JMA AHI Rayleigh Scattering with MODIS Yusuke YOGO and Masaya TAKAHASHI Meteorological Satellite Center Japan Meteorological Agency I'll talk about Rayleigh scattering method for JMA's Himawari-8 and 9 AHI visible and near infrared bands

Aim / Contents Aim Contents Himawari-8, 9/AHI VNIR vicarious calibration has some underestimation problems -> I'd like to ask how I can improve this method Contents AHI VNIR Vicarious Calibration using Radiative Transfer Calculation Method AHI Rayleigh Scattering Differences with CNES' Rayleigh Scattering Summary and Future Plans I was taken over this job from Arata Okuyama in the last April, and I'm facing on problems that there're some underestimations about Himawari-8, 9 AHI VNIR vicarious calibration so I'd like to ask how I can improve this method 22 Mar 2018 2018 GSICS annual meeting

AHI VNIR Vicarious Calibration Radiative transfer calculation using the model "RSTAR" for several types of targets Inputs: MODIS L1B, Aura/OMI Ozone L3, JRA-55 (JMA's re-analysis) Targets in use Sea surface for darker scene or zero radiance currently not used in bands 1-2 (0.47-0.51 μm) due to underestimation Water cloud for brighter scene Targets currently not in use Land surface, especially desert for middle scene No ideal deserts (e.g. Sahara) over AHI observation area DCC (RT calculation) for the brightest scene, < 1 μm To be implemented Result of MTSAT-2 in Feb. 2011 Simulated reflectivity Observed reflectivity DCC Water cloud Land Sea Firstly, I introduce the VNIR vicarious calibration method in JMA calculation results by the radiative transfer model are used Input data are MODIS L1B radiances to estimate aerosols, ozone amount and re-analysis to make radiative transfer calculation more accurately # We think this can be called intercalibration between AHI and MODIS Several types of targets are used in this method cloud-free ocean surface for darker scene and water clouds for brighter scene are in use, but results of bands 1-2, around 0.5 micro meter, are not used due to a underestimation problem We don't use cloud-free land surface calculation results, because no ideal deserts over AHI area DCC calculation for the brightest scene of visible bands needs information about reflection by non-spherical ice crystals, so it is under implementation 22 Mar 2018 2018 GSICS annual meeting

AHI Rayleigh Scattering RT calculation of ToA radiance over cloud-free ocean limitations JMA AHI CNES (IOCCG, 2013) RT model RSTAR (Nakajima and Tanaka 1986) 6S (Vermote et al., 1997) surface wind speed < 7.0 m/s < 5.0 m/s aerosol optical depth < 0.3 @ 500 nm < 0.05 @ 865 nm Sun zenith angle < 60 deg. (AHI & MODIS) < 60 deg. Viewing zenith angle sunglint "sunglint angle" > 40 deg. (AHI & MODIS) cone angle > 60 deg. region no limitation oligotrophic ocean only other limitations relative azimuth angle < 90 deg. > 10px distant from cloud RT calculation of cloud-free ocean is similar to the Rayleigh scattering method, which suggested by some agencies this time I chose CNES' method for comparison, because it has been well discussed in gsics # CNES' Rayleigh scattering method and it was revealed that JMA method is not as strict as CNES method 22 Mar 2018 2018 GSICS annual meeting

Vicarious calibration using water clouds Period: 30 days, 02-31 Jan 2017 Good agreement with VIIRS "ray-matching" method -> ☺ well estimated 0.0 < simulation > 1.0 0.0 < simulation > 1.0 band 1 band 2 (0.47 μm) (0.51 μm) band 3 band 4 (0.64 μm) (0.86 μm) band 5 band 6 (1.6 μm) (2.3 μm) 0.0 < observation > 1.0 0.0 < observation > 1.0 0.0 < simulation > 1.0 0.0 < simulation > 1.0 viirs比較図 0.0 < observation > 1.0 0.0 < observation > 1.0 I show the results of water clouds before the results of AHI Rayleigh scattering Every band shows a good agreement with the result by SuomiNPP VIIRS ray-matching method and 1:1 diagonal lines So these seem to be well estimated 0.0 < simulation > 1.0 0.0 < simulation > 1.0 0.0 < observation > 1.0 0.0 < observation > 1.0 22 Mar 2018 2018 GSICS annual meeting

Vicarious calibration using sea surface (ranges of axes differ) Bands 1-2 (0.47-0.51 μm) simulations < observations undetestimation caused by ocean color and polarization? Bands 3-4 (0.64-0.86 μm) simulations ~ observations ☺ show good agreements Bands 5-6 (1.6-2.3 μm) Instrument's noise? The estimation may be insufficient in NIR? 0.0 < simulation > 0.2 0.0 < simulation > 0.2 band 1 band 2 (0.47 μm) (0.51 μm) band 3 band 4 (0.64 μm) (0.86 μm) band 5 band 6 (1.6 μm) (2.3 μm) 0.0 < observation > 0.2 0.0 < observation > 0.2 0.0 < simulation > 0.2 0.0 < simulation > 0.05 0.0 < observation > 0.2 0.0 < observation > 0.05 Next, the results of sea surface in bands 1-2, simulated reflectances are slightly smaller than observations It is because of ocean color and polarization in bands 3-4, simulations and observations show good agreements in bands 5-6, reflectances are almost 0, and simulations are smaller than observations I'm not confident of the cause, it may be instrument's noise, or the estimation is insufficient 0.0 < simulation > 0.05 0.0 < simulation > 0.05 0.0 < observation > 0.05 0.0 < observation > 0.05 22 Mar 2018 2018 GSICS annual meeting

Differences with CNES' Method Region Selection VZA < 60 deg. is the only condition for target area selection much wider than CNES' method, which uses only certain oligotrophic (= less nutrient and plankton) areas Why NIR bands are calculated Almost no reflection nor Rayleigh scattering Instrument's noise could be validated Any ideas/applications in other agencies? Why we don't utilize in bands 1-2 (0.47-0.51 μm) Underestimation problems remain How to resolve consider polarization (handed over from Arata, under investigation) consider Chlorophyll-A (same as above) restrict to oligotrophic areas JMA CNES There are some differences between JMA method and CNES' method One of the major differences is about region selection, CNES method chooses only certain oligotrophic areas in JMA method, viewing zenith angle is smaller than 60 deg is the only condition and we're applying this method to not only visible bands, but also NIR bands practically, almost no reflection by sea surface, nor Rayleigh scattering effects, so we acquire almost 0 reflectances and instrument's noise could be validated by using this method as I said before, currently we have not utilized the results of bands 1-2 as final results, because underestimation problems remain this can be resolved by considering polarization and ocean color to consider ocean color, there are some ways, e.g. inputting chlorophyll-a, and restricting to oligotrophic area 22 Mar 2018 2018 GSICS annual meeting

Trial: restrict to oligotrophic areas RT calculation over "ClimZOO" North-West of Pacific (Fougnie et al., 2002) Lon: 10.0N to 22.7N, Lat: 139.5E to 165.6E - suitable geometry for AHI "ClimZOO" North-West Pacific I tried to do the RT calculation over only oligotrophic area, which is one of suggested by CNES method I chose ClimZOO north-west pacific area, that has suitable geometry for AHI (cited from Fougnie et al., 2002) 22 Mar 2018 2018 GSICS annual meeting

Entire Region band 1 band 2 band 3 (0.47 μm) (0.51 μm) (0.64 μm) 0.0 < simulation > 0.2 0.0 < simulation > 0.2 0.0 < simulation > 0.2 band 1 band 2 band 3 (0.47 μm) (0.51 μm) (0.64 μm) band 4 band 5 band 6 (0.86 μm) (1.6 μm) (2.3 μm) 0.0 < observation > 0.2 0.0 < observation > 0.2 0.0 < observation > 0.2 first, entire region with viewing zenith angle of less than 60 deg 0.0 < simulation > 0.05 0.0 < simulation > 0.05 0.0 < simulation > 0.05 0.0 < observation > 0.05 0.0 < observation > 0.05 0.0 < observation > 0.05 22 Mar 2018 2018 GSICS annual meeting

Limited to "ClimZOO" Region seem to be very stable In bands 1-2 and 5-6, still mismatched between obs and sim Data inputting or RT cal may have some problems 0.0 < simulation > 0.2 0.0 < simulation > 0.2 0.0 < simulation > 0.2 band 1 band 2 band 3 (0.47 μm) (0.51 μm) (0.64 μm) band 4 band 5 band 6 (0.86 μm) (1.6 μm) (2.3 μm) 0.0 < observation > 0.2 0.0 < observation > 0.2 0.0 < observation > 0.2 and these are the result with restriction to oligotrophic area in all bands, results are well converged into narrow ranges, but there're still mismatches between obs and sim so I think that data input or radiative transfer calculation may have problems 0.0 < simulation > 0.05 0.0 < simulation > 0.05 0.0 < simulation > 0.05 0.0 < observation > 0.05 0.0 < observation > 0.05 0.0 < observation > 0.05 22 Mar 2018 2018 GSICS annual meeting

Summary / Future Plans Summary Future plans JMA AHI Rayleigh scattering method has some issues Bands 1-2 (0.47 - 0.51 μm) are underestimated due to not considering ocean color and polarization enough Bands 5-6 (1.6 - 2.3 μm) also show mismatches, but apparently with a different reason Future plans Resolving underestimations in blue-green bands / NIR bands considering polarization, Chlorophyll-A, ... Transition to VIIRS finally I summarize this talk JMA's AHI Rayleigh scattering method has some issues, about mismatches in some bands bands 1-2 are underestimated due to not considering ocean color and polarization enough bands 5-6 NIR also show mismatches, but apparently with a different reason I plan to resolve underestimations in blue-green bands / NIR bands, considering polarization, Chlorophyll-A, and transit from MODIS to VIIRS 22 Mar 2018 2018 GSICS annual meeting

Thank you for your attention 22 Mar 2018 2018 GSICS annual meeting

References Fougnie et al., 2002: Identification and Characterization of Stable Homogeneous Oceanic Zones : Climatology and Impact on In-flight Calibration of Space Sensor over Rayleigh Scattering, Ocean Optics XVI Proceedings. IOCCG, 2013: In-flight Calibration of Satellite Ocean-Colour Sensors. Frouin, R. (ed.), Reports of the International Ocean-Colour Coordinating Group, No. 14. Nakajima, T. and M. Tanaka, 1986: Matrix formulation for the transfer of solar radiation in a plane-parallel scattering atmosphere. J. Quant. Spectrosc. Radiat. Transfer, 35, 13-21. Vermote, E., D. Tanré, J. L. Deuzé, M. Herman, and J. J. Morcrette, Second Simulation on the Satellite Signal in the Solar Spectrum, 1997: 6S: An overview, IEEE Trans. Geosci. Remote Sens., 35, 675-686. 22 Mar 2018 2018 GSICS annual meeting