1. 1 Agenda Topic: National Blend Presented By: Kathryn Gilbert (NWS/NCEP) Team Leads: Dave Myrick, David Ruth (NWS/OSTI/MDL), Dave Novak (NCEP/WPC), Jeff Craven, Jim Sieveking, John Gagan (NWS/CR), Tom Hamill (OAR/ESRL/PSD), Jack Settelmaier (NWS/SR) Contributors: NWS Regions, NWS HQ, NCEP Centers, OAR/ESRL

2. The Growing Challenge: Consistency Issue: Local forecast offices work primarily to serve local user requirements. However, are state, regional, and national needs being addressed? Consequence: Inconsistencies across CWA or Regional lines can lead to challenges for IDSS on the state, regional & national scale. What do our partners think when they see sharp changes in our forecast grids? -It impacts their confidence in our forecasts -They remove NWS products from their briefings Need : To develop nationally consistent methodologies 2

3. 3 Operational System Attribute(s) First implementation December 2015 System NameAcronymAreal CoverageHorz Res Cycle Freq Fcst Length (hr) National Blend of global Models - CONUS NBMCONUS NDFD expanded domain 2.5 km2/day192 NBM - OceanicNDFD Oceanic10 km2/day192 NBM - AlaskaNDFD Alaska3 km2/day192 NBM - Hawaii, Puerto RicoNDFD HI, PR2.5 km2/day192 SystemAttributes MOSCalibrated GFS, linear regression models EKDMOSCalibrated GEFS and CMCE, linear regression models GFS, GEFSBias-corrected GFS and GEFS direct model inputs CMC EnsemblesBias-corrected, direct model inputs (GEM) RTMA/URMAAnalysis for bias correction and verification, future calibration System Data Assimilation or Initialization Technique

4. 4 Why System(s) are Operational  Primary stakeholders and requirement drivers Stakeholders: NWS field offices, NCEP centers, all users of NDFD Drivers: NAPA Report, Disaster Relief Appropriations Act of 2013NAPA Report  What products are the models contributing to? National Digital Forecast Database  What product aspects are you trying to improve with your development plans? Consistency of products through a common starting point, improve collaboration between offices Increased Decision Support Services Quantifying uncertainty through probabilistic output from ensembles Increased accuracy  Top System Performance Strengths Runs on the same supercomputer as the operational models Spatially consistent Extensible and maintainable Project will downscale model data, analyses and forecasts to a common elevation/terrain dataset  Top System Performance Challenges Never enough disk space to easily process ensemble members Need to be as good or better than the current regional blending techniques Complex project, not known yet how to optimally blend all fields

5. 5 System Evolution Over the Next 5 Years  Major forcing factors NAPA Report – “Forecast for the Future: Assuring the Capacity of the National Weather Service” Recommendation: Improve consistency of products and services across the organization NAPA Report – “Forecast for the Future: Assuring the Capacity of the National Weather Service” Recommendation: Improve consistency of products and services across the organization Support the goals of the Weather Ready Nation, i.e. Improve Weather Decision Services Support the goals of the Weather Ready Nation, i.e. Improve Weather Decision Services  Science and development priorities Complete development of all NDFD weather elements (days 3 – 8) for all NDFD CONUS and OCONUS domains in Phase 1NDFD weather elements (days 3 – 8)NDFD CONUS and OCONUS domains Add mesoscale models and aviation weather elements to improve days 1-3 Develop probabilistic products from calibrated ensembles to quantify uncertainty  What are your top challenges to evolving the system(s) to meet stakeholder requirements? High expectations, i.e. timelines, maturity of techniques Skill of current GFS/GEFS inputs less than skill of comparable ECMWF fields Requires large amounts of high-quality training datasets and disk space Requires large amounts of high-quality training datasets  Potential opportunities for simplification going forward Subversion repositories accessible from R&D machines to share common libraries with potential contributors Rely on free and accessible data

6. 6 Top 3 4 Things You Need From the UMAC 1.Advocate for retrospective runs before the models are upgraded Build into timelines so calibrated model output is ready for implementation with the model upgrades 2.Commitment to an Analysis of Record of sufficient length and quality for calibration, bias correction and verification For all weather elements in the National Digital Forecast Database and all forecast domains, including the Oceanic domain 3.Evaluate NOAA’s ability to meet its future requirements in a way that includes post-processing Can requirements be met through a combination of less computationally expensive post-processing and models? 4.Commitment to make America’s NWP the best global model in the world with full and unrestricted access.