice cloud product derived from over ten years of global, near-continuous CALIOP measurements. The primary contents are monthly, vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud Product Version 1.0 is built from the CALIOP Version 4.20 Level 2 5-km Cloud Profile product, which, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops and should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Version 4.51 (V4.5) of the CALIPSO lidar level 1 (LL1) data products is targeted for public release in summer 2022. One of the most far-reaching changes implemented in this release is to the polarization gain ratio (PGR), which quantifies the gain between the 532 nm parallel and perpendicular channels in the CALIPSO receiver. The PGR is an essential calibration coefficient required for computing both the attenuated backscatter coefficients reported in the LL1 product and the volume depolarization ratios. Prior to V4.5, the PGR was assumed to be a slowly varying constant that remained invariant throughout both the day and night portions of any CALIPSO orbit. However, in response to recent discoveries, in V4.5 the PGR now varies diurnally. In this work, we describe the motivation for this change, briefly review the technique used to compute daytime PGR estimates, provide an overview of the V4.5 PGR implementation, and compare the V4.5 PGRs to those used in previous data releases.

The assimilation of hyperspectral infrared sounders (HIS) observations aboard Earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky obser-vations. Using collocated assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that nearly 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532 nm (COD532nm) below 0.10 and cloud-top temperatures between 240 and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) model showing a cirrus cloud with a COD532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus-contaminated HIS observations is estimated. Large differences of 2.5 Kin temperature and 11 Kin dewpoint are possible for a cloud with COD532nm of 0.10 and cloud-top temperature of 210 K. When normalized by the contamination statistics, global differences of nearly 0.11 K in tempera-ture and 0.34 K in dewpoint are possible, with temperature and dewpoint tropospheric root-mean-squared errors (RMSDs) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differ-ences are likely much larger in regions with high cirrus frequency.

Since mid-2016, the frequency of low energy laser pulses emitted by the CALIPSO lidar has been slowly increasing due to pressure losses in the canister housing the laser. While originally confined primarily to the South Atlantic Anomaly (SAA) region, these low energy pulses now occur intermittently around the globe. Low energy pulses can cause calibration biases and degrade the science quality of level 2 retrievals. We describe a new low energy mitigation (LEM) algorithm that will be implemented incrementally in future versions of the CALIOP data processing to identify and reject affected profiles during calibration and feature detection. The LEM algorithm effectively eliminates low energy calibration biases, improves level 2 retrievals, and minimizes level 2 data loss.

Using the so-called Hu diagram relating layer-integrated depolarization ratio (δv) and layer-integrated attenuated backscatter at 532 nm (γ′532), the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar can effectively discriminate between liquid water clouds, clouds composed of randomly oriented ice (ROI) crystals, and ice clouds that contain some fraction of horizontally oriented ice (HOI) crystals. A recently proposed Hu diagram update splits the original water region into a more compact pure liquid regime and two mixed-phase regions. We propose an extended Hu diagram that additionally identifies mixed-phase clouds by incorporating perfectly colocated microphysical index (βeff) and effective diameter (De) retrievals in the thermal infrared window obtained from the CALIPSO imaging infrared radiometer (IIR). First, using the βeff index, we identify regions of the Hu diagram which are seemingly associated with mixed-phase clouds. Second, we characterize opaque water clouds in the updated Hu diagram. Water clouds with top temperatures lower than 238 K, when ice is expected in the upper portion of the cloud, fall distinctly into a proposed mixed-phase region. IIR De varies as expected with lidar ratio in the tighter liquid water region and is larger due to the presence of ice in the mixed-phase region. Finally, we show that the HOI class clusters into two main families of δv-γ′532 relationships, suggesting mixed-phase clouds with smaller De and pure ice clouds with larger De, which is confirmed by Monte Carlo simulations published in the literature.

A new CALIPSO satellite retrieval for cirrus clouds has been developed over the last 1.5 years that retrieves ice particle number concentration, effective diameter, and ice water content. It compares favorably with in situ measurements from many field campaigns around the world. This talk would briefly describe the new method targeting single-layer cirrus clouds and focus on new findings resulting from this retrieval, relating them to climate model predictions. These results indicate that there are two types or categories of cirrus clouds. Type 1 cirrus appear to form through heterogeneous ice nucleation (het), have visible optical depths < 0.3, and are most abundant; they are what most people visualize as a “cirrus cloud”. Type 2 cirrus may form through a combination of het and homogeneous ice nucleation, have visible optical depths > 0.3 (with visible extinction coefficients typically 4 times greater than type 1 cirrus), and are often associated with warm fronts, orographic gravity waves, and other lifting processes. However, type 2 cirrus clouds constitute 76% to 88% (depending on latitude) of the estimated net cloud radiative effect of all cirrus clouds. Based on comparisons between retrieved and predicted ice particle number concentrations and effective diameters, these type 2 cirrus clouds are poorly represented in climate models, possibly partly due to the predicted dependence of ice nucleation on layer-average pre-existing ice (not realistic near cloud top where ice nucleation occurs). Predicted ice nuclei concentrations may also need revising.

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the CloudAerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 mu m (DAOD(10 mu m)) and the coarse-mode dust effective diameter (D-eff) over global oceans. The accuracy of the D-eff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in situ-measured dust PSDs from the AERosol Properties - Dust (AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol-CloudInteraction Experiment (SALTRACE) field campaigns through case studies. The new DAOD(10 mu m) retrievals were evaluated first through comparisons with the collocated DAOD(10.6 mu m) retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al., 2022). The pixel-topixel comparison of the two DAOD retrievals indicates a good agreement (R similar to 0.7) and a significant reduction in (similar to 50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (2 degrees x 5 degrees) mean DAOD(10 mu m) retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0 .7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse-mode dust D-eff over global oceans. We found that dust D-eff over IO and NP is up to 20% smaller than that over NA. Over NA in summer, we found a similar to 50% reduction in the number of retrievals with D-eff > 5 mu m from 15 to 35 degrees W and a stable trend of D-eff average at 4.4 mu m from 35 degrees Wthroughout the Caribbean Sea (90 degrees W). Over NP in spring, only similar to 5% of retrieved pixels with D-eff > 5 mu m are found from 150 to 180ffi E, while the mean Deff remains stable at 4.0 mu m throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust longwave radiative effects and coarse-mode dust particle size in models.

This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-mu m "cirrus" band). Calibration of this algorithm is based on coincident Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of similar to 1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

The features detected in monolayer atmospheric columns sounded by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) aboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are reclassified more appropriately as confident cloud layers through the use of the IIR observations in the tropics and in the midlatitudes, respectively. IIR observations are of relatively little help in deriving high-confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those of clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke, respectively, are found to be misclassified cloud layers by the IIR measurements.

Travel restrictions in the wake of the COVID‐19 pandemic resulted in an unprecedented decrease of 73% in global flight mileage in April–May 2020 compared to 2019. Here we examine the CALIPSO satellite observations and find a significant increase in ice crystal number concentrations (Ni) in cirrus clouds in the mid‐latitudes of the Northern Hemisphere, which we attribute to an increase in homogeneous freezing when soot from aircraft emissions is reduced. A relatively small positive global average radiative effect of 21 mW m−2 is estimated if a decrease in aircraft traffic continues, with an average of up to 64 mW m−2 over the area where aviation is most active. We infer from this analysis that the worldwide adoption of biofuel blending in aircraft fuels that lead to smaller soot emissions could lead to a significant change in the microphysical properties of cirrus clouds but a rather small positive radiative effect.

Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D-e, that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D-e has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D-e as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA's A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds. Significance StatementWe derived unique parameterizations of ice crystal effective size from global satellite measurements in an effort to more robustly and consistently represent ice clouds in numerical models for weather forecasting and climate energy balance studies. Based on our results, effective ice crystal size is easily solved based on temperature and visible cloud translucence. By knowing the size of the ice crystals, we can then estimate cloud scattering and absorption. In comparison with aircraft-based parameterizations, the satellite data reveal that ice crystal effective sizes are much smaller, on global average, for ice clouds occurring in relatively warm layers (>230 K), indicating that many ice clouds are more reflective than previously believed.