WRF模式與自動(dòng)站資料同化相結(jié)合的遼寧高分辨2012年1月氣溫場(chǎng)建立試驗(yàn)

doi:10.3969/2015jms.0033

首頁(yè) > 過(guò)刊瀏覽>2016年第36卷第5期 >639-646. DOI:10.3969/2015jms.0033

WRF模式與自動(dòng)站資料同化相結(jié)合的遼寧高分辨2012年1月氣溫場(chǎng)建立試驗(yàn)
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                        10.3969/2015jms.0033
                    
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基金項(xiàng)目:國(guó)家自然科學(xué)基金資助項(xiàng)目（41230528）；中國(guó)氣象局2016氣候變化專項(xiàng)（CCSF201608）

Construct experiment for high resolution temperature field of Liaoning in January 2012 combined with WRF model and automatic weather station data assimilation

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利用2012年1月遼寧省1 410個(gè)自動(dòng)站資料，通過(guò)資料質(zhì)量控制，采用WRF模式分析同化，結(jié)合自動(dòng)站資料觀測(cè)同化進(jìn)行了遼寧地區(qū)4 km分辨率1月氣溫場(chǎng)建立的試驗(yàn)，同時(shí)與不進(jìn)行同化觀測(cè)資料、同化稀疏場(chǎng)資料的模擬試驗(yàn)對(duì)比，評(píng)估3種不同試驗(yàn)下遼寧地區(qū)月平均以及逐日、逐時(shí)溫度的模擬能力，發(fā)現(xiàn)WRF模式與自動(dòng)站資料同化相結(jié)合能建立高質(zhì)量4 km分辨率的遼寧地區(qū)1月氣溫場(chǎng)，1月平均溫度的偏差范圍大部分低于±0.5℃，對(duì)逐日溫度絕對(duì)偏差一般低于0.6℃，溫度變化與觀測(cè)序列的相關(guān)系數(shù)高于0.95；逐時(shí)溫度的絕對(duì)偏差一般低于1℃，相關(guān)系數(shù)高于0.92。同化自動(dòng)站資料試驗(yàn)的模擬效果，無(wú)論對(duì)于逐月溫度還是逐日、逐時(shí)溫度都顯著優(yōu)于未同化試驗(yàn)和同化稀疏場(chǎng)資料試驗(yàn)。在復(fù)雜地形下，同化自動(dòng)站后模擬效果的改善尤為明顯，逐日和逐時(shí)溫度模擬的平均絕對(duì)偏差降低幅度都能接近0.5℃。

Abstract:

Based on 1 410 automatic weather station data of Liaoning province in January 2012, combined with data quality control, the WRF model analysis nudging skill and automatic station data observation nudging skill were used to simulate temperature field of Liaoning province with 4 km resolution in January. Three different experiments(without assimilation, assimilate sparse field data (NCEP global observation data) and assimilate automatic weather station data) were conducted to validate the capabilities of temporal and spatial variability in monthly average temperature, daily temperature and hourly temperature. WRF model combining with automatic station data assimilation can simulate high-quality temperature field of Liaoning province with 4 km resolution in January, the average January temperature bias is lower than ±0.5℃, the absolute bias of the daily temperature is less than 0.6℃, and the correlation coefficient with the observed temperature change is higher than 0.95. Hourly temperature of absolute bias is generally lower than 1℃, and the correlation coefficient is higher than 0.92. Experiments show that in both monthly temperature and daily temperature, the results of automatic weather station data assimilation are significantly better than that without assimilate and assimilate sparse field data. Over complex terrain, the result can be improved significantly with automatic weather station data assimilation, and the absolute bias of both daily temperature and hourly temperature can be reduced by nearly 0.5℃.

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周易,江志紅,邱欣,李偉,張強(qiáng). WRF模式與自動(dòng)站資料同化相結(jié)合的遼寧高分辨2012年1月氣溫場(chǎng)建立試驗(yàn).氣象科學(xué),2016,36(5):639-646 ZHOU Yi, JIANG Zhihong, QIU Xin, LI Wei, ZHANG Qiang. Construct experiment for high resolution temperature field of Liaoning in January 2012 combined with WRF model and automatic weather station data assimilation. Journal of the Meteorological Sciences,2016,36(5):639-646

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收稿日期:2015-04-04
最后修改日期:2015-05-21
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在線發(fā)布日期: 2016-11-01
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