Daily Precipitation over Southern Africa: A New Resource for Climate Studies

نویسندگان

  • R. Layberry
  • D. R. Kniveton
چکیده

This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract This paper describes a new high-resolution multi-platform multi-sensor satellite rainfall product for Southern Africa covering the period 1993-2002. The Microwave Infra-Red Algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using co-located data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only GOES Precipitation Index (GPI) demonstrates a consistent advantage using the former over the latter, for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.

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تاریخ انتشار 2017