Correcting diurnal cycle aliasing in satellite microwave humidity sounder measurements
Article first published online: 16 JAN 2013
©2012. American Geophysical Union. All Rights Reserved.
Journal of Geophysical Research: Atmospheres
Volume 118, Issue 1, pages 101–113, 16 January 2013
How to Cite
2013), Correcting diurnal cycle aliasing in satellite microwave humidity sounder measurements, J. Geophys. Res. Atmos., 118, 101–113, doi:10.1029/2012JD018545., , and (
- Issue published online: 29 JAN 2013
- Article first published online: 16 JAN 2013
- Manuscript Accepted: 5 DEC 2012
- Manuscript Revised: 5 DEC 2012
- Manuscript Received: 24 JUL 2012
- orbital drift correction;
- diurnal cycle of humidity
 Microwave humidity measurements from polar orbiting satellites are affected by diurnal sampling biases which are caused by changes in the local observation time of the satellites. The long-term data records available from these satellites thus have spurious trends, which must be corrected. Diurnal cycles of the microwave measurements have been constructed by combining data over the period 2001–2010 from five different satellite platforms (NOAA-15, -16, -17, -18, and MetOpA). This climatological diurnal cycle has been used to deduce and correct the diurnal sampling bias in Advanced Microwave Sounding Unit-B and microwave humidity sounder measurements. Diurnal amplitudes for channels which are sensitive to surface temperature variations show a sharp land-sea contrast with the amplitudes exceeding 10 K for land regions but less than 1 K for oceanic regions. The humidity channels sensitive to the upper and middle troposphere exhibit a seasonal variation with large diurnal amplitudes over convective land regions (often above 3 K) in comparison to oceanic regions. The diurnal peak times of these channels over land occur in the early mornings. The diurnal sampling bias correction has a greater impact over land regions when compared to oceanic regions due to the large diurnal amplitudes over land. The diurnal cycle of humidity generated as a part of this study could be used to evaluate diurnal cycles in climate models.