Advertisement

Impacts of AMSU-A inter-sensor calibration and diurnal correction on satellite-derived linear and nonlinear decadal climate trends of atmospheric temperature

  • Xinlu Xia
  • Xiaolei ZouEmail author
Article
  • 21 Downloads

Abstract

Satellite microwave temperature sounding data have been widely used in the study of climate trends over the past decades. When merging advanced microwave sounding unit-A (AMSU-A) data from 1998 to 2017 from different satellites, brightness temperature observations can be affected by differences in the center frequency, incidence angle, and local equator crossing time (LECT) among the instruments. Atmospheric drag and gravity variations with latitude can also cause orbital drift that leads to changes in LECT of the same instrument. Inter-sensor calibration and diurnal correction are thus necessary before applying AMSU-A data to climate studies. In this study, AMSU-A data from the National Oceanic and Atmospheric Administration’s (NOAA’s) -15, -18, and -19 satellites and the European Organization for the Exploitation of Meteorological Satellites MetOp-A/-B collected during 1998–2017 were first inter-calibrated by a double difference method to remove inter-sensor biases. AMSU-A data from NOAA-18 was used as the reference for the double difference inter-sensor calibration. A diurnal correction was then applied to data over the Amazon rainforest to eliminate the effects of different LECTs. Finally, linear and nonlinear climate trends were calculated to show that warming (cooling) trends over the Amazon rainforest for window and tropospheric AMSU-A channels 1–8 and 15 (stratospheric channels 9–14) significantly decreased (increased) if inter-sensor calibration and a diurnal correction was applied. The nonlinear climate trends reveal more rapid warming trends for tropospheric sounding channels 3–7 and 15 and less rapid cooling trends for stratospheric channels 10–12 during 1998–2008 than during 2008–2017. Channels 1–2 (channel 13) have cooling (warming) and warming (cooling) trends before and after 2008, respectively.

Keywords

AMSU-A Inter-calibration Diurnal correction Climate trend 

Notes

Acknowledgements

This research was supported by the National Key R&D Program of China (Grant 2018YFC1507004) and the National Natural Science Foundation of China (Grant 91337218).

References

  1. Andersson E, Hollingsworth A, Kelly G, Lönnberg P, Pailleux J, Zhang Z (1991) Global observing system experiments on operational statistical retrievals of satellite sounding data. Mon Weather Rev 119:1851–1865CrossRefGoogle Scholar
  2. Angell JK, Korshover J (1983) Global temperature variations in the troposphere and low stratosphere, 1958–1982. Mon Weather Rev 111:901–921CrossRefGoogle Scholar
  3. Barnett TP (1984) Long-term trends in surface temperature over the oceans. Mon Weather Rev 112:303–312CrossRefGoogle Scholar
  4. Bremen LV, Ruprecht E, Macke A (2002) Errors in liquid water path retrieval arising from cloud inhomogeneities: the beam-filling effect. Meteorol Z 11(1):13–19CrossRefGoogle Scholar
  5. Cao C, Weinreb M, Xu H (2004) Predicting simultaneous nadir overpasses among polar-orbiting meteorological satellites for the inter-satellite calibration of radiometers. J Atmos Ocean Technol 21:537–542CrossRefGoogle Scholar
  6. Chen H, Zou X, Qin Z (2018) Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data. Front Earth Sci 12:1–16CrossRefGoogle Scholar
  7. Christy JR, Spencer RW, Braswell WD (2000) MSU tropospheric temperatures: dataset construction and radiosonde comparisons. J Atmos Ocean Technol 17:1153–1170CrossRefGoogle Scholar
  8. Eyre JR, Kelly GA, McNally AP, Andersson E, Persson A (1993) Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteorol Soc 119:1427–1463CrossRefGoogle Scholar
  9. Ferraro RR, Weng F, Grody NC, Zhao L (2000) Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys Res Lett 27:2669–2672CrossRefGoogle Scholar
  10. Greenwald TJ, Stephens GL, Haar THV, Jackson DL (1993) A physical retrieval of cloud liquid water over the global oceans using special sensor microwave/imager (SSM/I) observations. J Geophys Res Atmos 98:18471–18488CrossRefGoogle Scholar
  11. Han Y, Weng F, Liu Q, Delst P (2007) A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J Geophys Res Atmos 112:D11121CrossRefGoogle Scholar
  12. Hansen J, Johnson D, Lacis A, Lebedeff S, Lee P, Rind D, Russell G (1981) Climate impact of increasing atmospheric carbon dioxide. Science 213:957–966CrossRefGoogle Scholar
  13. Hansen J, Ruedy R, Glascoe J, Sato M (1999) GISS analysis of surface temperature change. J Geophys Res Atmos 104:30997–31022CrossRefGoogle Scholar
  14. Huang NE, Wu Z (2008) A review on Hilbert–Huang transform: method and its applications to geophysical studies. Rev Geophys 46:RG2006CrossRefGoogle Scholar
  15. Jones PD, Wigley TML, Kelly PM (1982) Variations in surface air temperatures. Part I: Northern Hemisphere, 1881–1980. Mon Weather Rev 110:59–70CrossRefGoogle Scholar
  16. Marchand R, Ackerman T, Westwater ER, Clough SA, Cady-Pereira K, Liljegren JC (2003) An assessment of microwave absorption models and retrievals of cloud liquid water using clear-sky data. J Geophys Res Atmos 108:D24CrossRefGoogle Scholar
  17. Marquardt DW, Donald W (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441CrossRefGoogle Scholar
  18. Mears CA, Wentz FJ (2016) Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment. J Climate 29:3629–3646CrossRefGoogle Scholar
  19. Mears CA, Schabel MC, Wentz FJ, Santer BD, Govindasamy B (2002) Correcting the MSU middle tropospheric temperature for diurnal drifts. IEEE Int Geosci Remote Sens Sympos 3:1839–1841CrossRefGoogle Scholar
  20. Mo T (1996) Prelaunch calibration of the advanced microwave sounding unit-A for NOAA-K. IEEE Trans Microw Theory Tech 44:1460–1469CrossRefGoogle Scholar
  21. Mo T (2007) Diurnal variation of the AMSU-A brightness temperatures over the Amazon rainforest. IEEE Trans Geosci Electron 45:958–969CrossRefGoogle Scholar
  22. Mo T (2009) A study of the NOAA-15 AMSU-A brightness temperatures from 1998 through 2007. J Geophys Res Atmos 114:D11110CrossRefGoogle Scholar
  23. Po-Chedley S, Thorsen TJ, Fu Q (2015) Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: understanding tropical tropospheric trend discrepancies. J Climate 28:2274–2290CrossRefGoogle Scholar
  24. Privette JL, Fowler C, Wick GA, Baldwin D, Emery WJ (1995) Effects of orbital drift on advanced very high resolution radiometer products: normalized difference vegetation index and sea surface temperature. Remote Sens Environ 53:164–171CrossRefGoogle Scholar
  25. Qin Z, Zou X, Weng F (2012) Comparison between linear and nonlinear trends in NOAA-15 AMSU-A brightness temperatures during 1998–2010. Climate Dyn 39:1763–1779CrossRefGoogle Scholar
  26. Sasi M, Ramkumar G, Deepa V (1998) Nonmigrating diurnal tides in the troposphere and lower stratosphere over Gadanki (13.5°N, 79.2°E). J Geophys Res Atmos 103:19485–19494CrossRefGoogle Scholar
  27. Shindell DT, Miller RL, Schmidt GA, Pandolfo L (1999) Simulation of recent northern winter climate trends by greenhouse-gas forcing. Nature 399:452–455CrossRefGoogle Scholar
  28. Spencer RW, Christy JR, Braswell WD (2017) UAH Version 6 global satellite temperature products: methodology and results. Asia Pac J Atmos Sci 53:121–130CrossRefGoogle Scholar
  29. Tian X, Zou X (2016) ATMS- and AMSU-A-derived hurricane warm core structures using a modified retrieval algorithm. J Geophys Res Atmos 121:12630–12646CrossRefGoogle Scholar
  30. Wang W, Zou C (2014) AMSU-A-only atmospheric temperature data records from the lower troposphere to the top of the stratosphere. J Atmos Ocean Technol 31:808–825CrossRefGoogle Scholar
  31. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:41Google Scholar
  32. Zou X (2012) Climate trend detection and its sensitivity to measurement precision. Adv Meteorol Sci Technol 2:41–43Google Scholar
  33. Zou C, Wang W (2011) Inter-satellite calibration of AMSU-A observations for weather and climate applications. J Geophys Res Atmos 116:D23113Google Scholar
  34. Zou C, Goldberg MD, Cheng Z, Grody NC, Sullivan JT, Cao C, Tarpley D (2006) Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses. J Geophys Res Atmos 111:5455–5464Google Scholar
  35. Zou C, Gao M, Goldberg M (2009) Error structure and atmospheric temperature trends in observations from the microwave sounding unit. J Climate 22:1661–1681CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Joint Center of Data Assimilation for Research and ApplicationNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA

Personalised recommendations