Error correlations in High-Resolution Infrared Radiation Sounder (HIRS) Radiances

Holl, G., Mittaz, J. P.D. and Merchant, C. J. ORCID: https://orcid.org/0000-0003-4687-9850 (2019) Error correlations in High-Resolution Infrared Radiation Sounder (HIRS) Radiances. Remote Sensing, 11 (11). 1337. ISSN 2072-4292 doi: 10.3390/rs11111337

Abstract/Summary

The High-resolution Infrared Radiation Sounder (HIRS) has been flown on 17 polar-orbiting satellites between the late 1970s and the present day. HIRS applications require accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count deviations for sequential sets of calibration measurements, and then correlating deviations between channels (for a fixed view) or views (for a fixed channel). The inter-channel error covariance is usually assumed to be diagonal, but we show that large error correlations, both positive and negative, exist between channels and between views close in time. We show that correlated error exists for all HIRS and that the degree of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5 are not unusual. Correlations between calibration observations taken sequentially in time arise from periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that, for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies. These findings are significant for application of HIRS data in various applications, and related information will be made available as part of an upcoming Fundamental Climate Data Record covering all HIRS channels and satellites.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/83997
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	MDPI
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