Characteristics of the Forecast Jumpiness Based on TIGGE Data

Based on 500 hPa geopotential height, 850 hPa temperature and the mean sea level pressure forecasts from ECMWF, NCEP and CMA in TIGGE datasets, characteristics of the forecast jumpiness for the control and ensemble-mean forecasts and the comparison of their characteristics conducted using Jumpiness index and other different forecast jumps: The flip, flip-flop, flip-flop-flip and so on. Results indicate that in terms of the period-average forecast jumpiness features, the period-average jumpiness indices increase with the forecast range in agreement with the practical experience that forecasts are usually more consistent at short forecast ranges. And for ensemble prediction system, the ensemble-mean forecast is less jumping than its corresponding control forecast, especially at long forecast ranges, which indicates that the forecast jumpiness could be reduced using the ensemble prediction method. And both for the control forecast and ensemble-mean forecast, the forecast jumpiness of ECMWF is lower. In frequency statistics of the forecast jumpiness, frequencies of the flip, flip-flop and flip-flop-flip are in descending order. For these three types of forecast jumps, the frequency of ensemble-mean forecast is significantly lower than that of the control forecast especially at long forecast ranges. It indicates that the ensemble-mean forecast is less jumping than its corresponding control forecast, which also shows that the forecast jumpiness could be reduced using the ensemble prediction method. The frequency variation of parallel flip, parallel flip-flop and parallel flip-flop-flip indicates that the control forecast and ensemble-mean forecast have large difference at long forecast ranges. And the correlation coefficient of their Jumpiness indices also confirms this conclusion. At last, the sensitivity of the forecast jumpiness to areas, time and parameters are presented. Results show that the period-average forecast jumpiness has a strong sensitivity to the area and parameter. And the sensitivity of the control forecast to the area and parameter is stronger than that of ensemble-mean forecast. The smaller the studied area is, the larger the period-average forecast jumpiness becomes, which indicates that the forecast jumpiness intensity is stronger. As the weather and climate characteristics of the selected areas are not the same, the period-average forecast jumpiness is different. For different variables, the period-average forecast jumpiness is also various. The period-average Jumpiness index of mean sea level pressure is the maximum, the result of 500 hPa geopotential height is the second, and the minimum result is 850 hPa temperature. That is to say, the forecast jumpiness intensity of temperature is lower than geopotential height results. And the frequency of different forecast jumps and the difference of the jumpiness between control forecast and ensemble-mean forecast show little sensitivity to the choice of the area, time and parameter.