Impacts of AMSU-A inter-sensor calibration and diurnal correction on satellite-derived linear and nonlinear decadal climate trends of atmospheric temperature
- 17 Downloads
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.
KeywordsAMSU-A Inter-calibration Diurnal correction Climate trend
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).
- Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:41Google Scholar
- Zou X (2012) Climate trend detection and its sensitivity to measurement precision. Adv Meteorol Sci Technol 2:41–43Google Scholar
- Zou C, Wang W (2011) Inter-satellite calibration of AMSU-A observations for weather and climate applications. J Geophys Res Atmos 116:D23113Google Scholar
- 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