{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:28:42Z","timestamp":1762507722382,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,11]],"date-time":"2019-05-11T00:00:00Z","timestamp":1557532800000},"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>Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151\u2013247 kWhm\u22122 (43\u2013145 kWhm\u22122) in July (December). The maximum (minimum) seasonal solar energy potential is 457\u2013706 kWhm\u22122 (167\u2013481 kWhm\u22122) in summer (winter). The available annual solar energy in 2015 was 1295\u20132324 kWhm\u22122. The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud\/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid.<\/jats:p>","DOI":"10.3390\/rs11091130","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T05:35:39Z","timestamp":1557725739000},"page":"1130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions"],"prefix":"10.3390","volume":"11","author":[{"given":"Shuang","family":"Xia","sequence":"first","affiliation":[{"name":"Laboratory for Remote Sensing and Geoinformatics, Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA"},{"name":"Texas Sustainable Energy Research Institute, University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3546-3668","authenticated-orcid":false,"given":"Alberto M.","family":"Mestas-Nu\u00f1ez","sequence":"additional","affiliation":[{"name":"Laboratory for Remote Sensing and Geoinformatics, Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-1210","authenticated-orcid":false,"given":"Hongjie","family":"Xie","sequence":"additional","affiliation":[{"name":"Laboratory for Remote Sensing and Geoinformatics, Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"given":"Rolando","family":"Vega","sequence":"additional","affiliation":[{"name":"CPS Energy, San Antonio, TX 78205, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1016\/j.solener.2011.08.025","article-title":"Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed","volume":"85","author":"Chow","year":"2011","journal-title":"Sol. Energy"},{"key":"ref_2","first-page":"275","article-title":"US Department of Energy Workshop Report: Solar Resources and Forecasting","volume":"303","author":"Stoffel","year":"2012","journal-title":"Contract"},{"key":"ref_3","unstructured":"Kleissl, J. (2013). Solar Energy Forecasting and Resource Assessment, Academic Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s00704-014-1189-9","article-title":"Estimating probability distributions of solar irradiance","volume":"119","author":"Voskrebenzev","year":"2015","journal-title":"Theoret. Appl. Climatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1175\/1520-0450(2003)042<1421:CCBOAI>2.0.CO;2","article-title":"Cloud coverage based on all-sky imaging and its impact on surface solar irradiance","volume":"42","author":"Pfister","year":"2003","journal-title":"J. Appl. Meteor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.enconman.2006.07.002","article-title":"Assessing the distribution of monthly mean hourly solar irradiation at an African Equatorial site","volume":"48","author":"Mubiru","year":"2007","journal-title":"Energy Convers. Mgmt."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.energy.2015.07.103","article-title":"Retrieval of surface solar irradiance, based on satellite-derived cloud information, in Greece","volume":"90","author":"Nikitidou","year":"2015","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"63","DOI":"10.17159\/2413-3051\/2018\/v29i2a3376","article-title":"Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa","volume":"29","author":"Amillo","year":"2018","journal-title":"J. Energy S. Afr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.solener.2018.02.012","article-title":"Bias correction of a novel European reanalysis data set for solar energy applications","volume":"164","author":"Frank","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.renene.2016.07.003","article-title":"Estimation of Hong Kong\u2019s solar energy potential using GIS and remote sensing technologies","volume":"99","author":"Wong","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1175\/1520-0450(1992)031<0194:MSSIFS>2.0.CO;2","article-title":"Modeling surface solar irradiance for satellite applications on a global scale","volume":"31","author":"Pinker","year":"1992","journal-title":"J. Appl. Meteor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/0034-4257(94)00069-Y","article-title":"A review of satellite methods to derive surface shortwave irradiance","volume":"51","author":"Pinker","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pinker, R.T., Tarpley, J.D., Laszlo, I., Mitchell, K.E., Houser, P.R., Wood, E.F., Schaake, J.C., Robock, A., Lohmann, D., and Cosgrove, B.A. (2003). Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project: GEWEX Continental-Scale International Project, Part 3 (GCIP3). J. Geophys. Res., 108.","DOI":"10.1029\/2002JD003301"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xia, S., Mestas-Nu\u00f1ez, A.M., Xie, H., and Vega, R. (2017). An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA. Remote Sens., 9.","DOI":"10.3390\/rs9121268"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0038-092X(02)00045-2","article-title":"A new airmass independent formulation for the Linke turbidity coefficient","volume":"73","author":"Ineichen","year":"2002","journal-title":"Sol. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0038-092X(02)00122-6","article-title":"A new operational model for satellite-derived irradiances: description and validation","volume":"73","author":"Perez","year":"2002","journal-title":"Sol. Energy"},{"key":"ref_17","unstructured":"Reno, M.J., Hansen, C.W., and Stein, J.S. (2019, May 10). Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.651.1676&rep=rep1&type=pdf."},{"key":"ref_18","unstructured":"Schillings, C., Meyer, R., and Trieb, F. (2019, May 10). Solar and Wind Energy Resource Assessment (SWERA). Available online: https:\/\/openei.org\/datasets\/files\/712\/pub\/sri_lanka_10km_solar_country_report.pdf."},{"key":"ref_19","unstructured":"Jia, Y. (2016). Solar Shift: A perspective on Building Energy Performance under Haze Pollutions in China, Georgia Institute of Technology."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xia, S., Mestas-Nu\u00f1ez, A., Xie, H., Tang, J., and Vega, R. (2018). Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness. Remote Sens., 10.","DOI":"10.3390\/rs10122016"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1364\/AO.28.004735","article-title":"Revised optical air mass tables and approximation formula","volume":"28","author":"Kasten","year":"1989","journal-title":"Appl. Optics."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hahn, C.J., and Warren, S.G. (2007). A Gridded Climatology of Clouds over Land (1971-96) And Ocean (1954-97) from Surface Observations Worldwide.","DOI":"10.3334\/CDIAC\/cli.ndp026e"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5914","DOI":"10.1175\/2011JCLI3972.1","article-title":"Variations in cloud cover and cloud types over the ocean from surface observations, 1954\u20132008","volume":"24","author":"Eastman","year":"2011","journal-title":"J. Climate."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"D00H06","DOI":"10.1029\/2009JD011916","article-title":"Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat","volume":"114","author":"Sassen","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.atmosres.2017.10.018","article-title":"Enhanced daytime occurrence of clouds in the tropical upper troposphere over land and ocean","volume":"201","author":"Gupta","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kosmopoulos, P.G., Kazadzis, S., Taylor, M., Bais, A.F., Lagouvardos, K., Kotroni, V., Keramitsoglou, I., and Kiranoudis, C. (2017). Estimation of the solar energy potential in Greece using satellite and ground-based observations. Perspectives on Atmospheric Sciences, Springer.","DOI":"10.1007\/978-3-319-35095-0_165"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:07Z","timestamp":1760187067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,11]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091130"],"URL":"https:\/\/doi.org\/10.3390\/rs11091130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,5,11]]}}}