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Bridging Climate Research and Operation
NWS Science and Technology Infusion PlanNWS Science and Technology Infusion Plan
for Climate Servicesfor Climate Services
NWS Science and Technology Infusion PlanNWS Science and Technology Infusion Plan
for Climate Servicesfor Climate Services
Jiayu ZhouJiayu Zhou
NWS/Office of Science and TechnologyNWS/Office of Science and TechnologyUpdated, 14 April 2004Updated, 14 April 2004
OutlineOutline
I.I. Introduction Introduction
II.II. Developing R&D NeedsDeveloping R&D Needs
1.1. Prediction Prediction
2.2. Modeling Modeling
3.3. Regional Services Regional Services
4.4. Precipitation Data & Hydrological Science Precipitation Data & Hydrological Science
III.III. Collaboration with Strategic Partners Collaboration with Strategic Partners
IV.IV. NWS STIP Opportunities NWS STIP Opportunities
I.I. IntroductionIntroduction
1.1. Climate System Climate System
2.2. NWS Vision of Climate Services NWS Vision of Climate Services
3.3. NWS STIP Concept and NOAA Climate Matrix Management NWS STIP Concept and NOAA Climate Matrix Management **
4.4. STIP Climate MissionsSTIP Climate Missions Identify and explore important developing S&T issues and making
report and recommendation to the leadership (SPARC, CPASW, CSA, EMC/GMB Climate briefing etc.)
Discuss R&D needs with scientists in operational centers and communicate the issues to the research community (NCAR, CDC, ARL, OGP, UM, GMU/COLA, FSU/COAPS etc.)
Provide consultation on science issues to local forecasters.
Invite distinguished researchers to address important S&T issues in S&T Seminar Series.
Make decision briefing to NWS Science subcommittee, when needed.
5.5. 2003 Review2003 Review
Learning from 2002/03 winter forecast failure
Identify problems
Visit NCAR, CDC*
Back
II.II. Developing IssuesDeveloping Issues
1.1. PredictionPrediction
Provide seamless suite of products and services
• Further improvement based on identified predictable Further improvement based on identified predictable information (information (El Nino, Soil Moisture, Trends El Nino, Soil Moisture, Trends ))
Forecast tools improvement Forecast tools improvement
• Exploration of predictability beyond current Exploration of predictability beyond current knowledge *knowledge *
North American monsoon system & warm season North American monsoon system & warm season prediction prediction
AO/NAO, MJO predictability AO/NAO, MJO predictability
Predictability of week 2 forecast Predictability of week 2 forecast
Stratosphere and troposphere interaction Stratosphere and troposphere interaction
Impact of solar flux variabilityImpact of solar flux variability
Back
2.2. ModelingModeling
Unified model strategy:Unified model strategy:
To build confidence in Earth System Models used To build confidence in Earth System Models used for the climate predictions, it is required that the for the climate predictions, it is required that the same models should be able to simulate natural same models should be able to simulate natural phenomenon on shorter time scales. phenomenon on shorter time scales.
It is only by validating model simulation on It is only by validating model simulation on shorter time scales, can we be certain about their shorter time scales, can we be certain about their realism and believe in their credibility for making realism and believe in their credibility for making climate change projections.climate change projections.
• Understanding recent model improvement and Understanding recent model improvement and the impact on climate forecast the impact on climate forecast **
• Continue to improve simulation of physics, Continue to improve simulation of physics, dynamics & chemistry processes and coupling dynamics & chemistry processes and coupling of atmosphere, ocean, land and sea ice models of atmosphere, ocean, land and sea ice models
Back
Back
3. Regional Services
Expand Climate Products and Services Regionally and Locally
• Regional predictability *
• Role of downscaling
• Linking weather extreme events to climate anomalies
• Climate testbed for regional services (Example*)
• RRegional climate data reanalysis
• Software development for regional climate services
XCLIMATE - A regional climate information search engine developed by Alaska Regional Office
III.III. Collaboration with Strategic Partners Collaboration with Strategic Partners
1. Seasonal Diagnostics Consortium
2. Routine Attribution / Forecast Discussions (CPC, IRI, CDC and Others)
3. OGP Supported Programs (NAME, LDAS, Etc.)
4. NESDIS/NCDC and NWS Joint FY05 Initiative for Ensuring Data Continuity for Observing Systems
5. Climate System Analysis
6. UW Prof. Donald Johnson and NWS/NCEP Interaction
7. NSF/NCAR Dr. Trenberth’s Recommendation
8. Proposed NOAA Silver Spring In-house Capability of Weather-Climate Connection Assessment and Prediction Operational Development *
Back
IV.IV. NWS STIP OpportunitiesNWS STIP Opportunities
1.1. Collaborative Science, Technology, and Applied Collaborative Science, Technology, and Applied Research Program Research Program
2.2. Cooperative Program for Operational Meteorology, Cooperative Program for Operational Meteorology, Education and Training (COMET) Outreach Education and Training (COMET) Outreach Program Program
NWS/CSDNWS/CSD
• R. ReevesR. Reeves
• M. TimofeyevaM. Timofeyeva
Team Composition (2002):Team Composition (2002):• Jiayu ZhouJiayu Zhou NWS/OSTNWS/OST
• Bob LivezeyBob Livezey NWS/OCWWSNWS/OCWWS
• Ed O’lenicEd O’lenic NWS/NCEP/CPCNWS/NCEP/CPC
• Martin P. HoerlingMartin P. Hoerling OAR/CDCOAR/CDC
• Richard W. ReynoldsRichard W. Reynolds NESDIS/NCDCNESDIS/NCDC
• Simon MasonSimon Mason IRI/UCSDIRI/UCSD
• Fiona HorsfallFiona Horsfall NWS/OCWWSNWS/OCWWS
NWS/NCEPNWS/NCEP
• L. UccelliniL. Uccellini
CPCCPC
• J. D. LaverJ. D. Laver
• A. KumarA. Kumar
• V. KouskyV. Kousky
• H. M. van den DoolH. M. van den Dool
• W. HigginsW. Higgins
• D. UngerD. Unger
• P. XieP. Xie
• P. Peng P. Peng
• E. YaroshE. Yarosh
• W. EbisuzakiW. Ebisuzaki
• W. Shi W. Shi
• C. LongC. Long
• L. HeL. He
• A. MillerA. Miller
EMCEMC
• S. LordS. Lord
• H.-L. PanH.-L. Pan
• K. MitchellK. Mitchell
• B. FerrierB. Ferrier
• D. BehringerD. Behringer
• Y.-T. Hou Y.-T. Hou
• S. HarperS. Harper
• W. WangW. Wang
• W. YangW. Yang
• R. GrumbineR. Grumbine
OAR/OGPOAR/OGP
• M. JiM. Ji
• A. BamzaiA. Bamzai
• R. LawfordR. Lawford
• J. HuangJ. Huang
• M. PattersonM. Patterson
NWS/OHDNWS/OHD
• Q. DuanQ. Duan
OAR/CDCOAR/CDC
• R. DoleR. Dole
Acknowledgements:
UWUW
• D. JohnsonD. Johnson
Climate Services
ObservationO
utre
ach
Forecast
UMUM
• E. KalnayE. Kalnay
NSF/NCARNSF/NCAR
• K. TrenberthK. Trenberth
• J. TribbiaJ. Tribbia
• G.MeehlG.Meehl
OAR/ARLOAR/ARL
• D. SeidelD. Seidel
NESDIS/ORANESDIS/ORA
• X. LiX. Li