{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T16:33:09Z","timestamp":1767889989218,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000192","name":"National Oceanic and Atmospheric Administration","doi-asserted-by":"publisher","award":["NA19OAR4320073"],"award-info":[{"award-number":["NA19OAR4320073"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018YFC1507504"],"award-info":[{"award-number":["2018YFC1507504"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson\u2019s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson\u2019s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.<\/jats:p>","DOI":"10.3390\/rs13204030","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"4030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-9415","authenticated-orcid":false,"given":"Luyao","family":"Sun","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9795-3064","authenticated-orcid":false,"given":"Haonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA"}]},{"given":"Zhe","family":"Li","sequence":"additional","affiliation":[{"name":"Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6141-4595","authenticated-orcid":false,"given":"Lei","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1175\/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2","article-title":"The Operational GOES Infrared Rainfall Estimation Technique","volume":"79","author":"Vicente","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1175\/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2","article-title":"The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors","volume":"40","author":"Kummerow","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1175\/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2","article-title":"CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution","volume":"5","author":"Joyce","year":"2004","journal-title":"J. Hydrometeor."},{"key":"ref_4","unstructured":"Kuligowski, R.J. (2013). GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Rainfall Rate (QPE), NO-AA STAR. NO-AA\/NESDIS\/STAR Version 2.6."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1175\/JHM-D-16-0168.1","article-title":"Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998","volume":"18","author":"Xie","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Derin, Y., Anagnostou, E., Berne, A., Borga, M., Boudevillain, B., Buytaert, W., Chang, C.-H., Chen, H., Delrieu, G., and Hsu, Y.C. (2019). Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions. Remote Sens., 11.","DOI":"10.3390\/rs11242936"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TGRS.2019.2942280","article-title":"A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"359","DOI":"10.5194\/hess-25-359-2021","article-title":"A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: An experiment in the northeastern Tibetan Plateau","volume":"25","author":"Ma","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1175\/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2","article-title":"A Self-Calibrating Real-Time GOES Rainfall Algorithm for Short-Term Rainfall Estimates","volume":"3","author":"Kuligowski","year":"2002","journal-title":"J. Hydrometeorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1175\/2011BAMS-D-11-00047.1","article-title":"Coauthors. National Mosaic and Multi Sensor QPE (NMQ) system: Description, results, and future plans","volume":"92","author":"Zhang","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1175\/JHM-D-11-0139.1","article-title":"Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA\/NSSL Ground Radar\u2013Based National Mosaic QPE","volume":"13","author":"Kirstetter","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1002\/qj.3253","article-title":"How well were the early 2017 California atmospheric river precipitation events captured by satellite products and ground-based radars?","volume":"144","author":"Wen","year":"2018","journal-title":"Quart. J. Roy. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1175\/JHM-D-16-0179.1","article-title":"MRMS QPE Performance East of the Rockies during the 2014 Warm Season","volume":"18","author":"Cocks","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2484","DOI":"10.1175\/JHM-D-13-0199.1","article-title":"Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations","volume":"15","author":"Stenz","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1175\/JHM-D-15-0186.1","article-title":"Improvements to the GOES-R Rainfall Rate Algorithm","volume":"17","author":"Kuligowski","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/01431160010006935","article-title":"The role of orographic and parallax corrections on real time high resolution satellite rain rate distribution","volume":"23","author":"Vicente","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, R., Li, Z., Kuligowski, R.J., Ferraro, R., and Weng, F. (2011). A study of warm rain detection using A-Train satellite data. Geophys. Res. Lett., 38.","DOI":"10.1029\/2010GL046217"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1175\/JHM-D-15-0057.1","article-title":"Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth","volume":"17","author":"Stenz","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1175\/JHM-D-19-0136.1","article-title":"A Flexible Bayesian Approach to Bias Correction of Radar-Derived Precipitation Estimates over Complex Terrain: Model Design and Initial Verification","volume":"20","author":"Chen","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/TGRS.2019.2949214","article-title":"Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4030\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:11:02Z","timestamp":1760166662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,9]]},"references-count":21,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204030"],"URL":"https:\/\/doi.org\/10.3390\/rs13204030","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,9]]}}}