Re-calibrated Infrared and Water Vapor channel’s measurements from JMA and EUMETSAT historical geostationary meteorological satellites

Geostationary meteorological satellites have been observing the Earth for more than 40 years. Initially, the measurements of these satellites were intended for qualitative analysis. However, with time the need for using these measurements quantitatively has increased. Due to their long observation period and their good temporal sampling and spatial coverage, these measurements are of tremendous value for climate studies and climate monitoring. Using satellite observations in climate monitoring applications, such as reanalysis, requires these observations to be quantitative, stable in time, accurate, and (or at least) provide quantitative information on the changes in the characteristics of sensors that were operated on the different satellites.
This presentation introduces a common re-calibration approach that can be applied to the infrared (IR) channels (round ~11 um) and water vapour (WV) channels (round ~6 um) of the VISSR, JAMI, IMAGER, MVIRI, and SEVIRI instruments from the JMA or EUMETSAT historical geostationary satellites. Data from the IASI, AIRS and HIRS/2 are used as references for re-calibration. Spectral Band Adjustment Factors are used to correct radiances for small difference in spectral response from the different geostationary instruments and the reference instruments. IASI and AIRS spectra were directly convolved with the spectral response from geostationary instruments. We developed and applied a new method to correct for the gaps in the AIRS spectra. Finally, we applied a prime correction method to adjust the radiometric biases of HIRS/2 and AIRS against IASI, which is necessary to ensure temporal homogeneity of the re-calibrated geostationary data.
In our presentation we will analyze time-series of JMA and EUMETSAT re-calibrated data spanning more than 35 years. The re-calibrated IR data from the old JMA satellites (GMS to GMS-5) reveal significant seasonal changes in radiometric biases. This suggests that the sensors on the old JMA satellites were strongly affected by seasonal variations in solar illumination. The magnitude and amplitude of the biases differ from satellite to satellite, and tend to be smaller for more recent satellites (from ±3 K to ±0.2 K). The re-calibrated WV data on GMS-5 also exhibits seasonal variations (±0.5K) in bias. The MTSAT data, however, do not show seasonal variations. The MTSAT-1R and MTSAT-2 biases are respectively -0.3K and +0.1K in the IR channel and +0.3K and -0.2K in the WV channel. We observed significant biases and seasonal variations in the re-calibrated IR and WV data from MVIRI as well. For example, the Meteosat-7 IR channel data has a cold bias of about 3 K, whereas the WV channel data has a warm bias of about 2 K with a seasonal amplitude of about one kelvin. Similar to the JMA satellites, the newer Meteosat Second Generation data show much smaller biases (within ±0.5 K).
This research contributes to the WMO initiative Sustained and Coordinated Processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM) project No. 6: Inter-calibration of imager observations from time-series of geostationary satellites (IOGEO), which aims at establishing spatially and temporally homogeneous radiance data from all geostationary satellites. The inter-calibration algorithms used are based on those developed for the Global Space-based Inter-Calibration System (GSICS).