{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:08:15Z","timestamp":1761948495744,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T00:00:00Z","timestamp":1563235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2017YFA0603102"],"award-info":[{"award-number":["2017YFA0603102"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XAD19070103"],"award-info":[{"award-number":["XAD19070103"]}]},{"name":"the Natural Science Foundation of China","award":["41574004","41531069"],"award-info":[{"award-number":["41574004","41531069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41\u00b0. The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, &gt;73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error &lt; 3.9 mm\/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.<\/jats:p>","DOI":"10.3390\/rs11141686","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T02:44:03Z","timestamp":1563331443000},"page":"1686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets"],"prefix":"10.3390","volume":"11","author":[{"given":"Yihui","family":"Liu","sequence":"first","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China"},{"name":"Collaborative Innovation Center for Territorial Sovereignty and Maritime Rights, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7231-8956","authenticated-orcid":false,"given":"Weifeng","family":"Hao","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2112-9685","authenticated-orcid":false,"given":"Jean-Pierre","family":"Barriot","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China"},{"name":"Observatoire G\u00e9od\u00e9sique de Tahiti, University of French Polynesia, Tahiti 98702, French Polynesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2499-1147","authenticated-orcid":false,"given":"Yetang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250014, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10712-016-9398-7","article-title":"Greenland and Antarctica Ice Sheet Mass Changes and Effects on Global Sea Level","volume":"38","author":"Forsberg","year":"2017","journal-title":"Surv. 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