Monitoring global surface temperature variations using cloud data sets |
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| Institution: | 1. NOAA/NESDIS, Washington, DC 20233, U.S.A.;2. SASC, Riverdale, MD 20737, U.S.A.;1. Universidade Estadual Do Centro-Oeste, Departamento de Química, Guarapuava, Paraná, 85040-080, Brazil;2. Universidade Federal Do Pampa, Departamento de Química, Caçapava Do Sul, 96570-000, Rio Grande do Sul, Brazil;1. Département de neurologie, hôpital Pierre Paul Riquet, université de Toulouse, place du Docteur Baylac, TSA 40031, 31059 Toulouse cedex 09, France;2. Service de neuroradiologie diagnostique et thérapeutique, hôpital Pierre Paul Riquet, université de Toulouse, Toulouse, France;1. Department of Obstetrics & Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA;2. Department of Life Sciences, School of Sciences, Gujarat University, Ahmedabad, India;3. Department of Bioinformatics, Gujarat University, Ahmedabad, India;4. College of Information Science & Technology, University of Nebraska Omaha, Omaha, Nebraska, USA;5. Department of Clinical Research, KIMS ICON Hospital, Visakapatnam, India;6. Clinical Dermatology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA;7. Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy;8. Department of Dermatology, Zealand University Hospital, Roskilde, Denmark |
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| Abstract: | One of the by-products of cloud data sets such as that of the International Satellite Cloud Climatology Project (ISCCP) is global information on longwave window brightness temperatures for clear skies. These brightness temperatures depend mainly on the actual surface temperature with only a slight dependence on atmospheric water vapor. Thus, it may be possible to monitor long-term temperature variations using such data. The current methods for such monitoring depend on conventional surface observations and are subject to uncertainties due to inadequate spatial sampling. To test this idea monthly clear sky brightness temperatures from the six-year Nimbus-7 cloud data set are analyzed and compared to conventional estimates of surface temperature fluctuations. |
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