GK2A Infrared Calibration

     GK2A(Geo-Kompsat-2A) AMI (Advanced Meteorological Imager) has 10 infrared channels and its radiometric calibration is conducted in stage of Level 1A data processing by measuring the radiances of warm and cold targets, blackbody onboard GK2A and space-look, respectively. In addition to onboard calibration, NMSC has examined the inter-calibration as a part of GSICS (Global Space-based Inter-Calibration System) activities. It is helpful to check the quality of operational measurements for GK2A and sensor degradation with time. GSICS inter-calibration system is to compare two observation values measured by an instrument. We want to calibrate and reference instrument which is known as relatively accurate.

Infrared inter-calibration of imagers on GK2A using high-spectral-resolution sounders as references

     NMSC/KMA has performed inter-calibration between AMI(Advanced Meteorogical Imagers on GK2A satellites and high-spectral-resolution sounders on LEO satellites. Data from IASI(Infrared Atmospheric Sounding Interferometers) on Metop–B and -C satellites, CrIS(Cross_track Infrared Sounder) on SNPP and NOAA20 satellite are used for this inter-calibration. The infrared inter-calibration results are generated once a day.

Algorithm (ATBD)

     The inter-calibration system for GK2A infrared channels is thoroughly based on the GSICS Coordinate Center (GCC) Algorithm Theoretical Basis Document (ATBD). In order to conduct calibration system, first we should make collocation dataset for measured pixels of GK2A and LEOs. The FOV(field of view) of the hyper-spectral sounders is about 12 km diameter at nadir and that of AMI infrared channels are 2 km. The sounder radiance is compared with an average value for AHI radiances over a dimension of 7 × 7 pixels (FOV box) corresponding to the sounder FOV. Then, the several channel measurements of hyper-spectral sounder is converted into a simulated radiance according to spectral response function (SRF) of GK2A. It is called as constraint method, generating a super channel consisting of combination of a lot of channels to imitate a broadband channel. More details of the methods for collocation and spectral simulation are given in below paragraph.

Collocation method

     The collocation algorithms used in inter-calibration are determined by the GSICS Research Working Group. GEO imagers and hyper sounder data meeting the collocation criteria for following check are selected.

Difference check for observation time

     | Time LEO − Time GEO | < 300s ( =5 min)

Difference check for Satellite zenith angle

     | cos( SZA LEO ) / cos( SZA GEO ) − 1 | < 𝜀1

Uniformity check for environment

     To reduce the difference between the observation conditions of the two satellites caused by different observational condition, especially time, navigation, optical path and cloud advection, only measurements over the uniform scene are selected for inter-calibration. Uniformity test for environmental pixels is conducted over 21x21 area (called as ENV box) of GEO.

     STDV(GEO radiances, ENV BOX) < 𝜀2

Normality check

     The normality of the GEO radiance data in the FOV BOX is checked using the following condition as the length of FOV BOX is 7.

     | MEAN(GEO radiances, FOV BOX) − MEAN(GEO radiances, ENV BOX) | × 7 / STDV(ENV BOX) < 𝜀3(Gaussian)

     The tables below represents the criteria used in inter-calibration between GEO imagers and AIRS/IASI/CrIS. The values differ according to channels and weather conditions. If the brightness temperature of IR105 is higher than 275 K, the scene condition is categorized as clear, otherwise, it is categorized as cloudy.

data result
AMI channel condition 𝜀1 𝜀2 𝜀3
SW038 Clear 0.01 0.0238 2
Cloudy 0.03 0.0476
WV063 All 0.01 0.371 1
WV069 All 0.01 0.561 1
WV073 All 0.01 0.661 1
IR087 Clear 0.01 1.18 2
Cloudy 0.03 2.36
IR096 Clear 0.01 1.46 2
Cloudy 0.03 2.92
IR105 Clear 0.01 1.62 2
Cloudy 0.03 3.24
IR112 Clear 0.01 1.77 2
Cloudy 0.03 3.54
IR123 Clear 0.01 1.91 2
Cloudy 0.03 3.82
IR133 Clear 0.01 2.03 2
Cloudy 0.03 4.06

Spectral response compensation method

      CrIS applied gap-filling method which predict the CrIS gap channels based on the principal component-based regression method. (Please refer to Hui Xu(2018) )