Update to COPC 21 November 2013 Chuck Skupniewicz, FNMOC, UEO co-chair Yuejian Zhu, EMC, UEO co-chair Dave McCarren, NUOPC DPM

2 NUOPC National Unified Operational Prediction Capability 2 Agenda  NUOPC UEO Committee Update  NUOPC Verification Metrics through Oct 2013  NUOPC CMA Committee Update  Discussion

National Unified Operational Prediction Capability NUOPC 3 UEO Committee Update to COPC

4 NUOPC National Unified Operational Prediction Capability 4 NUOPC and the North American Ensemble Forecast System (NAEFS) Q: What’s the difference? A: Not much. NUOPCNAEFS Official PartnersNCEP, FNMOC, AFWA NWS, MSC, NMSM (Mexico) Global Model Systems NCEP+FNMOC+CMCNCEP+CMC Parameters, Metrics and Data Standards Same as NAEFSSame as NUOPC Data ServerNOMADS NAEFS focus is a coordination and sharing of research, development, production and distribution of ensemble members for weather forecasting at each agency. NUOPC focus is the centralized (common) and de-centralized (mission- unique) post-processing of a multi-model ensemble products.

5 NUOPC National Unified Operational Prediction Capability 5 Current Model Configurations

6 NUOPC National Unified Operational Prediction Capability 6 Future Model Configurations

7 NUOPC National Unified Operational Prediction Capability Evaluate candidate NUOPC products NCEP and FNMOC will execute reanalyses of agreed upon periods Reanalysis will include CMC ensemble members At a minimum reanalysis will use the NUOPC metrics Approve selection of NUOPC products to be made available for user access Approve final implementation decisions with oversight from NUOPC ES G 7 According to the UEO Operational Management Plan signed by AFWA, FNMOC, and NCEP, Operational Prediction Centers will

Delivery Schedule +6:00 +7:00 +8:00 +9:00 +10:00 +6:10 raw members delivery +7:00 Level 1 Single model products: - L1 debiased members - Single model statistics +9:00 to 10:00 Level 3 multi-model products: - group statistics -downscaled products +8:00 Level2 Multi-model products: - L2 group debiased members - group statistics Ensemble model runs completed transmit raw post-process, transmit L1 & statistics transmit partners’ L2 post-process, transmit L3 downscaled products & group stats 1.“statistics” include mean, std dev, and threshold probabilities 2.“group statistics” are for the multi-model ensemble 3. “L1” is single model bias corrections by each center partners’ raw post-process, transmit L2 & group statistics transmit partners’ L1 4 “L2” is multi-model calibration based on L1 (e.g. adjustment with NCEP analysis 5. “L3” is user (or OPC) specified products based on L2 (e.g. downscaling probabilistic products) 6.Partners may split responsibilities for group debiasing or statistics Notes Planned for 2014 Exists today

9 NUOPC National Unified Operational Prediction Capability UEO Plans  All 3 agencies have agreed to a multi-month, multi-model validation during this winter using most of the agreed upon NUOPC metrics.  Each agency will share their results and make recommendations through the UEO committee. Each agency’s focus will be on statistical products most useful to their customer base.  The UEO will present consensus recommendations at the COPC spring meeting. This will include recommendation on shared production responsibilities.  COPC-approved multi-model products will be produced and distributed to the NOMADS server.

NUOPC Verification Metrics EMC/NCEP November 1 st 2013 For all three individual bias corrected ensemble forecast (NCEP/GEFS, CMC/GEFS and FNMOC/GEFS) and combined (NUOPC) ensemble (equal weights) against UKMet analysis

Ratio of RMS error over spread Under-dispersion Over-dispersion NH 500hPa anomaly correlation NH 500hPa RMS errors NH 500hPa CRPS skill scores 5-day forecast Northern Hemisphere 500hPa height: 30-day running mean scores of day-5 CRPS skill score RMS error and ratio of RMS error / spread Anomaly correlation All other regions could be seen from: aefs/VRFY_STATS/T30_P500HGT aefs/VRFY_STATS/T30_P500HGT

10-day forecast Northern Hemisphere 500hPa height: 30-day running mean scores of day-10 CRPS skill score RMS error and ratio of RMS error / spread Anomaly correlation All other regions could be seen from: fs/VRFY_STATS/T30_P500HGT fs/VRFY_STATS/T30_P500HGT

5-day forecast for surface temperature NH RMS errors NA RMS errors NA CRPS skill scores NH CRPS skill scores

10-day forecast for surface temperature

5-day forecast for surface wind (U) 10-day forecast for surface wind (U)

5-day forecast for surface wind (V) 10-day forecast for surface wind (V)

AL01-13, EP01-17, WP03-29, May-October, 2013 Forecast hours CASES Track error(NM)

NA T2m

National Unified Operational Prediction Capability NUOPC 19 CMA Committee Update to COPC

National Unified Operational Prediction Capability NUOPC National Unified Operational Prediction Capability NUOPC Common Model Architecture Draft whitepaper sent to Liaisons for review on 4 Sep. Being developed into BAMS article to address National Research Council report “A National Strategy for Advancing Climate Modeling” Focus on advanced capability and interoperability through Earth System Prediction Suite ESPS is collection of Earth system component and model codes that are interoperable, documented, and available for integration and use ESPS implementation is part of a project awarded under ESPC entitled: An Integration and Evaluation Framework for ESPC Coupled Models ESPS website with draft inclusion criteria and list of candidate models (Coupled, Atmosphere, Ocean, Ice, and Wave) 20

National Unified Operational Prediction Capability NUOPC 21 NUOPC Layer Roadmaps The current set of roadmaps using NUOPC Layer involves the following codes: –Navy NAVGEM and HYCOM coupled system –Navy COAMPS coupled system –NOAA Environment Modeling System (NEMS) from NOAA NCEP EMC –NOAA Climate Forecast System (CFS) from NOAA NCEP EMC –WaveWatch 3 model from NOAA NCEP EMC and NRL –MOM5 ocean model from GFDL and CICE sea ice model from Los Alamos –GEOS-5 atmospheric general circulation model from NASA Goddard Space Flight Center –The Ionosphere Plasmasphere Electrodynamics model from the NOAA Space Weather Prediction Center –NASA Goddard Institute for Space Studies Model E –Community Earth System Model from NCAR/DOE There are development pathways that traverse multiple groups, and outcomes that are interrelated –Implementing GFDL MOM5 as a NUOPC component, coupling this to a NOAA NEMS atmosphere component, and exploring the use of this system as the architecture for the next version of CFS –Reconciling multiple versions of the HYCOM ocean model, and using the resulting NUOPC HYCOM version in NEMS. A proposed activity would also couple this version of HYCOM to CESM.

22 NUOPC National Unified Operational Prediction Capability 22 National Unified Operational Prediction Capability NUOPC Common Model Architecture ESMF v6.3.0r expected release Dec 2013 NUOPC Layer upgrades in ESMF v6.3.0r Developed new user orientation material with prototype codes -- Implemented standardization of component dependencies -- establishing a standard way for assembling NUOPC compliant components into a working application Implemented NUOPC Layer compliance testing tools: NUOPC Compliance Checker & NUOPC Component Explorer NUOPC Layer Reference and prototypes extended to include data-dependencies during initialize, standardization of component dependencies, compliance, and multi-time level coupling 22

National Unified Operational Prediction Capability NUOPC 23 Questions & Discussion

The Earth System Prediction Capability (ESPC) Inter-agency Project

Phase 0: Ongoing Collaborative Programs (Operational short-range weather forecasting, research seasonal outlooks ) Inter-agency Global and Mesoscale Atmospheric Model Ensembles Hurricane Forecast Improvement Program (HFIP: 3-7 days) National Unified Operational Prediction Capability (NUOPC: 5-20 days) National Multi-model Ensemble (NMME: 3-6 months) Multi-model Ensembles are more accurate for longer lead times. Distributed Production Centers leverage multi-agency and international computer infrastructure and investments. Skill improves with spatial resolution - All are run at sub-optimal but best affordable resolution. Next-generation Global Atmospheric Cloud Resolving Models (GCRM) – DCMIP Candidates NMMB, FIM/NIM, Cubed Sphere, MPAS, NUMA, CAM-SE High resolution for regional high impact and extreme events Adaptive/unstructured mesh allows computational efficiency Potentially Improved prediction at weather to short term seasonal climate variability scales (5-100 days)

Phase I: ESPC Demonstrations (10 days to 1-2 years) Extreme Weather Events: Predictability of Blocking Events and Related High Impact Weather at Leads of 1-6 Weeks (Stan Benjamin) Seasonal Tropical Cyclone Threat: Predictability of Tropical Cyclone Likelihood, Mean Track, and Intensity from Weekly to Seasonal Timescales (Melinda Peng) Arctic Sea Ice Extent and Seasonal Ice Free Dates: Predictability from Weekly to Seasonal Timescales (Phil Jones) Coastal Seas: Predictability of Circulation, Hypoxia, and Harmful Algal Blooms at Lead Times of 1-6 Weeks (Gregg Jacobs) Open Ocean: Predictability of the Atlantic Meridional Overturning Circulation (AMOC) from Monthly to Decadal Timescales for Improved Weather and Climate Forecasts (Jim Richman)

Phase II: Decadal Prediction (5-30+ years) The decadal to multi-decadal prediction issue is more complex and more focused on the forced problem and limits of predictability Physical – solar variability, aerosols, volcanic, albedo, glacial and sea ice melt, ocean circulation and acidification, desertification… Biogeochemical – ocean microbial, migrations including human, plant and animal…. Societal – deforestation, agriculture, urbanization, industrial… Political – carbon limits, economic cycles, policy, water resources, warfare, … Leverage National and International ongoing efforts in defining “operational” capability at these timescales: availability and reliability of information against decision requirements and format and mechanism for operational product generation, validation, and distribution.

Phase I: Demonstration Goals (2013) An Implementation Plan for each Demonstration Project ( ) A better understanding of the bounds on prediction skill at various time and space scales in the current “skill nadir” at sub-seasonal to ISI lead times for specific aspects of the earth system important to decision makers ( ) Improved operational prediction for informed decisions (Full Operational Capability (FOC) by 2025) The Phase I Demonstrations seek to define: the current state of scientific understanding the current technological approach and maturity common skill metrics and case studies to explore areas of predictability that could lead to future operational prediction some measure of return on investment, i.e. computational cost vs. prediction skill of various approaches, resolution, etc.