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dc.contributor.authorIslam, Md Atiqul
dc.date.accessioned2019-08-19T23:34:35Z
dc.date.available2019-08-19T23:34:35Z
dc.date.issued2018
dc.identifier.issn0143-1161
dc.identifier.doi10.1080/01431161.2018.1433890
dc.identifier.urihttp://hdl.handle.net/10072/384618
dc.description.abstractIn this investigation, six satellite-derived precipitation products namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Centre (CPC) Morphing Technique (CMORPH), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) final run both non gauge-calibrated (IMERG) and gauge-calibrated (IMERG-GC), and Global Satellite Mapping of Precipitation (GSMaP) for GPM both non gauge-calibrated (GSMaP) and gauge-calibrated (GSMaP-GC) are evaluated over Bangladesh, using ground-based rain gauge observations as reference over a 3 years period from January 2014 to December 2016. Nine widely used categorical and volumetric statistical matrices such as bias, probability of detection, volumetric hit index, false alarm ratio, volumetric false alarm ratio, critical success index, volumetric critical success index, miss index, and volumetric miss index are employed to exploit the performance of the precipitation products in detecting extremes above different quantile thresholds (i.e. 50%, 75%, and 90% quantiles) for various temporal window (i.e. 3 h, 6 h, 12 h, and 24 h). The bias values show that none of the satellite rainfall data sets are ideal for detecting extreme rainfall accumulations. In fact, all products lose their detection skills consistently as the extreme precipitation thresholds (50%, 75%, and 90% quantiles) increase. The results indicate that PERSIANN shows the worst performance over the study region. Overall, GSMaP-GC performs better than the other precipitation products. However, the FAR values of GSMaP are also higher over monsoon and post-monsoon months. The categorical and volumetric scores reveal that the detection skill increases remarkably for all rainfall data sets throughout the year with the increase of extreme quantile thresholds. At higher temporal accumulations, the detection capability of the products also improves considerably, and this improvement is more significant during monsoon period. The performance is relatively poor for all precipitation data sets over the cold months. In general, all six satellite precipitation products are doing well in detecting the occurrence of rainfall but are not so good in estimating the amount of rainfall.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofpagefrom2906
dc.relation.ispartofpageto2936
dc.relation.ispartofissue9
dc.relation.ispartofjournalInternational Journal of Remote Sensing
dc.relation.ispartofvolume39
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleStatistical comparison of satellite-retrieved precipitation products with rain gauge observations over Bangladesh
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2018 Taylor & Francis (Routledge). This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 07 Feb 2018, available online: https://doi.org/10.1080/01431161.2018.1433890
gro.hasfulltextFull Text
gro.griffith.authorIslam, Atiqul


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