1 / 18

Multimodel Superensemble Forecasts of Surface Temperature in the Northern Hemisphere

Multimodel Superensemble Forecasts of Surface Temperature in the Northern Hemisphere. Xiefei Zhi, Yongqing Bai, Chunze Lin, Haixia Qi, Wen Chen Nanjing University of Information Science & Technology Nanjing, China, 210044. Monterey, CA Sep 2009. Outline. Introduction Data and methods

bernard-caldwell
Télécharger la présentation

Multimodel Superensemble Forecasts of Surface Temperature in the Northern Hemisphere

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multimodel Superensemble Forecasts of Surface Temperature in the Northern Hemisphere Xiefei Zhi, Yongqing Bai, Chunze Lin, Haixia Qi, Wen Chen Nanjing University of Information Science & Technology Nanjing, China, 210044 Monterey, CA Sep 2009

  2. Outline • Introduction • Data and methods • Error evaluation • Multimodel superensemble forecast • Improved superensemble forecast • Summary

  3. Introduction Krishnamurti, T. N. et al (1999) in Science, Krishnamurti, T. N. et al (2000) in J. Climate Krishnamurti, T. N. et al (2001) in Mon. Wea. Rev. Postulated multimodel superensemble forecastmethod for weather and seasonal climateand compared the forecast skill of the multimodel forecasts with that of the individual models, the ensemblemean, and individually bias-removed ensemble mean.

  4. Introduction • The multimodelsuperensemble forecastsoutperform all the individual models. • The skillof the superensemble-based rain rates is higher than (a) individual model’s skills, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

  5. Ensemble Mean Model 1 Model 1 Evaluating the Forecast Skill of Superensemble Forecasts Superensemble Forecasting Model 2 Model 2 Model 3 Model 3 Model n Model n Error Evaluation of the Ensemble Mean Forecasts New Idea The work includes

  6. Data and Methods Data • 1) Ensemble forecasts of the temperature at 2m from ECMWF, JMA, NCEP and UKMO provided by TIGGE archives. Period: 1 June 2007 to 31 August 2007 Area: 10°-80°N ，0°-357.5°,with a resolution of 1.25°×1.25° Forecast: 24h-168h with a time interval of 24hrs • 2) NCEP/NCAR Reanalyses are used as “observational data” Period: 1 June 2007 to 7 September 2007 Area: same as that of dataset 1) with a resolution of 2.5°×2.5°

  7. Methods Superensemble: Bias-removed Ensemble Mean: Ensemble Mean: Root Mean Square Error:

  8. Creation of a superensemble forecast at a given grid point: . The weights aiare computed at each grid point by minimizing the function G in (1.5) Methods

  9. Error Evaluation Forecast errors of ensemble mean forecasts of each model

  10. b a c d Error Evaluation Mean RMSEs of the surface temperature in China, USA and Europe for (a) ECMWF, (b)JMA, (c)NCEP, and (d)UKMO (Unit：℃).

  11. JMA ECMWF NCEP UKMO Error Evaluation Comparisons among the four models Geographical distribution of the RMSEs of the 24h forecast

  12. (a) (b) (c) (d) (e) (f) (g) Multimodel superensemble forecast why? How to deal with it Mean RMSEs of the surface temperature forecast with fixed training period (a) 24h, (b)48h, (c)72h, (d)96h(e)120h, (f)144h and (g)168h

  13. Improved superensemble forecast with running training periods (b) (a) (c) (e) (d) (f) RMSEs of the improved surface temperature forecast for (a) 24h, (b)48h, (c)72h, (d)96h, (e)120h, (f)144h and (g)168h (Unit：℃). (g)

  14. 3 50 ECMWF 2.5 EMN 40 JMA 2 LRSUP 30 NCEP NNSUP 1.5 UKMO Rms error(℃) % Improvement 20 R-LRSUP 1 EMN 10 R-NNSUP LRSUP 0.5 0 NNSUP 0 24h 48h 72h 96h 120h 144h 168h R-LRSUP 24h 48h 72h 96h 120h 144h 168h Time (Hours) R-NNSUP Time (Hours) Improved superensemble forecast with running training periods Percentage improvement of the EMN, LRSUP, NNSUP, R-LRSUP, R-NNSUP over the best model Mean RMSEs of the 24-168h surface temperature forecast

  15. Improved superensemble forecast with running training periods Geographical distribution of the RMSEs for 24h、120h forecast from the best model, EMN, R-LRSUP and R-NNSUP 24h 120h

  16. Ave: 10°-80°N; 0°-357.5°E Ave: 10°-30°N; 0°-357.5°E (a) (b) Ave: 30°-60°N; 0°-357.5°E Ave: 60°-80°N; 0°-357.5°E (c) (d) Optimal length of the training period The mean RMSEs of the surface temperature forecasts versus the length of the running training period

  17. Summary • The superensemble with fixed training period gives a good improvement of 24h-72h temperature forecast with RMSEs reduction over the best single model forecast and the multimodel ensemble mean. • The superensemble forecast using running training period further improves 96h-168h temperature forecasts. • The optimal training length is different for different forecast time.

  18. Thank you! Wen Chen Monterey, CA Sep 2009

More Related