Introduction
The Meteorological Satellite Center of the Japan Meteorological Agency (JMA/MSC) contributes to the Global Spacebased InterCalibration System (GSICS) and
examines ways to improve vicarious calibration for visible and nearinfrared (VIS/NIR) bands of instrumentation on geostationary orbit (GEO) satellites.
Under the GSICS framework, various ways of validating VIS/NIRband sensor sensitivity are considered.
One such method, known as raymatching, involves using the sensor on the Low Earth Orbit (LEO) satellite as reference for comparison against GEO instrumentbased observation.
As the reference sensor is well calibrated and stable, the bias of GEO observation can be estimated and correction values derived.
JMA/MSC plans to implement validation of Himawari8/9 VIS/NIR data based on the raymatching technique with VIIRS as reference, in addition to the current validation based on radiative transfer simulation (RSTAR) data. This method has the following advantages over the existing approach:

Fig. 1: Raymatching process 

Outcomes
The outcomes of the raymatching approach are linear regression coefficients, scatter plots for individual bands, statistical CSV files and timeseries graphs.
Coefficients of regression between reference and GEO imager observation
Regression coefficients are displayed as scatter plots and stored in CSV files (described later). Linear regression via the origin (referred to as forcefit regression) and with offsetting are applied. A regression coefficient (Slope) can be applied in forcefit regression using the equation below to produce corrected AHI reflectance (R_{corr_AHI}) in reference to VIIRS reflectance.Here, R_{org_AHI} is the original AHI reflectance.
Scatter plots
Fig. 2: Relationship between Band 03 of AHI and I01 of VIIRS 
Figure 2 shows the reflectance relationship between reference values based on SNPP/VIIRS and HIMAWARI8/AHI.
Warm colors (e.g., orange, red) and cold colors (e.g., blue, grey) represent high and low numbers of collocations, respectively,
and the coefficients for forcefit and linear regression are shown in the upper left.
Values in parentheses represent standard errors. For simplicity, only the forcefit regression line is displayed (red line).
CSV files
Raymatching results are also stored in CSV file format, including slope, offset and statistics based on both regression methods.
Timeseries representation
Timeseries graphs consist of three parts (Fig. 3), with the upper and middle ones showing offset and the slope of linear regression since July 2015, respectively. Red and blue plots represent statistics for t  14 days to t + 14 days and for t + 0 days (t: date of validity), respectively. The bottom part shows collocation variations, with red bars representing half the number of collocation data gathered during the 29 days and blue bars representing the number of pixels gathered on the specific single day.

Fig. 3: Timeseries representations of slope, offset and numbers of collocations 

Methodology
VIIRS data are used as reference for ray matching, and the reflectance of the AHI VIS/NIR bands is validated (Fig. 1).
Input
 Monitoring data: AHI Himawari Standard Data (HSD) resampled with a spatial resolution of approximately 2 km at the subsatellite point are used, with AHI reflectance calculated as
 Reference data: VIIRS SNPP/VIIRS SDR data from the NOAA class server are used for reference (Table 1: band details). Although the center wavelengths of VIIRS and AHI are similar for each band, their spectral response functions (SRFs) differ slightly. The difference is considered via a spectral band adjustment factor (SBAF) as described later. As the spatial resolutions of VIIRS and AHI data differ, VIIRS data pixels are averaged with surrounding pixels (Mband: 3 x 3 pixels; Iband: 5 x 5 pixels).
Himawari8 AHI (resolution) 
Band 1 0.47 µm (2.0 km) 
Band 2 0.51 µm (2.0 km) 
Band 3 0.64 µm (2.0 km) 
Band 4 0.86 µm (2.0 km) 
Band 5 1.6 µm (2.0 km) 
Band 6 2.3 µm (2.0 km) 

SNPP VIIRS (resolution) 
M03 0.488 µm (750 m) 
M03 0.488 µm (750 m) 
I01 0.640 µm (375 m) 
M07 0.865 µm (750 m) 
M10 1.61 µm (750 m) 
M11 2.25 µm (750 m) 
Collocation
From AHI and VIIRS data, pixels meeting the geometric conditions (Table 2) are extracted. AHI and VIIRS collocation data are produced by matching data with same positions.
Condition  Threshold 

Observation time difference  < 5 min. 
Satellite zenith angle difference  < 10 deg. 
Satellite azimuth angle difference  < 10 deg. 
Sun glint angle*1 (AHI only)  >25 deg. 
Brightness temperature @ 10.4µm (AHI only)  < 273.15 K 
STDV of reflectance/Mean of reflectance  < 5% 
*1: The angle between vectors A and B, where A has the direction of specular reflection of solar light via the observation point, and B has the direction of the satellite via the observation point
Spectral band adjustment factors (SBAFs)
SBAFs are applied in raymatching to correct slight differences in AHI and VIIRS spectral responses using NASA' webbased SBAF Tool. SBAFs are derived using Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) data and AHI/ VIIRS SRF values. Table 3 shows SBAFs between scaled radiance for AHI and VIIRS with the “Force Fit” and “Allsky Tropical Ocean” conditions set by NASA’s SBAF tool. As AHI Band 06 (2.3µm) is not supported by the tool, the SBAF of this band vs. VIIRS M11 is estimated via radiative transfer simulation using RSTAR.
Band 1 vs. M03  Band 2 vs. M03  Band 3 vs. I01  Band 4 vs. M07  Band 5 vs. M10  Band 6 vs. M11  

SBAF  1.022  0.960  0.999  0.997  1.018  1.000 
For each band, SBAF is applied as
Comparison and monitoring
AHI reflectance is compared to VIIRS reflectance corrected using SBAF via linear regression, and the outcomes described above are derived.
Reference

Raymatching
Doelling D., R. Bhatt, D. Morstad, B. Scarino, 2011: Algorithm Theoretical Basis Document (ATBD) for raymatching technique of calibrating GEO sensors with AquaMODIS for GSICS (PDF, 302 kB)

SBAF
Scarino B. R, D. R. Doelling, P. Minnis, A. Gopalan, T. Chee, R. Bhatt, C. Lukashin, C. O. Haney, 2016: A webbased tool for calculating spectral band difference adjustment factors derived from SCIAMACHY hyperspectral data, IEEE Trans. Geosci. Remote Sens., 54, No. 5, 2,529  2,542

RSTAR
Nakajima, T., M. Tanaka, 1986: Matrix formulation for the transfer of solar radiation in a planeparallel scattering atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 35, 13  21
Nakajima, T., M. Tanaka, 1988: Algorithms for radiative intensity calculations in moderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Radiat. Transfer, 40, 51  69
Stamnes, K., S.C. Tsay, W. Wiscombe, K. Jayaweera, 1988: Numerically stable algorithm for discreteordinatemethod radiative transfer in multiple scattering and emitting layered media, Appl. Opt., 27, 2,502  2,509