Improving S2S Forecasting: The Science Perspective
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1 Duane Waliser Improving S2S Forecasting: The Science Perspective CAN WE REALLY PREDICT THE WEATHER? THE LATEST IN FORECASTING Sacramento Convention Center, Sacramento, CA November 9, 2017 Acknowledgements: B. Guan, M. DeFlorio, M. Ralph, S2S Project, J.Jones/DWR, NASA
2 Outline What is S2S forecas4ng? ü ü ü ü S2S defini4on How can we predict so far into future? Use hurricanes as an illustra4ve example and to introduce MJO Highlight MJO impact on long-lead mid-la4tude forecasts What does the U.S. NAS say about S2S? What is the WMO doing about S2S? What are we doing about S2S and ARs? ü ü Science: Quan4fying poten4al S2S forecast skill for CA Applica4on: Experimental S2S AR forecast ac4vity for CA
3 Forecast Lead Times Weather 0-14 Days Subseasonal 2-12 Weeks Seasonal 3-12 Months Interannual 1 year - Decade Climate Decades - Centuries Subseasonal to Seasonal (S2S) 2 weeks -12 months p.s. subseasonal aka intraseasonal
4 Weather Forecasts 0-14 Days Hurricanes Atmospheric Rivers For discussion on connec;ons between ARs, Tropical Moisture Exports (TMEs) and Warm Conveyor Belts (WCBs), see Cordeira (2015). cold spells, heat waves, thunderstorms/tornados, nor easters, santa ana winds, etc
5 S2S Forecasts: Hurricanes 2 12 weeks How can we predict hurricanes 2 weeks to 3 months in the future? Rather than try to predict the occurrence or evolu4on of a single hurricane at such long leads, we predict the likelihood of a hurricane or expected frequency of hurricanes? Can we do that? How do we do that?
6 S2S Forecasts: Hurricanes Typical Distribu;on
7 S2S Forecasts: Hurricanes More or Less than Usual Phase 2+3 Phase 4+5 Phase 6+7 CPC NCEP NOAA Phase 8+1 Weak/No MJO Courtesy M. Wheeler
8 Madden-Julian Oscilla4on (MJO) Important to S2S : MJO Life Cycle ~40-50 Days Intermidently Occur ~2-5 4mes/year Madden & Julian, 1972
9 Predic4ng the MJO Up to 4 Weeks Ahead MJO Forecast Skill vs Year 2015 > 30 days Forecast Lead 1995 ~7 days Courtesy ECMWF Vitart Madden & Julian, 1972 Great Progress in the last two decades!
10 S2S Forecasts: Hurricanes & MJO Predic4ng More or Less than Usual Up to 4 Weeks Ahead Phase 2+3 Phase 4+5 Phase 6+7 CPC NCEP NOAA Phase 8+1 Weak/No MJO Courtesy M. Wheeler
11 S2S Forecasts: MidLa4tudes & MJO Predic4ng Cold Spell 3-4 Weeks Ahead Cold March 2013 in N. America and N. Asia NCEP forecast at lead 4mes Days (when considering accurate forecasts of the MJO) Vitart and Robertson, 2017
12 S2S Forecasts: 3-9 Months? El Nino and La Nina is expected to provide an analogous source of predictability at lead 4mes of 3-9 months
13 U.S. Na?onal Academy of Sciences Study on S2S Forecas?ng Bold Vision: S2S forecasts will be as widely used a decade from now as weather forecasts are today Benefi4ng business, government and individuals Fulfilling this vision will take sustained effort and investment Supported by NASA, ONR and Heising-Simons Founda;on 2016
14 Fulfilling the Vision: Research Strategies 1. Engage Users 2. Increase S2S Forecast Skill 3. Improve Predic4on of Disrup4ve Events 4. Include More Earth System Components
15 Research Strategy 1 : Engage Users Example - Water Resource Management Improved S2S predic4ons of drought and the probability of atmospheric river events will: Support improved management of reservoirs, including drought management, flood control, and planning for hydropower Need engagement to understand cri4cal decisions and to produce forecast informa4on that fits water project/agency loca4on and 4ming needs
16 Research Strategy 2: Increase S2S Forecast Skill 1) Improve understanding of sources of S2S predictability Natural oscilla4ons e.g. ENSO, MJO, QBO, IOD, etc Slowly varying surface processes, e.g. snowpack, sea ice, soil moisture, etc 2) Improve models to beder represent these processes 3) Improve observa?ons to beder measure these processes. For long-lead S2S predic?ons, 1-3 mean globally. What happens far away mayers to a local S2S forecast.
17 World Meteorological Organiza?on (WMO) To improve S2S forecast skill. To promote use of S2S forecasts by decision makers Developed a 10-year ( ) research and applica4ons ini4a4ve referred to as the S2S Predic?on Project Involves 11 global opera4onal forecast centers A research quality database of hindcasts/forecasts Organizing research to improve S2S forecasts Engaging poten4al users, with trainng and pilot projects s2spredic?on.net
18 What are we doing about S2S and ARs? Global AR Detec4on Algorithm Based on modern understanding and datasets (e.g. Ralph et al. 2004). Developed for global & regional studies Applied to Reanalyses and Weather/ Climate Models Code and databases available at: o hdps://ucla.box.com/arcatalog Guan and Waliser (2015)
19 AR Distribu4on & Landfalls Guan and Waliser (2015)
20 S2S Forecasts: ARs Predic4ng More or Less than Usual Madden Julian Oscilla4on (MJO) El Nino Southern Oscilla4on (ENSO) La Niña El Niño Similar to hurricanes MJO and ENSO modulate number of ARs
21 S2S Forecasts: ARs Predic4ng More or Less than Usual Typical #ARs in 2 Week Period DeFlorio, Waliser, Guan, Vitart, Ralph (2017)
22 S2S Forecasts: ARs On average - How good are today s S2S models in forecas4ng #ARs in weeks 2&3? DeFlorio, Waliser, Guan, Vitart, Ralph (2017)
23 S2S Forecasts: ARs Orville Dam Region NCEP S2S Model Skill #ARs in Weeks 1&2 #ARs in Weeks 2&3 #ARs in Weeks 3&4 DeFlorio, Waliser, Guan, Vitart, Ralph (2017)
24 S2S Forecasts: ARs Northeast Pacific / W. Coast Forecast Skill for #ARs in 2 Week Period Forecast skill goes up if MJO is in Phase 7 Weeks 1&2 ECMWF Weeks 2&3 Weeks 3&4 Weeks 4&5 DeFlorio, Waliser, Guan, Vitart, Ralph (2017)
25 Future Ac4vi4es Con4nue fundamental research on ARs, MJO, ENSO, etc and related S2S predictability science and impacts studies, with an eye toward California applica4ons. In collabora4on with CW 3 E/M. Ralph and the WMO S2S Project, we are standing up an experimental S2S AR forecast ac4vity for winter and (u4lizing NCEP, EC and ECMWF forecasts). This ac4vity represents one of the proposed WMO S2S Project s Pilot Applica4on Projects.
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