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Integrating the Stony Brook Mesoscale Ensemble System into NWS Hydro-Meteorological Operations

This project focuses on integrating the Stony Brook University Mesoscale Ensemble System (MES) into National Weather Service (NWS) operations, specifically the Advanced Weather Interactive Processing System (AWIPS) and Site Specific Hydrologic Predictor (SSHP) tools. The objective is to enhance forecaster capabilities in performing mesoscale stochastic hydrology for small, fast-responding basins. Key data such as precipitation plumes, temperature, and wind members will be made accessible to enhance operational efficiency and improve predictive analysis for hydrology.

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Integrating the Stony Brook Mesoscale Ensemble System into NWS Hydro-Meteorological Operations

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  1. Integration of a Mesoscale Ensemble System into National Weather Service Hydro-Meteorological Operations Operational Aspects of the COMET Collaborative Project

  2. Participants • Stony Brook University: • Brian Colle, Mike Erickson • NWS New York City: • Jeff Tongue, Nancy Furbush, Matt Sardi, Nelson Vaz, Adrienne Leptich • NWS Philadelphia/Mount Holly: • Al Cope • NWS Eastern Region Headquarters • Josh Watson • NWS MARFC: • Joe Ostrowski

  3. Objective Integrate data from the Stony Brook University Mesoscale Ensemble System (MES) • Into AWIPS. • Into the Site Specific Hydrologic Predictor (SSHP) application.

  4. Why ? • Allow forecasters to perform mesoscale stochastic hydrology on small, fast responding basins.

  5. How?? • Provide WFO Forecasters with Mesoscale Ensemble Data to assimilate via AWIPS D2D (GFE) and the Web. • Provide WFO Forecasters with Mesoscale Ensemble Data to integrate into the Site Specific Hydrologic Predictor.

  6. Data • Web • AWIPS • GFE

  7. Webhttp://chaos.msrc.sunysb.edu/NEUS/nwp_graphics.html

  8. http://chaos.msrc.sunysb.edu/NEUS/nwp_graphics.html

  9. Precip Plumes

  10. AWIPS • 00Z 12/36 km 14 Member Ensemble data • Available ~11 UTC • 36 Hour Forecast • Volume Browser: • Members • Means/Standard Deviation • Probability of Exceedence

  11. AWIPS

  12. AWIPS – QPF Members

  13. AWIPS - Mean

  14. AWIPS – Temperature Members

  15. AWIPS – Wind Members

  16. AWIPS - Concerns • Installation Instructions are Available. • Still needs some “clean-up.” • New “build” issues. • Stony Brook is not an Operational Center.

  17. SSHP Integrating Stony Brook ESP into SSHP Deterministically: From the AWIPS GFE Stochastically: From the Stony Brook EPS

  18. Accessing SSHP

  19. 1. GFE QPF - Deterministic

  20. Convert 6 hour NDFD QPF to RR2 text product for use with SSHP Note that GFE 6-hr Pcpn is evenly distributed

  21. In SSHP Basins: • Lodi - Saddle R. • Gardiner – Wallkill R. • HoHoKus – Hohokus Brook • Springfield – Rahway R. • Beacon Falls – Naugatuck R. • Southbury – Pomperaug R. • Yantic – Yantic R.

  22. 2. EPS into SSHP (under development) • Experimenting with the Lodi Basin. • Stony Brook University is producing hourly resolution basin averaged precipitation. • RR2 Product (ftp’ed): • Mean • Worst Case scenario • Individual member data

  23. Ensemble mean accumulated 1 hr forecast for hour 32 Ensemble mean for the Lodi Basin

  24. GFE QPF SBU Ensemble Members Mean Average Worst case

  25. Selecting Basin QPF in SSHP

  26. Example Worse Case in SSHP

  27. Future Work • Automate the FTP of Data • Train Staff • Modify SSHP with help of OH to display ensemble hydrographs

  28. The Future in SSHP

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