| TRMM月降水量产品在新疆地区的订正 |
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| 引用本文： | 卢新玉,魏鸣,王秀琴.TRMM月降水量产品在新疆地区的订正[J].应用气象学报,2017,28(3):379-384. |
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| 作者姓名： | 卢新玉 魏鸣 王秀琴 |
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| 作者单位： | 1.中国气象局气溶胶-云-降水重点开放实验室, 南京信息工程大学, 南京 210044 |
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| 基金项目： | 江苏省普通高校研究生科研创新计划项目（KYLX15_0864），中国沙漠气象科学研究基金项目（SQJ2014012），中国气象科学研究院灾害天气国家重点实验室开放课题（2016LASW-B12），公益性行业（气象）科研专项（GYHY201506009），国家自然科学基金项目（41565003，41675029），地理信息科学教育部重点实验室开放研究基金资助项目（KLGIS2015A01） |
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| 摘 要： | 利用1998-2013年TRMM月降水量产品与新疆同期的105个气象站地面观测降水量，运用逐步回归与BP神经网络方法，选取1998-2010年数据建立新疆地区的降水订正模型，并利用2011-2013年月降水量进行检验。结果表明:加入地形因子对TRMM月降水量产品订正效果明显，整体上两种模型对TRMM月降水量产品订正的相关系数从最初的0.66分别提高到0.75和0.80，相对误差由10.75%分别降低为4.88%和3.19%；月尺度上，TRMM月降水量产品相对误差为-5.68%~54.44%，经逐步回归模型订正后为-4.26%~32.57%，而BP神经网络模型订正后为-5.33%~24.48%，表明BP神经网络模型订正效果更好；从综合时间技巧评分ST看，订正后TRMM月降水量产品在各月的效果均有不同程度提高，逐步回归模型订正后提高0.01~0.49，BP神经网络模型订正后提高0.03~0.70。因此，基于逐步回归模型与BP神经网络模型订正的TRMM降水量产品均能够准确、定量地再现降水分布，为TRMM降水量产品质量改进提供一种较实用的参考方法。
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| 关 键 词： | TRMM 降水 新疆 订正 |
| 收稿时间： | 2016/12/7 0:00:00 |
| 修稿时间： | 2017/2/15 0:00:00 |
Correction of TRMM Monthly Precipitation Data from 1998 to 2013 in Xinjiang |
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| Lu Xinyu,Wei Ming and Wang Xiuqin.Correction of TRMM Monthly Precipitation Data from 1998 to 2013 in Xinjiang[J].Quarterly Journal of Applied Meteorology,2017,28(3):379-384. |
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| Authors: | Lu Xinyu Wei Ming and Wang Xiuqin |
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| Affiliation: | 1.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 2100442.Institute of Desert Meteorology, China Meteorological Administration, Urumqi 8300023.Central-Asia Research Center of Atmosphere Science, Urumqi 8300024.Xinjiang Meteorological Bureau, Urumqi 830002 |
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| Abstract: | Using monthly TRMM precipitation data and precipitation observations from 105 national basic weather stations in Xinjiang region from 1998 to 2013, a stepwise regression model and a back-propagation (BP) neural network are established to correct TRMM precipitation. Results show that models added with geographical factors can increase the accuracy of TRMM precipitation effectively. Corrected by two models, overall correlation coefficients are 0.75 and 0.80, and relative errors are 4.88% and 3.19%. On the monthly scale, the relative error of TRMM monthly precipitation ranges from -5.68% to 54.44%, from -4.26% to 32.57% after stepwise regression and from 5.33% to 24.48% after neural network, respectively. In addition, results show that qualities of satellite precipitation products are improved in different degrees from ST, with 0.01-0.49 for stepwise regression model and 0.03-0.70 for neural network, respectively. Compared with TRMM data before correction, the stepwise regression model and BP neural network model can accurately and quantitatively reproduce the actual distribution of precipitation, and provide a more practical method for the area lack of precipitation data. |
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| Keywords: | TRMM precipitation Xinjiang correction |
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