基于集合卡爾曼濾波的土壤溫濕度同化試驗(yàn)

doi:10.3969/2015jms.0027

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基于集合卡爾曼濾波的土壤溫濕度同化試驗(yàn)
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                        10.3969/2015jms.0027
                    
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基金項(xiàng)目:國家自然科學(xué)基金資助項(xiàng)目(41101037,13231203804);華東師范大學(xué)地理信息科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室主任基金(KLGIS2011C06);中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)獎金項(xiàng)目

Experiments of soil temperature and moisture assimilation system based on Ensemble Kalman Filter

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以通用陸面模式CLM 3.0(Community Land Model 3.0)為模型算子,基于集合卡爾曼濾波(Ensemble Kalman Filter,EnKF)發(fā)展了一個土壤溫濕度同化系統(tǒng),主要用于改進(jìn)模式對土壤溫濕度和地表水熱通量的模擬精度,并考察集合樣本數(shù)、同化頻率及不同觀測量的組合對同化效果的影響。該系統(tǒng)同化了FLUXNET兩個站點(diǎn)(阿柔和Bondville)不同土壤深度、不同時間頻率的土壤溫度和濕度數(shù)據(jù)。通過對阿柔站不同集合樣本數(shù)的設(shè)計(jì),綜合考慮計(jì)算成本和計(jì)算精度,最終將集合樣本數(shù)設(shè)置為40。通過分析三種同化方案對同化頻率的敏感性得出,同化土壤溫度最為敏感,同時同化土壤溫濕度次之,同化土壤濕度最不敏感。對于阿柔站點(diǎn),同化系統(tǒng)對不同土壤深度溫度和濕度的模擬精度均能提高90%,潛熱通量的均方根誤差由94.0W·m^-2降為46.3W·m^-2,感熱通量均方根誤差由55.9W·m^-2降為24.6W·m^-2。Bondville站點(diǎn)淺層土壤溫度的改進(jìn)在30%左右,深層土壤溫度改進(jìn)達(dá)到60%,對土壤濕度的改進(jìn)均在70%以上,潛熱通量和感熱通量的均方根誤差分別從57.4W·m^-2和54.4W·m^-2降為51.0W·m^-2和42.5W·m^-2。試驗(yàn)結(jié)果表明,同化站點(diǎn)土壤溫濕度數(shù)據(jù)對土壤水熱狀況及通量的模擬改進(jìn)非常有效,同時也驗(yàn)證了同化土壤水分遙感產(chǎn)品的可行性和必要性。

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In this work,with the Community Land Model Version 3.0 (hereafter CLM) as a model operator, a soil temperature and moisture data assimilation scheme was developed, which was based on Ensemble Kalman Filter. The system was mainly used to improve the estimations of the soil temperature, moisture, sensible and latent heat fluxes. Also, the ensemble size, assimilation frequency, three assimilation programs with different observations were executed to evaluate the improvements of land surface process simulation.Soil temperature and moisture in different layers and time frequencies from two flux observing sites (Arou and Bondville) were assimilated into CLM.With considering the computational cost and accuracy, the optimal ensemble size was set as 40. The sensitivity analysis of three programs to assimilation frequencies showed that assimilating soil temperature was the most sensitive to assimilation frequency, then assimilating soil temperature and moisture simultaneously, and assimilating soil moisture was the least sensitive to assimilation frequency. At Arou site, assimilation system could improve the soil temperature and moisture accuracy in different layers by 90%. The RMSE of latent and sensible heat flux reduced from 94.0 W·m^-2 to 46.3 W·m^-2 and from 55.9 W·m^-2 to 24.6 W·m^-2 respectively. At Bondville site, the improvements of shallow layer and deep layer soil temperature were about 30 % and 60%respectively. The improvement of soil moisture was above 70%. The RMSE of latent and sensible heat flux dropped from 57.4 W·m^-2 to 51.0 W·m^-2 and from 54.4 W·m^-2 to 42.5 W·m^-2. The results indicated that assimilating the soil temperature and moisture site data could improve the simulation of land surface process effectively. It was feasible and significant to assimilate soil moisture remote sensing products.

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杜娟,劉朝順,高煒.基于集合卡爾曼濾波的土壤溫濕度同化試驗(yàn).氣象科學(xué),2016,36(2):184-193 DU Juan, LIU Chaoshun, GAO Wei. Experiments of soil temperature and moisture assimilation system based on Ensemble Kalman Filter. Journal of the Meteorological Sciences,2016,36(2):184-193

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收稿日期:2014-07-25
最后修改日期:2015-03-24
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在線發(fā)布日期: 2016-06-13
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