Improving seasonal forecast skill of surface temperature

• Main Authors: Cheng-Hsun Hsieh, 謝承勳
• Other Authors: Yung-An Lee
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/hd58qb

碩士 === 國立中央大學 === 大氣科學學系 === 107 === In this study, we develop a novel forecast post-processing method to improve multi-model mean (MME) seasonal forecast skill of surface temperature. Briefly speaking, this method uses variable transformation, Markov model, and principal components analysis to combine the information from observation to calibrate and improve MME forecast skill. We apply monthly mean 2m temperature hindcast data from the multi-system seasonal forecast service of the Copernicus Climate Change Service (C3S) to develop and evaluate this method. Forecast skills were evaluated using correlation coefficient and root-mean-square error (RMSE) between forecasts and observations. The performance of this method is evaluated by comparing forecast skills among this method, persistence forecasts, and original model MME forecasts. We first used K-fold cross-validation forecasts to build and test the post-process of forecast method. Results from ENSO indices forecast experiments show that the forecast skill is increased from east to west and the use of only one EOF mode in this method yields the largest forecast skill improvement. After finishing the development of the method, we conduct the 1° x 1° global surface temperature forecast experiment. Generally speaking, original model MME forecasts of global surface temperature field have better skill in land area at lead one month and worse skill in ocean area except the Niño region at all lead months than persistence forecasts. The forecast experiment shows that the calibrated MME forecasts show notable improvement over the original model MME forecasts. Furthermore, the degree of improvement in skill depends on the original model skill. Overall speaking, the calibrated MME forecasts combine the advantages of both the persistence forecasts and the original model MME forecasts to yield better forecast skill than both methods.