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Mei Hong
,
Dong Wang
,
Ren Zhang
,
Xi Chen
,
Jing-Jing Ge
, and
Dandan Yu

Abstract

Abnormal activity of the western Pacific subtropical high (WPSH) may result in extreme weather events in East Asia. However, because the relationship between the WPSH and other components of the East Asian summer monsoon (EASM) system is unknown, it is still difficult to forecast such abnormal activity. The delay-relevant method is used to study 2010 data for abnormal weather and it is concluded that the Indian monsoon latent heat flux, the Somali low-level jet, and the Tibetan high activity index can significantly affect anomalies in the WPSH in the EASM system. By combining genetic algorithms and statistical–dynamical reconstruction theory, a nonlinear statistical–dynamical model of the WPSH and these three influencing factors was objectively reconstructed from actual 2010 data and a dynamically extended forecasting experiment was carried out. To further test the forecasting performance of the reconstructed model, further experiments using data from nine abnormal WPSH years and eight normal WPSH years were performed for comparison. All the results suggest that the forecasts of the subtropical high area index, the Indian monsoon latent heat flux, the Somali low-level jet, and the Tibetan high activity index all have good performance in the short and medium terms (<25 days). Not only is the forecasting trend accurate, but the mean absolute percentage error is ≤9%. This work suggests new areas of research into the association between the WPSH and EASM systems and provides a new method for the prediction of the WPSH area index.

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Jie Feng
,
Jing Zhang
,
Zoltan Toth
,
Malaquias Peña
, and
Sai Ravela

Abstract

Ensemble prediction is a widely used tool in weather forecasting. In particular, the arithmetic mean (AM) of ensemble members is used to filter out unpredictable features from a forecast. AM is a pointwise statistical concept, providing the best sample-based estimate of the expected value of any single variable. The atmosphere, however, is a multivariate system with spatially coherent features characterized with strong correlations. Disregarding such correlations, the AM of an ensemble of forecasts removes not only unpredictable noise but also flattens features whose presence is still predictable, albeit with somewhat uncertain location. As a consequence, AM destroys the structure, and reduces the amplitude and variability associated with partially predictable features. Here we explore the use of an alternative concept of central tendency for the estimation of the expected feature (instead of single values) in atmospheric systems. Features that are coherent across ensemble members are first collocated to their mean position, before the AM of the aligned members is taken. Unlike earlier definitions based on complex variational minimization (field coalescence of Ravela and generalized ensemble mean of Purser), the proposed feature-oriented mean (FM) uses simple and computationally efficient vector operations. Though FM is still not a dynamically realizable state, a preliminary evaluation of ensemble geopotential height forecasts indicates that it retains more variance than AM, without a noticeable drop in skill. Beyond ensemble forecasting, possible future applications include a wide array of climate studies where the collocation of larger-scale features of interest may yield enhanced compositing results.

Free access
Jingzhuo Wang
,
Jing Chen
,
Hanbin Zhang
,
Hua Tian
, and
Yining Shi

Abstract

Ensemble forecasting is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by the ensemble transform Kalman filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread–skill relationship, a faster ensemble spread growth rate, and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

Open access
Jing Zhang
,
Jie Feng
,
Hong Li
,
Yuejian Zhu
,
Xiefei Zhi
, and
Feng Zhang

Abstract

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged. We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Open access
Yi Shang
,
Lijuan Miao
,
Yunpeng Shan
,
Kaushal Raj Gnyawali
,
Jing Zhang
, and
Giri Kattel

Abstract

Reliable ultra-short-term and short-term wind speed forecasting is pivotal for clean energy development and grid operation planning. During the wind forecasting process, decomposing the measured wind speed into data with different frequencies is a solution for overcoming the nonlinearity and the randomness of the natural wind. Existing forecasting methods, a hybrid method based on empirical mode decomposition and the back propagation neural network optimized by genetic algorithm (EMD-GA-BPNN), rely on partial decomposing the measured wind speed into data with different frequencies and subsequently achieving forecasting results from machine learning algorithms. However, such methods can roughly divide IMF signals in different frequency domains, but each frequency domain contains signals with multiple frequencies. The condition reflects that the method cannot fully distinguish wind speed into data with different frequencies and thus it compromises the forecasting accuracy. A complete decomposition of measured wind speed can reduce the complexity of machine learning algorithm, and has become a useful approach for precise simulations of wind speed. Here, we propose a novel hybrid method (CEEMDAN-GA-BPNN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) by completely decomposing the measured wind speed. The decomposition results are put into the back propagation neural network optimized by a genetic algorithm (GA-BPNN), and the final forecasting results are achieved by combining all the output values by GA-BPNN for each decomposition result from CEEMDAN. We benchmark the forecasting accuracy of the proposed hybrid method against EMD-GA-BPNN integrated by EMD and GA-BPNN. From a wind farm case in Yunnan Province, China, both for ultra-short-term forecasting (15 min) and short-term forecasting (1 h), the performance of the proposed method exceeds EMD-GA-BPNN in several criteria, including root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R 2). The forecasting accuracy in decomposed components of low frequencies outperform components of high and middle frequencies. Fine improvement of the error metric (in percentage) in ultra-short-term/short-term forecasting is found by the complete decomposition method CEEMDAN-GA-BPNN: RMSE (7.0% and 8.6%), MAE (7.41% and 7.9%), MAPE (11.0% and 8.7%), and R 2 (2.2% and 11.0%), compared with the incomplete decomposing method EMD-GA-BPNN. Our result suggests that CEEMDAN-GA-BPNN could be an accurate wind speed forecasting tool for wind farms development and intelligent grid operations.

Significance Statement

Nonlinearity and randomness of natural wind speed data are the limitations for short-term and ultra-short-term wind speed forecasting. By decreasing forecasting error in machine learning training process, data decomposition for the measured wind speed has become an effective method for overcoming this issue. Nonetheless, the normal incomplete decomposition method will compromise the extent of forecasting accuracy. We introduce a novel hybrid and complete decomposition method CEEMDAN-GA-BPNN (the complete decomposition method). Measured wind speed data from a wind farm in Yunnan Province, China, has been utilized. CEEMDAN-GA-BPNN outperforms EMD-GA-BPNN (the partial decomposition method) in forecasting accuracy both in the ultra-short-term and the short-term wind speed forecasting.

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