{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:00:12Z","timestamp":1772204412193,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Aeronautics and Space Administration"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The decades-long Clouds and Earth\u2019s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.<\/jats:p>","DOI":"10.3390\/rs15051349","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T04:28:41Z","timestamp":1677558521000},"page":"1349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8754-9594","authenticated-orcid":false,"given":"Christopher R.","family":"Yost","sequence":"first","affiliation":[{"name":"Science Systems and Applications, Inc., Hampton, VA 23666, USA"}]},{"given":"Patrick","family":"Minnis","sequence":"additional","affiliation":[{"name":"Science Systems and Applications, Inc., Hampton, VA 23666, USA"}]},{"given":"Sunny","family":"Sun-Mack","sequence":"additional","affiliation":[{"name":"Science Systems and Applications, Inc., Hampton, VA 23666, USA"}]},{"suffix":"Jr.","given":"William L.","family":"Smith","sequence":"additional","affiliation":[{"name":"Science Directorate, NASA Langley Research Center, Hampton, VA 23681, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9915-9147","authenticated-orcid":false,"given":"Qing Z.","family":"Trepte","sequence":"additional","affiliation":[{"name":"Science Systems and Applications, Inc., Hampton, VA 23666, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.asr.2011.03.009","article-title":"Clouds and the Earth\u2019s Radiant Energy System (CERES), a review: Past, present, and future","volume":"48","author":"Smith","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_2","unstructured":"Loeb, N.G., Su, W., Doelling, D.R., Wong, T., Minnis, P., Thomas, S., and Miller, W.F. (2018). Comprehensive Remote Sensing, Elsevier Ltd."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1175\/BAMS-D-12-00097.1","article-title":"First-light imagery from Suomi NPP VIIRS","volume":"93","author":"Hillger","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Minnis, P., Sun-Mack, S., Smith, W.L., Trepte, Q.Z., Hong, G., Chen, Y., Yost, C.R., Chang, F.-L., Smith, R.A., and Heck, P.W. (2023). VIIRS Edition 1 cloud properties for CERES. Part 1: Algorithm adjustments and results. Remote Sens., 15.","DOI":"10.3390\/rs15030578"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1175\/2009JTECHA1281.1","article-title":"Overview of the CALIPSO mission and CALIOP data processing algorithms","volume":"26","author":"Winker","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"674513","DOI":"10.1117\/12.737903","article-title":"Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data","volume":"6745","author":"Wielicki","year":"2007","journal-title":"Proc. SPIE Remote Sens. Clouds Atmos. XII"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"L12801","DOI":"10.1029\/2008GL033947","article-title":"Estimating the physical top altitude of optically thick ice clouds from thermal infrared satellite observations using CALIPSO data","volume":"35","author":"Minnis","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"D00A19","DOI":"10.1029\/2008JD009837","article-title":"Global moderate resolution imaging spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP","volume":"113","author":"Holz","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.5194\/amt-6-1271-2013","article-title":"On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: Example investigating the CM SAF CLARA-A1 dataset","volume":"6","author":"Karlsson","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.5194\/amt-7-3233-2014","article-title":"Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing","volume":"7","author":"Kox","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.5194\/amt-9-1587-2016","article-title":"MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP","volume":"9","author":"Marchant","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.5194\/amt-11-3177-2018","article-title":"Neural network cloud top pressure and height for MODIS","volume":"11","author":"Adok","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5194\/amt-11-633-2018","article-title":"Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: Demonstration of results based on the CM SAF CLARA-A2 climate data record","volume":"11","author":"Karlsson","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1029\/2018JD029681","article-title":"An assessment of the impacts of cloud vertical heterogeneity on global ice cloud data records from passive satellite retrievals","volume":"124","author":"Wang","year":"2019","journal-title":"J. Geophys. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.5194\/amt-13-3263-2020","article-title":"Evaluation of the Aqua MODIS Collection 6.1 multilayer cloud detection algorithm through comparisons with CloudSat CPR and CALIPSO CALIOP products","volume":"13","author":"Marchant","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Frey, R.A., Ackerman, S.A., Holz, R.E., Dutcher, S., and Griffith, Z. (2020). The continuity MODIS-VIIRS cloud mask. Remote Sens., 12.","DOI":"10.3390\/rs12203334"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, P.A. (2020). Assessment of the GOES-16 clear sky mask product over the contiguous USA using CALIPSO retrievals. Remote Sens., 12.","DOI":"10.3390\/rs12101630"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.5194\/amt-13-4035-2020","article-title":"Improvement in cloud retrievals from VIIRS through use of infrared absorption channels constructed from VIIRS+CrIS data fusion","volume":"13","author":"Li","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.1109\/TGRS.2020.3015155","article-title":"CERES MODIS cloud product retrievals for Edition 4, Part II: Comparisons to CloudSat and CALIPSO","volume":"59","author":"Yost","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9410","DOI":"10.1109\/TGRS.2019.2926620","article-title":"Global cloud detection for CERES Edition 4 using Terra and Aqua MODIS data","volume":"57","author":"Trepte","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"D00A18","DOI":"10.1029\/2008JD009982","article-title":"CloudSat mission: Performance and early science after the first year of operation","volume":"113","author":"Stephens","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1175\/2009JTECHA1228.1","article-title":"Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements","volume":"26","author":"Vaughan","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_23","unstructured":"Vaughan, M.A., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A., Getzewich, B., Hunt, W., Lambeth, J., and Lee, K.-P. (2016). Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) Data Management System Data Products Catalog, Release 4.10, NASA Langley Research Center. NASA Langley Research Center Document PC-SCI-503."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1175\/2009JTECHA1280.1","article-title":"CALIPSO\/CALIOP cloud phase discrimination algorithm","volume":"26","author":"Hu","year":"2009","journal-title":"J. Atmos. Ocean. Technol"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5701","DOI":"10.5194\/amt-11-5701-2018","article-title":"Extinction and optical depth retrievals for CALIPSO\u2019s Version 4 data release","volume":"11","author":"Young","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.5194\/amt-14-3371-2021","article-title":"Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks","volume":"14","author":"White","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1175\/1520-0493(1976)104<1209:TEOYFF>2.0.CO;2","article-title":"The evaluation of yes\/no forecasts for scientific and administrative purposes","volume":"104","author":"Woodcock","year":"1976","journal-title":"Mon. Weather Rev."},{"key":"ref_28","unstructured":"Heidinger, A., Botambekov, D., and Walther, A. (2020, July 21). A Na\u00efve Bayesian Cloud Mask Delivered to NOAA Enterprise\u2014Version 1.2, Available online: https:\/\/www.star.nesdis.noaa.gov\/goesr\/documents\/ATBDs\/Enterprise\/ATBD_Enterprise_Cloud_Mask_v1.2_Oct2016.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.5194\/amt-13-2257-2020","article-title":"A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations","volume":"13","author":"Wang","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2021GL096111","DOI":"10.1029\/2021GL096111","article-title":"Detecting supercooled water clouds using passive radiometer measurements","volume":"49","author":"Zhou","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"D00A12","DOI":"10.1029\/2008JD009972","article-title":"Global distribution of cirrus clouds from CloudSat\/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements","volume":"113","author":"Sassen","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Platnick, S., Meyer, K., Wind, G., Holz, R.E., Amarasinghe, N., Hubanks, P.A., Marchant, B., Dutcher, S., and Veglio, P. (2021). The NASA MODIS-VIIRS continuity cloud optical properties products. Remote Sens., 13.","DOI":"10.3390\/rs13010002"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4374","DOI":"10.1109\/TGRS.2011.2144601","article-title":"CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms","volume":"49","author":"Minnis","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1175\/JAMC-D-13-081.1","article-title":"Regional apparent boundary layer lapse rates determined from CALIPSO and MODIS data for cloud height determination","volume":"53","author":"Minnis","year":"2014","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_35","unstructured":"Rienecker, M.M., Suarez, M.J., Todling, R., Bacmeister, S., Takacs, L., Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R.D., and Gelaro, R. (2008). The GEOS-5 Data Assimilation System\u2014Documentation of Versions 5.0.1, 5.1.0, and 5.2.0, NASA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1109\/TGRS.2020.3008866","article-title":"CERES MODIS cloud product retrievals for Edition 4, Part I: Algorithm changes to CERES MODIS","volume":"59","author":"Minnis","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5499","DOI":"10.1029\/93JD02856","article-title":"Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis","volume":"99","author":"Baum","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12227","DOI":"10.1029\/2019JD030835","article-title":"Episodes of warm air advection causing cloud-surface decoupling during MARCUS","volume":"124","author":"Zheng","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1109\/TGRS.2011.2144602","article-title":"CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part II: Examples of average results and comparisons with other data","volume":"49","author":"Minnis","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"RG1004","DOI":"10.1029\/2008RG000267","article-title":"Tropical tropopause layer","volume":"47","author":"Fueglistaler","year":"2009","journal-title":"Rev. Geophys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1175\/JCLI-D-17-0426.1","article-title":"Impact of ice cloud microphysics on satellite retrievals and broadband flux radiative transfer calculations","volume":"31","author":"Loeb","year":"2018","journal-title":"J. Clim."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/JAMC-D-11-0203.1","article-title":"MODIS cloud-top property refinements for Collection 6","volume":"51","author":"Baum","year":"2012","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"881","DOI":"10.5194\/essd-9-881-2017","article-title":"Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project","volume":"9","author":"Stengel","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1175\/1520-0493(1990)118<2402:TOFICC>2.0.CO;2","article-title":"The 27-28 October 1986 FIRE IFO Case Study: Cirrus parameter relationships derived from satellite and lidar data","volume":"118","author":"Minnis","year":"1990","journal-title":"Mon. Weather Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"L11102","DOI":"10.1029\/2004GL019699","article-title":"Underestimation of deep convective cloud tops by thermal imagery","volume":"31","author":"Sherwood","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s40645-019-0295-9","article-title":"Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products","volume":"6","author":"Nakajima","year":"2019","journal-title":"Prog. Earth Planet. Sci."},{"key":"ref_47","first-page":"1115202","article-title":"Advances in neural network detection and retrieval of multilayer clouds for CERES using multispectral satellite data","volume":"11152","author":"Minnis","year":"2019","journal-title":"Proc. SPIE Remote Sens. Clouds and the Atmos. XXIV"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1349\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:44:00Z","timestamp":1760121840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,28]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051349"],"URL":"https:\/\/doi.org\/10.3390\/rs15051349","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,28]]}}}