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.

Hyperspectral Earth Observation from IASI: Five Years of Accomplishments

“…GSICS aims to ensure the comparability of satellite measurements performed from different instruments (sounders/imagers, infrared/microwave) and platforms, to interpret the differences, and to link the measurements to absolute references and standards. Intercomparisons have been performed with HIRS, AIRS, the Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Advanced Along-Track Scanning Radiometer (AATSR) using simultaneous nadir overpass (SNO) methods or double-difference (DD) methods (e.g., Cao et al 2009;Illingworth et al 2009;Hewison 2008a,b;Tobin 2008;Wang and Cao 2008). Biases are also characterized via the study of residuals between IASI observations and simulations based on radiative transfer code and collocated radiosoundings (Blumstein et al 2010).…”

Comparison of AIRS and IASI Radiances Using GOES Imagers as Transfer Radiometers toward Climate Data Records

“…The factors that may cause the systematic biases but are not related to AIRS and IASI are the calibration MARCH 2010 W A N G E T A L .…”

AIRS, IASI, and CrIS Retrieval Records at Climate Scales: An Investigation into the Propagation of Systematic Uncertainty

“…Statistical data reduction reduces random uncertainty caused by variations in local weather conditions (a necessity in this case), but propagate, amplify, and even distort systematic uncertainty across space-time scales (Stubenrauch et al 2013). With radiometric differences shown to be at or below the climate accuracy requirement of 0.1 K for most conditions, they concluded that data from these systems have the potential to create fundamental climate data records of radiances (e.g., Cao et al 2009;Gunshor et al 2009;Wang and Cao 2008;Wang et al 2009). Toward this end, we processed the AIRS, IASI, and CrIS radiance measurement records in the exact same manner to avoid the introduction of algorithm-related discrepancies.…”