本文選題：大氣環(huán)境學(xué) + 數(shù)據(jù)同化　； 參考：《熱帶氣象學(xué)報(bào)》2017年05期

【摘要】：集合卡爾曼濾波和粒子濾波是大氣海洋領(lǐng)域兩種先進(jìn)的數(shù)據(jù)同化方法。理論上講,粒子濾波克服了集合卡爾曼濾波中先驗(yàn)分布的高斯假定。但現(xiàn)有的關(guān)于兩種方法的比較研究不夠全面和系統(tǒng),基于簡(jiǎn)單的洛倫茲63模式,重點(diǎn)對(duì)基于確定性集合卡爾曼濾波和均權(quán)重粒子濾波的數(shù)據(jù)同化方法開(kāi)展對(duì)比分析,通過(guò)對(duì)觀測(cè)誤差和模式誤差的不同配置,設(shè)計(jì)了四組試驗(yàn)著重研究?jī)煞N方法相同試驗(yàn)條件下的同化效果。試驗(yàn)結(jié)果表明:與采用最優(yōu)膨脹系數(shù)的集合卡爾曼濾波的同化方法相比,均權(quán)重粒子濾波的均方根誤差更加依賴于觀測(cè)信息的質(zhì)量,但最優(yōu)膨脹因子的集合卡爾曼濾波的均方根誤差低于粒子濾波同化方法。
[Abstract]:Aggregate Kalman filter and particle filter are two advanced data assimilation methods in atmospheric and oceanic domain. Theoretically, particle filtering overcomes Gao Si's assumption of prior distribution in ensemble Kalman filtering. However, the existing comparative studies on the two methods are not comprehensive and systematic. Based on the simple Lorenz 63 model, the data assimilation methods based on deterministic set Kalman filter and average weighted particle filter are compared and analyzed. Through different configuration of observation error and mode error, four groups of experiments are designed to study the assimilation effect under the same experimental conditions. The experimental results show that the root mean square error of average weight particle filter is more dependent on the quality of observation information than the assimilation method using set Kalman filter with optimal expansion coefficient. However, the root mean square error of the set Kalman filter with the optimal expansion factor is lower than that of the particle filter assimilation method.
【作者單位】： 哈爾濱工程大學(xué)自動(dòng)化學(xué)院;國(guó)家海洋信息中心海洋環(huán)境信息保障技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFC1401800) 基于粒子濾波的海氣耦合數(shù)據(jù)同化方法研究(HEUCF041705) 中國(guó)南海內(nèi)波場(chǎng)數(shù)字化及其在數(shù)字化傳播方面的應(yīng)用(HEUCFP201708)共同資助
【分類號(hào)】：P732.6
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本文編號(hào)：1812374