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000724222 019__ $$a899277284$$a907303096$$a907559644$$a908089896
000724222 020__ $$a9783319120812$$qelectronic book
000724222 020__ $$a3319120816$$qelectronic book
000724222 020__ $$z9783319120805
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000724222 049__ $$aISEA
000724222 050_4 $$aQC925
000724222 08204 $$a551.57/70285$$223
000724222 1001_ $$aNasrollahi, Nasrin,$$eauthor.
000724222 24510 $$aImproving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery$$h[electronic resource] /$$cNasrin Nasrollahi.
000724222 264_1 $$aCham :$$bSpringer,$$c[2014]
000724222 264_4 $$c©2015
000724222 300__ $$a1 online resource (xxi, 68 pages) :$$billustrations (some color).
000724222 336__ $$atext$$btxt$$2rdacontent
000724222 337__ $$acomputer$$bc$$2rdamedia
000724222 338__ $$aonline resource$$bcr$$2rdacarrier
000724222 4901_ $$aSpringer Theses,$$x2190-5053
000724222 502__ $$bPh.D.$$cUniversity of California, Irvine$$d2013.
000724222 504__ $$aIncludes bibliographical references.
000724222 5050_ $$aSupervisor's Foreword ; Preface; Acknowledgements; Contents; List of Figures; List of Tables; Chapter-1; Introduction to the Current State of Satellite Precipitation Products; 1.1 The Importance of Precipitation in Water Resources; 1.2 Precipitation Observation; 1.3 Satellite-based Precipitation Estimation; 1.4 Research Motivation; 1.5 Objectives of this Dissertation; 1.6 Dissertation Outline; References; Chapter-2; False Alarm in Satellite Precipitation Data; References; Chapter-3; Satellite Observations; 3.1 MODIS; 3.2 CloudSat; References; Chapter-4
000724222 5058_ $$aReducing False Rain in Satellite Precipitation Products Using Cloudsat Cloud Classification Maps and Modis Multi-spectral Images4.1 The Role of Multi-spectral Data in Satellite Precipitation Algorithms; 4.2 Satellite Data; 4.3 Methodology; 4.4 Classification; 4.5 Training Data Set; 4.6 Application of the Model on Precipitation Events; 4.7 Results and Discussions; References; Chapter-5; Integration of CloudSat Precipitation Profile in Reduction of False Rain; 5.1 Classification; 5.2 Satellite Observations; 5.3 Training Data Set; 5.4 Application of the Model on Precipitation Events
000724222 5058_ $$a5.5 Results and Discussions5.6 Case Study; 5.7 Conclusion; References; Chapter-6; Cloud Classification and its Application in Reducing False Rain; 6.1 Introduction; 6.2 MODIS Cloud Mask; 6.3 Image Classification Using Self Organizing Maps; 6.4 Data Pre-processing; 6.5 Training and Validation Datasets, Summer Season; 6.6 Training and Validation Datasets, the Winter Season; 6.7 SOFM Model for Summer Season; 6.8 Validation of Cloud Classification Model, Summertime; 6.9 SOFM Model for the Winter Season; 6.10 Validation of Cloud Classification Model, the Winter Season; 6.11 Conclusion; References
000724222 5058_ $$aChapter-7Summary and Conclusions; 7.1 Future Work
000724222 506__ $$aAccess limited to authorized users.
000724222 520__ $$aThis thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved. The approach introduced in this thesis takes advantage of the recent NASA satellit.
000724222 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 19, 2014).
000724222 650_0 $$aPrecipitation (Meteorology)$$xRemote sensing.
000724222 650_0 $$aInfrared detectors.
000724222 77608 $$iPrint version:$$z3319120808$$z9783319120805
000724222 830_0 $$aSpringer theses.
000724222 852__ $$bebk
000724222 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-12081-2$$zOnline Access$$91397441.1
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