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GK-2A Visible/Near-infrared Calibration

     The meteorological instrument AMI(Advanced Meteorological Imager) of GK-2A has six visible channels of four visible channels and two near-infrared channels(Table 1).

Table 1. GK-2A AMI Visible/Near-infrared channels

On board calibrator

     The AMI has a solar diffuser as a Solar Calibration Target (SCT). The SCT is the first solar diffuser installed on KMA geostationary meteorological satellite imager. The SCT is used as a bright reference for the VIS(Visible) and NIR(Near-infrared) bands together with space view observations as dark references. Using these calibration targets, linear and bias calibration coefficients are derived to convert counts of raw detector sample data into radiances. However, in case there is a problem with the absolute calibration target, the vicarious calibration method should be used together and stable calibration monitoring should be performed.

Algorithm

     NMSC performs the vicarious calibration method by using earth target(ocean, desert, WC(Water Cloud) and DCC(Deep Convective Cloud)), ray-matching and GSICS DCC. Earth target algorithm is based on the development of meteorological data processing system for communication, ocean and meteorological satellite ATBD. Ray-matching and GSICS DCC algorithms are developed from GSICS community, and it can see the reference papers information under this page.

Table 2. GK2A AMI VIS/NIR calibration methods

※ RTM : Radiative Transfer Model
      6S : Second Simulation of the Satellite Signal in the Solar Spectrum
      SBDART : Santa Barbara DISORT Atmospheric Radiative Transfer

ㆍ Ocean(RTM)

     The ocean targets are usually homogeneous and dark but should be considered only a clear pixel without a cloud for sensor calibration. The reflectance of ocean is affected by aerosol, because the surface reflectance is so small. Therefore, AOT(Aerosol Optical Thickness) has to be considered as an ancillary data in radiative transfer modeling. In addition, it has the advantage that radiance can be calculated without BRDF (bidirectional reflectance distribution function) because the surface reflectance of the ocean is nearly homogeneous.

ㆍ Desert(RTM)

     KMA conducted desert calibration for 11 fixed desert targets among Simpson Desert in Australia. There is no need to use AOT as ancillary data from other satellites, because of the low effect of aerosol by high surface reflectivity of desert. However, accurate surface BRDF needs to be used for model input, since the variation of surface BRDF is low with time. The surface BRDF can be updated with longer period than other targets.

ㆍ Water Cloud(RTM)

     The atmosphere and surface effects can be minimized in the simulation of TOA radiance over the WC targets due to strong reflection of cloud layer. According to the actual test for WC reflectances sensitivity, TOA reflectance is more dependent on cloud optical properties than surface and atmospheric effects. In addition, the reflectance is more sensitive with cloud optical thickness(COT) than particle sizes of cloud since the absorption rate of visible rays into cloud particle is low. Thus, the cloud optical properties are necessary as the model input for more accurate result.

ㆍ DCC(RTM)

     DCC target has the largest reflectance among the targets: ocean, desert, WC and DCC. The vertical distribution of DCC is assumed from 1km to 15km. COT and particle size of the DCC are assumed to be 200 and 20µm, respectively. The pixel of which the brightness temperature (TB) of IR1(10.8µm) is less than 190K are selected as DCC for calibration.

ㆍ GSICS DCC

     The GSICS DCC method is which GEO and LEO observing DCC, respectively. Through this method, DCC pixels are selected for each month of GEO and LEO, and these selected pixels are compared using monthly statistics(This method assumes that two satellites (GEO and LEO) have observed the same DCC target). Using statistics such as mean, median, and mode, you can monitor the distribution of radiance values or the degradation rate of VIS/NIR channels over time.

ㆍ Ray Matching

     The ray-matching method is inter-comparison method between GEO(Geostationary Earth Orbit) satellite and well calibrated LEO(Low Earth Orbit) satellite. Each grids(pixels) of GEO and LEO are matched satellite and solar angle, spatial and temporal follow under the thresholds table 4. This method has the advantage of always getting results because it does not select a specific target. And It can monitoring wide and various range of reflectance(For example DCC method can monitoring only high reflectance).

Table 3. Matched channels for GSICS DCC and Ray Matching methods.

Table 4. Ray-matching method thresholds

Calibration

     NMSC provides the four types of outcomes in this web page. There are the two time series of regression coefficients and ratios for each methods, and the one scatter plot.

Regression coefficients between the observed and reference reflectances.

     The time series of regression slopes(C1) and intercepts(C0) are computed between the observed and reference reflectances. The C1 and C0 in the web page represent the 29 days moving averaged values(±14 days).

     Reflectance(Observed)= C1 * Reflectance(Reference) + C0

Ratios of the observed reflectance to reference reflectance for each target

     The time series of ratios of the observed reflectance to reference reflectance represents the 30days moving averaged value.

     Ratio=Reflectance(Observed)/Reflectance(reference)

Scatter plot between observed and reference reflectance

     The comparison of reflectance between observation and simulation is provided in the scatter plot with the regression line.

Reference

Doelling D., Dan Morstad, Rajendra Bhatt, Benjamin Scarino 2011: Algorithm Theoretical Basis Document (ATBD) for Deep Convective Cloud (DCC) technique of calibrating GEO sensors with Aqua-MODIS for GSICS, GSICS ATBDs.

Doelling, D., Rajendra Bhatt, Dan Morstad, Benjamin Scarino, 2011: Algorithm Theoretical Basis Document (ATBD) for ray-matching technique of calibrating GEO sensors with Aqua-MODIS for GSICS, GSICS ATBDs.

Chun, H. W., & Sohn, B. J., 2006: Calibration for the solar channel using MODIS-derived BRDF parameters over Australian desert targets. Target, 4(6), 8.

Ham, S. H. 2006: Improvement of cloud's spectral reflection simulation using MODIS cloud products. 12th Conference on Atmospheric Radiation.

KMA, 2009: Development of Meteorological Data Processing System for Communication, Ocean and Meteorological Satellite(ATBD).

Sohn, B. J., Seung-Hee Ham, Ping Yang, 2009: Possibility of the Visible-Channel Calibration Using Deep Convective Clouds Overshooting the TTL. J. Appl. Meteor. Climatol., 48, 2271-2283.