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1. FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012

1. FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012. 08. Title : Improving GOES Retrievals Status : Final Progress Report Project Leads: Daniel Birkenheuer/ ESRL/GSD/FAB – daniel.l.birkenheuer@noaa.gov Other Participants :

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1. FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012

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  1. 1. FY12-13 GIMPAP Project Proposal Title Pagedate: 7 August 2012 08 Title: Improving GOES Retrievals Status: Final Progress Report Project Leads: Daniel Birkenheuer/ ESRL/GSD/FAB – daniel.l.birkenheuer@noaa.gov Other Participants: Tomoko Koyama / CIRES (CU Boulder, graduate student) – tomoko.koyama@Colorado.edu Seth Gutman/ ESRL/GSD/FAB – seth.i.gutman@noaa.gov Kirk Holub / ESRL/GSD/FAB – kirk.l.holub@noaa.gov

  2. 2. Project Summary Supports weekly interactions with NESDIS/StAR and CIMSS to characterize and track improvement of retrieval development (Ma to Li algorithm). The major project examines the feasibility of using a moisture constraint in the retrieval processing to improve the thermal result. The goal is to characterize the impact to address its cost effectiveness, to help judge whether this should be pursued operationally. 2

  3. 3. Motivation / Justification • For the past 2.5+ years, GSD/FAB has been participating in a weekly telecon with CIMSS and NESDIS/StAR that has proven to be very useful for the retrieval developers to understand product performance using hourly GPS metrics for GOES moisture retrievals instead of synoptic metrics from RAOBS. Retrieval problems have been revealed in the course of routine collaboration and mutual comparison of retrieval quality. This proposal is in part to tie this into a project to help continue this interaction as currently we have no official project to really insure that this work can be suitably documented by GSD/FAB staff. • Can constraining the moisture in the retrieval processing to an accurate value (whether GPS-met or other data is used for this) improve the retrieved temperatures? • At the suggestion of Jun Li, a retrieval developer at CIMSS, GPS-met could play a stronger role in the retrieval processing, this proposal is a response to that suggestion and is a NEW and more direct approach. • Using CRTM sensitivity testing can be performed to establish the relative impact of such moisture constraints. 3

  4. 4. Methodology • Continue the GPS dialog with CIMSS and NESDIS developers that can take the modified retrieval algorithms and move them into operation. Much like the current implementation of the Li algorithm. You should know that the Li algorithm has been now shown, after about a year of work and comparison to GPS, to indeed be superior to the Ma retrieval system. Now we advance this technology to the Li algorithm checkout in DC. • The primary funded aspect of this proposal is to explicitly determine the impact of constraining the moisture retrieval’s total integrated water using the total moisture from GPS IPW during the retrieval processing. By this sensitivity study using CRTM, we can determine the impact of additional moisture constraints on the thermal retrieval outcome. • We used approximately 3400 soundings in the results that will be shown. • Surface data were treated as a separate level, soundings were cast to a uniform vertical grid. • This work had to terminate after the first year (initially planned for 2 years) due to unforeseen circumstances. • The advantage is that we will be able to understand whether this approach deserves attention before we spend any resources focusing on implementing such an approach. (This should be regarded as a cost/benefit or sensitivity assessment.) 4

  5. 4a. Product monitoring • Improved ESRL GPS-met tool (Kirk Holub) • Assist CIMSS and NESDIS StAR on Li and Ma algorithm performance • First guess improvement progress • Still see the first guess water vapor “out performing” the retrievals

  6. 4b. Questions to answer • Can constraining moisture in the retrieval processing improve the thermal result? • By how much? • Is this also important for GOES R and ABI? • Can we prepare now for GOES R in better fashion knowing the result?

  7. Illustrated methodology Begin with sounding 2 1 Create delta-td 3 4 CRTM – K matrix (Jacobians) CRTM – Forward Model Knowledge of delta-T uncertainly in the thermal retrieval Equations and step-by-step approach can be provided. Also refer to the appendix in the LOI. Derive expected delta-T from observed differences between forward modeled radiances and the thermal Jacobian. 7

  8. Execution • Discovered that the normalized channel weighting function needed to be applied to the derived vertical delta-T values for each channel. • The delta-T values from all channels used were then summed at each vertical level. • Excluded short-wave and ozone channels • Performed study for both current GOES and ABI • Used ~3400 sounding sample • Interpolated soundings to “standard” height grid.

  9. Relationship to Previous GIMPAP Projects(if applicable) • The first and minor aspect of this proposal is to use GPS data directly in the development phase of the retrieval algorithm not only to gauge success, but determine what changes in the retrieval algorithm are effective. Early successes in this regard are documented by Gary Wade (CIMSS) in a presentation to NWA*. • The retrieval sensitivity work relates to prior GIMPAP efforts in GSD/FAB insofar as earlier work only sought to improve retrievals after production. That approach was found to be less than optimal. • This approach was actually suggested by CIMSS as a potential way to improve retrievals during production. In the words of Jun Li, GPS-met could be looked upon as an independent “channel” to help satellite retrievals. This is the approach we are thinking of taking in the application of GPS data to the retrieval problem. And more specifically to investigate impact on the thermal retrieval by better moisture constraints. Moreover, the new improvements already have a built-in, direct path to operations. *Gary S. Wade, James P. Nelson III, Amerigo S. Allegrino, Seth I. Gutman, Daniel L. Birkenheuer, Zhenglong Li, Anthony J. Schreiner, Timothy J. Schmit, Jaime Daniels, and Jun Li, 2011: Transitioning Improvements in the GOES Sounder Profile Retrieval Algorithm into Operations, 36th NWA Annual Meeting, Birmingham, AL, 15-20 Oct. 2011 9

  10. 5. Expected Outcomes • Acceptance by developers that the Li algorithm is superior to the Ma and its advancement to operations (has occurred). • Ideally we would like to see GOES moisture products be superior to their first guess more frequently than the current situation. • Understanding of whether using GPS data to constrain moisture in the retrieval algorithm (directly) will offer any benefit to thermal results (shown to be true). • GPS-met technology or other moisture constraints can impact the retrieval processing to improve not only moisture profiles but thermal profiles. This potential payoff has been characterizedby this funded activity. 10

  11. 6a. First Year - Results GPS successfully tracked retrieval performance in transitioning code to DC Work continues and will into next year under AWG. Desire to continue working with CIMSS and StAR on this. Funding here was used exclusively by CIRES on the moisture impact study. 11

  12. Courtesy Gary Wade CIMSS – Li better than Ma, DC better than CIMSS

  13. 6b. First Year - Preliminary Results • Performed sensitivity test to moisture constraints on current GOES and ABI. • Added weighting function to the algorithm • Now have metrics to help determine the importance of constraining moisture to both current and future systems • Much more important in the ABI system (more than 2x) • Current GOES could be improved both near the surface and above 400 hPa, whether the magnitude of improvement is important is really a decision for the user. • Planned journal article

  14. Thermal Sensitivity to 1% Moisture Profile Uncertainty GOES blue ABI green

  15. Moisture Constraint Result • A 1% improvement in moisture will improve the thermal sounding slightly less than 0.1K near the surface and at upper levels. • The same improvement will be much more important to ABI. To achieve GOES 12-like ABI thermal soundings we need ~3x the water vapor constraint.

  16. 7. Possible Path to Operations • Include some means to constrain the moisture profile during retrievals. Suggest starting with GPS-met. • Follow-on study would be to see whether the impacts shown here are realistic. (CIMSS study) • Strongly consider applying a constraint to ABI where potential is shown here to be far more important. – Obvious spinup w/ current GOES is a natural progression.

  17. 8. FY13 Milestones Publish findings on sensitivity work Continue collaboration with CIMSS where possible using AWG funding unless we can apply GIMPAP funding to Federal labor.

  18. 9. Funding Request (K)

  19. 10. Spending Plan FY13 • FY13 $5,000 Total Project Budget • Grant to XXX– No satellite person to fund (lost graduate student) • Federal Travel – 2k – AMS fy13 satellite conference • Federal Publication Charges – 3k • Federal Equipment - None • Transfers to other agencies – None • Other - None

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