MULTIMODEL CONSENSUS FORECASTING OF LOW TEMPERATURE AND ICY WEATHER OVER CENTRAL AND SOUTHERN CHINA IN EARLY 2008

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1 Vol.21 No.1 JOURNAL OF TROPICAL METEOROLOGY March 2015 Article ID: (2015) MULTIMODEL CONSENSUS FORECASTING OF LOW TEMPERATURE AND ICY WEATHER OVER CENTRAL AND SOUTHERN CHINA IN EARLY 2008 ZHANG Ling ( ), ZHI Xie-fei ( ) (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/KLME, Nanjing University of Information Science and Technology, Nanjing China) Abstract: Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing conditions, which occurred in the southern part of China during early 2008, are investigated in this study. In addition, multimodel consensus forecasting experiments are conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Results show that more than a third of the stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan province as well. For the 24- to 216-h surface temperature forecasts, the bias-removed multimodel ensemble mean with running training period (R-BREM) has the highest forecast skill of all individual models and multimodel consensus techniques. Taking the RMSEs of the ECMWF 96-h forecasts as the criterion, the forecast time of the surface temperature may be prolonged to 192 h over the southeastern coast of China by using the R-BREM technique. For the sprinkle forecasts over central and southern China, the R-BREM technique has the best performance in terms of threat scores (TS) for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean. Key words: multimodel consensus forecasting; extreme low temperature and icy weather event; forecast skills CLC number: P423.3 Document code: A 1 INTRODUCTION During the last two decades, abnormal East Asian Winter Monsoon (EAWM) systems bring about frequent damage, for example, storms, cold wave, heavy rain and so on. In 2005 and 2008, extreme freezing rain and snow in southern China resulted in considerable loss of life and great financial losses (Fu et al. [1] ). Against the background of global warming, the intensity and frequency changes of extreme events become a hot topic in the climate change research. Compared to the climatology, the extreme events are more sensitive to climate changes (Katz and Brown [2] ) and exert great influence on the nature and society. The observations indicate that the trend of extreme precipitation underwent significant changes with regional differences. In China, extreme precipitation in western and mid-and lower-valley of Yangtze River is increasing, but the trend in northern and central China is negative (Zhai and Pan [3] ; Zhai et Received ; Revised ; Accepted Foundation item: Special Scientific Research Fund of Meteorological Public Welfare Industries of China (GYHY (QX) ); National Nature Science Foundation of China ( ). Biography: ZHANG Ling, Ph. D., associate researcher, primarily undertaking research on numerical simulation. Corresponding author: ZHANG Ling, lingzhang@nuist. edu.cn al. [4] ). Zhi et al. [5] indicated that against the background of global warming the intensity of extreme rainfall in South China is enhancing, resulting from a weakened East Asian Winter Monsoon. Wang et al. [6] studied the spatio-temporal variation of seasonal extreme wet days (EWDs) in China. The results show that the EWDs in winter have significant increasing trends in Yangtze River Valley, North China and Northwest China. During the last two decades, numerical weather prediction (NWP) has acquired considerable skill, playing an increasing role in the weather forecasting. However, the extended range forecast skill of the temperature (in terms of temperature minima and maxima) and rainfall of the available NWP models is still not satisfactory to address the detailed aspects of extreme weather events. Multimodel superensemble forecast method is a practical post-processing technique capable of reducing model output errors (Krishnamurti [7-9] ; Yun et al. [10] ; Lin et al. [11] ; Zhi et al. [12] ). It is well known that individual models differ in internal architectures, particularly in terms of resolution, initialization, data assimilation, description of physics and dynamics, and representation of topography etc. Consequently, even though these modeling aspects explain the differences in the model skill over different regions, the multimodel forecast dataset jointly lays an important foundation on which the superensemble forecast is developed. In the multimodel superensemble forecast, several model outputs are put together with appropriate weights to obtain

2 68 Journal of Tropical Meteorology Vol.21 a combined estimation of meteorological parameters. Weights are calculated by squared error minimization in a so-called training period. Zhi et al. [13] and Lin et al. [11] applied the multimodel superensemble technique in the forecasting of the surface temperature in Northern Hemisphere. The forecast skill of the multimodel superensemble with a fixed training period is higher than that of the ensemble mean and the best individual model for the 24- to 144-h surface temperature forecast. The superensemble with running training period has superior forecast skill to that with fixed training period for the 24- to 168-h forecast. Further study indicates that bias-removed ensemble mean can considerably reduce the RMSEs of the 24- to 144-h surface temperature forecast as well. In particular, the forecast skill of this technique may be superior to that of multimodel superensemble for the long forecast time. Cui and Zhi [14] showed that, for the 10- to 15-d extension forecasts of surface temperature, multimodel ensemble mean, bias-removed multimodel ensemble mean and multimodel superensemble have superior forecast skills to individual models, and multimodel superensemble has the best performance. In addition, for the quantitative precipitation forecast, the advantages of multimodel superensemble with running training period are not shown, which results from the discontinuity of rainfall field and quantity of rainfall samples. The weights in the multimodel superensemble should be calculated using the training period data. If the samples are not e- nough, some extreme values of precipitation may over-fit the linear regression coefficients, which result in the weights not representing the mean model dynamics. In this study, the bias-removed ensemble mean forecasting experiment was performed for the surface temperature and precipitation in central and southern China during the extreme weather events in early 2008 to improve the extended range forecast skill of the high-impact weather events. 2 DATA AND METHODOLOGY 2.1 Data Based on the daily mean surface temperature and 24-h accumulated total precipitation in central and southern China (102.5 E E, 22.5 N-35 N) during the period from 1952 to 2008, the features of the extreme events with low temperature and ample precipitation during the period from 10 January until 2 February 2008 have been investigated. The 24- to 216-h ensemble forecast data of the surface temperature and the 24-h accumulated total precipitation during the period from 1 January until 31 January 2008 are taken from European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), and China Meteorological Administration (CMA) in the TIGGE Data Archive Portal. In addition, the NCEP/NCAR reanalysis data and the TRMM 24-h accumulated total precipitation from 1 January to 10 February 2008 are used as the observed data for verification of the forecasts. 2.2 Methodology The consensus forecast experiments of the surface temperature and 24-h accumulated total precipitation are conducted by using the multimodel ensemble mean (EMN), the bias-removed multimodel ensemble mean with fixed training period (BREM) and the bias-removed multimodel ensemble mean with running training period (R-BREM), respectively. The RMSE and the precipitation rank threat score (TS) are utilized to evaluate the forecast skills of the surface temperature and 24-h accumulated total precipitation, respectively. The EMN is calculated as follows, where F i is the ith model forecast and n is the number of forecast models involved. The BREM is calculated as follows where F i is the ith model forecast value, F i is the time mean of the ith model forecast over the training period, O is the time mean of observed state, N is the number of models. The RMSE is written as where F i is the ith sample forecast value, and O i is the ith sample observed value. 3 FEATURES AND POSSIBLE CAUSES OF THE ICY WEATHER OVER CENTRAL AND SOUTHERN CHINA IN EARLY 2008 Due to cold surges, four successive major precipitation processes affected central and southern China during the period from 10 January until 2 February 2008, which caused low temperature, snowstorms and freezing rain over the southern part of China. In this paper, the Generalized Pareto Distribution (GPD) return values (Zhi et al. [4] ; Hosking [15] ; Hosking et al. [16] ) of the low temperature and precipitation over central and southern China during early 2008 have been calculated. As shown in Fig. 1, the extreme daily mean temperature with a 50-a return period is below freezing in the southern part of China. The minimum value center is located in the northeastern part of Hubei province and southeastern part of Henan province and the maximum value center is in the southwestern part of Guizhou province and southern part of Hunan and Jiangxi provinces. In the southern part of China, 24-h accumulated total precipitation with 50-a return period is more than 30 mm/d and the high value center is located in Hunan, Jiangxi (1) (2) (3)

3 No.1 ZHANG Ling ( ) and ZHI Xie-fei ( ) 69 and Zhejiang provinces with maximum around 50 mm/d. Fig. 2 indicates that more than a third of stations in the southern part of China were covered by the ex tremely abundant precipitation with a 50-a return period, and the extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan provinces as well. The concurrence of low temperature and large precipitation caused the extreme icy weather over the southern part of China. 4 MULTIMODEL CONSENSUS FORECASTS OF SURFACE TEMPERATURE Based on the surface temperature 24- to 216-h ensemble forecasts of ECMWF, JMA, NCEP and CMA in the southern part of China during the period from 1 January until 31 January 2008, the multimodel consensus forecasts have been conducted. In this study, the optimal training period was tested for the bias-removed ensemble mean forecast and 6-d training period was chosen for the multimodel consensus forecast of the surface temperature. The period from 16 January through 31 January was selected as the forecast period. The NCEP/NCAR reanalysis data were chosen as the observed data. Figure 3 shows the RMSE of the surface temperature forecast for 24- to 216-h forecast time averaged over the southern part of China from 16 until 30 January 2008, including the JMA, ECMWF, NCEP and CMA individual models, multimodel ensemble mean, bias-removed ensemble mean with fixed training period and running training period. Comparing the skills of the four numerical forecast centers, ECMWF has the best performance over the southern part of China, while the skill of CMA is the least satisfactory among the four individual models. In order to obtain the optimal forecast results, only three individual models, namely ECMWF, NCEP and JMA, were chosen to carry out the multimodel consensus forecasting experiment. As shown in Fig. 3, the RMSEs of the multimodel ensemble mean are smaller than that of the individual models, while the RMSEs of the bias-removed multimodel ensemble mean with fixed training period are much smaller than that of the multimodel ensemble mean. The forecast skill of the bias-removed multimodel ensemble mean with running training period has the best performance among all forecasts with different individual models or methods. Figure 1. The extreme daily mean temperature (a, unit: ) and 24-h accumulated total precipitation (b, unit: mm) distribution for a 50-a return period of GPD during the period from 10 January to 2 February Figure 2. The extreme daily mean temperature (a) and 24-h accumulated total precipitation (b) distribution for different return periods (units in year, represented by colored dots) during the period from 10 January to 2 February 2008.

4 70 Journal of Tropical Meteorology Vol.21 Figure 3. The RMSEs (unit: ) of the 24- to 216-h surface temperature forecast averaged over the southern part of China during the period from 16 to 31 January Figure 4 shows the geographical distribution of RMSEs in the 168-h surface temperature forecast for individual models and multimodel consensus forecasts during the forecast period. The RMSEs of CMA model for the 168-h surface temperature forecast (Fig. 4d) are more than 5 over the southern part of China, and those of JMA model (Fig.4a) are no less than 4, while the RMSEs of ECMWF (Fig. 4b) over the coast of the mainland are much smaller than those of JMA, and the maximum errors are located over the eastern Sichuan province, Chongqing and the middle and lower reaches of the Yangtze River. In addition, the maximum errors of NCEP (Fig. 4c) are located over the eastern Sichuan province, Chongqing, Guangdong, Guangxi, whereas the errors over the middle and lower reaches of the Yangtze River and the southern part of China are relatively small among the four models. Due to different forecast skills of four models in different regions, the RMSEs over most part of central and southern China are reduced considerably by using the multimodel ensemble mean (Fig.4e). However, the RMSEs over the eastern Sichuan province and Chongqing are not reduced noticeably, because the skills of all the ECMWF, NCEP and JMA model for the 168-h surface temperature forecast are not high. However, the forecast errors over East and South China are reduced significantly by using the bias-removed ensemble mean with fixed training period (Fig. 4f). Comparing the RMSEs of all the models and multimodel consensus forecasts, the bias-removed ensemble mean with running training period has the highest forecast skill, which reduces the RMSEs of the 168-h surface temperature forecast by 1.5 to 2.5 over the southern part of China. In order to investigate the forecast improvement of the multimodel consensus methods, the RMSEs of the ECMWF 96-h forecast for the period from 25 January to 1 February 2008 were selected as the criterion for comparison of the forecast time. As shown in Fig. 5, the forecast time of the multimodel ensemble mean is improved over the north part of central and southern China, but not improved over southern China, while the forecast time of the bias-removed ensemble mean with fixed training period is prolonged over central and southern China. The forecast time of the surface temperature may be extended to 192 h over the southeastern coast of China. The bias-removed multimodel ensemble mean with running training period may prolong the forecast time to 192 h over most part of central and southern China. However, the forecast skill of this technique over the western Guizhou province and Guangxi has not been improved compared with that of ECMWF 96-h forecast. All in all, the bias-removed ensemble mean, especially that with running training period, reduced the RMSEs significantly and may prolong the forecast time considerably. 5 MULTIMODEL ENSEMBLE FORECASTS OF PRECIPITATION Based on the precipitation 24- to 216-h ensemble forecasts of ECMWF, JMA, NCEP and CMA in the southern part of China during the period from 1 January until 31 January 2008, the multimodel consensus forecasts of the 24-h accumulated total precipitation have been conducted for the forecast period from 16 to 31 January Similar to the procedure mentioned in section 4, a 6-d training period was chosen as the optimal training period and the TRMM (Tropical Rainfall Measuring Mission) precipitation data were chosen as the observed data. TS, also known as the critical success index (Schaefer [17] ), or equitable threat score (ETS), which is a modification of the TS to explain the correct forecasts as a result of chance (Gilbert [18] ), is used at the National Centers for Environmental Prediction (NCEP) almost exclusively as the most important variable for verification of the skill in precipitation forecasting (Messinger [19] ). In this study, TS with different ranks was utilized to evaluate the precipitation forecast skills of different individual models and multimodel consensus forecasts. It is defined as follows: (4) with NA denoting the total number of correct forecast-

5 No.1 ZHANG Ling (张 玲 ) and ZHI Xie-fei ( 智协飞 ) 71 Figure 4. Geographical distribution of the RMSEs (unit: ) of the 168-h surface temperature forecast for JMA (a), ECMWF (b), NCEP (c), CMA (d), EMN (e), BREM (f) and R-BREM (g) during the period from 16 to 31 January 2008.

6 72 Journal of Tropical Meteorology Vol.21 Figure 5. Geographical distribution of surface temperature predictability in terms of the forecast time improvement (hours) by using the EMN (a), BREM (b), and R-BREM (c) technique relative to the RMSEs of the ECMWF 96-h forecast for the period from 25 January to 1 February Positive values represent improved forecast time as compared to the ECMWF 96-h forecast and negative values represent shorter forecast time. ing points, NB the total number of false alarm points, and NC the total number of forecast miss points. Figure 6 shows the TS score of 24-h accumulated total precipitation forecast for light (0.1 to 10 mm/24 h) and moderate (10 to 25 mm/24 h) rain over central and southern China during the period from 25 January to 1 February 2008 with the forecast time from 24 to 216 h, including the CMA, JMA, NCEP, ECMWF, multimodel ensemble mean and bias-removed ensemble mean with running training period. Comparing forecasts of the light rain of the individual models, it is known that the forecast skills of the JMA model are the best among the four models except for the 24-h forecasts. With increasing precipitation ranks, the TS scores of the individual models decrease considerably. For the moderate rain forecasts over central and southern China, the TS scores of the JMA model contain large fluctuations and the ECMWF forecasts are more stable, while the forecast skills of the CMA model are not satisfactory. In order to obtain better forecast skills, the ensemble forecasts of the JMA, ECMWF and NCEP models were selected to carry out the multimodel consensus forecasts. The TS scores have been improved by using the R-BREM technique for most forecast time except for the 72-h and 216-h forecasts compared with the ECMWF forecasts, which have the best performance among the four models. For the moderate rain forecasting, the 24- to 192-h TS scores of the R-BREM technique are higher than those of the individual models and the EMN technique. Figure 7 shows the RMSEs geographical distribu tion of the 144-h accumulated precipitation during 24 h for individual models(cma, JMA, ECMWF and NCEP) and multimodel consensus forecasts during the forecast period. The RMSEs of CMA model for the 144-h daily (24 h) accumulated rainfall forecast (Fig.7d) are more than 4 mm over the southern part of China, and the maximum errors are more than 21 mm in the eastern part of Guangxi and western part of Guangdong, while the errors are more than 10 mm in South China and southern part of Yangtze River. The errors distributions from JMA (Fig.7a), ECMWF (Fig.7b) and NCEP (Fig.7c) show similar patterns with that of CMA (Fig. 7d), which indicates that the maximum errors are more than 21 mm between Guangdong and Guangxi provinces. However, the forecast errors from individual models have some differences in the east and central part of South China. Due to different forecast skills of the four models in different regions, the RM SEs over Figure 6. TS scores of the 24-h accumulated total precipitation forecast using different individual models, EMN and R-BREM techniques for light rain(a) and moderate rain(b) over central and southern China during the period from 25 January to 1 February 2008.

7 No.1 ZHANG Ling (张 玲 ) and ZHI Xie-fei ( 智协飞 ) 73 Figure 7. Geographical distribution of the RMSEs (unit: mm) of the 144-h daily accumulated (unit: mm) forecast for JMA (a), ECMWF (b), NCEP (c), CMA (d), EMN (e) and R-BREM (f) during the period from 16 to 31 January 2008.

8 74 Journal of Tropical Meteorology Vol.21 most parts of central and southern China are reduced considerably by using the multimodel ensemble mean (Fig. 7e and 7f), especially in the southern part of Anhui province, western part of Zhejiang province and northern part of Jiangxi province. However, the RMSEs over the regions of maximum errors are not reduced noticeably. Comparing the RMSEs of all the models and multimodel consensus forecasts, the bias-removed ensemble mean with running training period has the highest forecast skill, which reduces the RMSEs of the 144-h daily (24 h) accumulated rainfall forecast by 3 mm over the southern part of China. 6 SUMMARY Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and possible causes of the extreme weather events with low temperature and icing conditions which occurred in the southern part of China dur ing early 2008 have been discussed in this study. In addition, the multimodel consensus forecasting experi ments have been conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Main results are summarized as follows. (1) Due to cold surges, more than a third of stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and the extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan provinces as well. The concurrence of low temperature and large precipitation caused the extreme icy weather events over the southern part of China. (2) For the 24- to 216-h surface temperature fore casts, the R-BREM has the best performance among those of all individual models and multimodel consensus techniques. The R-BREM technique reduced the RMSEs of the 168-h surface temperature forecast by 1.5 to 2.5 over the southern part of China during the extreme weather events. Taking the RMSEs of the ECMWF 96-h forecast as the criterion, the forecast time of the surface temperature may be extended to 192 h over the southeastern coast of China by using the R-BREM technique. (3) For the light rain forecasts over central and southern China, the R-BREM technique has the best performance in terms of TS scores for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean. The R-BREM technique has the best performance among those of all individual models, reducing the forecast RMSEs and extending the forecast time. It is favorable for improving capabilities of disaster prevention and reduction. It should be noted that the selection of the training period length is very important for the forecasting by using the R-BREM technique. The optimal length of the training period may vary with variable, region and season. Therefore, the optimal length of the training period should be determined after careful test ing, which is favorable for improvement of forecasting skills. In addition, the R-BREM technique applies the same weights to all individual models, so choosing models with similar forecasting skills is crucial for obtaining the optimal forecast results. In this paper, for the light rain forecasts, the 72-h forecast performance of the R-BREM technique is better than that of JMA, which results from the much better forecasting skills of JMA compared to the skills of ECMWF and NCEP for 72-h forecasts. Therefore, if the skills of different models have big differences, selecting different weights for individual models may be favorable for enhancing the forecasting results. REFERENCES: [1] FU Ming-ning, ZOU Hai-bo, WU Jun-jie, et al. 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