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The limit of numerical prediction and ensemble prediction can be further understood by the study of the forecast jump. By using the ensemble average forecast and control forecast product output data for the United States National Environmental Prediction Center (NCEP) global ensemble forecast system (GEFS), and the concept of Jumpiness index from Zsoter
*et al*., we analyzed the statistical characteristics of forecast jump. Results show that, on average, in the NCEP ensemble forecast product, the time average prediction jump index increases with the increase of the forecast aging, and the actual forecast experience can reflect this phenomenon. The consistency of ensemble average forecast is better than the corresponding control forecast. Also, in summer, the frequency of “forecast jump” phenomenon is fluctuating by 17.5%.

With the rapid development of numerical forecast and the ensemble forecast, many problems to be solved also appear, and these questions are included in the “forecast jump” [

Because the numerical prediction technology is not perfect yet, it is hard to avoid forecast jump, which not only seriously affects the forecasters’ judgment on the forecast results, but also undermines user’s confidence in weather forecasts. Its harm is very obvious [

At present, the studies of forecast jump just begin, and most of the research work is focused on the analysis of the NCEP and ECMWF and UKMO ensemble forecast product performance in North America or Europe [

In view of the above questions, we will use the NCEP ensemble mean forecast and control forecast product data to carry on the statistical analysis and the contrast research on its forecast jump in the Asian region in this paper. Through the study of this chapter, it is helpful to recognize the forecast jump features of NCEP ensemble forecast, and the forecaster’s ability of releasing with NCEP ensemble forecast products was improved, in order to provide more high quality weather forecast service to the users.

The data used in this paper is the ensemble average forecast and control forecast product output data for the United States National Environmental Prediction Center (NCEP) global ensemble forecast system (GEFS). The forecast factors are selected as 500 hPa height field, forecast the starting time is on March 1, 2011 to February 28, 2013, a total of 731 days. Forecast start time is 00 and 12 times per day (the same below), the forecast time is 6 hours. Forecast area is 15˚N - 18˚N and 40˚E - 160˚E, the horizontal spatial resolution is 1˚ × 1˚ latitude and longitude. This area was selected based on GEFS’ application of Northwest China.

At present, it is usually used to make objective and quantitative analysis of the forecast jump by the forecast jump index. In this paper, one of forecast jump index, Jumpiness index can be used to analyze and compare the forecast jump. Zsoter et al. first proposed the concept of Jumpiness index [

In the formula, f denotes forecast fields, d and d + δ denote starting time forecasts, t and t − δ denote prediction time, δ denote repay twice the time interval, in this study, for 12 hours. In this way, the forecast time of these two forecasts are d + t. Therefore this article says

The time averaged prediction jump index [

In Equation (2),

with N times. To forecast jump phenomena, the emergence of “flip” phenomenon is defined as to half of the jump time average forecast index as the critical value, the absolute value of prediction index jumping phenomenon more than the critical value. On the basis of the definition of “flip” phenomenon, it continues to define “flip-flop” and “flip-flop-flip” phenomenon. It is defined as “the flip-flop”, for a future in the same moment of forecast, forecasting the continuous found many times forecast of different starting moment occurred on the forecast of two adjacent to the starting time of forecast jump phenomena, and two times for the above forecast starting moment adjacent forecast jumping phenomenon, they calculated by jumping index is the opposite of the positive and the negative of the forecasts. “Flip-flop-flip” phenomenon is the adjacent three consecutive forecast has forecast jump, jump and arbitrary two adjacent forecast its forecast index sign instead.

so this article will point A, B and C are referred to as a “flip”; for any two adjacent forecast jump index exceeds the critical value of the point, such as point A and B or points B and C, to get two changes from point A to point B or from point B to point C, the call for a “flip-flop”; for the forecast of three adjacent jump index exceeds the critical value of the point, such as A, B and C, for the change from point A to point B and then to C, this article said the change as a “flip-flop-flip”.

According to this paper, the definition and the calculation formula of the jump index are given, the NCEP ensemble average forecast and the corresponding control forecast are calculated respectively, and the calculation results are shown in

The results of

According to the relevant definitions given in this paper, the frequency of “flip”,

“flip-flop” and “flip-flop-flip” phenomena in the ensemble average prediction in the NCEP ensemble forecast products are calculated respectively, the frequency of occurrence of “flip”, “flip-flop” and “flip-flop-flip” phenomena occurred in the control and prediction of the NCEP ensemble forecast products, As well as the frequency of “flip”, “flip-flop” and “flip-flop-flip” phenomena occurred simultaneously in the above two kinds of prediction, The results are shown in Figures 3-5.

Can be seen from

control prediction, corresponding to ensemble prediction, results are “flip”, the ensemble average forecast does not necessarily occur at the same time, “flip”, conversely, When the ensemble average forecast appears to predict jump “flip”, the control forecast does not appear to predict jump “flip”. At the same time, the frequency of “flip” of ensemble average forecast and the corresponding control forecast appearing at the same time is always lower than the frequency of “flip” of ensemble average forecast and the corresponding control forecast appearing alone.

It can be seen from

To synthesize the above results, for NCEP ensemble prediction products, the frequency difference of the “flip” phenomenon is not obvious in the ensemble average forecast and the corresponding set control forecast, however, the frequency of “flip- flop” and “flip-flop-flip” phenomenon is obviously smaller than that of the corresponding set control forecast, which should be greatly concerned is that the difference between the two frequencies will become easier to distinguish when the forecast time is relatively long. The above phenomena show that the ensemble average forecast is quite low compared with the control forecast.

In this paper, the variation characteristics of “forecast jump” in different seasons are also analyzed, namely, the frequency size of “forecast jump” in the NCEP ensemble average forecast and control forecast in each season (

ensemble in summer is compared. As shown in

The verification method of NCEP ensemble prediction is introduced firstly in this paper, and then the concept and the statistical analysis method of the forecast jump are described. Finally, the advantages and disadvantages of the forecast consistency of the NCEP ensemble mean forecast and the corresponding control forecast, and the consistency and variability of “forecast jumps” in the ensemble mean forecast and control forecast are studied. The results are summarized as follows:

Two main attributes of NCEP ensemble forecast product inspection system are reliability and resolution. The main methods are: SPRD, RMSE, histogram, CRPS score, RPS score, BS score, etc.

Through the statistical analysis of average prediction time jump index, we found that, on average, in the NCEP ensemble forecast product, the time average prediction jump index increases with the increase of the forecast aging, and the actual forecast experience can reflect this phenomenon. At the same time, the consistency of ensemble average forecast is better than the corresponding control forecast.

The frequency of “flip”, “flip-flop” and “flip-flop-flip”, three different grades of “forecast jumping” phenomenon in the NCEP ensemble mean forecasts and corresponding control forecast decrease in turn. The frequency of occurrence of “forecast jump” phenomenon in the ensemble average and the corresponding control forecast at the same time is lower than the frequency of occurrence of “forecast jump” phenomenon in the ensemble average or the corresponding control forecast alone. For NCEP ensemble prediction products, generally speaking, the difference between the frequency of occurrence of “flip” phenomenon in the ensemble average forecast and its corresponding control forecast is small, however, especially in the longer forecast period, the frequency of “flip-flop” and “flip-flop-flip” phenomenon is obviously smaller than that of the corresponding control forecast. This fully shows that the inconsistent level average level of the ensemble average forecast is lower than the ensemble control forecast.

The frequency of occurrence of “forecast jump” phenomenon in the NCEP ensemble average forecast and its corresponding control forecast in summer is greater than in spring and autumn season, which is more than that in winter. But the occurrence frequency of “forecast jump” is limited to the sensitivity of the season. In summer, the frequency of “forecast jump” phenomenon is fluctuating by 17.5%, which does not appear significant growth.

This work was supported by the National Meteorological Center’s Youth Fund (No. Q201603), China.

Zhang, X.K., Zhang, L.P., Fu, J. and Zhang, L.X. (2017) Analysis of Characteristics of the Forecast Jump in the NCEP Ensemble Forecast Products. Atmospheric and Climate Sciences, 7, 151- 159. http://dx.doi.org/10.4236/acs.2017.71011