對流尺度集合預(yù)報初值擾動技術(shù)及系統(tǒng)應(yīng)用研究進展

doi:10.12306/2025jms.0019

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對流尺度集合預(yù)報初值擾動技術(shù)及系統(tǒng)應(yīng)用研究進展
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                        10.12306/2025jms.0019
                    
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中圖分類號:P456.7
基金項目:國家自然科學(xué)基金資助項目(42205166；42475167)；中國氣象局氣象能力聯(lián)合提升研究專項(24NLTSQ010)；中國氣象局青年創(chuàng)新團隊項目(CMA2024QN05)

Advances in initial perturbation techniques and systemic application research for convective-scale ensemble prediction

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摘要:

對流尺度集合預(yù)報系統(tǒng)(Convective-scale Ensemble Prediction System， CEPS)具有時空高分辨率，能提供概率預(yù)報，對于提高強對流天氣的可預(yù)報性具有重要意義。目前，CEPS已經(jīng)成為業(yè)務(wù)中心建設(shè)的重點，而初值擾動方法是攻關(guān)技術(shù)難點之一，目前業(yè)務(wù)上主要的初值擾動方案有：動力降尺度、集合資料同化及奇異向量等，也有學(xué)者將中尺度集合預(yù)報的初值擾動方法如增長模繁殖法方案及混合擾動方法沿用到對流尺度集合預(yù)報中。然而現(xiàn)有方案尚未考慮快速增長的非線性擾動，開發(fā)能夠描述初始場非線性增長信息的初值擾動方法，是CEPS的一個發(fā)展方向。國際上一些先進的數(shù)值預(yù)報中心和機構(gòu)已成功地將CEPS應(yīng)用到業(yè)務(wù)天氣預(yù)報中，從現(xiàn)有的CEPS對強降水天氣事件預(yù)報可見，CEPS能夠為大量級降水提供概率預(yù)報，對暴雨的極端性有較好的參考意義，而集合平均的參考性并不強，其對強降水的預(yù)報仍然存在改進空間。人工智能技術(shù)的進步為集合預(yù)報的發(fā)展帶來了新契機，首先集合預(yù)報能夠為人工智能模型訓(xùn)練提供優(yōu)質(zhì)的數(shù)據(jù)基礎(chǔ)；其次，構(gòu)建基于人工智能的集合預(yù)報模型避免了傳統(tǒng)集合預(yù)報的高耗能問題；此外，利用人工智能技術(shù)對集合預(yù)報結(jié)果進行后處理，可提升集合預(yù)報的使用效率。隨著數(shù)值天氣預(yù)報逐步實現(xiàn)精細化，CEPS與人工智能相結(jié)合也將是未來發(fā)展的一個重要方向。

Abstract:

Convective-scale Ensemble Prediction System (CEPS)， characterized by its high spatiotemporal resolution and capability to provide probabilistic forecasts， plays a critical role in improving the predictability of severe convective weather. Currently， CEPS has become a key focus in operational center development， with initial perturbation methods being one of the challenging technical issues. Primary operational initial perturbation schemes include dynamic downscaling， ensemble data assimilation， and singular vectors. Some researchers have also adapted mesoscale ensemble forecast perturbation methods such as the BGM(Breeding of Growing Modes) scheme and hybrid perturbation approaches to CEPS. However， existing methodologies have yet to account for rapidly amplified nonlinear perturbations. The development of initial perturbation methods capable of characterizing nonlinear growth dynamics in initial fields represents a pivotal direction for CEPS innovation. Some international numerical weather prediction centers and institutions have successfully implemented CEPS in operational weather forecasting. Evaluations of CEPS performance in forecasting heavy precipitation events reveal its effectiveness in providing probabilistic predictions for extreme rainfall magnitudes， offering valuable insights into the extremity of storm events. However， ensemble mean forecasts demonstrate limited utility， indicating substantial room for improvement in predicting intense precipitation. Advancements in Artificial Intelligence (AI) technology present new opportunities for advancing ensemble forecasting. Firstly， ensemble forecasts provide high-quality datasets for training AI models. Secondly， AI-driven ensemble forecasting frameworks could circumvent the high energy consumption inherent in traditional ensemble methods. Furthermore， AI-based post-processing of ensemble forecast outputs could enhance operational efficiency. As numerical weather prediction progresses toward finer precision， the integration of CEPS with AI technologies is poised to become a vital future development direction.

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王靜,張楠,李紅祺,楊曉君,韓雨盟,王婧卓,劉昕.對流尺度集合預(yù)報初值擾動技術(shù)及系統(tǒng)應(yīng)用研究進展.氣象科學(xué),2025,45(4):525-534 WANG Jing, ZHANG Nan, LI Hongqi, YANG Xiaojun, HAN Yumeng, WANG Jingzhuo, LIU Xin. Advances in initial perturbation techniques and systemic application research for convective-scale ensemble prediction. Journal of the Meteorological Sciences,2025,45(4):525-534

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收稿日期:2024-09-19
最后修改日期:2025-01-07
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在線發(fā)布日期: 2025-11-29
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