1. 1 Group 1: Modeling and Data Assimilation Summary: 1. HPC access, HPC access, HPC access 2. resources: personnel inside (e.g. EMC) and externally funded (NOAA, other projects, visitors), code documentation/wikis, computing (e.g. CFSv3 50PB!) 3. "Easy" access to model codes and development tools, e.g. a "hierarchy" of model components, metrics 4. communication --on above with EMC points of contact 5. Efficient R2O process involves above 6. sustained investment (by EMC, NCEP, NWS, NOAA...)

2. Facilitating external collaboration in CFS development The primary task of CFS is operational coupled ensemble prediction between 2 weeks and a year. Our vision is that all NCEP prediction models be the most skillful in the world, and their outputs be freely, widely and wisely used. The US has the largest and strongest scientific community in weather and climate science in the world. This community would embrace a CFS designed to facilitate community use, testing and collaborative model development, in which they have a stake. This process has begun but to transform CFS it must go much further. Keeping the CFS tightly unified with GFS will leverage the resources of the US weather community and allow the software engineering and infrastructure needed for this vision. Chris Bretherton, UW 2

3. Examples of external CFS users Use CFS outputs and reanalyses (already active) for diverse applications (hydro, fire, ag, health, etc.) Use CFS as part of an MME (already active) Run CFS for fundamental analysis of climate variability or extreme events Run CFS for predictability and attribution studies Improve data assimilation methodology / OSSEs Improve model physics and dynamics; reduce biases Add new earth system processes (e.g. chemistry) Improve ensemble statistics … Chris Bretherton, UW 3

4. Moving to this vision With enough R2O support, a large active pool of external model users accelerates system improvement, as they are motivated to help solve the problems they encounter Required elements of such R2O support ✓ CPT-like support for transitioning promising high-priority improvements to operations ✓ Clear metrics to measure model improvement ✓ Clear internal strategic plan for model development - Comprehensive on-line documentation - Extensive, user-friendly run scripts, input files, and basic diagnostic packages on accessible HPC. - Clear internal EMC points of contact A sustained investment in these elements pays off! Chris Bretherton, UW 4

5. TOWARDS BUILDING A PROTOTYPE OF THE NEXT GENERATION UNIFIED GLOBAL COUPLED ANALYSIS AND FORECAST SYSTEM AT NCEP (UGCS ) Earth-Next Dreamliner 5

6. Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere  Data Assimilation cycle  Analysis frequencyHourly  Forecast length9 hours  Resolution10 km  Ensemble members100  Testing regime3 years (last 3 years)  Upgrade frequency1 year UNIFIED GLOBAL COUPLED SUITE DATA ASSIMILATION 6

7. Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere  Weather Forecasts  Forecast length10 days  Resolution 10 km  Ensemble members10/day  Testing regime3 years (last 3 years)  Upgrade frequency1 year UNIFIED GLOBAL COUPLED SUITE WEATHER FORECASTS 7

8. Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere  Sub-seasonal forecasts  Forecast length6 weeks  Resolution30 km  Ensemble members20/day  Testing regime20+ years (1999-present)  Upgrade frequency2 year UNIFIED GLOBAL COUPLED SUITE SUB-SEASONAL FORECASTS 8

9. Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere  Seasonal Forecasts  Forecast length9 months  Resolution50 km  Ensemble members40 (lagged)  Testing regime40+ years (1979-present)  Upgrade frequency4 year UNIFIED GLOBAL COUPLED SUITE SEASONAL FORECASTS 9

10. Resources: Human (Building the Dream Team) Climate Team Lead: Suru Saha ( Care-Taker-Acting) 10 ComponentData AssimilationModel AtmosphereJack Woollen, Malaquias Pena Daryl Kleist Shrinivas Moorthi Yu-Tai Hou LandJiarui Dong Jesse Meng Helin Wei Rongqian Yang OceanDave Behringer (MOM) Steve Penny (MOM) Xu Li (NSST) Avichal Mehra (HYCOM) Jiande Wang (MOM) Zulema Garaffo (HYCOM) Sea-IceXingren Wu Bob Grumbine WavesPaula EtalaArun Chawla Henrique Alves AerosolsSarah Lu Code ManagerNEMS InfrastructurePatrick TrippMark Iredell Diagnostics/ Verification/single column model, physics component simulators Suru Saha, NCEP/EMC

11. Resources: Compute/Storage 11 Development work on WCOSS, with collaborations on Theia, Gaea, etc. Model code/scripts on EMC/Svn. Production/reforecasts -- “Cloud Computing”? (has to be done on a computer we can manage ourselves with dedicated queues and computer hours –”dedicated resources”). TBD NLT Jan 2017. Storage/Dissemination: HPSS, the “Cloud”(?) –e.g. Amazon and/or Internal/NCO. TBD NLT Jan 2017. Resolution upgrade (4x) and number of ensemble members (5x) = 20x ~50PB! Suru Saha, NCEP/EMC

12. Collaborations 12 Svn: codes, scripts to be checked out by collaborators, including single column model, physics component simulators. “Test Harness” 9 years/seasons of warm/cold/neutral ENSO (for fast turnaround to test e.g. physics changes): May starts -summer (JJA) Nov starts -winter (DJF) Control runs to be done at EMC, with initial & boundary conditions, etc available. Corresponding Diagnostics and Verification Packages, “Climate Scorecard”. Visiting Scientist Program to promote R2O, example to invite leading scientists to visit EMC for 1-12 months. Suru Saha, NCEP/EMC

13. Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere  Predictions for all spatial and temporal scales will be ensemble based.  There will be a continuous process of making coupled Reanalysis and Reforecasts for every implementation (weather, sub-seasonal and seasonal)  Since the resolution of all parts of the system is usually increased with every new implementation (in proportion to the increased computing power currently available), there is the possibility of exploring cheaper cloud computing and storage options for making these reanalysis/reforecasts. UNIFIED GLOBAL COUPLED SUITE NEW PARADIGM 13

14. Coupled Model Ensemble Forecast NEMS OCEAN SEA-ICE WAVE LAND AERO ATMOS Ensemble Analysis (N Members) OUTPUT Coupled Ensemble Forecast (N members) INPUT Coupled Model Ensemble Forecast NEMS OCEAN SEA-ICE WAVE LAND AERO ATMOS NCEP Coupled Hybrid Data Assimilation and Forecast System 14