孙丽
, 赵姝慧
辽宁省人工影响天气办公室,辽宁 沈阳 110166
Sun Li, Zhao Shuhui
中图分类号: P412.23
文献标识码: A
文章编号: 1001-8166(2018)01-0085-08
通讯作者:
收稿日期: 2017-04-17
修回日期: 2017-10-5
网络出版日期: 2018-01-10
版权声明: 2018 地球科学进展 编辑部
基金资助:
作者简介:
First author:Sun Li (1987-), female, Linyi City, Shandong Province, Engineer. Research areas include aerosol and weather modification.E-mail:sunli_2006_abc@126.com
作者简介:孙丽(1987-),女,山东临沂人,工程师,主要从事气溶胶及人工影响天气的相关观测、分析技术研究.E-mail:sunli_2006_abc@126.com
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摘要
探空湿度观测是获取湿度资料的重要手段,探空仪湿度传感器的性能会直接影响湿度的测量结果并对基于探空湿度资料识别的云层准确性造成影响。为更好地了解目前国内外探空仪湿度测量的准确性,回顾了大量国内外研究成果,简要介绍了国内外探空仪的类型及湿度传感器性能,归纳了探空仪湿度测量误差并探讨了湿度测量误差对云识别的影响。分析发现,探空仪湿度测量误差来源多样,是多种因素综合作用的结果。一般而言,在对流层低层温度较高的条件下,湿度测量的结果较为准确,云层识别较为可靠;但湿度传感器在低温条件下响应时间变长、灵敏度下降,导致云底的识别准确性要高于云顶,而识别的中、高云偏少;而高湿条件下,测湿元件易被沾湿,导致湿度异常偏高,从而使得识别的云层偏厚;探空仪普遍存在湿度异常偏低的情况,尤其是湿度较高的测站,从而导致云层漏判。
关键词：
Abstract
Sounding observation of humidity is an important means of obtaining the atmospheric humidity data. The measurement results of humidity and accuracy of cloud recognition based on that are directly affected by the performance of the radiosonde humidity sensor. In order to better understand the accuracy of the current measurement of the radiosonde at home and abroad, a large number of research results are reviewed. The types of radiosonde and the performance of its humidity sensor are briefly introduced. Moreover, the influence of humidity measurement error on cloud recognition is also discussed. The results show that the error sources of radiosonde humidity measurement are various and it’s a comprehensive result of many factors. In general, accuracy of humidity measurement is more reliable in the low troposphere with high temperature and so with the cloud identification by the humidity. However, the response time is longer and sensitivity of humidity sensor is lower at low temperature, which results in the accuracy of cloud bottom recognition being higher than that of cloud top while the medium and high cloud recognized by radiosonde being less than the reality. Moreover, under high-humidity conditions, the humidity sensors are easily wetted, which leads to the abnormally high value of humidity and resulting in thicker cloud. Furthermore, the radiosonde generally has low humidity anomalies, especially when the synoptic station with high humidity, resulting in missing report of cloud.
Keywords：
湿度是气象领域一个基础而又非常重要的物理量,在降水预报、雾霾预报、云识别和气象服务等方面都起着至关重要的作用[1~7]。探空湿度观测可以获取由地面到30 km高空的湿度变化信息,作为时间序列最长、最为便捷的湿度资料获取来源而被广泛使用。经研究证明,探空湿度的变化对于云的识别具有重要的指示意义[8]。因此,探空湿度数据的测量准确性不仅会影响各种预报的准确性,还将直接影响云识别以及基于探空湿度资料构建的全球云数据集的准确性。
国内外的众多学者建立了较为完善的基于探空湿度数据进行云识别的方法[9~19]。但由于探空湿度数据进行云识别所使用的探空仪各不相同且探空仪探测精度和测量误差的差异较大,因此会对云识别的结果产生一定的影响。本文简要介绍了国内外几种常见的探空仪并阐述了探空仪器湿度测量误差的研究现状及其对云识别准确性的影响,从而为利用探空湿度测量资料开展相关研究和业务应用工作以及应用其进行云识别提供一定的订正依据。
探空的湿度数据是通过湿度感应元件获取的,而数据的质量则取决于湿度感应元件的性能和数据的订正技术等[20]。国际市场上近7成的探空仪来自芬兰Vaisala公司,其较高的技术水平被世界所公认。自20世纪30年代该公司发明第1台机电式探空仪以来,相继推出了RS80,RS90和RS92型探空仪[21],完全实现了探空系统和探空仪数字化[20],是目前国际上应用最为广泛的探空仪。RS80,RS90和RS92探空仪的湿度测量均采用薄膜电容型传感器,通过测量电容变化换算为相对湿度。目前常用的Vaisala探空仪包括RS80-A,RS80-H,RS90和RS92,测量范围为0~100%,测量误差低于5%。其中,H和A代表了2种不同的聚合物。H型传感器的滞后作用较小,尤其是在高相对湿度条件下,但在低温条件下其反应较慢,因此具有较大的时间延迟误差。RS90和RS92中均使用了H型聚合物但相比RS80-H,聚合物层更小更薄,使得传感器在低温条件下也能快速反应。除此之外,RS92与RS90的湿度传感器均采用双传感器交替加热的设计,可以有效防止传感器出云入云时结露或者结霜的问题。
美国低温霜点式湿度仪(Cryogenic Frost point Hygrometer, CFH)的湿度测量是基于冷镜原理。镜面与低温液体相连,采用沸点接近-83 ℃的三氟甲烷液体作为冷却剂,通过加热冷却槽控制镜面温度,使其表面始终存在一层凝结层,从而使得镜面温度等于通过传感器的空气的霜点或露点温度。测量水汽在镜面凝结成露(霜)时的温度就可以反算大气中水汽体积混合比。其温度测量范围为-100~30 ℃,湿度测量范围为0~100%。CFH相对湿度测量误差主要来源于对镜面上水汽相变的响应快慢以及微控制器电路的稳定性[22]。CFH可以测量对流层到平流层范围内的水汽条件,是目前测量对流层低温条件下水汽浓度的最为可靠的仪器,通常被用作验证地基、探空以及卫星测量的湿度的参照仪器。
瑞士Snow White(SW)露(霜)点式湿度探空仪的湿度测量也是基于冷镜原理,是目前对流层以下湿度测量最好的仪器[23]。仪器分为日、夜2种类型。日间型传感器和散热器被封装在带有导管的聚苯乙烯塑料壳中,其表面附有防水材料以防止太阳光污染测量,而夜晚则直接暴露在大气中。其相对湿度的测量下限为3%~6%[24],整体测量误差为2%[23]。
目前我国业务上广泛使用的是L 波段探空系统,主要由 GTS1 电子探空仪与地面 L 波段探空雷达组成,因此L波段探空系统又常被简称为L波段探空仪[25]。L波段探空仪采用的相对湿度传感器有碳湿敏电阻和湿敏电容2种,大多采用的是碳湿敏电阻,测量范围为2%~100%。相比59型探空仪的肠膜测湿元件其在性能上有了很大的改进[26]。但是,温度对碳湿敏元件的影响较为显著,感湿元件在不同环境温度下的感湿特性曲线相差较大,从而使得获取的相对湿度数据与大气实际相对湿度之间误差较大[27]。随着湿敏电容技术的不断发展,GTS1-2型L波段电子探空仪湿度传感器采用中国科学院空间科学与应用研究中心研制的HS02型湿敏电容湿度传感器[28]。XC06型和HC103M2型GPS探空仪湿度传感器分别采用我国航天科工集团第 23 研究所和中国华云技术开发公司研制的湿敏电容湿度传感器[29]。实验表明,在采用新型测试条件以及湿度传感器情况下,相对湿度的测量误差在±2%之内,可以达到世界气象组织(World Meteorological Organization,WMO)常规高空探测的技术要求[30]。
湿度测量的精度受观测方法、制造商及传感器型号的影响,即使对同一型号的传感器,硬件、制造过程以及校正方法的变化也会影响测量精度。除此之外,大量研究已经证实相对湿度的测量精度还会随温度(高度)、干湿状况以及太阳高度角等变化。国外学者对于湿度传感器的测量误差研究较为系统和深入[31],在鉴定和减小误差方面做过大量的工作,认为湿度传感器的误差可能是诸多原因造成的[32~35]。常见的传感器误差包括:
(1) 污染误差
由于传感器的材质不稳定以及相对湿度等原因造成的传感器污染误差导致测量的相对湿度要小于实际相对湿度,产生干偏差。以RS80-A型和RS80-H型传感器为例,薄膜型电容传感器的化学污染会导致产生干偏差,在高湿条件下(80%~90%),由传感器化学污染导致的两者的系统性偏差分别为-13%±3%[36]和-12%±0.3%[37]。而且随着时间的推移,传感器受污染的程度会增加,Wang等[32] 研究认为在饱和状态下,2年的RS80-A型的干偏差大约为5%,并以每年0.5%递增。但从2000年6月开始,RS80型探空仪在运输过程中添加了密封帽,从而较大地改善了化学污染的问题[38]。解决此误差的方法是将传感器臂置于一种含有除湿物质、气体流动性差的特别塑料包装中,该方法已被应用于2000年6月以后芬兰Vaisala公司生产的传感器中。由于RS90探空仪采用的是特殊材料,污染误差几乎可以忽略不计。
(2) 标校方法误差
造成该误差的主要原因是温度与相对湿度之间的关系函数,低湿条件下为线性关系,在低温接近冰面饱和的条件下为非线性。低温(-25 ℃)冰面饱和条件下的温度关系函数很不准确。Miloshevich等[39]通过同时比对观测CFH和RS80-A导出校准算法,当温度高于-25 ℃时校准因子为1.0;温度为-30 ℃(大约为10 km高度)时校准因子为1.1;当温度低于-50 ℃(大约为12 km高度)时校准因子大于1.4。低于-40 ℃时,温度关系误差是造成测量误差的最主要原因。RS80-H和RS90传感器的温度关系采用了多项式形式更为准确,校准因子更低。同时,也对芬兰Vaisala公司的湿度传感器的测量结果进行了低温订正、时间滞后订正和偏干订正[33,35],建立了相应的校正方法,但是这些方法本身也存在不确定性,所以不可能校正相对湿度的所有偏差[40]。研究发现RS80-H测量水汽混合比在不同批次之间的平均误差为-2%~24%。Turner等[35]也认为这种误差来源于校准程序算法。RS90和RS92探空仪目前使用的是新的具有标准化程序的校准设备,可以降低不同批次之间校准的变化。
我国探空仪测湿元件的标校主要由仪器厂家完成,但目前获取的资料表明,工厂对L波段探空湿度传感器在云区相对湿度的订正效果不理想。姚雯等[41]指出,水云包括其上部的过冷水云内的相对湿度都应接近100%,即使测量元件有系统误差,达不到100%,也应该是一个恒定值。但探空记录表明,在很厚的云体内,只要温度随高度降低,经工厂订正过的相对湿度也随高度明显减小。
(3) 时滞误差
随着温度的降低,传感器的响应时间将增加,当其大于相对湿度变化的时间尺度时便会产生时滞误差。响应时间T的定义为:dUe/dt=-1/T·(Ue-U),其中Ue代表传感器湿度示值,而U代表实际大气相对湿度,T代表传感器恢复湿度测量所需要的时间。WMO给出了湿敏电容湿度传感器在不同温度条件下的时间常数,随着温度降低湿度传感器反应速度大大降低,特别是在-20 ℃以下时间常数显著增加[42],变化范围可从0.1 s增加至200~300 s。其中,CFH与SW在-20 ℃以上,时间常数低于4 s,而在-70 ℃,一般低于25 s[37]。而对L波段探空仪而言,在-30~-50 ℃范围内,探空仪湿度传感器的时间常数增加显著,变化幅度减小,反应滞后,而当温度降至-80 ℃以下时,湿度传感器失去对空气湿度的反应能力[43]。低温条件下响应时间的增长会导致相对湿度随高度的变化梯度减小,湿度廓线被“平滑”。
(4) 太阳辐射误差
太阳辐射误差主要是由于太阳加热湿度传感器造成的干偏差,与气压(或高度)和太阳高度角有关[39],不同的传感器也有所区别。Turner等[35]指出RS80-H日间的干偏差要比夜间大3%~4%。Miloshevich等[34]指出RS90探空仪的太阳辐射干偏差为6%~8%。Vömel等[22,40]研究发现在Alajuela和Costa Rica等地,太阳高度角在10°~30°时,RS92探空仪相对湿度干偏差随着高度的升高而增加,900 mb(0 km)为9%,而200 mb(15 km)可达到50%。Rowe等[44]研究发现RS90探空仪在太阳天顶角为83°时,在微波窗区相对湿度干偏差为8%±5%,在谱线中心相对湿度干偏差为9%±3%;太阳天顶角为62°时对应谱段的相对湿度干偏差分别为20%±6% 和 24%±5%。CFH的镜面由于放置在通风良好的采样管内,因此不受太阳辐射的影响。
(5) 其他误差
L波段探空的测湿元件采用湿敏电阻进行湿度测量,湿敏电阻的局限性以及软件处理也会带来测量误差。碳湿敏电阻存在湿滞现象,湿敏元件吸湿和脱湿的响应时间各不相同,吸湿和脱湿的特性曲线也不相同,而且这一现象随温度降低而变得更为显著[41]。当测湿元件从高湿到低湿反复变化后,湿度测量的灵敏度变低,导致入云高湿条件下不能达到饱和,出云时降湿条件下变化滞后,产生较大的测量误差。另外,L波段探空仪测量的相对湿度存在异常偏低的情况,导致这一现象的原因除了仪器本身存在缺陷外,还可能是由于大气中存在干气层或探空仪在观测过程中遇到了云[45]。如果湿度传感器元件被沾湿,或者在低温条件下被冻结,会使得测湿元件瘫痪,相对湿度测量失败。因此,L波段探空的湿度测量在高对流层会失效,甚至有时会出现在中对流层[46]。而且L波段探空仪测湿元件存在过饱和条件下无法及时恢复的问题,软件在处理时会自动将这部分相对湿度数据处理成2%,从而导致平均系统差为负值。
尽管探空仪湿度测量误差的来源有很多,但大多数研究是针对湿度测量的整体误差进行分析的。一般用作对照的探空仪包括CFH, SW和RS92。
Verver等[31]比对热带地区对流层上层不同的湿度传感器的性能,发现RS80-A在对流层低层与SW相比干偏差为4%~8%,在对流层上层,干偏差通过平均值能够被有效地修正,而RS80-H在对流层的中高层存在2%~5%的湿偏差,RS90在7 km以下表现出2%~3%的湿偏差。采用Miloshevich等[39]的时间延迟误差修正算法后,相对湿度廓线分别在9 km(RS80-A),8 km(RS80-H)和11 km(RS90)高度以上与SW探测数据相比表现出更好的一致性。
CFH测量相对湿度的误差在对流层底层小于4%,平流程中层(28 km)小于10%,对流层顶区域的误差小于9%[18]。对比CFH与RS80的湿度测量结果发现,在整个对流层RS80湿度测量值比CFH测值偏干23.7%±18.5%;由于太阳的加热作用,白天RS80的干偏差比夜间显著,较夜间偏干13.5%±14.8%,而且RS80基本无法测量对流层上层到平流层过渡区域内的相对湿度。
相比于老式探空系统,L波段探空仪的数据精度有了明显的提升,虽然在综合性能上要低于RS92型探空仪,相对湿度的测量仍然存在干偏差[25,29,41,45],但已达到芬兰RS80型探空仪的测量精度。对比CFH发现,500 hPa以下,L波段探空仪的平均相对湿度干偏差大约为10%,500 hPa以上迅速增加到30%,而310 hPa增长至55%[46]。
随着国产探空仪的发展,郭启云等[47]研究认为GTS1A型探空仪湿度测量结果不论是在测量稳定性还是准确性上较GTS1型探空仪均有了显著的提升,相比RS92型探空仪湿度测量结果,GTS1A型探空仪主要表现为高湿区偏高,低湿区偏干;在对流层顶附近,偏差值在7%RH左右,以负偏差为主。全量程系统偏差在±5%RH以内。除了可以较准确地反映大气湿度结构特征,在低温条件下特别是对流层顶附近表现也较好。国产GPS探空仪的动态测量性能虽然在相对湿度准确性方面仍有一定差距,但与目前业务使用的L波段探空系统相比,测量准确性已有较大提高[30,48],与RS92型探空仪一致性较好,系统误差基本在15%RH以内,标准偏差在12%RH以内[49]。对比SW和大桥HS02型湿度传感器,除了白天20 ℃以上与-30 ℃以下HS02湿度探测偏干外,其他均呈偏湿状态,夜间所有湿度段均呈偏湿状态,最大系统偏差在30%RH左右[28]。
虽然目前我国生产的探空仪在制作工艺得到了较大地提高,与国际上先进的探空仪测量性能的差距也逐渐缩小,但相比Vaisala公司的高空探测技术,我国现有的探空仪发展水平还相对落后[50]。目前我国业务布网的L波段探空湿度测量仍存在比较大的问题,测量结果的一致性上相对较差,与其他国家的探空结果相比观测值明显偏低,这与湿度感应元件的性能和数据订正技术等有关,迫切需要技术改进[29,45]。各探空仪生产厂家之间技术水平参差不齐,会直接影响探测资料的质量,因此有必要从多个方面,如传感器的性能、生产工艺以及误差补偿方法等方面展开广泛的研究[51]。
由于湿度传感器型号、批次、制造厂商、储存方式的差异以及部分研究中未明确指出对湿度数据所做的校正等原因,本文无法定量给出由于湿度测量误差导致的云识别误差,仅做定性分析。
基于探空湿度数据进行云识别的研究有很多,包括PWR95法[9]、WR95法[10]、CE96法[12]、MN05[13]法以及ZHA10法[17]等,这些方法主要利用相对湿度阈值、温度露点差或者温、湿度随高度的二阶导数进行云层判断,均取得了一定的效果[52]。针对这些云识别方法,国内外开展了许多验证研究。Chernykh等[12]利用美国CARDS(Comprehensive Aerological Reference Dara Set)数据集以及VIZ探空仪对CE96法进行了验证,指出该方法可以确定90%以上的云层,而基于该方法判别的高云云量与云层的准确率相对较小,湿度传感器在低温条件下灵敏度下降是可能的原因之一。Naud等[53]基于1996—2000年ARM项目在SGP的雷达、激光雷达、云高仪等观测数据,利用WR95和CE96 2种云层识别算法,验证了利用RS80探空数据反演云边界的准确性,通过比较发现,2种云识别方法识别的云底高度的准确性要好于云顶高度,并指出这种差异可能是由于探空仪的干偏差及湿度响应滞后从而使得识别的云层位置偏高导致的。Zhang等[17]利用RS92探空观测数据反演的云垂直结构信息与云雷达MACR、微脉冲激光雷达MPL和激光云高仪的结果进行了对比,发现与MPL及云高仪测量的云底高度偏差小于500 m的概率分别为77.1%和68.4%,但也存在部分MPL与探空测量的云底高度相差较大的情况,可能的原因除了气球漂移以及MPL探测能力的限制,还可能是由于探空仪的干偏差导致的。Miloshevich等[39]则指出在对流层中上层到平流层底层,由于湿度感应元件的性能问题,在低温条件下灵敏度差,容易冻结,观测的湿度值不能反映大气的真实状态,而且无法测出卷云中比较高的相对湿度值。由此可知,测湿元件在低温条件下响应时间变长,灵敏度下降,加之太阳辐射误差在高层更为明显,使得测得的云顶湿度准确性低于云底,反演的云顶高度与实况相差更大,甚至出现云层的误判和漏判[19,54]。
相比低温、低湿条件下的测量结果,探空仪在高温条件下的湿度测量相对准确,因此云识别的结果也较为可靠。但部分条件下存在传感器入云或者在低层高湿的环境下,测湿元件被沾湿的情况。以CFH为例,CFH侧重在低温低湿条件下进行湿度测量,但在对流层高温、高湿条件下,易出现凝结水。而CFH的微控制器无法在下对流层快速除去镜面上的凝结水,而导致测量湿度偏大。另外,低空云的出现比例比较高,特别是积雨云里液态水滴容易对镜面造成污染,从而导致测值不正常偏高[54],如果出云后还不能快速降到正常测值,会使识别的云层厚度增加,甚至会影响之上云层的探测。
当使用L波段探空数据进行云层识别时,如3.1所述,在对流层中低层经常出现成片相对湿度观测数值异常偏低的情况(2%的截断值)。为了与对流层高层相对湿度普遍偏干的问题区分开,唐南军[45]对这种情况进行了统计,定义异常偏低需满足:①观测的相对湿度值<5%,且对应的气压值低于300 hPa;②符合条件①的数据段从开始到结束,气压差>200 hPa,对应的气压值<300 hPa,统计发现,约有12.63%的探空曲线出现异常偏低的情况,而且这种异常偏低的现象在湿度较大测站出现的频率更高,部分测站异常偏低的次数甚至占到总观测次数的一半以上。另外,受不同季节及高度云层出现差异的影响,异常偏低发生的高度也有所不同,一般在600~550 hPa最容易发生。对全球探空湿度数据进行分析发现,异常偏低现象具有普遍性(4%),这无疑会导致对云层的漏判。因此,在利用探空湿度数据进行云识别的过程中要充分考虑探空仪测湿元件测量误差、性能特点以及数据处理上的差异等对云识别造成的影响。
本文简要介绍了探空仪的主要类型及其湿度传感器性能,对探空仪湿度测量误差的来源进行了简单归纳。探空湿度的测量误差来源较多,除了传感器的差异外,不同原因引起的湿度测量误差在不同温度、湿度以及高度上也有所不同。总体而言,芬兰、瑞士和美国等国生产的测湿元件性能相对优良,我国业务上广泛使用的L波段探空仪略显滞后,技术水平仍有待于进一步提高。
尽管,基于探空湿度的云识别方法研究有很多,但由湿度测量误差引起的云识别准确性还没有定量的研究结果。一般而言,随着温度的下降,探空仪测湿元件的响应时间变长、灵敏度下降,会使得到的云底高度较云顶高度更为准确,而且导致对流层中高层低温、低湿条件下,识别的中、高云减少。但在对流层下层则相反,由于相对湿度较高,云层的出现频率较大,容易出现测湿元件被沾湿而导致湿度偏高,云层厚度较实际偏大的状况。除此之外,探空湿度测量普遍存在异常偏低的现象,尤其是湿度较高的测站,这会导致对部分云层的漏判和误判。值得注意的是,湿度测量的误差是各种因素的综合结果,受温度、辐射、传感器材质等多种因素的影响。因此在利用探空湿度进行云识别时,应充分考虑探空仪型号、测量误差以及数据处理差异对云系识别造成的影响。
The authors have declared that no competing interests exist.
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<FONT face=Verdana>针对造成湖南省特大暴雨过程的“碧利斯”和“圣帕”两次台风， 利用气象、 水文加密观测资料及NCEP再分析资料， 结合中尺度数值预报模式AREM的模拟结果， 对它们独特的水汽场特征进行了对比分析。结果表明： 这两次台风的共同特点是有两条主要水汽输送通道， 即与西南季风相联系的偏南风水汽通道和与台风低压环流相联系的偏北风水汽通道； 凝结降水的水汽主要来源于低层风场辐合和水汽平流， 并通过局地垂直运动再将其输送到中高层； 在湘东南强降水区上空始终存在强的水汽水平辐合和水汽垂直输送， 比较而言， “圣帕”台风暴雨区上空水汽通量更强， 但水汽通量辐合强度却小于“碧利斯”台风， 水汽辐合层也不及后者深厚， 但前者由于自身旋转性强， 低压环流中心南部的切变较长时间维持， 并自东向西转动， 使得强降水持续时间更长， 过程雨量更大， 影响范围也更大。“碧利斯”水汽主要源地较“圣帕”更加偏南， 水汽辐合更强， 与南海季风的相互作用更显著， 降水时段集中， 局部地区短时间内的降雨强度甚至超过了“圣帕”。</FONT>
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云作为地球大气系统的重要组成部分,不仅影响着气候变化和天气系统的发展演变,还与航空活动密切相关,一直以来是空军和民航部门非常关注的气象要素之一。在云探测、资料同化和反演方法发展的基础上,从实际业务保障和数值模式发展需求出发,综述国内外云分析、预报方法和云分析预报系统开发的研究成果,分析各类方法的优势和不足,明确国内外研究的主要差距,并探讨国内未来研究的方向。云分析方法中,探空对云廓线识别较好,卫星可见光和红外资料在云顶信息反演方面优势明显,多普勒雷达能够获取对流层中层和底层的云信息,而毫米波雷达能够很好地反映云三维结构信息,发展潜力巨大。云预报方法中,传统的统计和诊断方法发展较为成熟,而考虑了大气温湿和云微物理状况的大气辐射传输模式正演模拟云顶亮温的方法是未来的发展趋势。加强云探测技术,综合利用云分析预报方法,借鉴国外先进云分析预报系统的设计理念,积极开发我国自主的云分析预报系统,推动天气预报、航空气象保障和数值预报模式的发展将会是我国云研究的重要方面。
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The method of cloud vertical structure analysis using rawinsonde observation and its applied research [J].利用探空数据分析云垂直结构的方法及其应用研究 [J].
云的垂直结构特征,无论是对天气、气候还是人工影响天气都十分重要,但业务上直接有效的观测手段十分缺乏。本文通过对比分析,确定了相对湿度阈值法分析云垂直结构的方法;利用我国气象业务探空秒数据,计算分析了不同云垂直结构,将得到的分析结果同CloudSat云雷达实测云垂直结构进行多个例的对比分析,验证了相对湿度阈值判断云垂直结构方法的可行性,及利用我国业务探空资料分析云结构的可用性;为业务应用,开发了探空秒数据的实时读取和计算方法,设计制作了云垂直结构探空分析显示图,初步形成基于我国业务探空的云结构分析技术;并实时将这些技术应用到我国60周年国庆期间几次不同天气系统云结构的时间和空间演变特征分析中,得到有意义的结果。
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Relationships between radiosonde and RUC-2 meteorological conditions and cloud occurrence determined from ARM data [J].
[1] Relationships between modeled and measured meteorological state parameters and cloudy and cloud-free conditions are examined using data taken over the ARM (Atmospheric Radiation Measurement) Southern Great Plains Central Facility between 1 March 2000 and 28 February 2001. Cloud vertical layering was determined from the Active Remotely Sensed Cloud Location product based on the ARM active sensor measurements. Both temperature and relative humidity (RH) observations from balloon-borne Vaisala RS80-15LH radiosonde (SONDE) and the Rapid Update Cycle (RUC) 40-km resolution model are highly correlated, but the SONDE RHs generally exceed those from RUC. Inside cloudy layers, the RH from SONDE is 2芒聙聯14% higher than the RH from RUC at all pressure levels. Although the layer mean RH within clouds is much greater than the layer mean RH outside clouds or in clear skies, RH thresholds chosen as a function of temperature can more accurately diagnose cloud occurrence for either data set than a fixed RH threshold. For overcast clouds (cloud amount greater than or equal to 90%), it was found that the 50% probability RH threshold for diagnosing a cloud, within a given upper tropospheric layer, is roughly 90% for the SONDE and 80% for RUC data. For partial cloud cover (cloud amount is less than 90%), the SONDE RH thresholds are close to those for RUC at a given probability in upper tropospheric layers. Cloud probability was found to be only minimally dependent on vertical velocity. In the upper troposphere, SONDE ice-supersaturated air occurred in 8 and 35% of the clear and cloudy layers, respectively. The RH was distributed exponentially in the ice supersaturated layers as found in previous studies. The occurrence of high-altitude, ice-supersaturated layers in the RUC data was roughly half of that in the SONDE data. Optimal thresholds were derived as functions of temperature to define the best RH thresholds for accurately determining the mean cloud cover. For warm clouds the typical SONDE threshold exceeds 87%, while the RH thresholds for cold clouds are typically less than 80% and greater than 90% with respect to liquid and ice water, respectively. Preliminary comparisons with satellite data suggest that the relationships between cloudiness and RH and T determined here could be useful for improving the characterization of cloud vertical structure from satellite data by providing information about low-level clouds that were obscured by high-level clouds viewed by the satellite. The results have potential for improving computations of atmospheric heating rate profiles and estimates of aircraft icing conditions. Similar analyses are recommended for later versions of the RUC analyses and forecasts.
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| [14] |
Variability of cloud vertical structure during ASTEX observed from a combination of rawinsonde, radar, ceilometer, and satellite [J].
The macroscale cloud vertical structure (CVS), including cloud-base and -top heights and layer thickness, and characteristics of multilayered clouds, is studied at Porto Santo Island during the Atlantic Stratocumulus Transition Experiment (ASTEX) by using rawinsonde, radar, ceilometer, and satellite data. The comparisons of CVS parameters obtained from four different approaches show that 1) by using the method developed by Wang and Rossow rawinsonde observations (raob’s) can sample all low clouds and determine their boundaries accurately, but oversample low clouds by about 10%, mistaking clear moist layers for clouds; 2) cloud-base heights less than 200 m in the radar data are ambiguous, but can be replaced by the values measured by ceilometer; and 3) the practical limit on the accuracy of marine boundary layer cloud-top heights retrieved from satellites appears to be about 150–300 m mainly due to errors in specifying the atmospheric temperature and humidity in the inversion layer above the cloud. The vertical distribution of clouds at Porto Santo during ASTEX is dominated by low clouds below 3 km, a cloud-free layer between 3 and 4 km, and 6520% high clouds with a peak occurrence around 7–8 km. Low clouds have mean base and top heights of 1.0 km and 1.4 km, respectively, and occur as single layers 90% of the time. For double-layered low clouds, the tops of the uppermost layers and the bases of the lowermost layers have similar distributions as those of single-layered clouds. The temporal variations of low clouds during ASTEX are apparently dominated by advecting mesoscale (20–200 km) horizontal variations. Coherent time variations are predominately synoptic (timescale 4.5–6.8 days) and diurnal variability. On the diurnal timescale, all cloud properties show maxima in the early morning (around 0530 LST) decreasing to minima in the late afternoon. Diurnal variations appear to be altered when high clouds are present above low clouds. The general characteristics of CVS in three ASTEX and the First ISCCP Regional Experiment (FIRE87) regions derived from a 20-yr rawinsonde dataset are also presented. The results suggest that CVS characteristics obtained from data collected at Porto Santo during ASTEX (June 1992) are not representative of other marine stratiform cloud regions.
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| [15] |
A cloud parameterization scheme derived from GATE data for use with a numerical model [J].
Abstract A cloud parametrization scheme which allows for low, medium, high and convective clouds has been developed from GATE data for use in the Meteorological Office 11-layer tropical model. The problems involved in using synoptic observations to derive methods of predicting clouds are discussed. Only limited success was obtained in relating observed cloud amounts to relative humidity and atmospheric temperature structure. The restrictions imposed on the cloud scheme by the model's resolution and by its inability to produce a perfect simulation are considered. In the light of these difficulties a simple approach was adopted based on the assumption that condensation on the smallest scales is part of a larger-scale condensation regime related to the synoptic scale situation. The scheme has been designed to reproduce the main features of a cloud field by relating the large-scale meteorological features associated with a cloud distribution to model variables. Low, medium and high cloud amounts are determined from a quadratic relationship with relative humidity. Low cloud has also been related to the temperature lapse rate in an attempt to model the persistent areas of sub-tropical stratocumulus occurring under inversions. A relative humidity relationship is inappropriate for convective cloud which has, therefore, been related to the convective mass flux calculated in the convection scheme of the model. The scheme has been reasonably successful in predicting the cloudiness associated with the ITCZ and the NE. and SE. trades. The cloud fields showed a good degree of coherence from day to day and there were no signs of unrealistic feedbacks between radiation, cloud and dynamics.
|
| [16] |
A experimental technique for testing the validity of cumulus cloud parameterizations for longwave radiation calculations [J]. |
| [17] |
Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud radar [J].
[1] The Atmospheric Radiation Measurement Mobile Facility (AMF) was deployed in Shouxian, Anhui Province, China from 14 May to 28 December 2008. Radiosonde data obtained during the AMF campaign are used to analyze cloud vertical structure over this area by taking advantage of the first direct measurements of cloud vertical layers from the 95 GHz radar. Single-layer, two-layer, and three-layer clouds account for 28.0%, 25.8%, and 13.9% of all cloud configurations, respectively. Low, middle, high and deep convective clouds account for 20.1%, 19.3%, 59.5%, and 1.1% of all clouds observed at the site, respectively. The average cloud base height, cloud top height, and cloud thickness for all clouds are 5912, 7639, and 1727 m, respectively. Maximum cloud top height and cloud thickness occurred at 1330 local standard time (LST) for single-layer clouds and the uppermost layer of multiple layers of cloud. For lower layer clouds in multiple-layer cloud systems, maximum cloud top height and cloud thickness occurred at 1930 LST. Diurnal variations in the thickness of upper level clouds are larger than those of lower level clouds. Multilayer clouds occurred more frequently in the summer. The absolute differences in cloud base heights from radiosonde and micropulse lidar/ceilometer comparisons are less than 500 m for 77.1%/68.4% of the cases analyzed.
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| [18] |
Development of cloud detection methods using CFH, GTS1, and RS80 radiosondes [J]. |
| [19] |
Discriminating cloud area by using L-band sounding data [J].L波段探空判别云区方法的研究 [J].
利用2008年1月到2009年12月的L波段探空资料，和与之时空匹配的Cloudsat云观测资料，首先分析了云内和云外相对湿度的累积频率分布，发现以75%作为相对湿度阈值判断云准确率可达81%。随后利用BS（Bias Score）和TS（Threat Score）评分方法，对不同相对湿度阈值进行评分分析，发现以81%作为相对湿度阈值TS评分可达0.66，为最高。接着利用BS和TS评分方法分不同高度对相对湿度阈值进行评分分析，发现随高度的增加该高度上具有最好TS评分的相对湿度阈值在减小。利用这些阈值对云判断时，总的TS评分高于0.6，且其准确率达到84%以上，比利用单一相对湿度阈值判断云准确率要高。最后对这些阈值进行优化，得到一套适合于我国L波段探空秒数据的云垂直结构的判别方法。
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| [20] |
Analysis of GTS radiosonde humidity sensor testing data and correction of upper-air relative humidity radiosonde data [J].GTS探空仪碳湿敏元件性能测试数据分析及相对湿度订正 [J].
碳湿敏元件受温度影响明显,造成相对湿度探空数据测量的误差。利用温湿控制设备对该元件进行静态试验,测试不同温度对元件感湿特性的影响。通过对试验数据分析计算得到元件的相对湿度温度项订正数据,以及相对湿度的温度项订正拟合公式,可以有效订正由温度引起的相对湿度探空数据误差。对实际相对湿度探空数据资料订正效果的对比分析表明,经过温度项订正后的相对湿度探空数据,不但其准确度得到了提高,而且清楚地体现出在订正之前所不能体现的高空大气相对湿度在低湿段的变化细节。
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| [21] |
Tropospheric comparisons of vaisala radiosondes and balloon-borne frost-point and lyman-αhygrometers during the LAUTLOS-WAVVAP experiment [J].
The accuracy of all types of Vaisala radiosondes and two types of Snow White chilled-mirror hygrosondes was assessed in an intensive in situ comparison with reference hygrometers. Fourteen nighttime reference comparisons were performed to determine a working reference for the radiosonde comparisons. These showed that the night version of the Snow White agreed best with the references [i.e., the NOAA frost-point hygrometer (FPH) and University of Colorado cryogenic frost-point hygrometer (CFH)], but that the daytime version had severe problems with contamination in the humid upper troposphere. Since the RS92 performance was superior to the other radiosondes and to the day version of the Snow White, it was selected to be the working reference. According to the reference comparison, the RS92 has no bias in the mid- and lower troposphere, with deviations -30脗掳C it is ineffective and does not correct the RS80-A dry bias in high ambient RH.
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| [22] |
Accuracy of tropospheric and stratospheric water vapor measurements by the cryogenic frost point hygrometer: Instrumental details and observations [J].
[1] The cryogenic frost point hygrometer (CFH), currently built at the University of Colorado, is a new balloon borne hygrometer, which is capable of continuously measuring water vapor between the surface and the middle stratosphere. The design is loosely based on the old NOAA/CMDL frost point hygrometer, with improved accuracy and a number of significant new features that overcome some limitations of the older instrument. The measurement uncertainty of the new instrument depends on altitude and ranges between less than 4% in the tropical lower troposphere to no more than 10% in the middle stratosphere at 28 km. In the tropopause region the uncertainty is less than 9%. This instrument is used routinely at several sites for validation of satellite measurements and process studies in the upper troposphere and lower stratosphere region. It has proved to be particularly well suited for dehydration observations in the tropical upper troposphere, because the effects of cloud contamination have been significantly reduced. Results of this instrument are compared with the old NOAA/CMDL hygrometer, the Russian Fluorescent Lyman Alpha Stratospheric Hygrometer, the Vaisala RS92, the AURA/MLS satellite instrument, a cloud lidar, the NOAA/CSD frost point hygrometer and the Harvard Lyman-alpha hygrometer, both of the later instruments flown on board the NASA WB-57F high-altitude research aircraft. These comparisons demonstrate the level of accuracy of tropospheric and stratospheric water vapor measurements made by this instrument and point to areas where more research and development are needed.
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| [23] |
Capacity analysis on China-made HS02 humidity sensitive capacitor sensor [J].国产HS02型湿敏电容湿度传感器性能分析 [J].
采取同球比对施放方式， 选择瑞士SNOWWHITE露点式湿度探空仪作为比对标准， 使用29次同球比对数据， 分白天与夜间， 利用随高度变化、 温度分段， 以及高度、 温度与湿度综合分段3种方法， 对大桥HS02型湿度传感器进行评估。结果表明： （1）与国外先进湿度测量仪器相比， 大桥HS02型湿度传感器在准确性与稳定性方面还存在一定差距， 特别表现在低温性能方面； （2）除了白天20 ℃以上与-30 ℃以下温度段大桥HS02型湿度探测偏干外， 其他均呈偏湿状态， 夜间所有湿度段均呈偏湿状态； （3）大桥HS02型湿度探测基本上呈现中间湿度段随机误差偏大， 而两端逐渐减小的趋势， 近似于“＞”字型变化。
|
| [24] |
Performance of the meteolabor “Snow White” chilled mirror hygrometer in the tropical troposphere: Comparisons with the Vaisala RS-80 A/H humicap sensors [J].
The ‘‘Snow White’’ hygrometer is a low-cost, chilled-mirror hygrometer for radiosonde applications provided by a Swiss company, Meteolabor AG. A total of 54 Snow White soundings were conducted at five tropical stations in different seasons in 2000–01. All soundings were made with Vaisala RS80 radiosondes equipped either with the A-Humicap (22 soundings) or H-Humicap (32) relative humidity (RH) sensor. Comparisons of the RH with respect to liquid water between the Snow White and the different RS80 Humicap sensors are made. The Snow White measurements show reasonable agreement with the H-Humicap measurements from the surface up to ;12 km (above 2508C air temperature), the region where the H-Humicap sensor can be considered reliable. Above 12 km, the H-Humicap sensor tends to miss small vertical-scale structures in RH due to the time lag error, but on average both instruments show no significant difference up to 14 km (2658C). The comparison between the Snow White and A-Humicap sensors shows the known A-Humicap dry bias error at low temperatures and second dry bias error in the wet lower troposphere. The latter error [(A-Humicap RH) . 0.9 3 (Snow White RH) above 50% RH] may be a common problem for the recent A-Humicap sensors. These intercomparisons confirm the validity of the Snow White measurements at least up to the tropical upper troposphere and above 3%–6% RH.
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| [25] |
The analysis and comparison between GTS1 radiosonde made in China and RS92 radiosonde of Vaisala company [J].GTS1 探空仪与 VAISALA公司 RS92 探空仪对比分析 [J].
文章从静态指标与动态对比两个方面,对中国国产GTS1探空仪与芬兰Vaisala公司RS92探空仪的综合性能进行对比。对比结果表明,GTS1探空仪温度存在滞后误差,RS92探空仪湿度测量结果明显好于GTS1探空仪,200hPa以上RS92探空仪气压变化低于GTS1探空仪,测风精度方面RS92探空仪高于GTS1探空仪,850hPa以下和150hPa以上GTS1探空仪测风与RS92探空仪存在1m·s-1以内的系统差,RS92探空仪在整体性能上高于GTS1探空仪。
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| [26] |
Some humidity sensors in digital radiosondes [J].用于数字式电子探空仪的几种湿度传感器 [J].
本文介绍了目前在探空仪上使用的几种湿度传感器.着重介绍了集成湿度传感器的组成、原理和性质.
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| [27] |
Analysis of GTS radiosonde humidity sensor testing data and correction of upper-air relative humidity radiosonde data [J].GTS探空仪碳湿敏元件性能测试数据分析及相对湿度订正 [J].
碳湿敏元件受温度影响明显,造成相对湿度探空数据测量的误差。利用温湿控制设备对该元件进行静态试验,测试不同温度对元件感湿特性的影响。通过对试验数据分析计算得到元件的相对湿度温度项订正数据,以及相对湿度的温度项订正拟合公式,可以有效订正由温度引起的相对湿度探空数据误差。对实际相对湿度探空数据资料订正效果的对比分析表明,经过温度项订正后的相对湿度探空数据,不但其准确度得到了提高,而且清楚地体现出在订正之前所不能体现的高空大气相对湿度在低湿段的变化细节。
|
| [28] |
Capacity analysis on China-made HS02 humidity sensitive capacitor sensor [J].国产HS02型湿敏电容湿度传感器性能分析 [J].
采取同球比对施放方式， 选择瑞士SNOWWHITE露点式湿度探空仪作为比对标准， 使用29次同球比对数据， 分白天与夜间， 利用随高度变化、 温度分段， 以及高度、 温度与湿度综合分段3种方法， 对大桥HS02型湿度传感器进行评估。结果表明： （1）与国外先进湿度测量仪器相比， 大桥HS02型湿度传感器在准确性与稳定性方面还存在一定差距， 特别表现在低温性能方面； （2）除了白天20 ℃以上与-30 ℃以下温度段大桥HS02型湿度探测偏干外， 其他均呈偏湿状态， 夜间所有湿度段均呈偏湿状态； （3）大桥HS02型湿度探测基本上呈现中间湿度段随机误差偏大， 而两端逐渐减小的趋势， 近似于“＞”字型变化。
|
| [29] |
The international radiosonde intercomparison results for China-made GPS radiosonde [J].国产GPS 探空仪国际比对试验结果 [J]. |
| [30] |
Analysis on measure ability of Chinese-developed GPS sounding technology [J].国产GPS探空系统探测能力分析 [J].
实验室测试和外场比对试验表明,我国研制的GPS探空系统采用卫 星导航测风体制进行测风,GPS高度反算气压取代气压传感器,较之雷达探空体制要更为准确和精确.其在电气性能和稳定性、可靠性方面满足CIMO的探空要 求.实验室测试表明,在采用新型温、湿、压传感器和测试条件情况下,准确性误差分别在±0.1℃、±2％、±1 hPa 之内,满足WMO对常规高空探测要求.与RS92型探空系统相比,国产GPS探空仪的动态测量性能除相对湿度准确性方面由于技术和工艺水平仍有一定差距 外,其余要素已接近RS92的水平,尤其与GPS定位相关的气压、位势高度、风向和风速,其一致性标准差已与RS92相当,达到了较高的水平.与我国现有 高空探测业务使用的L波段探空系统相比,测量准确性方面已优于L波段探空系统.
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| [31] |
Performance of the Vaisala RS80A/H and RS90 humicap sensors and the meteolabor “Snow White” chilled-mirror hygrometer in Paramaribo, Suriname [J].
In climate research there is a strong need for accurate observations of water vapor in the upper atmosphere. Radiosoundings provide relative humidity profiles but the accuracy of many routine instruments is notoriously inadequate in the cold upper troposphere. In this study results from a soundings program executed in Paramaribo, Suriname (5.8°N, 55.2°W), are presented. The aim of this program was to compare the performance of different humidity sensors in the upper troposphere in the Tropics and to test different bias corrections suggested in the literature. The payload of each sounding consisted of a chilled-mirror “Snow White” sensor from Meteolabor AG, which was used as a reference, and two additional sensors from Vaisala, that is, either the RS80A, the RS80H, or the RS90. In total 37 separate soundings were made. For the RS80A a clear, dry bias of between 4% and 8% RH is found in the lower troposphere compared to the Snow White observation, confirming the findings in previous studies. A mean dry bias was found in the upper troposphere, which could be effectively corrected. The RS80H sensor shows a significant wet bias of 2%–5% in RH in the middle and upper troposphere, which has not been reported before. Comparing observations with RS80H sensors of different ages gives no indication of sensor aging or sensor contamination. It is therefore concluded that the plastic cover introduced by Vaisala to avoid sensor contamination is effective. Finally, the RS90 sensor yields a small but significant wet bias of 2%–3% below 7-km altitude. The time-lag error correction from Miloshevich et al. was applied to the Vaisala data, which resulted in an increased variability in the relative humidity profile above 9- (RS80A), 8- (RS80H), and 11-km (RS90) altitude, respectively, which is in better agreement with the Snow White data. The averaged Snow White profile is compared with the average profiles of relative humidity from the European Centre for Medium-Range Weather Forecasts (ECMWF). No significant bias is found in either the analyses or the forecasts. The correlation coefficient for the Snow White and ECMWF data between 200 and 800 hPa was 0.66 for the 36-h forecast and 0.77 for the analysis.
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| [32] |
Corrections of humidity measurement errors from the Vaisala RS80 radiosonde-application to TOGA COARE data [J].
Abstract A series of laboratory tests have been conducted on several different batches of Vaisala RS80 radiosondes to understand and develop methods to correct six humidity measurement errors, including chemical contamination, temperature dependence, basic calibration model, ground check, sensor aging, and sensor arm heating. The contamination and temperature-dependence (TD) errors dominate total errors. The chemical contamination error produces a dry bias, and is due to the occupation of binding sites in the sensor polymer by nonwater molecules emitted from the sonde packaging material. The magnitude of the dry bias depends on sensor polymer type (RS80-A and RS80-H), age of the sonde, relative humidity (RH), and temperature, and it exists throughout the troposphere. The contamination error generally increases with age and RH, and is larger for the RS80-H than the RS80-A. It is 652% and 6510% at saturation for 1-yr-old RS80-A and RS80-H sondes, respectively. The TD error for the RS80-A results from an appro...
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| [33] |
Development and validation of a time-lag correction for Vaisala radiosonde humidity measurements [J].
Abstract This study presents a method of improving the accuracy of relative humidity (RH) measurements from Vaisala RS80 and RS90 radiosondes by applying sensor-based corrections for well-understood sources of measurement error. Laboratory measurements of the sensor time constant as a function of temperature are used to develop a correction for a time-lag error that results from slow sensor response at low temperatures. The time-lag correction is a numerical inversion algorithm that calculates the ambient (“true”) humidity profile from the measured humidity and temperature profiles, based on the sensor time constant. Existing corrections for two sources of dry bias error in RS80 humidity measurements are also included in the correction procedure: inaccuracy in the sensor calibration at low temperatures, and chemical contamination of sensors manufactured before June 2000 by nonwater molecules from the radiosonde packaging material. The correction procedure was evaluated by comparing corrected RS80-H measur...
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| [34] |
111: D09S10. DOI: 10.1029/2005JD006083. |
| [35] |
Dry bias and variability in Vaisala RS80-H radiosondes: The ARM experience [J].
Thousands of comparisons between total precipitable water vapor (PWV) obtained from radiosonde (Vaisala RS80-H) profiles and PWV retrieved from a collocated microwave radiometer (MWR) were made at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains Cloud and Radiation Testbed (SGP CART) site in northern Oklahoma from 1994 to 2000. These comparisons show that the RS80-H radiosonde has an approximate 5% dry bias compared to the MWR. This observation is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean-揂tmosphere Response Experiment (TOGA COARE). In addition to the dry bias, analysis of the PWV comparisons as well as of data obtained from dual-sonde soundings done at the SGP show that the calibration of the radiosonde humidity measurements varies considerably both when the radiosondes come from different calibration batches and when the radiosondes come from the same calibration batch. This variability can result in peak-to-peak differences between radiosondes of greater than 25% in PWV. Because accurate representation of the vertical profile of water vapor is critical for ARM's science objectives, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor. By using an independent set of observations and radiative transfer models to test the correction, it is shown that the constant humidity scaling method appears both to improve the accuracy and reduce the uncertainty of the radiosonde data. The ARM data are also used to examine a different, physically based, correction scheme that was developed recently by scientists from Vaisala and the National Center for Atmospheric Research (NCAR). This scheme, which addresses the dry bias problem as well as other calibration-related problems with the RS80-H sensor, results in excellent agreement between the PWV retrieved from the MWR and integrated from the corrected radiosonde. However, because the physically based correction scheme does not address the apparently random calibration variations observed, it does not reduce the variability either between radiosonde calibration batches or within individual calibration batches.
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| [36] |
RA III radiosonde training workshop, Buenos Aires, May 2006 [ |
| [37] |
Measurement of upper-air pressure, temperature and humidity [C]∥ |
| [38] |
Absolute accuracy of water vapor measurements from six operational radiosonde types launched during AWEX-G and implications for AIRS validation [J].
[1] A detailed assessment of radiosonde water vapor measurement accuracy throughout the tropospheric column is needed for assessing the impact of observational error on applications that use the radiosonde data as input, such as forecast modeling, radiative transfer calculations, remote sensor retrieval validation, climate trend studies, and development of climatologies and cloud and radiation parameterizations. Six operational radiosonde types were flown together in various combinations with a reference-quality hygrometer during the Atmospheric Infrared Sounder (AIRS) Water Vapor Experiment-Ground (AWEX-G), while simultaneous measurements were acquired from Raman lidar and microwave radiometers. This study determines the mean accuracy and variability of the radiosonde water vapor measurements relative to simultaneous measurements from the University of Colorado (CU) Cryogenic Frostpoint Hygrometer (CFH), a reference-quality standard of known absolute accuracy. The accuracy and performance characteristics of the following radiosonde types are evaluated: Vaisala RS80-H, RS90, and RS92; Sippican Mark IIa; Modem GL98; and the Meteolabor Snow White hygrometer. A validated correction for sensor time lag error is found to improve the accuracy and reduce the variability of upper tropospheric water vapor measurements from the Vaisala radiosondes. The AWEX data set is also used to derive and validate a new empirical correction that improves the mean calibration accuracy of Vaisala measurements by an amount that depends on the temperature, relative humidity, and sensor type. Fully corrected Vaisala radiosonde measurements are found to be suitably accurate for AIRS validation throughout the troposphere, whereas the other radiosonde types are suitably accurate under only a subset of tropospheric conditions. Although this study focuses on the accuracy of nighttime radiosonde measurements, comparison of Vaisala RS90 measurements to water vapor retrievals from a microwave radiometer reveals a 6芒聙聯8% dry bias in daytime RS90 measurements that is caused by solar heating of the sensor. An AWEX-like data set of daytime measurements is highly desirable to complete the accuracy assessment, ideally from a tropical location where the full range of tropospheric temperatures can be sampled.
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| [39] |
Characterization and correction of relative humidity measurements from Vaisala RS80—A radiosondes at cold temperatures [J].
Presents a study which characterized radiosonde relative humidity (RH) measurements from Vaisala RS80-A thin-film capacitive sensors at cold temperatures. RS80-A sensor principles and calibration; Sources of RS80-A measurement error; Individual RS80-A measurement errors.
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Radiation dry bias of the Vaisala RS92 humidity sensor [J].
Abstract The comparison of simultaneous humidity measurements by the Vaisala RS92 radiosonde and by the Cryogenic Frostpoint Hygrometer (CFH) launched at Alajuela, Costa Rica, during July 2005 reveals a large solar radiation dry bias of the Vaisala RS92 humidity sensor and a minor temperature-dependent calibra- tion error. For soundings launched at solar zenith angles between 10掳 and 30掳, the average dry bias is on the order of 9% at the surface and increases to 50% at 15 km. A simple pressure- and temperature-dependent correction based on the comparison with the CFH can reduce this error to less than 7% at all altitudes up to 15.2 km, which is 700 m below the tropical tropopause. The correction does not depend on relative humidity, but is able to reproduce the relative humidity distribution observed by the CFH.
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Relative humidity error of L-band electronic radiosonde and its application [J].L波段电子探空仪相对湿度误差研究及其应用 [J].
为了提高高空大气探测数据准确度，我国从2002年1月开始推广使用L波段雷达-电子探空仪探测系统，用携带碳湿敏元件的数字式电子探空仪代替用肠膜测湿元件的59型探空仪进行相对湿度探测。但大量的实测探空资料表明：相对湿度探空曲线仍然存在较大误差。利用能测到-30℃低温的高精度湿度校准设备在-30～30℃试验温度范围内对碳湿敏元件进行大量静态测试，在进一步了解碳湿敏电阻校准线随温度变化的特征基础上，结合实际探空资料，修正工厂的相对湿度订正原理和公式，从而可以提高碳湿敏电阻在高湿端的测量准确度，提高判断云层垂直位置的准确性，进而提高L波段探空仪温度测量的精度。
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Guide to Meteorological Instruments and Methods of Observation(V7.0)[Z]. |
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The analysis of Yangjiang international radiosonde intercomparison results for Chinese GTS1-2 electronic radiosonde [J].中国GTS1-2型电子探空仪阳江国际比对结果分析 [J].
根据世界气象组织阳江第八届国际探空比对资料,对中国GTS1-2型探空仪系统开展了系统性评估。初步评估结果表明:GTS1-2型探空仪温度传感器系统偏差在0.2℃之内(高度在33 km以下),标准偏差在1℃之内;气压传感器系统偏差在0.7 hPa之内,标准偏差在1 hPa之内;位势高度系统偏差在40 gpm之内,标准偏差在320 gpm之内;温度、气压、位势高度一致性较好,但是还需进一步改善高空辐射误差修正软件;湿度测量结果与其他国家有一定的差距,具体表现温度在-30℃～-50℃,时间常数明显增大,变化幅度变小,反应滞后;风向风速与GPS导航卫星定位测风的结果比较接近。
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Dry bias in Vaisala RS90 radiosonde humidity profiles over Antarctita [J].
Middle to upper tropospheric humidity plays a large role in determining terrestrial outgoing longwave radiation. Much work has gone into improving the accuracy of humidity measurements made by radiosondes. Some radiosonde humidity sensors experience a dry bias caused by solar heating. During the austral summers of 2002/03 and 2003/04 at Dome C, Antarctica, Vaisala RS90 radiosondes were launched in clear skies at solar zenith angles (SZAs) near 8300° and 6200°. As part of this field experiment, the Polar Atmospheric Emitted Radiance Interferometer (PAERI) measured downwelling spectral infrared radiance. The radiosonde humidity profiles are used in the simulation of the downwelling radiances. The radiosonde dry bias is then determined by scaling the humidity profile with a height-independent factor to obtain the best agreement between the measured and simulated radiances in microwindows between strong water vapor lines from 530 to 560 cm-1 and near line centers from 1100 to 1300 cm-1. The dry biases, as relative errors in relative humidity, are 8% 00± 5% (microwindows; 10306) and 9% 00± 3% (line centers) for SZAs near 8300°; they are 20% 00± 6% and 24% 00± 5% for SZAs near 6200°. Assuming solar heating is minimal at SZAs near 8300°, the authors remove errors that are unrelated to solar heating and find the solar-radiation dry bias of 9 RS90 radiosondes at SZAs near 6200° to be 12% 00± 6% (microwindows) and 15% 00± 5% (line centers). Systematic errors in the correction are estimated to be 3% and 2% for microwindows and line centers, respectively. These corrections apply to atmospheric pressures between 650 and 200 mb.
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| [45] |
L-band Radiosonde System Relative Humidity Observation Error and Its Feature[D]. Nanjing:Nanjing University of Information, L波段探空系统相对湿度的观测误差特征[D] . |
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Intercomparison of humidity and temperature sensors: GTS1, Vaisala RS80, and CFH [J].
GTS1 digital radiosonde, developed by the Shanghai Changwang Meteorological Science and Technology Company in 1998, is now widely used in operational radiosonde stations in China. A preliminary comparison of simultaneous humidity measurements by the GTS1 radiosonde, the Vaisala RS80 radiosonde, and the Cryogenic Frostpoint Hygrometer (CFH), launched at Kunming in August 2009, reveals a large dry bias produced by the GTS1 humidity sensor. The average relative dry bias is in the order of 10% below 500 hPa, increasing rapidly to 30% above 500 hPa, and up to 55% at 310 hPa. A much larger dry bias is observed in the daytime, and this daytime effect increases with altitude. The GTS1 radiosonde fails to respond to humidity changes in the upper troposphere, and sometimes even in the middle troposphere. The failure of GTS1 in the middle and upper troposphere will result in significant artificial humidity shifts in radiosonde climate records at stations in China where a transition from mechanical to digital radiosondes has occurred. A comparison of simultaneous temperature observations by the GTS1 radiosonde and the Vaisala RS80 radiosonde suggests that these two radiosondes provide highly reproducible temperature measurements in the troposphere, but produce opposite biases for daytime and nighttime measurements in the stratosphere. In the stratosphere, the GTS1 shows a warm bias (0.5 K) in the daytime and a relatively large cool bias (-0.2 K to -1.6 K) at nighttime.
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| [47] |
Technical improvement and experimental analysis of GTS1 radiosonde [J].GTS1型探空仪技术改进对比试验 [J].
GTS1型探空仪在中国气象局探空网站使用已超过10年,其技术略显滞后,迫切需要技术改进。2010年,GTS1型探空仪在其原技术体制基础上进行了技术改进。中国气象局气象探测中心分别于2010年9月和2011年12月,在阳江探空站组织开展了动态比对试验和温度辐射修正算法改进试验。试验结果表明,改进后的GTS1型探空仪夜间温度偏差较小,在0.1℃左右,日间温度偏差在0.3℃以内;气压测量结果偏小于RS92型探空仪,稳定性优于GTS1型探空仪;湿度传感器反应灵敏,测量结果与RS92型一致性很好,偏差在5%RH以内。
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| [48] |
Intercomparison of GPS radiaosonde soundings during the eastern tropical Indian Ocean experiment [J]. |
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Comparative analysis of new GPS and GTS1-2 radiosonde [J].新型GPS探空仪与业务GTS1-2探空仪对比分析 [J].
2012年8月,中国气象局气象探测中心在广东阳江开展自动探空 系统新型GPS探空仪比对试验,对比分析其技术改进后的准确性,试验结果表明:温度测量性明显优于GTS1-2型探空仪.湿度测量结果与RS92型探空仪 一致性较好,系统误差在15％RH内,标准偏差在12％RH内.气压系统误差全量程在±1.0 hPa内,标准偏差在0.8 hPa内.位势高度系统误差在±20 gpm以内,标准偏差在70 gpm内.GPS定位测风性能优于GTS1-2型探空仪配合L波段二次测风雷达测风性能结果.
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Vaisala’s radiosonde technology and advancement in radiosonde technology in China [J].Vaisala探空技术及中国探空技术的发展 [J]. |
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Performance analysis of RS92 radiosonde for sounding temperature, pressure, and humidity [J].RS92探空仪温压湿测量性能分析 [J].
Vaisala RS92探空仪代表了当今探空仪的较高水平,通常可以作为比对标准用来评估其他探空仪的性能。除了从其提供的指标确定其性能外,还可以根据实际施放过程中的探测数据进行评估。采用双Vaisala RS92探空仪同球施放比对法,对多天同一时次的探测数据进行统计,分析了其温压湿探测性能。结果表明,RS92型探空仪温压湿传感器的测量性能一致性较好,可作为比对施放时的标准探空仪来衡量其他类型探空仪的测量性能。
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Comparing the cloud vertical structure derived from several methods based on radiosonde profiles and ground-based remote sensing measurements [J].
The cloud vertical distribution and especially the cloud base height, which is linked to cloud type, are important characteristics in order to describe the impact of clouds on climate. In this work, several methods for estimating the cloud vertical structure (CVS) based on atmospheric sounding profiles are compared, considering the number and position of cloud layers, with a ground-based system that is taken as a reference: the Active Remote Sensing of Clouds (ARSCL). All methods establish some conditions on the relative humidity, and differ in the use of other variables, the thresholds applied, or the vertical resolution of the profile. In this study, these methods are applied to 193 radiosonde profiles acquired at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site during all seasons of the year 2009 and endorsed by Geostationary Operational Environmental Satellite (GOES) images, to confirm that the cloudiness conditions are homogeneous enough across their trajectory. The perfect agreement (i.e., when the whole CVS is estimated correctly) for the methods ranges between 26 and 64%; the methods show additional approximate agreement (i.e., when at least one cloud layer is assessed correctly) from 15 to 41%. Further tests and improvements are applied to one of these methods. In addition, we attempt to make this method suitable for low-resolution vertical profiles, like those from the outputs of reanalysis methods or from the World Meteorological Organization's (WMO) Global Telecommunication System. The perfect agreement, even when using low-resolution profiles, can be improved by up to 67% (plus 25% of the approximate agreement) if the thresholds for a moist layer to become a cloud layer are modified to minimize false negatives with the current data set, thus improving overall agreement.
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| [53] |
Comparison between active sensor and radiosonde cloud boundaries over the ARM Southern Great Plains site [J].
[1] In order to test the strengths and limitations of cloud boundary retrievals from radiosonde profiles, 4 years of radar, lidar, and ceilometer data collected at the Atmospheric Radiation Measurements Southern Great Plains site from November 1996 through October 2000 are used to assess the retrievals of Wang and Rossow [1995] and Chernykh and Eskridge [1996]. The lidar and ceilometer data yield lowest-level cloud base heights that are, on average, within approximately 125 m of each other when both systems detect a cloud. These quantities are used to assess the accuracy of coincident cloud base heights obtained from radar and the two radiosonde-based methods applied to 200 m resolution profiles obtained at the same site. The lidar/ceilometer and radar cloud base heights agree by 0.156 00± 0.423 km for 85.27% of the observations, while the agreement between the lidar/ceilometer and radiosonde-derived heights is at best 0908080.044 00± 0.559 km for 74.60% of all cases. Agreement between radar- and radiosonde-derived cloud boundaries is better for cloud base height than for cloud top height, being at best 0.018 00± 0.641 km for 70.91% of the cloud base heights and 0.348 00± 0.729 km for 68.27% of the cloud top heights. The disagreements between radar- and radiosonde-derived boundaries are mainly caused by broken cloud situations when it is difficult to verify that drifting radiosondes and fixed active sensors are observing the same clouds. In the case of the radar the presence of clutter (e.g., vegetal particles or insects) can affect the measurements from the surface up to approximately 30900095 km, preventing comparisons with radiosonde-derived boundaries. Overall, Wang and Rossow [1995] tend to classify moist layers that are not clouds as clouds and both radiosonde techniques report high cloud top heights that are higher than the corresponding heights from radar.
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| [54] |
Inter-comparison and application of atmospheric humidity profiles measured by CFH and Vaisala RS80 radiosondes [J].2012. 两种探空仪观测湿度垂直分布及其应用比较 [J].
对2010年8月在云南腾冲利用芬兰Vaisala RS80和低温霜点仪(Cryogenic Frostpoint Hygrometer,CFH)两种探空仪测量大气湿度的垂直分布进行对比分析,同时比较它们白天和夜间测量误差的差别,并对国产GTS1,RS80和 CFH共3种探空仪测量水汽总量与地基GPS遥测结果进行比较.结果表明:RS80湿度测值在整个对流层比CFH测值偏干(23.7士18.5)％；因太 阳辐射白天RS80偏干较夜间更明显,比夜间偏干(13.5±14.8)％.而在对流层上层向平流层过渡区域内RS80湿度数据基本无效.CFH在低温、 低湿环境下对湿度能有效测量,但在湿度较高的对流层低层测值偏高,导致比较中CFH水汽总量平均比GPS遥测的水汽总量偏高(4.3士2.0)mm(样本 数为11),而RS80,GTS1与GPS的水汽总量差别分别是(0.2士1.4)mm(样本数为12),(-0.2士2.2)mm(样本数为43).地 基GPS遥测的水汽总量对对流层上层至平流层的水汽变化不敏感.由于RS80测量相对湿度在高空偏低,通过RS80相对湿度测值来确定中、高云结果是偏低 的,特别是对6000 m以上的高云判别上,RS80相对湿度的探测几乎很难甄别到云的存在.
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