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1 . Fy13 GIMPAP Project Proposal Title Page version 01 August 2013

1 . Fy13 GIMPAP Project Proposal Title Page version 01 August 2013. 02. Title : Fusing Goes Observations and RUC/RR Model Output for Improved Cloud Remote Sensing Status : Continuation Duration : 2 years (1 year remaining) Project Leads:

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1 . Fy13 GIMPAP Project Proposal Title Page version 01 August 2013

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  1. 1. Fy13 GIMPAP Project Proposal Title Pageversion 01 August 2013 02 Title: Fusing Goes Observations and RUC/RR Model Output for Improved Cloud Remote Sensing Status: Continuation Duration: 2 years (1 year remaining) Project Leads: Stan Benjamin / NOAA/OAR/ESRL / Stan.Benjamin@noaa.gov Andrew Heidinger / NOAA/NESDIS/STAR / Andrew.Heidinger@noaa.gov Other Participants: Andi Walter / UW-CIMSS

  2. 2. Project Summary • The GOES-R AWG cloud algorithms have been modified for and are running 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 Refresh Model (RR) is the most successful NWP model in its use of cloud information from satellites. RR 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. • This project aims to develop techniques that allow the Rapid Refresh (RR) to use the GOES-R 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. 3. 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 • RR is the only current NCEP model/assimilation system to assimilate either GOES cloud data or METAR cloud data • The Rapid Refreshreplaced 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 retrievaluncertainties with the RUC/RR background fields to remove the RH biases introduced currently. Retrievals will lean on NWP vertical structure where observations are ambiguous.

  4. 4. Methodology • Implement a real-time feed of GOES-R analog 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 GOEScloud properties and lead the efforts to ensure the satellite derived 3d hydrometer fields are beneficial to the RUC/RR.

  5. Methodology Schematic We propose two techniques to increase the utility of GOES cloud products: 1. Use OE metrics to allow RR model to ignore uncertain satellite results True State Retrieved State + O.E Uncertainty Assimilation Impact 2. Use NWP Vertical Structure to make NWP-consistent retrieval where observations are uncertain. Retrieved State + O.E Uncertainty Assimilation Impact True State NWP Profile

  6. 5. Expected Outcomes • Increased usage of the NESDIS GOESImager by the RUC/RR ESRL team. • A new version of the GOESImager cloud height algorithm that is optimized for RUC/RR assimilation and help solve their RH Bias issue. • 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.

  7. 6. First Year - Preliminary Results • Funding arrived at ERSL in June, 2012 and work will commence in August 2012. • Experimental feed of CIMSS/STAR cloud properties setup at CIMSS and serves all GOES E/W data. (excluding RSO). • Hardware for this processing is ordered but not installed. Hardware will allow for processing of full resolution (currently data is sampled along scan-direction) • Bob Lipschutz from GSD has already accessed the GOES data on CIMSS ftp server. Methodology to translate to BUFR or GRIB is being finalized.

  8. 6. First Year - Preliminary Results • Recent Rapid Refresh cloud developments: • Feb 2012 - completed testing of partial restoration of GOES cloud building in ESRL RAP but only for GOES cloud top < 1200m AGL (above model ground level) • May 2012 - tested full-column GOES cloud building using 3km HRRR background as a demonstration for 3km cloud assimilation.  No evaluation of induced moist bias, but successful 3km application was a first. • July 2012 - new list of hypotheses and initial tests now being developed to address moist bias from full-column GOES cloud building (the target for our GIMPAP project).

  9. Preliminary NWP Fusion with Cloud Height Algorithm • Images show comparison of satellite derived cloud-top pressure (left) and NWP forecasted cloud-top pressure (right). • Satellite retrieval used NWP as first guess where deemed appropriate. • These results used GFS but work with HRR is beginning (same technique). • Results are preliminary and proper weighting of NWP results in Optimal Estimation is just beginning. • Fusion will tap cloud vertical structure in NWP models to try and get satellite retrievals that are more consistent with NWP where observations allow.

  10. 7. Possible Path to Operations • The GOES-R AWG cloud algorithms are already slated to become operational via GSIP-fd (short-term) and other efforts funded by G-PSDI and led by Walter Wolf. 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.

  11. 8. FY13 Milestones • CIMSS setups real-time data stream to ESRL on data server purchased in Fy12 • 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 / cloud building issue.

  12. 9. Funding Request (K)

  13. 10. Spending Plan FY13 • FY13 $90,000 Total Project Budget • Grant to CI - • % FTE - 0 – we will cover this effort on AWG and JPSS projects • Travel - $3K – we would like Andi Walther to travel to ESRL • Publication charge - $0K • Federal Travel – $3K - Andrew Heidinger travel to ESRL • Federal Publication Charges – $0 K • Federal Equipment - $0K • Transfers to other agencies – $84 K to the ESRL RUC/RR team • Other -

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