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Comparing satellite, reanalysis, fused and gridded (In Situ) precipitation products over Türkiye

dc.contributor.authorAkbaş, Abdullah
dc.contributor.authorÖzdemir, Hasan
dc.contributor.buuauthorAKBAŞ, ABDULLAH
dc.contributor.buuauthorÖZDEMİR, HASAN
dc.contributor.departmentCoğrafya Bölümü
dc.contributor.departmentFiziki Coğrafya Ana Bilim Dalı
dc.contributor.orcid0000-0003-2024-0565
dc.contributor.orcid0000-0001-8885-9298
dc.contributor.researcheridY-4236-2018
dc.contributor.researcheridAAI-6814-2021
dc.date.accessioned2025-01-30T12:03:16Z
dc.date.available2025-01-30T12:03:16Z
dc.date.issued2024-10-31
dc.description.abstractPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground-based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge-Based Analysis of Daily Precipitation), ERA5-Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN-PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi-Satellite Retrievals for GPM), and TRMM-TMPA (Tropical Rainfall Measuring Mission/Multi-satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi-Source Weighted-Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite-based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.
dc.identifier.doi10.1002/joc.8671
dc.identifier.endpage5889
dc.identifier.issn0899-8418
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85208053324
dc.identifier.startpage5873
dc.identifier.urihttps://doi.org/10.1002/joc.8671
dc.identifier.urihttps://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.8671
dc.identifier.uri1097-0088
dc.identifier.urihttps://hdl.handle.net/11452/49951
dc.identifier.volume44
dc.identifier.wos001345071300001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherWiley
dc.relation.bapSGA-2022-735
dc.relation.journalInternational Journal of Climatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.relation.tubitak121Y578
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGlobal precipitation
dc.subjectFlood seasonality
dc.subjectForcing data
dc.subjectRainfall
dc.subjectNetwork
dc.subjectModel
dc.subjectClassification
dc.subjectUncertainty
dc.subjectPerformance
dc.subjectDataset
dc.subjectEvaluation metrics
dc.subjectFused/reanalysis/gridded/satellite precipitation estimations
dc.subjectPrecipitation
dc.subjectT & uuml;rkiye
dc.subjectScience & technology
dc.subjectPhysical sciences
dc.subjectMeteorology & atmospheric sciences
dc.titleComparing satellite, reanalysis, fused and gridded (In Situ) precipitation products over Türkiye
dc.typeArticle
dspace.entity.typePublication
local.indexed.atWOS
local.indexed.atScopus
relation.isAuthorOfPublicationd163aa44-8100-4aeb-8113-639868e48722
relation.isAuthorOfPublicationc1965f59-d207-4cbe-abf5-566d81db51ec
relation.isAuthorOfPublication.latestForDiscoveryd163aa44-8100-4aeb-8113-639868e48722

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