论文

三江源及其周边地区多源水汽资料对比检验

  • 马学谦 ,
  • 张小军 ,
  • 马玉岩 ,
  • 蔡淼 ,
  • 韩辉邦 ,
  • 康晓燕
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  • 青海省人工影响天气办公室, 青海 西宁 810000;中国气象局人工影响天气中心, 北京 100000

收稿日期: 2018-01-03

  网络出版日期: 2019-02-28

基金资助

国家自然科学基金项目(41665008,41565008,41705121);青海省基础研究项目(2017-ZJ-799);青海省自然科学基金项目(2017-ZJ-944Q);中央级公益性科研院所基本科研专项(CAFYBB2016SY003);国家留学基金委员会西部地区人才培养特别项目

Comparative Analysis of Multi-Source Water Vapor Data in Three River Source and Nearby Areas

  • MA Xueqian ,
  • ZHANG Xiaojun ,
  • MA Yuyan ,
  • CAI Miao ,
  • HAN Huibang ,
  • KANG Xiaoyan
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  • Office of WeatherModification in Qinghai Province, Xining 810000, Qinghai, China;Weather modification center of China Meteorological Bureau, Beijing 100000, China

Received date: 2018-01-03

  Online published: 2019-02-28

摘要

采用三江源人工增雨工程建设的35通道微波辐射仪、全球导航卫星系统气象观测(GNSS/Met)、L波段探空系统以及美国国家环境预报中心(NCEP)再分析资料,经多源连续监测和计算大气水汽含量,对比检验多源数据的差异性和影响因素,了解多源大气水汽含量在三江源及其周边地区的适用性。文中以L波段探空计算的大气水汽含量为基准值进行检验,结果表明GNSS/Met反演的大气水汽含量与基准值保持一致,总偏差为1.45 mm,不受温度、降水、季节、地区的明显影响,可代表三江源及其周边地区的大气水汽特征;NCEP大气可降水量总趋势与基准值比较一致,其值明显偏小,仅为基准值的69%,且随降水越大偏差增大,温度、地区、季节对其有明显影响;微波辐射仪能代表无降水或弱降水条件下的大气水汽特征,其值明显偏大,且受温度、液态水含量等多种因素影响。对三江源及其周边地区的旬大气水汽分析表明,三江源核心区大气水汽含量差异较小,从东到西呈波谷型分布;周边地区分布差异巨大,东边最高,西边最低,此分布特征与地理位置、地形和年内天气过程等密切相关。这些监测数据的对比检验和分析为三江源及其周边地区的云水资源精确定量评估起到基础性作用,也为改善人工增雨(雪)技术和保护当地生态环境起到关键性作用。

本文引用格式

马学谦 , 张小军 , 马玉岩 , 蔡淼 , 韩辉邦 , 康晓燕 . 三江源及其周边地区多源水汽资料对比检验[J]. 高原气象, 2019 , 38(1) : 78 -87 . DOI: 10.7522/j.issn.1000-0534.2018.00073

Abstract

The 35-channel microwave radiometer, global navigation satellite system meteorological observations (GNSS/Met), L-band sounding and National Center for Environmental Prediction (NCEP) reanalysis data constructed by enhancement precipitation project of Three Rivers Source were used to continuously monitor and calculated the precipitation water vapor (PWV) in this paper. The differences and influencing factors of multi-source data were compared, grasped and identified the applicability of multi-source data in Three River Source and nearby areas. The results show that GNSS/Met PWV is basically consistent with the reference value, which is not affected by temperature, precipitation, seasons and regions, and the total deviation is 1.45 mm. It can completely represent the water vapor characteristics of Three River Source and nearby areas. The general trend of NCEP PWV is consistent with the reference value, its value is obviously small, only 69% of the reference value, and with the larger deviation of precipitation increases. Temperature, region and season have a significant impact on its value. PWV of microwave radiometer can represent the water vapor characteristics of no or weak precipitation conditions, the value is significantly larger, and affected by the temperature, liquid water content and other factors, the data applicability need a lot of neural network training. The analysis of the ten-day PWV in Three River Source and nearby areas shows that there is little difference in the core area of Three River Source, and it is a trough-type distribution from east to west; the distribution characteristics of nearby areas are very different, with the highest in the east and the lowest in the west. the characteristics closely relate with geographic location, topography, and annual weather processes. These monitoring data analysis and comparative verification have played a fundamental role in the accurate quantitative assessment of cloud water resources in Three River Source and nearby areas. and also played a key role in protecting the ecological environment of the plateau ecology by enhancement precipitation.

参考文献

[1]Anthes R, Rocken C, Kou Y, 2000.Applications of COSMIC to meteorology and climatology[J]. Terrestrial Atmospheric & Oceanic Sciences, 11(1):115-156.
[2]Baumgardner D, 1983. An analysis and comparison of five water droplet measuring instruments[J]. Journal of climate and applied meteorology, 22(5):891-910.
[3]Huang C Y, Kuo Y H, Chen S H, et al, 2005. Improvements in Typhoon Forecasts with Assimilated GPS Occultation Refractivity[J]. Weather & Forecasting, 20(6):931-953.
[4]Kursinski E R, Hajj G A, Bertige W L, et al, 1996. Initial Results of Radio Occultation Observations of Earth's Atmosphere Using the Global Positioning System[J]. Science, 271(5252):1107-1110.
[5]Revercomb H E, Turner D C, Tobin D D, et al, 2003. The ARM program's water vapor intensive observation periods:Overview, initial accomplishments, and future challenges[J]. Bulletin of American Meteorological Society, 84(2):217-236.
[6]Rocken C, Anthes R, Exner M, et al, 1997. Analysis and validation of GPS/MET data in the neutral atmosphere[J]. Journal of Geophysical Research Atmospheres, 1022(D25):29849-29866.
[7]Sokolovskiy S, Kuo Y H, Wang W, 2005. Assessing the accuracy of a 1inearized observation operator for assimilation of radio occultation data:Case simulations with a high-resolution weather model[J]. Monthly Weather Review, 133(8):2200-2212.
[8]Syndergaarda S, Kursinskia E R, Herman B M, et al, 2005. A refractive index mapping operator for assimilation of occultation data[J]. Monthly Weather Review, 133(9):2650-2668.
[9]曹玥瑶, 张鹏, 马刚, 等, 2016. FY-3 IRAS水汽通道亮温正演精度改进方法[J].应用气象学报, 27(6):698-708.
[10]杜晓勇, 毛节泰, 2008. GPS-LEO掩星探测现状和展望[J].高原气象, 27(4):918-931.
[11]郭丽君, 郭学良, 2015.利用地基多通道微波辐射计遥感反演华北持续性大雾天气温、湿度廓线的检验研究[J].气象学报, 73(2):368-381.
[12]郭洁, 李国平, 2007.地基GPS探测水汽的发展与气象业务应用[J].大地测量与地球动力学, 27(6):35-42.
[13]郭巍, 尹球, 杜明斌, 等, 2015.利用地基北斗站反演大气水汽总量的精度检验[J].应用气象学报, 26(3):346-353.
[14]郭志梅, 李黄, 缪启龙, 2008. GPS探测气象参数的技术进展[J].气候与环境研究, 13(2):212-224.
[15]韩珏靖, 陈飞, 张臻, 等, 2015. MP-3000A型地基微波辐射计的资料质量评估和探测特征分析[J].气象, 41(2):226-233.
[16]黄建平, 何敏, 阎虹如, 等, 2010.利用地基微波辐射计反演兰州地区液态云水路径和可降水量的初步研究[J].大气科学, 34(3):548-558.
[17]敬文琪, 崔园园, 刘瑞霞, 等, 2017.影响长江中下游夏季降水的青藏高原水汽抽吸作用和水汽路径的定量化研究[J].高原气象, 36(4):900-911. DOI:10.7522/j. issn. 1000-0534.2016.00084.
[18]李国翠, 李国平, 连志鸾, 等, 2008.不同云系降水过程中GPS可降水量的特征-华北地区典型个例分析[J].高原气象, 27(5):1066-1073.
[19]李国平, 2011.地基GPS水汽监测技术及气象业务化应用系统的研究[J].大气科学学报, 34(4):385-392.
[20]李国平, 黄丁发, 2005. GPS气象学研究及应用的进展与前景[J].气象科学, 25(6):651-661.
[21]李昊睿, 丁伟钰, 薛纪善, 等, 2014.广东省GPS/PWV资料的质量控制及其对前汛期降水预报影响的初步研究[J].热带气象学报, 30(3):455-462.
[22]李娜, 张武, 陈艳, 等, 2015.基于微波辐射计的大气温湿廓线遥感探测[J].兰州大学学报(自然科学版), 51(1):61-71.
[23]李伟, 赵培涛, 郭启云, 等, 2011.国产GPS探空仪国际比对试验结果[J].应用气象学报, 22(4):453-462.
[24]刘春蓁, 2004.气候变化对陆地水循环影响研究的问题[J].地球科学进展, 19(1):115-119.
[25]刘红燕, 王迎春, 王京丽, 等, 2009.由地基微波辐射计测量得到的北京地区水汽特性的初步分析[J].大气科学, 33(2):388-396.
[26]刘亚亚, 毛节泰, 刘钧, 等, 2010.地基微波辐射计遥感大气廓线的BP神经网络反演方法研究[J].高原气象, 29(6):1514-1523.
[27]罗梦森, 曾明剑, 景元书, 等, 2013. GPS反演的大气可降水量变化特征及其与降水的关系研究[J].气象科学, 33(4):418-423.
[28]马再忠, 郭英华, 王斌, 2011. GPS掩星观测的发展及其在气象业务中的应用现状[J].气象学报, 69(1):208-218.
[29]任菊章, 孙绩华, 李建, 等, 2014.云南地区GPS探测与3类再分析可降水量的对比分析[J].高原气象, 33(6):1480-1489. DOI:10.7522/j. issn. 1000-0534.2013.00114.
[30]申彦波, 王炳忠, 王香云, 等, 2016.整层大气水汽含量统计外推方法应用讨论[J].高原气象, 35(1):181-187. DOI:10.7522/j. issn. 1000-0534.2014.00139.
[31]施闯, 王海深, 曹云昌, 等, 2016.基于北斗卫星的水汽探测性能分析[J].武汉大学学报(信息科学版), 41(3):285-289.
[32]向玉春, 陈正洪, 徐桂荣, 等, 2009.三种大气可降水量推算方法结果的比较分析[J].气象, 35(11):48-54.
[33]谢璞, 张朝林, 王迎春, 等, 2006.北京地区单双频地基GPS大气水汽遥测试验与研究[J].应用气象学报, 17(增刊):28-34.
[34]万蓉, 李武阶, 陈波, 等, 2010.日全食对地基GPS和微波辐射计气象观测影响分析[J].华中师范大学学报(自然科学版), 44(1):145-151.
[35]王红伟, 华灯鑫, 王玉峰, 等, 2013.水汽探测拉曼激光雷达的新型光谱分光系统设计与分析[J].物理学报, 62(12):120701-120701.
[36]王云, 王振会, 李青, 等, 2014.基于一维变分算法的地基微波辐射计遥感大气温湿廓线研究[J].气象学报, 72(3):570-582.
[37]魏东, 孙继松, 雷蕾, 等, 2011.用微波辐射计和风廓线资料构建探空资料的定量应用可靠性分析[J].气候与环境研究, 16(6):697-706.
[38]韦凯华, 黄兴友, 黄佳欢, 等, 2015.毫米波云雷达与地基微波辐射计联合反演云微物理参数[J].科学技术与工程, 2015(24):8-17.
[39]徐桂荣, 孙振添, 李武阶, 等, 2010.地基微波辐射计与GPS无线电探空和GPS/MET的观测对比分析[J].暴雨灾害, 29(4):315-321.
[40]徐国森, 方家熊, 朱三根, 等, 2000."风云二号"卫星用水汽/热红外双波段探测器[J].红外与激光工程, 29(4):50-54.
[41]姚宜斌, 雷祥旭, 张良, 等, 2016.青藏高原地区1979-2014年大气可降水量和地表温度时空变化特征分析[J].科学通报, 61(13):1462-1477.
[42]赵从龙, 蔡化庆, 宋玉东, 等, 1991.对流层水汽和液态水的地基微波遥感探测[J].应用气象学报, 2(2):200-207.
[43]周长艳, 邓梦雨, 齐冬梅, 2017.青藏高原湿池的气候特征及其变化[J].高原气象, 36(2):294-306. DOI:10.7522/j. issn. 1000-0534.2016.00042.
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