Abstract
Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.
Highlights
Precipitation is a critical hydrometeorogical variable that plays a key role in energy and water cycles, and impacts the weather, climate, hydrology, ecosystem, and Earth system [1,2,3]
LTs for the whole day (LTwd), LTd, or LTn spatially differ during seasons, while in a given season, a generally similar spatial pattern is observed among LTwd, LTd, and LTn, including for locations with significant (p < 0.05) linear trend (LT)
As important surrogate for precipitation estimates, various satellite-based and reanalysis precipitation products need to be validated from different perspectives
Summary
Precipitation is a critical hydrometeorogical variable that plays a key role in energy and water cycles, and impacts the weather, climate, hydrology, ecosystem, and Earth system [1,2,3]. We should note that gauges are related to high variability of the rain-bearing systems at different spatio-temporal scales and have an uneven spatial distribution [19,20] These factors limit the representativeness of gauge precipitation observations to a large extent, and introduce uncertainty into gauge data-based conclusions. Evaluating the multi-source precipitation products with sub-daily observations (daytime and nighttime datasets at least) could provide more detailed information, e.g., flexibility for a precipitation product on sub-daily scale This is very useful to further improve satellite-based algorithms and models/reanalysis systems from the perspective of sub-daily precipitation mechanisms, and even correct the precipitation products using the sub-daily rather than daily measurements. CinoFnisgiudreer1in).g the gaps in the previous works of precipitation evaluations, we used China as an example toCoenxsaimdeinriengthtehemgualptis-sionutrhceepprreevciiopuistawtiornkps roofdpurcetcsip’ citaaptiaocniteyvtaoludaetitoencts,pwreeciupsietdatiCohninlianaesarantrends durinegxadmayptliemtoe aexnadmninigehtthtiemmeu. lTtih-suosu,rtcheepmreacinpiotabtjieocntipvreosdoufcths’iscawpoacriktywtoerdeettoec(t1)pirnecviepsitaigtiaotne ltihneeasrpatial distritbruentidosndoufripnrgedciapyittiamtieoanncdhnainghgtetsimues.inThguds,atihlye, mdaayintiombeje,catinvdesnoifgthhtistimwoerkrewcoerrde stofr(o1)min2v3e9st3igwateeather sites athcerosspsaCtiahlindais;t(r2ib)uttoioqnuoafnptirfeycitphietaptieornfocrhmanagnecseuosfinsgeldecatielyd, dparoytdimucet,sa(ni.de.n, isgixhtstiamteellriteec-obradssefdroamnd six reana2bly3as9sie3sdwdaeanatdathsseeirxtssr)ietiaensndaalceyrtsoeicssstdiCnaghtaipnseraet;sc()2ipi)nittodaqetituoeacnntittnrifegynptdhrseecopipneirtafaostriuombna-tdnrcaeenilodyfsssoceanlelaectsweudibtp-hdrodadiilfuyfecsrtcsean(liet.ewv.,aistliihxddsaaitftifoeelnrlietmne-tetrics (correvlaaltiidoantioconemffeictriiecnst(,cobriraesl,atriooontcmoeeffainciesnqtu, bairaes,errorootrm, aenadn ssqigunareacecrruorra,cayn)dtshigronuagcchuaraccoy)mthpraoruisgohna with gaugecoombpsearrvisaotniownist;hagnadug(3e)otbosiedrveanttiiofnyst;haendm(e3t)rtioc-ibdaesnetdifyotphteimmaeltrpicr-obdausecdtsoapttiamsaulbp-rdoadiulyctsscaatlea.subdaily scale
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