Spectral Bias Estimation of Historical HIRS Using IASI Observations for Improved Fundamental Climate Data Records

Abstract

A prerequisite for climate change detection from satellites is that the measurements from a series of historical satellites must be consistent and ideally made traceable to the International System of Units (SI). Unfortunately, this requirement is not met for the 14 High Resolution Infrared Radiation Sounders (HIRS) on the historical NOAA satellites, because the instrument was developed for weather forecasts and lacks accuracy and consistency across satellites. It is well known that for HIRS, differences in the spectral response functions (SRF) between instruments and their prelaunch measurement uncertainties often lead to observations of the atmosphere at different altitudes. As a result of the atmospheric lapse rate, they both can introduce significant intersatellite biases. The SRF-dependent biases are further mixed with other effects such as the diurnal cycle because of observation time differences and orbital drifts, on board calibration, and algorithm issues. In this study, the Infrared Atmospheric Sounding Interferometer (IASI) observations are used to calculate the radiances for the 14 Television Infrared Observation Satellite series N (TIROS-N; to MetOp-A) HIRS instruments in different climate regimes and seasons to separate the SRF-induced intersatellite biases from other factors. It is found that the calculated radiance ratio (a bias indicator) using IASI observations for the HIRS satellite pairs forms bell-shaped curves that vary with the HIRS model and channel as well as climate regimes. This suggests that a bias found in the polar regions at the Simultaneous Nadir Overpass (SNO) cannot be blindly used for bias correction globally; instead, the IASI/HIRS spectral bias bell curves should be used as a complement to more fully address the biases. These bell curves also serve as lookup charts for separating the bias due to true SRF differences from that caused by SRF prelaunch measurement errors to resolve the inconsistency, which sheds new light on reprocessing and reanalysis in generating fundamental climate data records from HIRS.