FR1.T03.2 Zou_IGARSS_2011.ppt

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  • Thanks Bruce for the introduction. The title of my talk is ‘MSU/AMSU/SSU CDR development. I gave a talk three years ago in the STAR science forum about the MSU inter-calibration and trend and I kept talking this subject in the last few years in various occasions and hope people don’t get tired of it. But today I try to provide a comprehensive review of the current status and a discussion of various science issues include various bias correction, validation, inter-comparisons, web data support, and so on. I have reserved the room for two hours, but I will only talk about 45 minutes to one hour and allow plenty of time for questions. So don’t get scared about the length of the talk as suggested in the announcement.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • With that, now lets talk about SNOs and calibration. I guess most audience already know what is SNO, and we have Changyong Cao in the audience, who is the pioneer to think and generate the SNO datasets for satellite calibration. Basically, a SNO event is defined when two satellites meet each other and look at the same position of the atmosphere at the same time at nadir direction. The cross position in these orbits roughly shows a SNO event of two NOAA satellites.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • To obtain the coefficients, we establish a radiance error model for the SNO matchup, say satellite k and j. The calibration equation for each satellite is like this, when you subtract them from each other, you obtain the error model like this. Here the Z terms and delta Rl are measured variables, the E are error residual term related to the spatial and time differences in the SNO matchup datasets. Using some statistical chracteristics, this E term can be ignored here. Then we can use regressions to obtain the calibration coefficients. When we do regression, we need to consider the colinearity between the nonlinear terms for the SNO matchups. As a mater of fact, we find there is a high degree of colinearity between satellite pairs for the Z terms. This plot gives you an example for NOAA 11 and 10, here the correlation of the Z terms reach as high as 0.95. So the colinearity equation is part of the regression model, which serves to reduce the independent variables in the regression.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.
  • The advantage of this recalibration is that the warm target contamination and intersatellite biases have been largely removed at the radiance level. So we expect that if they are used in reanalysis, the reanalysis trend and variability will be close to the what we obtained from retrievals. So we recommend the community to try the new one. Currently,
  • The recalibration that I am talking about is the The purpose of the recalibration is to remove intersatellite bias and bias drift, on benefit is that it results in more accurate merged satellite climate products, The recalibration can also affect the modeling reanalysis effort. Recalibration Current reanalyses directly assimilate satellite radiance data, reprocessing can help us understanding the bias structure of the radiance data which leads to understanding of the reanalysis uncertainties, Then recalibration can generate consistent radiance dataset to minimize bias correction effort in reanalyses Reprocessing can also help us to better understand the overall quality of the radiance data for optimal use And we also develop algorithm to generate consistent high level data products from the re-calibrated radiance for reanalysis validation
  • FR1.T03.2 Zou_IGARSS_2011.ppt

    1. 1. Detection and Determination of Channel Frequency Shift in AMSU-A Observations Cheng-Zhi Zou and Wenhui Wang IGARSS 2011, Vancouver, Canada, July 24-28, 2011 NOAA/NESDIS/Center for Satellite Applications and Research (Thanks Y. Han and Y. Chen at JCSDA for their CRTM calculation support)
    2. 2. Background Weighting functions for AMSU-A. All weighting functions are corresponding to nadir or near-nadir observations. AMSU-A: 1998-present on NOAA-15 through NOAA-19 and MetOp-A, NASA Aqua <ul><li>AMSU-A observations are being assimilated into NWP models for accurate weather prediction in most weather centers in the world </li></ul><ul><li>AMSU-A observations are being assimilated into climate reanalysis systems to constrain model climate </li></ul><ul><li>AMSU-A observations are merged with MSU by different research groups to generate atmospheric temperature time series for climate change monitoring </li></ul><ul><li>In all these applications, channel frequency values are specified to be </li></ul><ul><li>the pre-launch measurements </li></ul><ul><li>Bias corrections of unknown error sources are conducted before AMSU-A data are being assimilated into NWP and reanalysis models </li></ul><ul><li>This study identify one of these error sources using inter-satellite bias analysis method </li></ul>
    3. 3. AMSU-A Orbit Information Satellites Launch Date LECT at lunch NOAA-16 SEPT 2000 1400 Ascending NOAA-15 MAY 1998 0730 Descending NOAA-17 JUNE 2002 1000 Descending NOAA-18 MAY 2005 1400 Ascending MetOp-A October 2006 0930 Descending Local Equator Crossing Time of the Descending Orbits of the NOAA and MetOp-A satellites
    4. 4. SNO Datasets <ul><li>For polar orbiting satellites, SNO events are generally found over the polar region </li></ul><ul><li>Use Cao’s (2004) orbital method to find SNO events </li></ul>Schematic viewing SNO and its locations
    5. 5. Examples of SNO Inter-Satellite Biases Channel 6 of MetOp-A minus NOAA-18 Channel 6 of NOAA-15 minus NOAA-18
    6. 6. k j Radiance Error Model for SNO Matchup K and J SNO Radiance Error Model Remove relative mean inter-satellite biases Remove non-uniformity in inter-satellite biases Remove instrument temperature signals
    7. 7. Effect of Calibration Non-linearity Channel 6 of MetOp-A minus NOAA-18 Channel 6 of MetOp-A minus NOAA-18 Before Inter-Calibration After SNO Inter-Calibration
    8. 8. Lapse Rate Climatology Average over the 70 0 S <ul><li>The averaged lapse rate around 350 hPa being steeper in winters (July) than in summers (January). </li></ul><ul><li>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. </li></ul><ul><li>NOAA-15 should have a positive frequency shift </li></ul>Channel 6 Measurement NOAA-15 Minus NOAA-18
    9. 9. Pre-launch Measured Frequencies for AMSU-A Channel 6 Frequency characteristics for AMSU-A Channel 6 from Mo [1996; 2006; 2007]. Units are in MHz. <ul><li>Measured frequency differences between different satellites are within 0.5 MHz. </li></ul><ul><li>These errors are so small that they wouldn't result in noticeable T b differences between satellites (0.01K) </li></ul><ul><li>Practically, these measured channel frequencies can be considered as the same for different satellites </li></ul><ul><li>The shift is a post-launch error </li></ul>Differences for all pairs: 0.5 MHz Measured Channel Frequency (Specification =54400 for all satellites) NOAA-15 54399.53 NOAA-16 54399.78 NOAA-18 54400.97 MetOp-A 54400.07
    10. 10. Methods to Determine the Actual Channel Frequency <ul><li>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 </li></ul><ul><li>Use NASA MERRA reanalysis surface data and atmospheric profiles (temperature, humidity, ozone, cloud liquid water, trace gases etc.) as inputs to the CRTM </li></ul><ul><li>MERRA data were interpolated into the N15-N18 SNO sites before being used by CRTM </li></ul><ul><li>Select different frequency shift values ( df ) in the simulation experiments </li></ul><ul><li>Analyze  T b (N15, df ) = T b (N15, f m + df ) - T b (N15, f m ) </li></ul><ul><li>f m : Measured Channel Frequency Value </li></ul><ul><li>df: Frequency Shift </li></ul>
    11. 11. Experimental Results <ul><li>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 </li></ul>Observed SNO time series over the Antarctic between NOAA-15 and NOAA-18 Simulated  Tb (N15, df )
    12. 12. Determine the Final Channel Frequency Value     <ul><li>Examine  Tb’, which is the T b differences between NOAA-15 and NOAA-18 at their SNO sites when NOAA-15 T b is adjusted by its simulated frequency shift </li></ul><ul><li>We expect the seasonal cycles in  Tb’ disappear when df equals to the actual channel frequency shift’ </li></ul><ul><li>The seasonal cycles can be measured by the amplitude, which should be equal to zero for df =actual channel frequency shift </li></ul>df o = 36.25±1.25MHz f a = f m + df o = 54435.73±1.25 MHz
    13. 13. Impact on SNO Time Series Channel 6 of NOAA-15 vs NOAA-18 Before Frequency adjustment Channel 6 of NOAA-15 vs NOAA-18 After NOAA-15 Frequency adjustment
    14. 14. Conclusion <ul><li>Method is developed to detect and determine the post-launch </li></ul><ul><li>channel frequency shift in AMSU-A observations onboard polar </li></ul><ul><li>orbiting satellites </li></ul><ul><li>NOAA-15 channel-6 frequency shift is determined </li></ul><ul><li>Methods are expected to be applicable to other satellites and other </li></ul><ul><li>channels, but analysis has to be done for each channel, since all </li></ul><ul><li>channels have different lapse rate climatology </li></ul><ul><li>Call for impact experiments on NWP accuracy improvement at </li></ul><ul><li>JCSDA; if positive, we need to work on more channels </li></ul><ul><li>Also call for provisional parameters for future AMSU-type instruments, </li></ul><ul><li>allowing calculating the frequency shift after launch </li></ul>

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