Pre-Study of the Rain Enhancing Technique Series

1 Field ExperimentConducted jointly by the Chinese Academy of Meteorological Sciences and the Meteorological Bureau of Qinghai Province and assisted by Atmospheric Environment Institute of the PLA General Staff, the PAL Air Force and the related institutions of the Civil Aviation Administration of China, a field experiment on comprehensive monitoring and cloud seeding operation was carried out under different weather systems around the upper reaches of Huanghe River in May 2001, in which two airplanes (a Y-8 and an AN-26) were used.     

Modelling of Indian Monsoon Rainfall Series by Univariate Box-Jenkins Type of Models

The time domain approach, i.e. Autoregressive (AR) processes, of time series analysis is applied to the monsoon rainfall series of India and its two major regions, viz. North-West India and Central India. Since the original time series shows no modelable structure due to the presence of high interannual variability, a 3-point running filter is applied before exploring and fitting appropriate stochastic models. Out of several parsimonious models fitted, AR(3) is found to be most suitable. The usefulness of this fitted model is validted on an independent datum of 18 years and some skill has been noted. These models therefore can be used for low skill higher lead time forecasts of monsoon. Further the forecasts produced through such models can be combined with other forecasts to increase the skill of monsoon forecasts.     

Determination of Kolmogorov Entropy of Chaotic Attractor Included in One-Dimensional Time Series of Meteorological Data

The 1970-1985 day to day averaged pressure dataset of Shanghai and the extension method in phase space are used to calculate the correlation dimension D and the second-order Renyi entropy K2 of the approximation of Kolmogorov's entropy, the fractional dimension D = 7.7-7.9 and the positive value K2 - 0.1 are obtained. This shows that the attractor for the short-term weather evolution in the monsoon region of China exhibits a chaotic motion. The estimate of K2 yields a predictable time scale of about ten days. This result is in agreement with that obtained earlier by the dynamic-statistical approach.The effects of the lag time i on the estimate of D and K2 are investigated. The results show that D and K2 are convergent with respect to i. The day to day averaged pressure series used in this paper are treated for the extensive phase space with T = 5, the coordinate components are independent of each other; therefore, the dynamical character quantities of the system are stable and reliable.     

Preliminary Study of Reconstruction of a Dynamic System Using an One－Dimensional Time Series

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Urban Bias in Temperature Time Series – a Case Study for the City of Vienna, Austria

Compared with other large cities Vienna shows different urban development characteristics. The city has had a zero population growth during 1951–1995, a period of rapid growth elsewhere. In spite of its stagnating population of about 1,6 million Vienna has had development in other areas: a doubling of living floor space, a two and a half-fold increase in total energy consumption, a 60% rise of traffic area. In contrast, forests have been reduced by 20% and grasslands within the city borders by 30%. Of the 34 temperature recording stations in the study area of 1450 km², nine series passed the quality tests after careful homogenization. Three of these were in the rural environment and were used as reference series for the urban temperature excess at the other six stations in the urbanized area. The urban excess temperatures vary from site to site: from 0.2 K in suburban areas up to 1.6 K in densely built-up areas. The Vienna case study illustrates two features of more than local interest which should be considered in urban climatology as well as in time series studies where the urban temperature excess is regarded as a bias. Firstly, in a city with constant population the urban heat excess shows significant to strongly significant trends of up to 0.6 K in 45 years due to changes in urban morphology and energy consumption. Secondly, the urban heat island and its trend cannot be regarded simply for the city as a whole. There are different absolute levels, different annual variations and different increases of the urban temperature excess in different parts of a city. The urban effect is more strongly influenced by the local surroundings of the site than by the city as a whole. So, if possible, urban heat islands should not be described by a two station approach only (the typical airport-downtown comparison), nor should it rely on regression between population number and heat island.     

Some Unique Characteristics of Atmospheric Interannual Variability in Rainfall Time Series over India and the United Kingdom

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Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.     

Estimation of Winds at Different Isobaric Levels Based on the Observed Winds at 850 hPa Level Using Double Fourier Series

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The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series

1. Introduction Let us suppose that the meteorological element series is the set of solution by integrating a perfect cli- matic numerical model with certain initial conditions, boundary conditions etc., thus it is also the concen- trated expression of nonlinear interaction between all climatic factors (including itself) in the model. Be- cause of limited understanding the mechanism of cli- matic system changes, the unsolved problems are not less than the solved ones in the climatic numerical …     

A New Multidimensional Time Series Forecasting Method Based on the EOF Iteration Scheme

In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments of Nino3 SST anomalies and Tahiti-Darwin SO index. The results show that the scheme is feasible and ENSO predictable.     

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature(SAT) series at 32 stations in China back to the 19 th century had previously been developed based on the RHtest method by Cao et al.,but some inhomogeneities remained in the dataset.The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization(MASH) method.The MASH procedure detects 33 monthly temperature records as erroneous outliers and152 meaningful break points in the monthly SAT series since 1924 at 28 stations.The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series.The new data show significant warming trends during 1924–2016 at all the stations,ranging from 0.48 to 3.57?C(100 yr)~(-1),with a regional mean trend of 1.65?C(100 yr)~(-1);whereas,the previous results ranged from a slight cooling at two stations to considerable warming,up to 4.5?C(100 yr)~(-1).It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century.     

Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013

Two homogenized datasets of daily maximum temperature (Tmax), mean temperature (Tm), and minimum temperature (Tmin) series in China have recently been developed. One is CHTM3.0, based on the Multiple Analysis of Series for Homogenization (MASH) method, and includes 753 stations for the period 1960–2013. The other is CHHTD1.0, based on the Relative Homogenization test (RHtest), and includes 2419 stations over the period 1951–2011. The daily Tmax/Tm/Tmin series at 751 stations, which are in both datasets, are chosen and compared against the raw dataset, with regard to the number of breakpoints, long-term climate trends, and their geographical patterns. The results indicate that some robust break points associated with relocations can be detected, the inhomogeneities are removed by both the MASH and RHtest method, and the data quality is improved in both homogenized datasets. However, the differences between CHTM3.0 and CHHTD1.0 are notable. By and large, in CHHTD1.0, the break points detected are fewer, but the adjustments for inhomogeneities and the resultant changes of linear trend estimates are larger. In contrast, CHTM3.0 provides more reasonable geographical patterns of long-term climate trends over the region. The reasons for the differences between the datasets include: (1) different algorithms for creating reference series for adjusting the candidate series—more neighboring stations used in MASH and hence larger-scale regional signals retained; (2) different algorithms for calculating the adjustments—larger adjustments in RHtest in general, partly due to the individual local reference information used; and (3) different rules for judging inhomogeneity—all detected break points are adjusted in CHTM3.0, based on MASH, while a number of break points detected via RHtest but without supporting metadata are overlooked in CHHTD1.0. The present results suggest that CHTM3.0 is more suitable for analyses of large-scale climate change in China, while CHHTD1.0 contains more original information regarding station temperature records.     

中国均一化日平均温、最高温和最低温序列1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects i     

多种均一性检验方法比较研究

气候资料的均一性检验对气候观测及变化研究有着重要作用。本文从四川省160个台站近50年气温资料中,选取具有连续观测记录的站点105个,综合SNHT(标准正态均一性检验)、Buishand检验、Pettitt检验、MASH(均一性序列多元分析)、TPR(二位相回归检验)和von Neumann比率法,对该省年平均气温进行均一性检验。结果表明有42个台站存在序列间断点,占40%;而不均一台站中,因迁站导致的有29个,更换仪器引起的有19个。对气温序列分年代统计发现不均一台站数目有增加的趋势,其中20世纪60—70和80—90年代不均一性均由台站迁移所致。评估不同检验方法的敏感度和适用性,发现SNHT法与Buishand法、Pettitt法检验结果相符率为48.6%,TPR法与其他方法相符率偏低,但该法不涉及邻近站是否均一,在分析中可做补充判断。参考四川省地形特征分析,SNHT法在盆地及山地台站的断点显露率最高,分别为67.6%和57.1%,漏检和误判较少;高原地区SNHT法和Buishand法显露率较高,其中Buishand法漏检和误判率最低。鉴于减小对元数据依赖度的均一化检验思路,应采用多种检验方法综合,有助于气候资料在区域气候变化研究中的有效利用。     

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are ?0.004°C/10 year and ?0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change.     

Homogenization of the Daily Land Surface Temperature over the Mainland of China from 1960 through 2017

Land surface temperature(LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other non-climatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017. The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40°N due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization(MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68 × 103 effective breakpoints in 1.65 × 106 monthly records(about 20%) are detected. A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China. After the MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/– 0.5°C, and of the remaining breakpoints, only 10% are adjusted over 1.5°C.Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend [0.22°C(10 yr)–1]. The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).     

基于均一化观测序列的京津冀地区气候变化格局分析

利用1960~2015年京津冀地区88个国家级气象站观测资料（包括日平均气温、日最高气温、日最低气温、日降水、日平均风速等）,使用MASH（Multiple Analysis of Series for Homogenization）方法剔除台站迁址、仪器变更等因素所致偏差后,生成均一化的观测资料集。基于新资料集计算了各站气温、降水和凤速序列的线性趋势和Morlet小波等统计特征,分析了京津冀地区气候变化格局。结果表明:MASH方法能较准确地检测并校订观测序列中迁站、仪器变更等因素所导致的非均一性;1960~2015年期间京津冀年平均气温显著上升,上升幅度为0.261℃/10 a;降水减少,平均减少11.27 mm/10 a;风速显著减小,平均减小0.193 m s-1（10 a）-1。     

资料均一化对气温变化趋势及其城市化偏差估计的影响：以北京地区为例

当前的地面气候观测资料普遍存在非气候性因素导致的非均一性,对气候变化监测和研究结论可靠性造成重要影响。结合观测台站的历史沿革数据,使用ACMANT和Pairwise Comparisons方法以及RHtest V4软件,对北京地区20个台站均一化前的月平均气温序列进行了非均一性检验和订正,最后评估了均一化对北京地区气温序列变化趋势及其城市化偏差估算的影响。结果表明：除元数据中记录的断点外,无元数据记录的断点也会对序列的趋势变化造成明显影响,其中乡村站最显著;经过订正,1958—2018年整个北京地区、乡村站以及城市站增温趋势分别为0.27℃/(10 a)、0.10℃/(10 a)和0.32℃/(10 a),较订正前分别上升了0.03℃/(10 a)、0.06℃/(10 a)和0.02℃/(10 a)。利用均一化资料估算,1958—2018年北京观象台的城市化影响为0.24℃/(10 a),城市化贡献率为70.2%,评估结果较前人结论有所降低。可见,在现有的北京地区气温资料序列中,仍可能存在较明显的非均一性和未被记录的断点,对区域平均气温趋势估算具有显著影响。     

Climate Responses to Direct Radiative Forcing of Anthropogenic Aerosols,Tropospheric Ozone,and Long-Lived Greenhouse Gases in Eastern China over 1951–2000

A unified chemistry-aerosol-climate model is applied in this work to compare climate responses to chang- ing concentrations of long-lived greenhouse gases(GHGs,CO2,CH4,N2O),tropospheric O3,and aerosols during the years 1951–2000.Concentrations of sulfate,nitrate,primary organic carbon(POA),secondary organic carbon(SOA),black carbon(BC)aerosols,and tropospheric O3 for the years 1950 and 2000 are obtained a priori by coupled chemistry-aerosol-GCM simulations,and then monthly concentrations are in- terpolated ...