1. a. FY12-13 GIMPAP Project Proposal Title Page version 04 August 2011 Title: Fusing Goes Observations and RUC/RR Model Output for Improved Cloud Remote Sensing Status: New Duration: 2 years Project Leads: Andrew Heidinger / NOAA/NESDIS/STAR / Andrew.Heidinger@noaa.govAndrew.Heidinger@noaa.gov Stan Benjamin / NOAA/OAR/ESRL / Stan.Benjamin@noaa.gov Other Participants: Andi Walter / UW-CIMSS 1

2. b. Project Summary 2 With support from past GIMPAP and AWG funding, the GOES-R AWG cloud algorithms are running in real-time on current GOES data. Our validation indicates that this data is high quality and approaches the expected GOES-R performance for many scenarios. The Rapid Update Cycle Model (RUC) is the most successful NWP model in its use of cloud information from satellites. RUC is the only current NCEP model/assimilation system to assimilate either GOES cloud data or METAR cloud data. Currently, direct use of the cloud fields observed by satellites is found to introduce significant relative humidity (RH) biases in the RUC. This project aims to develop techniques that allow the RUC and the succeeding Rapid Refresh (RR) to use the GOES-R AWG cloud products and remove the RH biases. This will will lead to the greater use of GOES and GOES-R cloud products for NWP.

3. Example of GOES-R AWG Algorithms Running on GOES 3

4. c. Motivation / Justification 3-d hydrometeor fields for cloud water and ice mixing ratios are modified based on current METAR ceiling and GOES cloud retrieval data RUC is the only current NCEP model/assimilation system to assimilate either GOES cloud data or METAR cloud data The Rapid Refresh, scheduled to replace the RUC as an NCEP operational model in November 2011, uses a similar cloud/hydrometeor analysis (within GSI). Problem: It was discovered in Dec 2010 that cloud-building from GOES cloud retrievals resulted in a significant RH moist bias and higher RH forecast error. Solution: Revisit the cloud assimilation with the GOES-R AWG products. Develop ways to use the AWG uncertainties with the RUC/RR background fields to remove the RH biases introduced currently. 4

5. Incremental adjustment based on information from multiple observation types Cloud and Hydrometeor Analysis - Uses METAR, satellite, radar, lightning data - Updates RR 1h-fcst RR hydrometeor, water vapor fields - Generates latent heating from radar and lightning data

6. Discovery in Dec 2010 – Saturation from cloud building from satellite-based cloud data was causing a significant RH moist bias

7. d. Methodology Implement a real-time feed of GOES-R AWG cloud products from CIMSS to the ESRL RUC team to allow for experimentation. Work with the RUC/RR team to optimize the 3-d cloud hydrometer distributions using the initial RUC analysis and the results (including the uncertainties) from the Optimal Estimation (OE) cloud retrievals with the existing Radar and METAR data. Use CALIPSO and CloudSat data when possible for verification. The RUC/RR team will develop the techniques to use the GOES-R AWG properties and lead the efforts to ensure the satellite derived 3d hydrometer fields are beneficial to the RUC/RR. 7

8. e. Expected Outcomes Usage of the NESDIS GOES-R AWG cloud products by the RUC/RR ESRL team. A new version of the GOES-R/GOES AWG algorithms that is optimized for RUC/RR support. Modification of the RUC/RR assimilation system to use the GOES-R/GOES cloud products (with Radar and METAR) in way the improves model forecast skill. 8

9. e. Possible Path to Operations The GOES-R AWG cloud algorithms are already slated to become operational via GSIP-fd. GSIP-fd updates can occur via the G-PSDI program. Improvement to the RUC/RR data assimilation scheme would be made operational via the GSI. 9

10. f. Milestones Year 1: CIMSS generates test cases as directed by ESRL and initiates real-time feed of satellite data to ESRL. ESRL conducts experiments during test cases and works with CIMSS to modify the GOES-R AWG retrieval output and the RUC/RR assimilation scheme. Year 2: CIMSS setups real-time data stream to ESRL on data server purchased in Year 1. CIMSS generates final version of GOES-R algorithms that include all modifications and enhancements needed for RUC/RR use. ESRL RUC/RR team finalizes assimilation scheme and generates a final report on the use of the GOES-R cloud products and their impact on the RH bias issue. 10

11. g. Funding Request (K) Funding SourcesProcurement Office Purchase Items FY12FY13 GIMPAPStAR Total Project Funding 9390 StAR Grant to CI 65 StAR Federal Travel 35 StAR Federal Publication StAR Federal Equipment 10 StAR Transfers to other agencies 7480 Other Sources AWG/JPSS 11

12. g. Spending Plan FY12 FY12 $93,000 Total Project Budget 1.Grant to CI - –% FTE - 0 – we will cover this effort on AWG and JPSS projects –Travel - $6K – we would like Andi Walther to travel to ESRL in hr 4 th quarter of Fy2012 –Publication charge - $0K 2.Federal Travel – $3K - Andrew Heidinger travel to ESRL in the 4 th quarter of Fy2012. 3.Federal Publication Charges – $0 K 4.Federal Equipment - $10 K – data server to be installed at CIMSS 5.Transfers to other agencies – $74 K to the ESRL RUC/RR team 6.Other - 12

13. g. Spending Plan FY13 FY12 $90,000 Total Project Budget 1.Grant to CI - –% FTE - 0 – we will cover this effort on AWG and JPSS projects –Travel - $5K – we would Andi Walther to travel to ESRL –Publication charge - $0K 2.Federal Travel – $3K - Andrew Heidinger travel to ESRL 3.Federal Publication Charges – $0 K 4.Federal Equipment - $0K 5.Transfers to other agencies – $80 K to the ESRL RUC/RR team 6.Other - 13