气象
氣象
기상
METEOROLOGICAL MONTHLY
2015年
8期
1007-1016
,共10页
李亚丽%任芝花%陈高峰%夏巧利%贺音%余鹏
李亞麗%任芝花%陳高峰%夏巧利%賀音%餘鵬
리아려%임지화%진고봉%하교리%하음%여붕
自动观测%气温%差异%仪器示值误差%仪器零点漂移%惩罚最大t检验(RMT)
自動觀測%氣溫%差異%儀器示值誤差%儀器零點漂移%懲罰最大t檢驗(RMT)
자동관측%기온%차이%의기시치오차%의기영점표이%징벌최대t검험(RMT)
automatic observation%temperature%difference%instrument calibration error value%zero drift%penalized maximal t test (RMT)
提利用143个国家基准站2002—2010年自动与人工逐日平行观测资料进行对比分析，评估自动观测与人工观测气温的差异，着重分析两者存在的较大差异及其发生原因，并利用惩罚最大 t 检验（RMT）方法结合台站元数据中自动观测仪器变化信息，客观评价自动观测对气温序列均一性的影响。结果表明：（1）51．29％、54．14％、67．18％的自动观测日平均、日最高、日最低气温大于人工观测值，差值在±0．2℃之间的百分率分别为78．8％、63．1％、60．9％，平均对比差值分别为0．05、0．09、0．15℃，标准差为0．14、0．22和0．15℃，各气温要素的差值、绝对差值和标准差随自动观测时间的增长并无明显的增大或减小的趋势，且空间分布各有不同；（2）通过对对比差值、绝对差值、标准差的分类比较、逐步筛选发现，少数台站自动与人工观测值差异较大，对于采集自同一传感器的不同气温要素，平均、最高、最低气温的差值表现也不尽一致。经 RMT 检验，在平均气温、最高气温和最低气温的绝对差值最大的20个站中分别有35％的台站的月平均气温序列、35％的台站的月平均最高气温序列和25％的台站的月平均最低气温序列由于自动观测仪器变化引起序列的非均一；（3）分析认为：温度传感器检定更换而导致的仪器示值误差变化会造成自动与人工观测对比差值跳变，而温度传感器或数据采集器等电子元器件的零点漂移会导致自动观测气温严重偏离人工观测值，这两种因素会导致自动与人工观测气温差异偏大，也是自动观测仪器变化导致气温序列产生非均一断点的可能原因。建议加强自动观测数据的监测与质量控制，增加观测仪器检定示值误差订正，并采取硬件、软件补偿等方法，实现温度零点补偿，尽可能地减小或消除仪器误差，提高自动观测资料的准确性。
提利用143箇國傢基準站2002—2010年自動與人工逐日平行觀測資料進行對比分析，評估自動觀測與人工觀測氣溫的差異，著重分析兩者存在的較大差異及其髮生原因，併利用懲罰最大 t 檢驗（RMT）方法結閤檯站元數據中自動觀測儀器變化信息，客觀評價自動觀測對氣溫序列均一性的影響。結果錶明：（1）51．29％、54．14％、67．18％的自動觀測日平均、日最高、日最低氣溫大于人工觀測值，差值在±0．2℃之間的百分率分彆為78．8％、63．1％、60．9％，平均對比差值分彆為0．05、0．09、0．15℃，標準差為0．14、0．22和0．15℃，各氣溫要素的差值、絕對差值和標準差隨自動觀測時間的增長併無明顯的增大或減小的趨勢，且空間分佈各有不同；（2）通過對對比差值、絕對差值、標準差的分類比較、逐步篩選髮現，少數檯站自動與人工觀測值差異較大，對于採集自同一傳感器的不同氣溫要素，平均、最高、最低氣溫的差值錶現也不儘一緻。經 RMT 檢驗，在平均氣溫、最高氣溫和最低氣溫的絕對差值最大的20箇站中分彆有35％的檯站的月平均氣溫序列、35％的檯站的月平均最高氣溫序列和25％的檯站的月平均最低氣溫序列由于自動觀測儀器變化引起序列的非均一；（3）分析認為：溫度傳感器檢定更換而導緻的儀器示值誤差變化會造成自動與人工觀測對比差值跳變，而溫度傳感器或數據採集器等電子元器件的零點漂移會導緻自動觀測氣溫嚴重偏離人工觀測值，這兩種因素會導緻自動與人工觀測氣溫差異偏大，也是自動觀測儀器變化導緻氣溫序列產生非均一斷點的可能原因。建議加彊自動觀測數據的鑑測與質量控製，增加觀測儀器檢定示值誤差訂正，併採取硬件、軟件補償等方法，實現溫度零點補償，儘可能地減小或消除儀器誤差，提高自動觀測資料的準確性。
제이용143개국가기준참2002—2010년자동여인공축일평행관측자료진행대비분석，평고자동관측여인공관측기온적차이，착중분석량자존재적교대차이급기발생원인，병이용징벌최대 t 검험（RMT）방법결합태참원수거중자동관측의기변화신식，객관평개자동관측대기온서렬균일성적영향。결과표명：（1）51．29％、54．14％、67．18％적자동관측일평균、일최고、일최저기온대우인공관측치，차치재±0．2℃지간적백분솔분별위78．8％、63．1％、60．9％，평균대비차치분별위0．05、0．09、0．15℃，표준차위0．14、0．22화0．15℃，각기온요소적차치、절대차치화표준차수자동관측시간적증장병무명현적증대혹감소적추세，차공간분포각유불동；（2）통과대대비차치、절대차치、표준차적분류비교、축보사선발현，소수태참자동여인공관측치차이교대，대우채집자동일전감기적불동기온요소，평균、최고、최저기온적차치표현야불진일치。경 RMT 검험，재평균기온、최고기온화최저기온적절대차치최대적20개참중분별유35％적태참적월평균기온서렬、35％적태참적월평균최고기온서렬화25％적태참적월평균최저기온서렬유우자동관측의기변화인기서렬적비균일；（3）분석인위：온도전감기검정경환이도치적의기시치오차변화회조성자동여인공관측대비차치도변，이온도전감기혹수거채집기등전자원기건적영점표이회도치자동관측기온엄중편리인공관측치，저량충인소회도치자동여인공관측기온차이편대，야시자동관측의기변화도치기온서렬산생비균일단점적가능원인。건의가강자동관측수거적감측여질량공제，증가관측의기검정시치오차정정，병채취경건、연건보상등방법，실현온도영점보상，진가능지감소혹소제의기오차，제고자동관측자료적준학성。
Based on the parallel daily air temperature data of automatic and manual observations at 143 na-tional benchmark stations from 2002 to 2010,systematic comparative analysis and objective evaluation of differences are made,especially focusing on the large differences and their causes.The impact of automatic observation on the homogeneity of temperature time series is evaluated using the penalized maximal t test (RMT)combined with the metadata of observation instrument changes.The results show that:(1 ) 51.29%,54.14%,and 67.18% of daily average,highest,lowest temperatures obtained by AWS (auto-matic weather station)are greater than the values by manual observation and the percentage of difference between ±0.2℃ respectively accounts for 78.8%,63.1%,and 60.9%.Average difference values of daily average,highest,lowest temperature are 0.05℃,0.09℃ and 0.15℃,and the standard deviations are 0.14℃,0.22℃ and 0.15℃,respectively.The difference,absolute difference value and standard deviation of all temperature elements have no apparent increasing or decreasing trend along with the observation time of AWS and their spatial distributions are different.(2)By the classification comparison and screening of the difference value,absolute difference value and standard deviation step by step,some differences are found greater at a few stations and the differences of the average,the highest and lowest temperatures col-lected from the same sensor are different as well.By the check of RMT,the inhomogeneous breakpoints are found in the monthly average temperature time series,monthly average maximum temperature time se-ries of 35% station and monthly average minimum temperature time series of 25% stations among the 20 stations with largest absolute difference values of average,maximum and minimum temperatures.(3)The change of calibration error of temperature sensor is the important reason for difference jump between auto-matic and manual observations.The instrument failures,such as zero drift of electronic components of temperature sensor or data collector can lead temperature obtained by AWS to deviate greatly from the val-ue of manual observation.The above two facts are the main causes for greater differences in temperature between automatic and manual observations,and also possible reasons for inhomogeneous breakpoints of temperature series because of observational instrument changes.Therefore,we suggest strengthening mo-nitoring and quality control for automatic observation data,increasing the observation instrument calibra-tion error correction and realizing the zero temperature compensation adopting methods of hardware and software compensation to reduce or eliminate the instrument error as much as possible and improve the ac-curacy of the automatic observation data.
