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姓 名:汪君
职 务
职 称:高级工程师
研究方向:气候模式、气象水文地质灾害、卫星遥感
进所时间:2006-09-01
教育
2002.9-2006.7,南京大学大气科学系,气象学专业,理学学士学位。
2006.9-2012.7,中科院大气物理研究研究所,气象学专业, 理学博士学位
2006年,南京大学优秀毕业生
2006年,本科毕业论文《一种诊断入梅的新方法:使用GPS-PWV资料》获南京大学优秀毕业论文

研究经历
2006.9至今,中国科学院大气物理研究所。
2009年1月-2009年12月,俄克拉荷马大学,土木工程系、国家天气中心。
2012年9月-2012年12月,南森环境遥感研究中心,挪威卑尔根。

任职经历


研究项目


重要著作
专著:汪君,王会军,洪阳,2015:《中国洪涝、滑坡灾害监测和动力数值预报系统研究》。气象出版社,北京。
博士论文:《中国地区洪涝、滑坡灾害监测和动力数值预报系统的研制》

代表著作:

1) 汪君, 王会军, HONG Yang, 2016: 一个新的高分辨率洪涝动力数值监测预报系统. 科学通报, 61(4-5), 518-528 <摘要>

2) Jun Wang, Huijun Wang, Yang Hong, 2016: Comparison of satellite-estimated and model-forecasted rainfall data during a deadly debris-flow event in Zhouqu, Northwest China. Atmospheric and Oceanic Science Letters, 9(2), 139-145 <摘要>

<span style="color: rgb(0, 0, 0); font-family: 'Open Sans', sans-serif; font-size: 13px;">The data of several rainfall products, including those estimated from satellite measurements and those forecasted via numerical weather modeling, for a severe debris-flow event in Zhouqu, Northwest China, are compared and analyzed in this paper. The satellite products, including CPC MORPHing technique (CMORPH), TMPA-RT, and PERSIANN are all near-real-time retrieved with high temporal and spatial resolutions. The numerical weather model used in this paper for precipitation forecasting is WRF. The results show that all three satellite products can basically reproduce the rainfall pattern, distribution, timing, scale, and extreme values of the event, compared with gauge data. Their temporal and spatial correlation coefficients with gauge data are as high as about 0.6, which is statistically significant at 0.01 level. The performance of the forecasted results modeled with different spatial resolutions are not as good as the satellite-estimated results, although their correlation coefficients are still statistically significant at 0.05 level. From the total rainfall and extreme value time series for the domain, it is clear that, from the grid-to-grid perspective, the passive microwave-based CMORPH and TRMM products are more accurate than the infrared-based PERSIANN, while PERSIANN performs very well from the general point of view, especially when considering the whole domain or the whole convective precipitation system. The forecasted data &mdash; especially the highest resolution model domain data &mdash; are able to represent the total or mean precipitation very well in the research domain, while for extreme values the errors are large. This study suggests that satellite-retrieved and model-forecasted rainfall data are a useful complement to gauge data, especially for areas without gauge stations and areas not covered by weather radars.</span>

3) DONG Xiao, SU Tong-Hua, WANG Jun, LIN Ren-Ping, 2014: Decadal Variation of the Aleutian Low-Icelandic Low Seesaw Simulated by a Climate System Model (CAS-ESM-C). Atmos. Oceanic Sci. Lett., 7(2), 110-114 <摘要>

<div>&nbsp;</div> <span style="font-family: STHeiti; font-size: 14px;">Based on a simulation using a newly developed climate system model (Chinese Academy of Sciences-Earth System Model-Climate system component, CAS-ESM-C), the author investigated the Aleutian Low-Icelandic Low Seesaw (AIS) and its decadal variation. Results showed that the CAS-ESM-C can reasonably reproduce not only the spatial distribution of the climatology of sea level pressure (SLP) in winter, but also the AIS and its decadal variation. The period 496&ndash;535 of the integration by this model was divided into two sub-periods: 496&ndash;515 (P1) and 516&ndash;535 (P2) to further investigate the decadal weakening of the AIS. It was shown that this decadal variation of the AIS is mainly due to the phase transition of the Pacific Decadal Oscillation (PDO), from its positive phase to its negative phase. This transition of the PDO causes the sea surface temperature (SST) in the equatorial eastern (northern) Pacific to cool (warm), resulting in the decadal weakening of mid-latitude westerlies over the north Pacific and north Atlantic. This may be responsible for the weakening of the inverse relation between the Aleutian Low (AL) and the Icelandic Low (IL).</span><br style="font-family: STHeiti; font-size: 14px;" /> <br style="font-family: STHeiti; font-size: 14px;" /> <span style="font-family: STHeiti; font-size: 14px;">Cite this paper:</span><br style="font-family: STHeiti; font-size: 14px;" /> <br style="font-family: STHeiti; font-size: 14px;" /> <span style="font-family: STHeiti; font-size: 14px;">DONG Xiao, SU Tong-Hua, WANG Jun, LIN Ren-Ping, 2014: Decadal Variation of the Aleutian Low-Icelandic Low Seesaw Simulated by a Climate System Model (CAS-ESM-C) . Atmos. Oceanic Sci. Lett., 7(2), 110-114, doi: 10.3878/j.issn.1674-2834.13.0061.</span><br style="font-family: STHeiti; font-size: 14px;" /> <span style="font-family: STHeiti; font-size: 14px;">URL:</span><br style="font-family: STHeiti; font-size: 14px;" /> <br style="font-family: STHeiti; font-size: 14px;" /> <span style="font-family: STHeiti; font-size: 14px;">http://159.226.119.58/aosl/EN/10.3878/j.issn.1674-2834.13.0061 OR http://159.226.119.58/aosl/EN/Y2014/V7/I2/110</span>

4) Sheng Chen, Yang Hong, Qing Cao, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, Youcun Qi, Jian Zhang, Ken Howard, Junjun Hu, Jun Wang, 2013: Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events?. Journal of Hydrology, 506, 4-13 <摘要> PDF


<br /> <div> <table style="font-family: STHeiti;" width="100%"> <tbody> <tr> <td class="abstract" style="font-size: 14px; word-wrap: break-word; padding-left: 10px;"> <div id="abstc_951">This study evaluated rainfall estimates from ground radar network and four satellite algorithms with a relatively dense rain gauge network over Taiwan Island for the 2009 extreme Typhoon Morakot at various spatiotemporal scales (from 0.04&deg; to 0.25&deg; and hourly to event total accumulation). The results show that all the remote-sensing products underestimate the rainfall as compared to the rain gauge measurements, in an order of radar (&minus;18%), 3B42RT (&minus;19%), PERSIANN-CCS (28%), 3B42V6 (&minus;36%), and CMORPH (&minus;61%). The ground radar estimates are also most correlated with gauge measurements, having a correlation coefficient (CC) of 0.81 (0.82) at 0.04&deg; (0.25&deg;) spatial resolution. For satellite products, CMORPH has the best spatial correlation (0.70) but largely underestimates the total rainfall accumulation. Compared to microwave ingested algorithms, the IR-dominant algorithms provide a better estimation of the total rainfall accumulation but poorly resolve the temporal evolution of the warm cloud typhoon, especially for a large overestimation at the early storm stage. This study suggests that the best performance comes from the ground radar estimates that could be used as an alternative in case of the gauge denial. However, the current satellite rainfall products still have limitations in terms of resolution and accuracy, especially for this type of extreme typhoon.</div> </td> </tr> </tbody> </table> </div> <br />

http://nzc.iap.ac.cn/uploadfile/2015/0204/20150204100643511.pdf

5) 汪君, 王会军, 2013: WRF模式对江苏如东地区风速预报的检验分析. 气候与环境研究, 18(2), 145-155 <摘要>

<br /> <div> <table style="font-family: STHeiti;" width="100%"> <tbody> <tr> <td class="abstract" style="font-size: 14px; word-wrap: break-word; padding-left: 10px;"> <div id="abstc_882">探讨了WRF模式在风电场的风速或者功率预报中应用的可行性, 主要研究和评估了WRF模式对地处东亚季风区及海陆交界的江苏如东地区夏季和冬季风速的短期预报效能。研究发现WRF模式可以比较好地预报如东站冬季的风速, 24 h预报的风速时间序列和观测资料的相关系数可以达到0.61, 通过置信度99%的检验, 48 h和72 h的预报与观测风速相关系数分别为0.54和0.47, 也能通过置信度99%的检验;相对而言, 模式对夏季风速的预报则要差一些, 24 h的相关系数有0.59, 48 h和72 h的相关系数只有0.47和0.30, 但仍能通过置信度99%的检验。在量值上, 模式预报的风速比观测值都略偏大一些。而江苏南通市预报结果显示, 模式的预报效能要比如东稍高一些, 和如东类似, 模式对该地冬季的预报要好于对夏季风速的预报。从更大尺度范围的分析也表明, 模式对不同地区预报的准确度是不一样的, 对海面以及海陆交界的海岸预报精度要高一些, 在平坦的内陆地区预报也比较好, 但在山区预报效能则较差。总体说来, WRF能胜任风速短期预报, 值得进一步研究和应用。<br /> <br /> 链接http://www.dqkxqk.ac.cn/qhhj/ch/reader/view_abstract.aspx?file_no=20130201&amp;flag=1 <p>引用:汪君,王会军.2013.WRF模式对江苏如东地区风速预报的检验分析[J].气候与环境研究,18(2):145-155,doi:10.3878/j.issn.1006-9585.2013.11152.</p> Citation:WANG Jun and WANG Huijun.2013.Forecasting of Wind Speed in Rudong, Jiangsu Province, by the WRF Model[J].Climatic and Environmental Research(in Chinese),18(2):145-155,doi:10.3878/j.issn.1006-9585.2013.11152.</div> </td> </tr> </tbody> </table> </div> <br />

6) WANG Jun and WANG Hui-Jun, 2010: The Relationship between Total Ozone and Local Climate at Kunming Using Dobson and TOMS Data. Atmospheric and Oceanic Science Letters, 3(4), 207-212 <摘要>

<span style="color: rgb(0, 0, 0); font-family: STHeiti; font-size: 14px;">This paper uses Dobson spectrometer total&nbsp;</span><span style="color: rgb(0, 0, 0); font-family: STHeiti; font-size: 14px;">ozone data, Total Ozone Mapping Spectrometer (TOMS) data and radiosonde reports from Kunming, which is located in southwest China, from 1980 to 2008 to analyze the total ozone-climate relationship. The total ozone decadal long-term trend and abrupt change were studied using enhanced Dobson data whose missing data were amended by the TOMS data. Stepwise linear regression was used for the selection of the key factors that influence total ozone, including temperatures, geopotential heights, depressions of the dew point, wind velocities, and total solar radiation. The relationship between the selected factors and total ozone was analyzed using the methods of stepwise regression and partial least squares regression (PLSR). Results showed that although the PLSR method was slightly better and more reasonable to study the relationship than stepwise regression, while the two regression results were only slightly different. It was also suggested that local climate, especially local circulation and temperature, were important for the variations in total ozone, and the local climate could almost linearly explain 80% of&nbsp;the variance of total ozone. The relationship also indicated that the abrupt change of total ozone in the year 1994 may be related to abrupt local climate change.</span>

7) Zonghu Liao, Yang Hong, Jun Wang , etc., 2010: Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets. Landslides, 7, 317-324 <摘要>

Abstract An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major compo- nents: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topo- graphical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system (http://trmm.gsfc.nasa.gov) and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface &ldquo;where&rdquo; information will be integrated with the &ldquo;when&rdquo; rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Admin- istration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system&rsquo;s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.

8) 曹晓岗,丁金才,叶其欣,汪君,邱黎华, 2007: 利用水汽总量资料诊断入梅时间的方法. 应用气象学报, 18(6), 791-801 <摘要>

链接: http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=200706121&flag=1

 

 

梅雨期是江淮流域从春季到夏季一个重要的过渡时期。传统诊断入梅的方法主要根据雨日和温度及副热带高压位置等来确定。由于雨日的不连续,天气形势的多变,常会引起诊断入梅日期的分歧。利用长江三角洲地区地基GPS网所反演的连续的大气水汽总量(GPS/PWV)资料详细分析了长江三角洲地区2002—2005年入梅情况,发现GPS/PWV资料可以反映出入梅前后大气中水汽发生显著季节性跳跃的特征,总结出利用大气中水汽变化特征来诊断入梅时间的 方法(PWV方法)。采用1980—2000年的历史探空资料计算的大气水汽总量(PWV)资料,对该方法进行了检验:21年中有13年的入梅日期与历史上传统方法诊断的入梅日期相吻合;对两种方法诊断的入梅日期相差较大的3年的入梅情况进行的分析表明,PWV方法诊断出的入梅日比原定入梅日更合理。该方法在2006年入梅诊断的应用也得到验证。
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