{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:32:04Z","timestamp":1769916724805,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T00:00:00Z","timestamp":1548633600000},"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>The Global Positioning System (GPS) Radio Occultation (RO) technique allows valuable information to be obtained about the state of the atmosphere through vertical profiles obtained at various processing levels. From the point of view of data assimilation, there is a consensus that less processed data are preferable because of their lowest addition of uncertainties in the process. In the GPSRO context, bending angle data are better to assimilate than refractivity or atmospheric profiles; however, these data have not been properly explored by data assimilation at the CPTEC (acronym in Portuguese for Center for Weather Forecast and Climate Studies). In this study, the benefits and possible deficiencies of the CPTEC modeling system for this data source are investigated. Three numerical experiments were conducted, assimilating bending angles and refractivity profiles in the Gridpoint Statistical Interpolation (GSI) system coupled with the Brazilian Global Atmospheric Model (BAM). The results highlighted the need for further studies to explore the representation of meteorological systems at the higher levels of the BAM model. Nevertheless, more benefits were achieved using bending angle data compared with the results obtained assimilating refractivity profiles. The highest gain was in the data usage exploring 73.4% of the potential of the RO technique when bending angles are assimilated. Additionally, gains of 3.5% and 2.5% were found in the root mean square error values in the zonal and meridional wind components and geopotencial height at 250 hPa, respectively.<\/jats:p>","DOI":"10.3390\/rs11030256","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T03:40:55Z","timestamp":1548733255000},"page":"256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Assimilation of GPSRO Bending Angle Profiles into the Brazilian Global Atmospheric Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3821-1770","authenticated-orcid":false,"given":"Ivette H.","family":"Banos","sequence":"first","affiliation":[{"name":"Center for Weather Forecast and Climate Studies, National Institute for Space Research, Cachoeira Paulista, S\u00e3o Paulo 12630-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8420-8033","authenticated-orcid":false,"given":"Luiz F.","family":"Sapucci","sequence":"additional","affiliation":[{"name":"Center for Weather Forecast and Climate Studies, National Institute for Space Research, Cachoeira Paulista, S\u00e3o Paulo 12630-000, Brazil"}]},{"given":"Lidia","family":"Cucurull","sequence":"additional","affiliation":[{"name":"NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL 33149, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5402-7979","authenticated-orcid":false,"given":"Carlos F.","family":"Bastarz","sequence":"additional","affiliation":[{"name":"Center for Weather Forecast and Climate Studies, National Institute for Space Research, Cachoeira Paulista, S\u00e3o Paulo 12630-000, Brazil"}]},{"given":"Bruna B.","family":"Silveira","sequence":"additional","affiliation":[{"name":"Center for Weather Forecast and Climate Studies, National Institute for Space Research, Cachoeira Paulista, S\u00e3o Paulo 12630-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature14956","article-title":"The quiet revolution of numerical weather prediction","volume":"525","author":"Bauer","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Palmer, T., and Hagedorn, R. 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