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JMA Activity in Sub-seasonal Forecasting

JMA Activity in Sub-seasonal Forecasting. Climate Prediction Division / JMA Yuhei Takaya WWRP/THORPEX/WCRP Sub-seasonal to Seasonal Implementation Planning Meeting 2-3 December 2011, Geneva Switzerland

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JMA Activity in Sub-seasonal Forecasting

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  1. JMA Activity in Sub-seasonal Forecasting Climate Prediction Division / JMAYuhei Takaya WWRP/THORPEX/WCRP Sub-seasonal to Seasonal Implementation Planning Meeting2-3 December 2011, Geneva Switzerland Thanks to M. Harada, N. Adachi, S. Matsueda

  2. Table of Contents • Review of plans • Integrated EPS on the next HPC(2012.6-) • Ongoing activity • More user-oriented and seamless products: Extreme Forecast Index (EFI), meteogram • MJO diagnosticswith Emphasis on sources of predictability at monthly time scale Climate Prediction Division, JMA

  3. Review of plansin sub-seasonal forecasting Toward “Seamless EPS (weekly - monthly)” • Further seamless forecast • Climate information • Forecast products • Monitoring-forecasting • Model development • Forecast system • More efficient • More accurate Images from http://visibleearth.nasa.gov/ Climate Prediction Division, JMA

  4. Integrated “seamless” EPS on the next HPC Week-1 Week-2 Week-3 Week-4 Weekly EPS (TL319L60) present 2-Week EPS (Early Warning) Monthly EPS (TL159L60) reforecast 2-Week EPS (TL479L100) reforecast FY2014 FY2013 Monthly EPS (TL319L100?) 2-Week EPS (Early Warning) reforecast 2-Week EPS (TL479L100) Monthly EPS (TL319L100) FY201? reforecast

  5. Ongoing activity • More user-oriented and seamless products: Extreme Forecast Index (EFI), meteogram Masashi Harada and Yuhei Takaya Climate Prediction Division, JMA

  6. Seamless climate Information on the extreme weather events “Seamless” Climate Information Future Past http://ds.data.jma.go.jp/tcc/tcc/products/climate/ Climate Prediction Division, JMA

  7. Extreme weather forecasting based on EFI • EFI (Extreme forecast index): Lalaurette (2003) • Measure of the difference betweena probabilistic forecast and a climate distribution Cumulative distributions offorecast and climate • Definition of the EFI(ECMWF Newsletter, No.107) climate Probability not toexceed threshold All forecast membersexceedthe 100 percentile of the climate forecast All forecast membersare below the 0 percentile of the climate Threshold(eg. 10m wind) from Lalaurette(2003)

  8. How to obtain probabilistic distributions? Forecast CDF JMA operational 1-month forecast (25 members for each initial day) average period(ex. 7days) time Hindcast data(150 members) Hindcast (reforecast) data (150 members: 5 members for each initial day, 30years) Climate CDF We produce climate distributionsfrom hindcast runs of two initial days

  9. Examples of EFI-products (1) EFI-Map • Time series of the EFI(top) and the probability distribution of the forecast and the model climate(7-days average, bottom) EFI time series - Meteogram EFI-map for 850hPa temperature averaged from 11/11 to 11/16 EFI-Meteogram at London(initial date: 2011/11/10) EFI forecast and model climate Extreme warm Extreme cold

  10. Examples of EFI-products (2) • Warning map for extreme weather events • Possibilities for various extreme weather eventsare summarized on one map. Extreme: EFI ≧ 0.8 Above normal: EFI ≧ 0.5

  11. Ongoing activity • Research related to MJO • MJO diagnostics: Emphasis on sources of predictability at monthly time scale Satoko Matsueda and Yuhei Takaya • Case study: MJO influence on extratropic circulation Kengo Miyaoka and Yuhei Takaya Shuhei Maeda Climate Prediction Division, JMA

  12. MJO index skill • MJO Skill of JMA monthly models • correlation coefficient falls below 0.6 on day 13 • predicted phase speed is faster than observed phase speed • predicted amplitude is smaller than observed amplitude RMSE COR Analysis Forecast Phase error(PERR) faster slower COR or PERR PC2 AERR Relative amplitude difference (AERR) larger smaller Lead time (day) RMSE PC1

  13. MJO Life cycle composite OLR/Wind200 Winter analysis model FT=2week

  14. Lag-correlation of OLR/U850 in Winter analysis model (FT=10day : lag=0) Lag correlation OLR : shaded U850 : contour Lag correlation OLR : shaded U850 : contour 15 15 FT=15day FT=10day FT=1day 0 0 -15 -15 180 180 0 0 0 0 Fig. Lag correlation of intraseasonal OLR (shaded) and U850 (contour) averaging 10S-10N at all longitudes against OLR and U850 at an Indian Ocean reference point (OLR:10S-5N,75-100E, U850:1.25-16.25S,68.75-96.25E). For hindcast, a forecast time of 10 days corresponds lag = 0. Eastward propagationof OLR/U850 anomaly is not well simulated.

  15. Case study of MJO influence on the extratropic circulation Hovmöller diagram of 200-hPavelocity potential anomaly averaged over 10N-10S. (2011/9/1-11/10) The second strongest MJO during last 30 years [W/m2] Climate Prediction Division, JMA

  16. Wave train dispersed from wave source generated by MJO OLR & Velocity Potential 200-hPa anomalies (2011/10/29 - 11/2) 5-day running mean temperature anomalies (Sep.-Nov. 2011) Nov. Sep. Oct. NorthernJapan [W/m2] EasternJapan Temperatures in Western Japan (+3.4) and Okinawa & Amami area (+2.2) were highest records (since 1961) for the first 10 days of Nov. 2011. WesternJapan Okinawa & Amami

  17. Was the event predicted?JMA monthly forecast for 10/28-11/3 (I.C. 10/20) 200-hPa velocity potential and divergent wind anomalies 850-hPa temperature Western Japan [K] 850-hPa temperature 10-6 [m2/s] 200-hPa stream function, rain anomalies [K] The week-2 forecast (I.C.: Oct. 20th) successfully predicted the event. [mm/day]

  18. Influence of MJO on Asian climate Composite : initial Phase 2(active convection in Indian Ocean) CHI200 analysis T850 analysis T850 JMA model FT= 1day FT= 6day -7 -3 0 3 7 10-6 [m2/s] [K] When active convection is in Indian Ocean, T850 anomaly pattern can be reproduced in Asia.

  19. Summary • Review of plans • Integrated “seamless” EPS on the next HPC(2012.6-) • Ongoing activity • More user-oriented and seamless products: Extreme Forecast Index (EFI), meteogram • Emphasis on sources of predictability at monthly time scale (MJO diagnostics) Climate Prediction Division, JMA

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