Abstract

Infrared sounding measurements of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to recalibrate infrared (IR; ~11 µm) channels and water vapor (WV; ~6 µm) channels of the Visible and Infrared Spin Scan Radiometer (VISSR), Japanese Advanced Meteorological Imager (JAMI), and IMAGER instruments onboard the historical geostationary satellites of the Japan Meteorological Agency (JMA). The recalibration was performed using a common recalibration method developed by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), which can be applied to the historical geostationary satellites to produce Fundamental Climate Data Records (FCDR). Pseudo geostationary imager radiances were computed from the infrared sounding measurements and regressed against the radiances from the geostationary satellites. Recalibration factors were computed from these pseudo imager radiance pairs. This paper presents and evaluates the result of recalibration of longtime-series of IR (1978–2016) and WV (1995–2016) measurements from JMA’s historical geostationary satellites. For the IR data of the earlier satellites (Geostationary Metrological Satellite (GMS) to GMS-4) significant seasonal variations in radiometric biases were observed. This suggests that the sensors on GMS to GMS-4 were strongly affected by seasonal variations in solar illumination. The amplitudes of these seasonal variations range from 3 K for the earlier satellites to <0.4 K for the recent satellites (GMS-5, Geostationary Operational Environmental Satellite-9 (GOES-9), Multi-functional Transport Satellite-1R (MTSAT-1R) and MTSAT-2). For the WV data of GOES-9, MTSAT-1R and MTSAT-2, no seasonal variations in radiometric biases were observed. However, for GMS-5, the amplitude of seasonal variation in bias was about 0.5 K. Overall, the magnitude of the biases for GMS-5, MTSAT-1R and MTSAT-2 were smaller than 0.3 K. Finally, our analysis confirms the existence of errors due to atmospheric absorption contamination in the operational Spectral Response Function (SRF) of the WV channel of GMS-5. The method used in this study is based on the principles developed within Global Space-based Inter-calibration System (GSICS). Moreover, presented results contribute to the Inter-calibration of imager observations from time-series of geostationary satellites (IOGEO) project under the umbrella of the World Meteorological Organization (WMO) initiative Sustained and Coordinated Processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM).

Highlights

  • Geostationary meteorological satellites have been observing the Earth for more than 40 years

  • The standard radiances of the historical Japan Meteorological Agency (JMA) satellites were calculated for each channel using RTTOV-11 with the 1976 US Standard Atmosphere for nadir condition in clear sky at night over an ocean surface with a Sea Surface Temperature (SST) of 288.15 K and a wind speed of 7 m/s

  • Kobayashi et al [23] used RTTOV-10 to evaluate the corrected Spectral Response Function (SRF), and found that the rroneous SRF causes a bias of about 0.6 K in brightness temperature at typical atmospheric onditions in mid-latitudes

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Summary

Introduction

Geostationary meteorological satellites have been observing the Earth for more than 40 years. 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 [1,2]. One of the methods is to recalibrate radiances of one instrument ( referred to as the monitored instrument) with radiances of superior instruments operated on another satellite or on an aircraft ( referred to as the reference instrument) using Simultaneous Nadir Overpass (SNO) observations [3,4,5]. When the accuracy of the measurements of the reference instrument is superior to the accuracy of the monitored instrument, it is meaningful to recalibrate the monitored instrument taking into account differences in the instrument’s spectral response and spatial resolution, as well as temporal and spatial uncertainties of the SNOs

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