NOAA Inter-Calibrated MSU/AMSU Radiance FCDR Cheng-Zhi Zou 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. NOAA/NESDIS/Center for Satellite Applications and Research GSICS GRWG/GDWG Annual Meeting, March 25-28, 2014

MSU/AMSU Sounding Instruments MSU; 1978-2007 AMSU; 1998-present MSU/AMSU covering time period from 1979-present Have a total of 14 channels Generally, each channel has its own characteristics for calibration Involving 15+ satellites This project focus on developing MSU/AMSU atmospheric temperature CDRs. The window channels will be addressed by another NOAA group. In summary, we work on 15 atmospheric temperature channels, involving 15 satellites, with tempral coverage from late 1978 to present. Each channel needs to be intercalibrated seperately. 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. Left: Weighting functions for the MSU and SSU instruments, where the black curve represent the MSU weighting functions and the dashed and red curves are the SSU weighting functions for different time period, showing a shift due to an instrument CO2 cell pressure change; Right: Weighting functions for AMSU-A. All weighting functions are corresponding to nadir or near-nadir observations.

IMICA Methodology Formerly known as SNO Method Physically based approach to remove satellite biases Use simultaneous nadir overpass as applicable Use global ocean means as applicable—diurnal drift effects are small, can be used to characterize calibration errors Use CRTM simulations as applicable Develop consistent FCDRs for climate applications Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

Calibration Equation—based on observational principles and allowing inter-calibration Nonlinear Calibration: one set of calibration coefficients for all scan positions (Cw, Rw) (Ce, Re) RL is the linear calibration term Radiance (R) { It is a design goal to make the calibration as linear as possible. If the calibration is linear, then the two calibration points will uniquely determine a linear calibration equation, where the earth scene radiance can be obtained as linear interpolation from the two calibration points. This is shown by the straight line in this plot. However, in reality, the MSU instrument contains a week quadratic nonlinear term, and the final equation looks like this. The nonlinearity is measured by this nonlinear coefficient MU. mZ (Ce, RL) S Slope (Cc , 2.73K) Z is the quadratic nonlinear term Digital Counts (C)

Bias Types I and II: Constant bias and relatively stable bias drift NOAA-16 raw counts drifted for all Earth and target views, suggesting degradation of certain parts Inter-satellite difference time series showing NOAA-16 had a relatively stable drift Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members. Before Inter-calibration After inter-calibration (allowing calibration coefficients to change with time)

Bias Type III: Instrument Temperature Related Radiance Variability – much more complicated NOAA operational calibrated inter-satellite difference time Series (Before Inter-satellite calibration); Ocean Mean NOAA-15 CH6 has strong calibration nonlinearity; pre-launch calibration inaccurate After recalibration, inter-satellite differences close to zero Recalibrated trend is expected to be different from NOAA-15 σ : ~ 0.1 K The project is recently funded by the NOAA SDS program. A science team for the CDR development is established. Inter-satellite difference time series after SNO Inter-satellite calibration, AMSU-A CH6 σ: ~ 0.03 K Warm Target temperature highly correlated with Solar Beta Angle

Bias Type IV: Scene Temperature Dependent Biases These plots compare scatter plots before and after SNO calibration. These plots are the SNO matchups between NOAA 10 and 11. The upper left panel is a SNO radiance comparison for linear calibration. We see there is a bias of 0.3 K, not bad for weather applications. Also this bias appears to be dependent on the temperature. The lower left panel shows the differences of the radiance which can see this more clearly. When the temperature is larger, these differences become larger. This can be seen that for large temperature, most points fall below the zero line. The right panel is after the SNO nonlinear calibration. By definition of regression, the bias is removed completely. Also, the temperature dependency in the biases disappear. This suggest the nonlinear calibration works well. Before inter-calibration After non-linear calibration Root causes: Inaccurate calibration nonlinearity

Bias Type V: Channel Frequency Shift 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 Channel 6 of NOAA-15 vs NOAA-18 Before Frequency adjustment Channel 6 of NOAA-15 vs NOAA-18 After NOAA-15 Frequency adjustment

Data Production Determine calibration coefficients and channel frequency shifts offline channel by channel (multiple years of work) Use SNOs, global ocean means, and CRTM simulations, and other tools as needed in the process Reprocess Level-1b data--use new calibration coefficients to generate a new set of Level-1c radiances for all channels Use quality control inherited in level-1b files and plus more quality assurance procedure And also, for temperature channels, we don’t have the dynamic range problem. Which means the SNO ranges covers 80% of the global temperature, which is not bad at all compare to the water vapor channels.

Operational Distribution NCDC website: http://www.ncdc.noaa.gov/cdr/operationalcdrs.html Data name: AMSU Brightness Temperature--NOAA Use Agreement, FTP, Algorithm Description, Data Flow Diagram, Maturity Matrix Data name: MSU Brightness Temperature--NOAA AMSU updated every month Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

GSICS Demonstration/Pre-Operational GSICS Review Completed: 12/03/2013 Products go to GSICS Pre-Operational Phase (NDCD website is linked to GSICS products catalog) Open to GSICS users Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

NSMC/ECMWF MSU/AMSU Work Assimilated Operational Calibrated MSU/AMSU-A radiances into NWP models Hypothesized MSU/AMSU-A channel frequency had a shift/drift errors Find optimal channel frequency shift values to minimize daily/hourly model-observation errors Interesting work, results sparks some personal thought on methodology and conclusion Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

NOAA MSU/AMSU Work In Zou and Wang (2011), most NOAA-16 AMSU-A channels had calibration drifts and they were removed Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

NOAA MSU/AMSU Work In Zou and Wang (2011), most NOAA-15 AMSU-A channels had calibration drifts and they were removed Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

Comparisons between NOAA and NSMC/ECMWF work – channel 7 NOAA-16 frequency drift – Is it possible that calibration drift interpreted as channel frequency drift? Zou and Wang (2011) work: Lu and Bell (2014) work: Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

Comparisons between NOAA and NSMC/ECMWF work – channel 6 In Zou and Wang, bias drift, nonlinearity, and frequency shift all contributed to the NOAA-15 differences; In Lu and Bell, all model-obs differences were assumed to be from frequency shift and drift in NOAA-15 Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members. Lu and Bell (2014) Zou and Wang (2011)

Reasons for the difference? Was the drift related to frequency shift or some other reasons ? Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

Reasons for the model-observation difference Is it possible that the shift actual a model errors in geopotential or hight? Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

Future work Repeat Lu and Bell work using NOAA IMICA calibrated FCDR Understand if radiosonde observations can have pressure measurement errors that may result in model simulated height errors? Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.