{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:37:16Z","timestamp":1784821036234,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Falling snow is a key component of the Earth\u2019s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat\u2019s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by \u221210.16% and the mean snowfall rate changes by \u22128.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.<\/jats:p>","DOI":"10.3390\/rs13112041","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0498-1021","authenticated-orcid":false,"given":"Lisa","family":"Milani","sequence":"first","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"},{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norman B.","family":"Wood","sequence":"additional","affiliation":[{"name":"Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4294","DOI":"10.1002\/2017JD028213","article-title":"Scale-aware and definition-aware evaluation of modeled near-surface precipitation frequency using CloudSat observations","volume":"123","author":"Kay","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","first-page":"25","article-title":"Status of the CloudSat mission","volume":"Volume 1","author":"Levizzani","year":"2020","journal-title":"Satellite Precipitation Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, G. (2009). Deriving snow cloud characteristics from CloudSat observations. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2007JD009766"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.atmosres.2018.05.015","article-title":"CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis","volume":"213","author":"Milani","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"King, F., and Fletcher, C.G. (2020). Using CloudSat-CPR retrievals to estimate snow accumulation in the Canadian Arctic. Earth Space Sci., 7.","DOI":"10.1029\/2019EA000776"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4468","DOI":"10.1002\/2015JD024546","article-title":"Status of high-latitude precipitation estimates from observations and reanalyses","volume":"121","author":"Behrangi","year":"2016","journal-title":"J. Geophys. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1175\/JHM-D-15-0123.1","article-title":"A shallow cumuliform snowfall census using spaceborne radar","volume":"17","author":"Kulie","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Behrangi, A., Lebsock, M., Wong, S., and Lambrigtsen, B. (2012). On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res. Atmos., 117.","DOI":"10.1029\/2012JD017979"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cho, H., Jun, S.Y., Ho, C.H., and McFarquhar, G. (2020). Simulations of winter Arctic clouds and associated radiation fluxes using different cloud microphysics schemes in the Polar WRF: Comparisons with CloudSat, CALIPSO, and CERES. J. Geophys. Res. Atmos., 125.","DOI":"10.1029\/2019JD031413"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s41467-019-14074-5","article-title":"Midwinter Arctic leads form and dissipate low clouds","volume":"11","author":"Li","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1002\/2016GL072242","article-title":"Polar clouds and radiation in satellite observations, reanalyses, and climate models","volume":"44","author":"Lenaerts","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5431","DOI":"10.1029\/2018JD029021","article-title":"Observation-based radiative kernels from CloudSat\/CALIPSO","volume":"124","author":"Kramer","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_13","unstructured":"Kelly, A.C., and Macie, E.J. (2003, January 7\u201311). The A-Train: NASA\u2019s Earth Observing System (Eos) satellites and other Earth observation satellites. Proceedings of the 4th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1002\/qj.3222","article-title":"Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective","volume":"144","author":"Kulie","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.2528\/PIER14030405","article-title":"Snowfall detectability of NASA\u2019s Cloudsat: The first cross-investigation of its 2C-Snow-Profile product and National Multi-sensor Mosaic QPE (NMQ) snowfall data","volume":"148","author":"Cao","year":"2014","journal-title":"Prog. Electromagn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1175\/BAMS-D-15-00306.1","article-title":"The global precipitation measurement (GPM) mission for science and society","volume":"98","author":"Petersen","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The Global Precipitation Measurement mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1175\/JAMC-D-18-0124.1","article-title":"Satellite estimation of falling snow: A Global Precipitation Measurement (GPM) core observatory perspective","volume":"58","author":"Kulie","year":"2019","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lenaerts, J., Camron, M.D., Wyburn-Powell, C., and Kay, J. (2020). Present-day and future Greenland Ice Sheet precipitation frequency from satellite observations and an Earth System Model. Cryosph. Discuss., 1\u201318.","DOI":"10.5194\/tc-2020-31"},{"key":"ref_20","unstructured":"Wood, N.B., and L\u2019Ecuyer, T.S. (2018). Level 2C Snow Profile Process Description and Interface Control Document, Product Version P1_R05, National Aeronautics and Space Administration, Jet Propulsion Laboratory. Available online: http:\/\/www.cloudsat.cira.colostate.edu\/sites\/default\/files\/products\/files\/2C-SNOW-PROFILE_PDICD.P1_R05.rev0_.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Milani, L., and Wood, N.B. (2021, May 19). Supplementary Data: Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations, Available online: https:\/\/zenodo.org\/record\/4749304.","DOI":"10.3390\/rs13112041"},{"key":"ref_22","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor\u00e1nyi, A., Sabater, J.M., Nicolas, J., Peubey, C., Radu, R., and Rozum, I. (2021, April 16). ERA5 Hourly Data on Single Levels from 1979 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-single-levels?tab=overview."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1175\/BAMS-D-12-00227.1","article-title":"The EarthCARE satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation","volume":"96","author":"Illingworth","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ehsani, M.R., Behrangi, A., Adhikari, A., Song, Y., Huffman, G.J., Adler, R.F., Bolvin, D.T., and Nelkin, E.J. (2021). Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in high latitudes using CloudSat and machine learning. J. Hydrometeorol., early online release.","DOI":"10.1175\/JHM-D-20-0240.1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rysman, J.-F., Panegrossi, G., San\u00f2, P., Marra, A.C., Dietrich, S., Milani, L., and Kulie, M.S. (2018). SLALOM: An all-surfce snow water path retrieval algorithm for the GPM Microwave Imager. Remote. Sens., 10.","DOI":"10.3390\/rs10081278"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13593","DOI":"10.1029\/2019GL084576","article-title":"Retrieving surface snowfall with the GPM Microwave Imager: A new module for the SLALOM algorithm","volume":"46","author":"Rysman","year":"2019","journal-title":"Geophys. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2041\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:05:52Z","timestamp":1760162752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,21]]},"references-count":26,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112041"],"URL":"https:\/\/doi.org\/10.3390\/rs13112041","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,21]]}}}