a. FY12-13 GIMPAP Project Proposal Title Page version 04 August 2011 Title: Improving GOES retrievals through applied constraints Status: New (Type 2: Product Improvement – Retrievals) Duration: 2 years Project Leads: Daniel Birkenheuer/ ESRL/GSD/FAB – Other Participants: Seth Gutman/ ESRL/GSD/FAB – Kirk Holub / ESRL/GSD/FAB – Tomoko Koyama / CIRES (CU Boulder, graduate student) – 1

b. Project Summary Continue and support weekly interactions with NESDIS/StAR and CIMSS to characterize and track improvement of retrieval development (Ma to Li algorithm). Also, to improve the new algorithm after it becomes operational. Examine the feasibility of using a moisture constraint in the retrieval processing to improve the thermal result, an approach suggested by Jun Li, but yet to be explored. The work under GIMPAP would simply answer whether this is a path that would be cost effective. 2

c. Motivation / Justification Several years of effort have been invested in improving the retrieval products using GPS-met data including the generation of bias corrections, but this has proved a sub- optimal approach. 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. For the past 1.5+ 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. 3

d. Methodology Begin by continuing 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. This activity has proven itself to be useful in algorithm checkout and its future role should not only include new algorithm development, but also in the incremental improvements of the operational algorithm. 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. The advantage here is that the moisture profile is a primary dependent variable processed by the algorithm and the thermal retrieval could be likened to a secondary dependent variable. By constraining the moisture solution we hope to show that the thermal solution can be improved. This will be done by using the K-Matrix CRTM output and perturbing the moisture profile. The resulting derived thermal perturbation will allow us to characterize thermal improvement and understand whether or not actually pursuing this approach in future retrieval algorithm development will lead to thermal profile improvement. After the development of a test environment using CRTM for this experiment, several cases will be examined to understand whether this is a viable approach for achieving better retrievals. The advantage here is that we will be able to understand whether this approach deserves attention before we spend any resources on developing this solution. If it proves to be a worthy approach, additional resources in future proposals will follow to invest in direct exploitation of GPS-met in the retrieval system. 4

Illustrated methodology 5 Create delta-td CRTM –K matrix (Jacobians) Knowledge of delta T uncertainly in the thermal retrieval Begin with sounding 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 actual GPS-TPW and integrated retrieval moisture profiles

Relationship to Previous GIMPAP Projects (if applicable) This 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 taking in this new 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. The other 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 planned presentation to NWA*. * 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, Oct

e. Expected Outcomes Acceptance by developers that the Li algorithm is superior to the Ma and its advancement to operations (now occurring). Incremental improvements to the new Li algorithm after day-1 implementation in operations. (in the words of Jamie Daniels, incremental adjustments to the algorithm in coming years) Understanding of whether using GPS data to constrain moisture in the retrieval algorithm (directly) will offer any benefit to thermal results. Ideally we would hope that GPS-met technology can be transferred into the retrieval processing to improve not only moisture profiles but thermal profiles. This potential can be answered by funding this proposal. Ideally we would like to see GOES moisture products be superior to their first guess more frequently than the current situation. 7

e. Possible Path to Operations Current retrieval development has an established path to operations through the network currently in place. This proposal simply enhances development of algorithm improvements that normally take place. The established path to operations would be by current means. The investigation as to the possibility of constraining the moisture in the retrieval algorithm and its implication to thermal retrieval is a cost-benefit estimate. Whether this will be pursued through to operations will depend on the results revealed by this funded work, the decisions made by management, and the realities present in one to two years time. 8

f. Milestones Year 1: 2012 Create the CRTM-based algorithm to generate thermal profile sensitivity with regard to constrained moisture. (finished by Feb 2012) Begin evaluating thermal improvement sensitivity for various cases spanning season and latitude, and maybe weather event (type). (summer 2012) Continue interaction with retrieval developers at CIMSS and NESDIS. React to needs for web interface changes, manage the GPS network with GOES science needs in mind. Eliminate plans for publication on collaborated work on retrieval development with CIMSS using GPS. Year 2: 2013 Continue weekly teleconferences and make adjustments to our web pages for ease of use in comparing with developing retrieval changes. Depend on NESDIS-StAR and CIMSS for moving new code to operations per the normal operating procedures. (ongoing) Finish case studies with the thermal impact. (Nov 2012, compilation Feb 2013) Possibly begin closer interaction with developers depending on the results. Report – offer the value to assist in a course of action on implementation of using GPS data in retrieval moisture constraint. Journal Publication (submitted early summer 2013) 9

g. Funding Request (K) Funding Sources Procurement Office Purchase Items FY12FY13 GIMPAPStAR Total Project Funding 4340 StAR CIRES support funded through GSD 4030 StAR Federal Travel 35 StAR Federal Publication 05 Other Sources 00 10

g. Spending Plan FY12 FY11 $43,000 Total Project Budget note: needs to be updated to actual needs 1.Grants to CIRES via GSD – $40,000 2.Federal Travel – 2,000 (AMS, Dan), 1,000 (GIMPAP meeting, Dan) 3.Federal Publication Charges – none 4.Federal Equipment - none 5.Transfers to other agencies – none 6.Other - none 11

g. Spending Plan FY13 FY11 $40,000 Total Project Budget note: needs to be updated to actual needs 1.Grants to CIRES via GSD – $30,000 2.Federal Travel – $5,000 (refined at renewal time) 3.Federal Publication Charges – $5,000 4.Federal Equipment - none 5.Transfers to other agencies – none 6.Other - none 12