IGARSS 2011, Vancouver, Canada, July 24-28, 2011

1. Detection and Determination of Channel Frequency Shift in AMSU-A Observations Cheng-Zhi Zou and Wenhui Wang NOAA/NESDIS/Center for Satellite Applications and Research (Thanks Y. Han and Y. Chen at JCSDA for their CRTM calculation support) IGARSS 2011, Vancouver, Canada, July 24-28, 2011

2. Background • AMSU-A observations are being assimilated into NWP models for accurate weather prediction in most weather centers in the world • AMSU-A observations are being assimilated into climate reanalysis systems to constrain model climate • AMSU-A observations are merged with MSU by different research groups to generate atmospheric temperature time series for climate change monitoring • In all these applications, channel frequency values are specified to be • the pre-launch measurements • Bias corrections of unknown error sources are conducted before AMSU-A data are being assimilated into NWP and reanalysis models • This study identify one of these error sources using inter-satellite bias analysis method AMSU-A: 1998-present on NOAA-15 through NOAA-19 and MetOp-A, NASA Aqua Weighting functions for AMSU-A. All weighting functions are corresponding to nadir or near-nadir observations.

3. AMSU-A Orbit Information Satellites Launch Date LECT at lunch 0930 Descending MetOp-A October 2006 1400 Ascending MAY 2005 NOAA-18 1000 Descending JUNE 2002 NOAA-17 1400 Ascending NOAA-16 SEPT 2000 NOAA-15 MAY 1998 0730 Descending Local Equator Crossing Time of the Descending Orbits of the NOAA and MetOp-A satellites

4. SNO Datasets • For polar orbiting satellites, SNO events are generally found over the polar region • Use Cao’s (2004) orbital method to find SNO events Schematic viewing SNO and its locations

5. Examples of SNO Inter-Satellite Biases Channel 6 of NOAA-15 minusNOAA-18 Channel 6 of MetOp-A minusNOAA-18

6. SNO Radiance Error Model k j Radiance Error Model for SNO Matchup K and J Remove relative mean inter-satellite biases Remove non-uniformity in inter-satellite biases Remove instrument temperature signals

7. Effect of Calibration Non-linearity After SNO Inter-Calibration Before Inter-Calibration Channel 6 of MetOp-A minusNOAA-18 Channel 6 of MetOp-A minusNOAA-18

8. Lapse Rate Climatology Average over the 700S • The averaged lapse rate around 350 hPa being steeper in winters (July) than in summers (January). • Time series with winter values being at the negative side of the summer values when the frequency shift is positive (weighting function peaking higher than prelaunch measured), and the other way around for negative frequency shift. • NOAA-15 should have a positive frequency shift Channel 6 Measurement NOAA-15 Minus NOAA-18

9. Pre-launch Measured Frequencies for AMSU-A Channel 6 • Measured frequency differences between different satellites are within 0.5 MHz. • These errors are so small that they wouldn't result in noticeable Tb differences between satellites (0.01K) • Practically, these measured channel frequencies can be considered as the same for different satellites • The shift is a post-launch error Frequency characteristics for AMSU-A Channel 6 from Mo [1996; 2006; 2007]. Units are in MHz. Differences for all pairs: 0.5 MHz

10. Methods to Determine the Actual Channel Frequency • Use NOAA Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) to simulate NOAA-15 observations at its SNO sites relative to NOAA-18 • Use NASA MERRA reanalysis surface data and atmospheric profiles (temperature, humidity, ozone, cloud liquid water, trace gases etc.) as inputs to the CRTM • MERRA data were interpolated into the N15-N18 SNO sites before being used by CRTM • Select different frequency shift values (df) in the simulation experiments • Analyze Tb(N15, df) = Tb(N15, fm + df) - Tb(N15, fm) • fm : Measured Channel Frequency Value • df: Frequency Shift

11. Experimental Results Simulated dTb (N15, df) • Comparisons between simulations and observed N15-N18 SNO data confirms a positive frequency shift in the NOAA-15 channel 6 relative to its measured frequency value Observed SNO time series over the Antarctic between NOAA-15 and NOAA-18

12. Determine the Final Channel Frequency Value • Examine DTb’, which is the Tb differences between NOAA-15 and NOAA-18 at their SNO sites when NOAA-15 Tb is adjusted by its simulated frequency shift • We expect the seasonal cycles in DTb’ disappear when df equals to the actual channel frequency shift’ • The seasonal cycles can be measured by the amplitude, which should be equal to zero for df=actual channel frequency shift dfo= 36.25±1.25MHz fa= fm+ dfo = 54435.73±1.25 MHz

13. Impact on SNO Time Series Channel 6 of NOAA-15 vsNOAA-18 Before Frequency adjustment Channel 6 of NOAA-15 vsNOAA-18 After NOAA-15 Frequency adjustment

14. Conclusion • Method is developed to detect and determine the post-launch • channel frequency shift in AMSU-A observations onboard polar • orbiting satellites • NOAA-15 channel-6 frequency shift is determined • Methods are expected to be applicable to other satellites and other • channels, but analysis has to be done for each channel, since all • channels have different lapse rate climatology • Call for impact experiments on NWP accuracy improvement at • JCSDA; if positive, we need to work on more channels • Also call for provisional parameters for future AMSU-type instruments, • allowing calculating the frequency shift after launch