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GEO-LEO Aerosol from Himawari and SGLI Onboard GCOM-C

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GEO-LEO Aerosol from Himawari and SGLI Onboard GCOM-C GEO-LEO aerosol from Himawari and SGLI onboard GCOM-C Motivation Our final goal Current and UpCominG • produce synergistic global Aerosol Monitoring Satellite aerosol data set − using JAXA Polar-orbiting and geostationary GOSAT2/CAI2 satellites (2018) − Provided in near real time GCOM-C/SGLI EarthCARE/ (2017) This study MSI • A common aerosol (JFY2021) retrieval algorithm is developed Ta r ge t s e n s o r s − for various satellite imaging sensors Geostationary: − over both land and ocean Himawari-8/AHI, (GOES-R, MTG) Polar-orbiting: Aqua,Terra/MODIS, GCOM-C/SGLI, GOSAT2/CAI2, EarthCARE/MSI 2 Himwari-8/AHI characteristics GCOM-C/SGLI characteristics IFOV CH l(nm) IFOV(m) CH l(nm) (m) VN1 380 VN2 412 1 471 1000 VN3 443 2 510 VN4 490 3 639 500 VN5 530 4 857 1000 VN6 565 250 5 1610 VN7 673.5 6 2257 VN8 673.5 7 3885 VN9 763 8 6243 VN10 868.5 9 6941 VN11 868.5 10 7347 POL1 673.5 2000 11 8592 POL2 868.5 1000 12 9637 SW1 1050 13 10407 SW2 1380 14 11240 SW3 1630 250 15 12381 SW4 2210 1000 16 13311 TIR1 10800 250 16 bands in Visible-Infrared TIR2 12000 250 3 10 minutes interval 19 bands in Visible-Infrared L2 Algorithm (aerosol retrieval) satellite sensor • based on the method developed by Higurashi and (AHI, MSI, SGLI, CAI) NaKajima (1998) and FuKuda et al. (2013) • 3 ideas for common retrieval cloud mask Aura/OMI total ozone Automatically select water vapor the optimum channels GANAL pressure by considering gas abs. correction uncertainty in TOA wind speed reflectance resulted ETOPO1 height from the surface observed TOA reflectance uncertainty 09: reflectance !* simulated <Optimal estimation> # TOA minimize object surface reflectance !" reflectance function J set common #"$ @ CD ! = < − >(?) AB < − >(?) " @ CD Aerosol candidate models + ? − ?F AF ? − ?F over land and ocean models Apply to various sensors without %&', )*+, -./*0, recalculation -,)-.1/*2, *+3,4 (*67) LUTs sensor for every response LUTs for sensor Ångström exponent a 1nm function single-scattering albedo w (300 ~ 2500nm) Retrieval Results (Himawar-8/AHI) 16 JST 27 Apr. 2018 : continental air pollutant transported to Kyusyu L2 AOT (every 10 min) Quality controlled L3 merged AOT (every hour) (cloud screening) AOT using difference in spatiotemporal variability between aerosol and cloud 1 hour (6 L2 AOT) Yoshida et al., 2018 KiKuchi et al., 2018 • The high and nearly continuous AOT over land and ocean are estimated • High AOT caused by local noise or insufficient cloud screening was eliminated and interpolated smoothly in L3 5 Retrieval Results (Himawar-8/AHI) • The high and nearly continuous AOT over land and ocean are estimated • High AOT caused by local noise or insufficient cloud screening was eliminated and interpolated smoothly in L3 • Aerosol transport are captured using frequent observation from AHI 6 Retrieval Results (GCOM-C/SGLI) 29 Jan. 2019 Thailand (school closed due to air pollution at Bangkok) AOT@500nm AE@500-380nm Bangkok SGLI polarization radiance • The high AOT and AE (i.e. fine particles) are estimated corresponding to local air pollution report • Estimated AOT and AE are consistent with SGLI polarization observation 7 Validation (AHI vs AERONET) Frequency distributions Time variation : 1 year, all AERONET site Baeksa in Korea 2.0 AERONET AHIL2,L3 AOT AHI L2 AOT L2 AHI 0.0 5/15 6/12 L2: snapshot retrievals every 10 min AERONET AOT L3: cloud screening data using 1hour data • AHI AOT is generally • AHI AOT successfully represent the consistent with AERONET time variation of AERONET Data distribution Himawari-8/AHI GCOM-C/SGLI: JAXA Himawari Monitor G-Portal User Registration→ 2016/04/25 Yellow dust https://gportal.jaxa.jp/ http://www.eorc.jaxa.jp/ ptree/index.html • distribute original (Level 1) and geophysical (Level 2) products • Data can be achieved with simple user registration 9 Next step: Utilization of aerosol transport model aerosol data assimilation system collaborated with MRI and Kyusyu-Univ. A priori Data Sattellite Aerosol forec retrieval assimila Analysis Forecast observation parameters ast tion Osberved RGB AOT (satellite) AOT (DA) AOT (FT=24) Yoshida et al.,2018 Yumimoto et al.,2018 Retrieval Data assimilation and forcast @JAXA @MRI Preliminary Results 02UTC, 19 May 2016: Aerosol originated from wildfires Retrieval (current) New Results A priori (model forcast) AOT Fine ratio • less noisy AOT and fine ratio • AOT seems to well capture observed aerosol front 11 • Fine ratio is highly dependent on a priori, but slightly updated by the observation • Should be validated in future Summary • We developed a common algorithm to retrieve aerosol properties for various satellite sensors over land and ocean. – common aerosol models – common lookup tables – automatic selection of the optimum channels • This method was applied to the Advanced Himawari Imager (AHI) /Himawari-8 and SGLI/GCOM-C. • The retrieved AOT are generally consistent with MODIS and AERONET product. • The retrieved product is distributed at JAXA Himawari Monitor and G-portal. • The utilization of aerosol properties forecasted by a global aerosol transport model for a priori of retrieval seems to improve the retrieval, but should be validated in future..
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