In-orbit Microwave Reference Records

The basic requirement for a data record to be able to serve as an in-orbit reference is that the data record should have zero or known biases relative to an on-orbit SI-traceable standard reference – e.g., a chosen reliable instrument or scene reference target of known quality. This allows scientists to be able to estimate the accuracy or bias of other observations when they are compared to the reference near real-time. But this is not easily achieved for the microwave observations because the accuracy of the satellite microwave observations is known to be in a range of 0.5-1K and these biases also vary with time. It is also difficult to find a target at the Earth’s surface to serve as an in-orbit reference since the target itself may be radiometrically unstable due to climate change. To resolve the microwave in-orbit reference problem, a Fundamental Climate Data Record (FCDR) is proposed here.

For a consistent FCDR, bias drifts have been removed or minimized by recalibration and inter-calibration of multiple satellites based on instrument calibration principles. For instance, Zou et al. (2006, 2009, 2011) have developed MSU/AMSU-A FCDRs by inter-calibrating/recalibrating them using an Integrated Microwave Inter-Calibration Approach (IMICA). The IMICA removes or minimizes time-varying inter-satellite biases arising from different error sources in the microwave calibration processes using available analysis tools such as simultaneous nadir overpasses, global mean differences, and radiative transfer model (RTM) simulations, etc.

The recalibrated satellites span nearly 40 years, including satellites from NOAA TIROS-N through NOAA-14 for the MSU instrument and from NOAA-15 through NOAA-18, Metop-A, and NASA Aqua for the AMSU-A instrument. The multiple types of biases among these satellites that were removed or minimized by the IMICA method include: constant inter-satellite biases between most satellite pairs, inter-satellite bias drifts, sun-heating-induced instrument temperature variability in radiances, scene temperature dependency in biases due to inaccurate calibration nonlinearity, and biases due to channel frequency shift from its prelaunch measurement in certain satellite channels (Zou and Wang 2011). This recalibration resulted in minimized inter-satellite biases within 0.1-0.2 K and a relative bias drift of 0.1-0.2K/Decade between satellite pairs, compared to relative bias drift of higher than 0.5K/Dec for some satellite pairs before recalibration. Furthermore, recent findings suggested that the SNPP/ATMS and Metop-A/AMSU-A observations have achieved an absolute radiometric stability within 0.004K/Year (Zou et al. 2018). Such a small radiometric stability satisfies the requirement for reliable measurement of the the long-term climate trends (Zou et al. 2018). If the MSU/AMSU-A FCDR is further inter-calibrated to the SNPP/ATMS and Metop-A/AMSU-A observations, it is expected that radiometric stability for satellites in the FCDRs could be further improved and their bias drifts be further reduced.

Once stability meets requirement or bias drifts are small enough, constant biases in the FCDR can be removed or minimized by comparing with other observations with known small biases. For instance, when compared with GPS-RO data, the AMSU-A FCDR biases are known to achieve within 0.1-0.2K (Isoz et al. 2015). In addition, the IMICA is a dynamic approach that can be repeatedly applied to the satellites within the FCDR as well as to satellites that are new to the existing FCDR. As such, FCDR accuracy could be further improved by repeated recalibration using the IMICA approach when new calibration tools, ideas, and new satellite observations are available.

The current MSU/AMSU-A FCDRs are updated monthly using calibration coefficients obtained from the recalibration process (Zou et al. 2013, 2016). Given resources, these FCDRs could also be produced near real-time, serving as an in-orbit reference in near rear-time applications. Given the spectral response function and scanning angles for each channel in the AMSU-A FCDRs, other microwave instrument observations could be compared to the FCDRs using double difference approach with available radiative transfer models to simulate different types of instrument observations.

Author: Cheng-Zhi Zou, NOAA

Key Scientific Papers

1. Hans, I., M. Burgdorf, S. Buehler, M. Prange, T. Lang and V. John, An uncertainty quantified fundamental climate data record for microwave humidity sounders, Remote Sens., 11(5), 548, 2019, doi:https://doi.org/10.3390/rs11050548.
2. Isoz, O., S. A. Buehler, and P. Eriksson (2015), Intercalibration Of Microwave Temperature Sounders Using Radio Occultation Measurements, J. Geophys. Res., 120, doi:10.1002/2014JD022699.
3. Prabhakara, C., R. Iacovazzi, Jr., J. M. Yoo, and G. Dalu, 2000, Global Warming: Evidence from satellite observations. Geophy. Res. Letters, (27), No. 21, 3517-3520.
4. Yang, Hu and Xiaolei Zou, X, 2014. Optimal ATMS remapping algorithm for climate research. Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, 52(11), 7290-7296.
5. Zou, C.-Z., and W. Wang, 2011: Inter-satellite calibration of AMSU-A observations for weather and climate applications. J.Geophys. Res., 116, D23113, DOI:10.1029/2011JD016205.
6. Zou, C.-Z., M. Goldberg, Z. Cheng, N. Grody, J. Sullivan, C. Cao, and D. Tarpley, 2006: Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses, J. Geophys. Res, 111 , D19114, DOI:10.1029/2005JD006798
7. Zou, C.-Z., and W. Wang, 2013: MSU/AMSU radiance fundamental climate data record derived from integrated microwave inter-calibration appraoch, Climate Algorithm Theoretical Basis Document (C-ATBD), NOAA/NESDIS, available from https:/www1/ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AMSU-A_Brightness_Temperature/AlgorithmDescription_01B-18_18a.pdf
8. Zou, C.-Z., M. Gao, M.Goldberg, 2009, Error structure and atmospheric temperature trends in observations from the Microwave Sounding Unit, J. Climate, 22, 1661-1681, DOI: 10.1175/2008JCLI2233.1
9. Zou, C.-Z., W. Wang, and X. Hao, 2016: AMSU Brightness Temperature –NOAA and AMSU Brightness Temperature –NOAA Gridded, Climate Algorithm Theoretical Basis Document (C-ATBD), Revision 2, NOAA/NESDIS, Available from https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AMSU-A_Brightness_Temperature/AlgorithmDescription_01B-18_18a.pdf
10. Zou, C.-Z., M. Goldberg and X. Hao, 2018: New Generation of US Microwave Sounder Achieves High Radiometric Stability Performance for Reliable Climate Change Detection, Science Advances , Vol. 4, No 10, eaau0049, DOI: 10.1126/sciadv.aau0049

-- RobbieIacovazzi - 14 Apr 2020