The author investigates the prediction of Northeast China's winter surface air temperature (SAT),and first forecast the year to year increment in the predic-tand and then predict the predictand.Thus,in the first s...The author investigates the prediction of Northeast China's winter surface air temperature (SAT),and first forecast the year to year increment in the predic-tand and then predict the predictand.Thus,in the first step,we determined the predictors for an increment in winter SAT by analyzing the atmospheric variability associated with an increment in winter SAT.Then,multi-linear re-gression was applied to establish a prediction model for an increment in winter SAT in Northeast China.The pre-diction model shows a high correlation coefficient (0.73) between the simulated and observed annual increments in winter SAT in Northeast China throughout the period 1965-2002,with a relative root mean square error of -7.9%.The prediction model makes a reasonable hindcast for 2003-08,with an average relative root mean square error of -7.2%.The prediction model can capture the in-creasing trend of winter SAT in Northeast China from 1965-2008.The results suggest that this approach to forecasting an annual increment in winter SAT in North-east China would be relevant in operational seasonal forecasts.展开更多
This paper presents a statistical scheme for the seasonal forecasting of North China's surface air temperature (NCSAT) during winter. Firstly, a prediction model for an decrease or increase of winter NCSAT is esta...This paper presents a statistical scheme for the seasonal forecasting of North China's surface air temperature (NCSAT) during winter. Firstly, a prediction model for an decrease or increase of winter NCSAT is established, whose predictors are available for no later than the previous September, as this is the most favorable month for seasonal forecasting up to two months ahead.The predicted NCSAT is then derived as the sum of the predicted increment of NCSAT and the previous NCSAT. The scheme successfully predicts the interannual and the decadal variability of NCSAT. Additionally, the advantages of the prediction scheme are discussed.展开更多
基金supported by the Major State Basic Research Development Program of China (973 Program) under grant No.2009CB421406the Research Program for excellent Ph. D dissertations in the Chinese Academy of Sciences
文摘The author investigates the prediction of Northeast China's winter surface air temperature (SAT),and first forecast the year to year increment in the predic-tand and then predict the predictand.Thus,in the first step,we determined the predictors for an increment in winter SAT by analyzing the atmospheric variability associated with an increment in winter SAT.Then,multi-linear re-gression was applied to establish a prediction model for an increment in winter SAT in Northeast China.The pre-diction model shows a high correlation coefficient (0.73) between the simulated and observed annual increments in winter SAT in Northeast China throughout the period 1965-2002,with a relative root mean square error of -7.9%.The prediction model makes a reasonable hindcast for 2003-08,with an average relative root mean square error of -7.2%.The prediction model can capture the in-creasing trend of winter SAT in Northeast China from 1965-2008.The results suggest that this approach to forecasting an annual increment in winter SAT in North-east China would be relevant in operational seasonal forecasts.
基金jointly supported by the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KZCX2-YW-QN202)the National Basic Research Program of China(Grant Nos.2010CB9503042 and 2009CB421406)strategic technological program of the Chinese Academy of Sciences(Grant No.XDA05090426)
文摘This paper presents a statistical scheme for the seasonal forecasting of North China's surface air temperature (NCSAT) during winter. Firstly, a prediction model for an decrease or increase of winter NCSAT is established, whose predictors are available for no later than the previous September, as this is the most favorable month for seasonal forecasting up to two months ahead.The predicted NCSAT is then derived as the sum of the predicted increment of NCSAT and the previous NCSAT. The scheme successfully predicts the interannual and the decadal variability of NCSAT. Additionally, the advantages of the prediction scheme are discussed.