Abstract
Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.
Highlights
As one of the most active variables in atmospheric circulation, precipitation is a critical linkage between global water and energy cycles
Spatial Distributions of Precipitation Estimates from FY-2E, FY-2G, and Integrated Multi-satellitE Retrievals for GPM (IMERG) The spatial distributions of FY-2E quantitative precipitation estimates (QPE), FY-2G QPE, and IMERG data in the summer of 2018 over maiTnhlaensdpaCtihalindaistarriebusthioonws nofinFYF-i2gEuQreP2Eb, –FdY,2rGesQpePcEt,ivaenldy,IMwhERileGFdiagtuarien2tahedsiuspmlmayesr tohfe20sp18atial distrodibvisuetrtriimobnuatoiinoflnpanroedfciCpprhieticanitapioiatnraetoisobhntoaowinbnetadiinnbeFydiignbuvyreeri2nsev(bedr–isdset)a,dnriecssetpawnecceteiigvwhelteyeig,dwh(tIheDdilWe(IF)DiignWutre)eripn2oateldarptisiooplnalatbiyoasnstehbdeaossepndagtoriaonlund obsegrrvoautinodnso.bsAerlvlatthiornees. sAaltleltlhirteee-bsaasteeldlitpe-rbeacsipeditaptrieocnippitraotidouncptsropdruecstesnptraesdenisttaindcitstdinecctredaescirnegassinpgatial variastpioatniaolfvparrieactiiponitaotfiopnrefcriopmitatthioensforuomthetahsetstooutthheeansot rttohtwheesnto, rwthhwicehsti,swcohnicshisitsecnotnwsiistthentthawtitphrethseant ted by grporuesnedntoebdsbeyrvgartoiounnsd. oTbhseersvpaatitoianlsp
Evaluations of satellite-based quantitative precipitation estimates are of great importance when applying these datasets in related fields, such as hydrology, meteorology, and agriculture
Summary
As one of the most active variables in atmospheric circulation, precipitation is a critical linkage between global water and energy cycles. Obtaining spatiotemporal information on precipitation is of great importance for water resource management, climatological modeling, and many other applications [1,2,3]. Reliable precipitation datasets gathered from different sources, including ground stations, ground-based weather radars, and satellites, are essential [4,5]. Collecting precipitation information from ground rain gauge stations is the traditional and common method of measurement. As for ground-based weather radars, they have certain superiorities when observing precipitation in local areas. Due to the limitations of the scope of observation and the huge cost of equipment acquisition and maintenance, ground-based weather radars are not the first choice for large-scale precipitation observations
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