Stochastic Interpolation of Precipitation Data from MultipleSensors
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'J. Eleonora Sabadell', 'PO_EMAI': None, 'PO_PHON': None}
This research will investigate the feasibility of utilizing stochastic          interpolation techniques for merging precipitation data from three              sensors:  rain gauges, radars, and satellites.  Linear and nonlinear            interpolators for stationary and nonstationary spatial processes will           be studied.  The interpolation techniques will include universal and            disjunctive kriging.  Emphasis will be given to comparing the new               techniques with the methods currently in use.  The comparison will be           carried out using synthetic spatial precipitation data.  The results of         this study are expected to have implications for flood hazard                   mitigation.
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{'Code': '1474', 'Text': 'NAT & MAN-MADE HAZARD MITIGATI'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1984&All=true', 'xml': '8419189.xml'}