遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在地面溫度多模式集成預(yù)報的應(yīng)用研究

doi:10.3969/2018jms.0034

首頁 > 過刊瀏覽>2018年第38卷第6期 >806-814. DOI:10.3969/2018jms.0034

遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在地面溫度多模式集成預(yù)報的應(yīng)用研究
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                        10.3969/2018jms.0034
                    
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基金項(xiàng)目:國家自然科學(xué)基金資助項(xiàng)目（41275091）

Application research of BP neural network optimized by genetic algorithm in multi-model ensemble forecast about ground temperature

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基于TIGGE資料集中的ECMWF、CMA和JMA的數(shù)值預(yù)報產(chǎn)品，利用加權(quán)集成、回歸集成和消除偏差集成等線性集成方式與遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)（GABP）集成，對我國大部開展地面2 m溫度在24 h、48 h和72 h預(yù)報時效的多模式集成預(yù)報試驗(yàn)。通過對2013年1-6月的預(yù)報檢驗(yàn)，結(jié)果表明：GABP集成預(yù)報效果有較大提升，均方誤差明顯小于各單一模式預(yù)報。GABP集成的誤差分布在新疆和華北均方誤差較大，但是在預(yù)報效果改進(jìn)上GABP集成在西部地區(qū)相對單一模式的誤差減小更加明顯。在進(jìn)行幾種多模式集成方式時，GABP集成相比線性方法預(yù)報結(jié)果更加精準(zhǔn)。對于天氣過程個例的預(yù)報，GABP集成預(yù)報出預(yù)報量的變化趨勢，預(yù)報效果優(yōu)于單一模式和線性集成預(yù)報。無論是較長時間段還是短時間的天氣過程，在改進(jìn)預(yù)報效果上GABP集成都起到了最佳的作用。

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Based on the numerical forecast products of ECMWF, CMA and JMA gathered in the TIGGE data, the multi-model ensemble forecasting experiment about temperature at 2 m above ground in most parts of China at the 24 h, 48 h and 72 h forecasting lead time is carried out using some linear ensemble methods such as weighted ensemble, regression ensemble and bias-removed ensemble as well as BP neural network ensemble optimized by genetic algorithm (GABP). Through the test for forecast from January to June in 2013, results show that forecasting result of GABP ensemble has been greatly improved, whose mean square error is obviously less than that of any single model. The error distribution of GABP ensemble forecast shows relatively larger mean square error in Xinjiang and North China than in other parts, but in terms of forecast result improvement, the error of GABP ensemble has reduced more obviously compared with that of single-model forecast in western China. When several multi-model ensemble experiments are being carried out, the forecasting result of GABP ensemble is more accurate than that of linear ensemble methods. For forecast about a case of synoptic process, GABP ensemble can reveal variation trend of the predict, and its forecasting result is better than that of single-model forecast and linear-ensemble forecast. No matter for long-time or short-time synoptic process, GABP ensemble plays the best role in improving forecasting results.

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雷彥森,蔡曉軍,王文,李江峰,李倩文.遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在地面溫度多模式集成預(yù)報的應(yīng)用研究.氣象科學(xué),2018,38(6):806-814 LEI Yansen, CAI Xiaojun, WANG Wen, LI Jiangfeng, LI Qianwen. Application research of BP neural network optimized by genetic algorithm in multi-model ensemble forecast about ground temperature. Journal of the Meteorological Sciences,2018,38(6):806-814

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收稿日期:2018-03-01
最后修改日期:2018-03-14
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在線發(fā)布日期: 2019-01-30
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