doi:10.1007/s00376-016-6128-5

Atomic Energy Regulatory Board of India, 2008: Extreme Values of Meteorological Parameters. Atomic Energy Regulatory Board of India, Mumbai, 37 pp.

Chen J., Y. G. Zheng, X. L. Zhang, and P. J. Zhu, 2013: Distribution and diurnal variation of warm-season short-duration heavy rainfall in relation to the MCSs in China. Acta Meteorologica Sinica,27, 868-888, doi: 10.1007/s13351-013-0605-x.10.1007/s13351-013-0605-x

2cfd5cc1979a2f2650a1129b7a990310

http%3A%2F%2Flink.springer.com%2F10.1007%2Fs13351-013-0605-x

http://d.wanfangdata.com.cn/Periodical/qxxb-e201306008Short-duration heavy rainfall (SDHR) is a type of severe convective weather that often leads to substantial losses of property and life. We derive the spatiotemporal distribution and diurnal variation of SDHR over China during the warm season (April–September) from quality-controlled hourly raingauge data taken at 876 stations for 19 yr (19912-2009), in comparison with the diurnal features of the mesoscale convective systems (MCSs) derived from satellite data. The results are as follows. 1) Spatial distributions of the frequency of SDHR events with hourly rainfall greater than 10–40 mm are very similar to the distribution of heavy rainfall (daily rainfall 82 50 mm) over mainland China. 2) SDHR occurs most frequently in South China such as southern Yunnan, Guizhou, and Jiangxi provinces, the Sichuan basin, and the lower reaches of the Yangtze River, among others. Some SDHR events with hourly rainfall 82 50 mm also occur in northern China, e.g., the western Xinjiang and central-eastern Inner Mongolia. The heaviest hourly rainfall is observed over the Hainan Island with the amount reaching over 180 mm. 3) The frequency of the SDHR events is the highest in July, followed by August. Analysis of pentad variations in SDHR reveals that SDHR events are intermittent, with the fourth pentad of July the most active. The frequency of SDHR over mainland China increases slowly with the advent of the East Asian summer monsoon, but decreases rapidly with its withdrawal. 4) The diurnal peak of the SDHR activity occurs in the later afternoon (1600–1700 Beijing Time (BT)), and the secondary peak occurs after midnight (0100–0200 BT) and in the early morning (0700–0800 BT); whereas the diurnal minimum occurs around late morning till noon (1000–1300 BT). 5) The diurnal variation of SDHR exhibits generally consistent features with that of the MCSs in China, but the active periods and propagation of SDHR and MCSs differ in different regions. The number and duration of local maxima in the diurnal cycles of SDHR and MCSs also vary by region, with single, double, and even multiple peaks in some cases. These variations may be associated with the differences in large-scale atmospheric circulation, surface conditions, and land-sea distribution.

Chen L. X., Q. G. Zhu, H. B. Luo, J. H. He, M. Dong, and Z. Feng, 1991: East Asian Monsoon. China Meteorological Press, Beijing, 362 pp. (in Chinese).

Chen X. C., K. Zhao, and M. Xue, 2014: Spatial and temporal characteristics of warm season convection over Pearl River Delta region,China, based on 3 years of operational radar data. J. Geophys. Res., 119, 12 447-12 465, doi: 10. 1002/2014JD021965.10.1002/2014JD021965

02feb9f0ccf76057b32dff154651390e

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JD021965%2Fpdf

http://onlinelibrary.wiley.com/doi/10.1002/2014JD021965/pdfThis study examines the temporal and spatial characteristics and distributions of convection over the Pearl River Delta region of Guangzhou, China, during the May-September warm season, using, for the first time for such a purpose, 3 years of operational Doppler radar data in the region. Results show that convective features occur most frequently along the southern coast and the windward slope of the eastern mountainous area of Pearl River Delta, with the highest frequency occurring in June and the lowest in September among the 5 months. The spatial frequency distribution pattern also roughly matches the accumulated precipitation pattern. The occurrence of convection in this region also exhibits strong diurnal cycles. During May and June, the diurnal distribution is bimodal, with the maximum frequency occurring in the early afternoon and a secondary peak occurring between midnight and early morning. The secondary peak is much weaker in July, August, and September. Convection near the coast is found to occur preferentially on days when a southerly low-level jet (LLJ) exists, especially during the Meiyu season. Warm, moist, and unstable air is transported from the ocean to land by LLJs on these days, and the lifting along the coast by convergence induced by differential surface friction between the land and ocean is believed to be the primary cause for the high frequency along the coast. In contrast, the high frequency over mountainous area is believed to be due to orographic lifting of generally southerly flows during the warm season.

Chen X. C., K. Zhao, M. Xue, B. W. Zhou, X. X. Huang, and W. X. Xu, 2015: Radar-observed diurnal cycle and propagation of convection over the Pearl River Delta during Mei-Yu season. J. Geophy. Res. Atmos.,120, 12 557-12 575, doi: 10.1002/2015JD023872.10.1002/2015JD023872

667205f58e038eeb7bdcd37c55a642a8

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2015JD023872%2Ffull

http://onlinelibrary.wiley.com/doi/10.1002/2015JD023872/fullUsing operational Doppler radar and regional reanalysis data from 2007-2009, the climatology and physical mechanisms of the diurnal cycle and propagation of convection over the Pearl River Delta (PRD) region of China during the Mei-Yu seasons are investigated. Analyses reveal two hot spots for convection: one along the south coastline of PRD and the other on the windward slope of mountains in the northeastern part of PRD. Overall, convection occurs most frequently during the afternoon over PRD due to solar heating. On the windward slope of the mountains, convection occurrence frequency exhibits two daily peaks, with the primary peak in the afternoon and the secondary peak from midnight to early morning. The nighttime peak is shown to be closely related to the nocturnal acceleration and enhanced lifting on the windward slope of southwesterly boundary layer flow, in the form of boundary layer low-level jet. Along the coastline, nighttime convection is induced by the convergence between the prevailing onshore wind and the thermally induced land breeze in the early morning. Convection on the windward slope of the mountainous area is more or less stationary. Convection initiated near the coastline along the land breeze front tends to propagate inland from early morning to early afternoon when land breeze cedes to sea breeze and the prevailing onshore flow.

Coles S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer,London, 223 pp.10.1007/978-1-4471-3675-0

726293a625cd4893ac321a6dc41967fa

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F230663933_An_introduction_to_statistical_modeling_of_extreme_values_-_springer

http://www.researchgate.net/publication/230663933_An_introduction_to_statistical_modeling_of_extreme_values_-_springerDirectly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.

Davis R. S., 2001: Flash flood forecast and detection methods. Severe Convective Storms, C. A. Doswell III, Ed., American Meteorological Society, 481- 525.

Ding Y. H., J. Y. Zhang, 2009: Heavy Rain and Flood. China Meteorological Press, 290 pp. (in Chinese)

Dong Q., X. Chen, and T. X. Chen, 2011: Characteristics and changes of extreme precipitation in the Yellow-Huaihe and Yangtze-Huaihe Rivers Basins, China. J.Climate, 24, 3781- 3795.10.1175/2010JCLI3653.1

184be61bf0a0937d3885a0032aec9b66

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011JCli...24.3781D

http://adsabs.harvard.edu/abs/2011JCli...24.3781DMany works suggest that the intensity of extreme precipitation might be changing under the background of global warming. Because of the importance of extreme precipitation in the Yellow--Huaihe and Yangtze--Huaihe River basins of China and to compare the spatial difference, the generalized Pareto distribution (GPD) function is used to fit the daily precipitation series in these basins and an estimate of the extreme precipitation spatial distribution is presented. At the same time, its long-term trends are estimated for the period between 1951 and 2004 by using a generalized linear model (GLM), which is based on GPD. High quality daily precipitation data from 215 observation stations over the area are used in this study. The statistical significance of the trend fields is tested with a Monte Carlo experiment based on a two-dimensional Hurst coefficient, H2. The spatial distribution of the shape parameter of GPD indicates that the upper reaches of the Huaihe River (HuR) basin have the largest probability of extreme rainfall events, which is consistent with most historical flood records in this region. Spatial variations in extreme precipitation trends are found and show significant positive trends in the upper reaches of Poyang Lake in the Yangtze River (YaR) basin and a significant negative trend in the mid- to lower reaches of the Yellow River (YeR) and Haihe River (HaR) basins. The trends in the HuR basin and the lower reaches of Poyang Lake in the YaR basin are nearly neutral. All trend fields are significant at the 5%% level of significance from the Monte Carlo experiments.

Frich P., L. V. Alexand er, P. Della-Marta B. Gleason, M. Haylock, A. M. G. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research,19, 193-212, doi: 10.3354/cr019193.10.3354/cr019193

81589def43f2db979886ad985d85dfe7

http%3A%2F%2Fdx.doi.org%2F10.3354%2Fcr019193

http://dx.doi.org/10.3354/cr019193ABSTRACT A new global dataset of derived indicators has been compiled to clarify whether frequency and/or severity of climatic extremes changed during the second half of the 20th century, This period provides the best spatial coverage of homogenous daily series, which can be used for calculating the proportion of global land area exhibiting a significant change in extreme or severe weather. The authors chose 10 indicators of extreme climatic events, defined from a larger selection, that could be applied to a large variety of climates. It was assumed that data producers were more inclined to release derived data in the form of annual indicator time series than releasing their original daily observations. The indicators are based on daily maximum and minimum temperature series, as well as daily totals of precipitation, and represent changes in all seasons of the year. Only time series which had 40 yr or more of almost complete records were used, A total of about 3000 indicator time series were extracted from national climate archives and collated into the unique dataset described here. Global maps showing significant changes from one multi-decadal period to another during the interval from 1946 to 1999 were produced. Coherent spatial patterns of statistically significant changes emerge, particularly an increase in warm summer nights, a decrease in the number of frost days and a decrease in intra-annual extreme temperature range. All but one of the temperature-based indicators show a significant change. Indicators based on daily precipitation data show more mixed patterns of change but significant increases have been seen in the extreme amount derived from wet spells and number of heavy rainfall events. We can conclude that a significant proportion of the global land area was increasingly affected by a significant change in climatic extremes during the second half of the 20th century. These clear signs of change are very robust; however, large areas are still not represented, especially Africa and South America.

Gao R., X. K. Zou, Z. Y. Wang, and Q. Zhang, 2012: The Atlas of Extreme Weather and Climate Events in China. China Meteorological Press, 188 pp. (in Chinese)

Garrett C., P. Müller, 2008: Supplement to extreme events. Bull. Amer. Meteor. Soc.,89, ES45-ES56, doi: 10.1175/2008 BAMS2566.2.10.1186/1297-9686-1-2-147

e9ba96dc-a767-474a-b390-0d8fbd231762

d9ab4b8984e218a535a800c6dbc7caba

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008BAMS...89S..45G

refpaperuri:(bc0a5375a143e8c99b4989c626993a5b)

http://adsabs.harvard.edu/abs/2008BAMS...89S..45GNo Abstract available.

Hosking J. R. M., 1990: L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B, 52, 105- 124.10.2307/2345653

3144146b-84bc-4955-85aa-75d7fdaaf1c3

995ca37fb39f5659a2106def6521cc66

http%3A%2F%2Fwww.jstor.org%2Fstable%2F2345653

refpaperuri:(0195b52ba5700af69c360fc41047e23a)

http://www.jstor.org/stable/2345653L-moments are expectations of certain linear combinations of order statistics. They can be defined for any random variable whose mean exists and form. the basis of a general theory which covers the summarization and description of theoretical probability distributions, the summarization and description of observed data samples, estimation of parameters and quantiles of probability distributions, and hypothesis tests for probability distributions. The theory involves such established procedures as the use of order statistics and Gini's mean difference statistic, and gives rise to some promising innovations such as the measures of skewness and kurtosis described in Section 2, and new methods of parameter estimation for several distributions. The theory of L-moments parallels the theory of (conventional) moments, as this list of applications might suggest. The main advantage of L-moments over conventional moments is that L-moments, being linear functions of the data, suffer less from the effects of sampling variability: L-moments are more robust than conventional moments to outliers in the data and enable more secure inferences to be made from small samples about an underlying probability distribution. L-moments sometimes yield more efficient parameter estimates than the maximum likelihood estimates.

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T. F. Stocker et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.59dceddb1377b53b578b73a8eefab495

http%3A%2F%2Fboris.unibe.ch%2F71452

http://boris.unibe.ch/71452This is the report of Working Group I of the Intergovernmental Panel on Climatic Change (IPCC) Fourth Assessment, which describes progress in understanding of the human and natural drivers of climatic change, observed climatic change, climate processes and attribution and estimates of projected future climate change. It builds on past IPCC assessments and incorporates new findings from the past...

Li J., R. C. Yu, and W. Sun, 2013a: Calculation and analysis of the thresholds of hourly extreme precipitation in mainland China. Torrential Rain and Disasters,32, 11-16, doi: 10.3969/j.issn.1004-9045.2013.01.002. (in Chinese)

Li J., R. C. Yu, and W. Sun, 2013b: Duration and seasonality of hourly extreme rainfall in the central eastern China. Acta Meteor. Sinica,27, 799-807, doi: 10.1007/s13351-013-0604-y.10.1007/s13351-013-0604-y

63593d9ec521267a1f376d47b2de517d

http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical%2Fqxxb-e201306003

http://d.wanfangdata.com.cn/Periodical/qxxb-e201306003Compared with daily rainfall amount, hourly rainfall rate represents rainfall intensity and the rainfall process more accurately, and thus is more suitable for studies of extreme rainfall events. The distribution functions of annual maximum hourly rainfall amount at 321 stations in China are quantified by the Gen-eralized Extreme Value (GEV) distribution, and the threshold values of hourly rainfall intensity for 5-yr return period are estimated. The spatial distributions of the threshold exhibit significant regional differ-ences, with low values in northwestern China and high values in northern China, the mid and lower reaches of the Yangtze River valley, the coastal areas of southern China, and the Sichuan basin. The duration and seasonality of the extreme precipitation with 5-yr return periods are further analyzed. The average duration of extreme precipitation events exceeds 12 h in the coastal regions, Yangtze River valley, and eastern slope of the Tibetan Plateau. The duration in northern China is relatively short. The extreme precipitation events develop more rapidly in mountain regions with large elevation differences than those in the plain areas. There are records of extreme precipitation in as early as April in southern China while extreme rainfall in northern China will not occur until late June. At most stations in China, the latest extreme precipitation happens in August-September. The extreme rainfall later than October can be found only at a small por-tion of stations in the coastal regions, the southern end of the Asian continent, and the southern part of southwestern China.

Luo Y. L., Y. Gong, and D.-L. Zhang, 2014: Initiation and organizational modes of an extreme-rain-producing mesoscale convective system along a Mei-yu front in East China. Mon. Wea. Rev., 142, 203- 221.10.1175/MWR-D-13-00111.1

f7e06671004c87149ca403b878e4ceb8

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F273635799_Initiation_and_organizational_modes_of_an_extreme-rain-producing_mesoscale_convective_system_along_a_Mei-yu_front_in_East_China

http://www.researchgate.net/publication/273635799_Initiation_and_organizational_modes_of_an_extreme-rain-producing_mesoscale_convective_system_along_a_Mei-yu_front_in_East_ChinaAbstract The initiation and organization of a quasi-linear extreme-rain-producing mesoscale convective system (MCS) along a mei-yu front in east China during the midnight-to-morning hours of 8 July 2007 are studied using high-resolution surface observations and radar reflectivity, and a 24-h convection-permitting simulation with the nested grid spacing of 1.11 km. Both the observations and the simulation reveal that the quasi-linear MCS forms through continuous convective initiation and organization into west–east-oriented rainbands with life spans of about 4–10 h, and their subsequent southeastward propagation. Results show that the early convective initiation at the western end of the MCS results from moist southwesterly monsoonal flows ascending cold domes left behind by convective activity that develops during the previous afternoon-to-evening hours, suggesting a possible linkage between the early morning and late afternoon peaks of the mei-yu rainfall. Two scales of convective organization are found during the MCS's development: one is the east- to northeastward “echo training” of convective cells along individual rainbands, and the other is the southeastward “band training” of the rainbands along the quasi-linear MCS. The two organizational modes are similar within the context of “training” of convective elements, but they differ in their spatial scales and movement directions. It is concluded that the repeated convective backbuilding and the subsequent echo training along the same path account for the extreme rainfall production in the present case, whereas the band training is responsible for the longevity of the rainbands and the formation of the quasi-linear MCS.

Ma Y., X. Wang, and Z. Y. Tao, 1997: Geographic distribution and life cycle of mesoscale convective system in China and its vicinity. Progress in Natural Science, 7, 701- 706.10.1007/s002690050078

fc184ddc8713047fd86c7602bd6c1f3d

http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-ZKJY199705009.htm

http://www.cnki.com.cn/Article/CJFDTotal-ZKJY199705009.htm正 A census of mesoscale convective systems (MCS) has been extended from mesoscale convective complexes (MCCs) to more general meso-a scale convective systems (Ma CSs) and meso-β scale convective systems (Mβ CSs). 234 Ma CSs and 585 Mβ CSs were found in China and its vicinity during the summers of 1993-1995 by the GMS satellite infrared images. The geographic distribution with higher representative shows that the Ma CSs occurred in three favorable zones. One of them, the middle-to-lower reaches basin of the Yellow River and the Yangtze River, were not found in the past researches of MCC census. There are two kinds of life cycles of Ma CS: one is similar to the life cycle of MCC occurring at night and dissipating early in the morning; the other occurs in the afternoon and dissipates in the evening.

Sen Roy, S., 2009: A spatial analysis of extreme hourly precipitation patterns in India. International Journal of Climatology,29, 345-355, doi: 10.1002/joc.1763.10.1002/joc.1763

fdd24c667c2148211a1ffb11894c3c30

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.1763%2Ffull

http://xueshu.baidu.com/s?wd=paperuri%3A%2863b2f9c15f0f257d5aa28b61348575c9%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.1763%2Ffull&ie=utf-8&sc_us=6643647539805733401Station level hourly precipitation data from 1980 to 2002 spread across the Indian subcontinent were analysed for trends in extreme hourly precipitation events. The analyses were conducted for the main seasons of winter, dry-summer, and wet-summer monsoon seasons, respectively. The results of the study indicated rising trends in extreme heavy precipitation events, mostly in the high-elevation r...

Tao S. Y., 1980: Heavy Rains in China. China Science Press, 225 pp. (in Chinese)

Tao Z. Y., Y. G. Zheng, 2013: Forecasting issues of the extreme heavy rain in Beijing on 21 July 2012. Torrential Rain and Disasters,32, 193-201, doi: 10.3969/j.issn.1004-9045.2013.03.001. (in Chinese)aec6a1ce8453060a3b33859d41fe4348

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-HBQX201303001.htm

http://en.cnki.com.cn/Article_en/CJFDTOTAL-HBQX201303001.htmBased on the upper air sounding,NWP model forecasts,satellite,radar and surface weather data,the extreme heavy rain event in Beijing during July 21-22,2012 is analyzed and its forecasting process is summarized.The results are as follows.(1) The"7-21"heavy rain was produced by a typical mesoscale convective complex(MCC) that occurred in the left front of a low-level jet in the warm and moist southerly,on the right of the entrance jet stream,with deep warm advection and clockwise wind direction with altitude,which are very favorable for the occurrence of MCC.(2) A variety of numerical models have predicted the heavy rain with skill in various degrees,and the lead time is up to 3-4 d.The short-term forecasting with lead time 1-2 d can be more accurately predict the occurrence area and intensity of heavy rainfall.But the prediction of heavy rain's start and end times is noticeably delayed about 6 h.(3) Satellite and radar monitoring indicates that 12 h before the arrival of a large-scale rain band,the initial warm convection has occurred ahead of the front.According to the movement,enhancement and organization of radar echoes,it can be extrapolated that the first stage of the heavy rain will affect Beijing at around noon,and thus timely corrections can be made to the numerical predictions.Using comprehensive analysis of radar echo and the surface weather(such as wind,dew point) fields,we can roughly determine the convective instability condition and the uplift condition favorable for the initial occurrence of convection,which can provide the basis for echo extrapolation forecasts.

Wang Y., Z. W. Yan, 2011: Changes of frequency of summer precipitation extremes over the Yangtze river in association with large-scale oceanic-atmospheric conditions. Adv. Atmos. Sci.,28, 1118-1128, doi: 10.1007/s00376-010-0128-7.10.1007/s00376-010-0128-7

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http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical_dqkxjz-e201105013.aspx

http://d.wanfangdata.com.cn/Periodical_dqkxjz-e201105013.aspxChanges of the frequency of precipitation extremes (the number of days with daily precipitation exceeding the 90th percentile of a daily climatology,referred to as R90N) in summer (June-August) over the mid-lower reaches of the Yangtze River are analyzed based on daily observations during 1961-2007.The first singular value decomposition (SVD) mode of R90N is linked to an ENSO-like mode of the sea surface temperature anomalies (SSTA) in the previous winter.Responses of different grades of precipitation events to the climatic mode are compared.It is notable that the frequency of summer precipitation extremes is significantly related with the SSTA in the Pacific,while those of light and moderate precipitation are not.It is suggested that the previously well-recognized impact of ENSO on summer rainfall along the Yangtze River is essentially due to a response in summer precipitation extremes in the region,in association with the East Asia-Pacific (EAP) teleconnection pattern.A negative relationship is found between the East Asian Summer Monsoon (EASM) and precipitation extremes over the mid-lower reaches of the Yangtze River.In contrast,light rainfall processes are independent from the SST and EASM variations.

Yu R. C., J. Li, 2012: Hourly rainfall changes in response to surface air temperature over eastern contiguous China. J.Climate, 25, 6851- 6861.10.1175/JCLI-D-11-00656.1

cff43ede691866c68834b8bacd2d889c

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012JCli...25.6851Y

http://adsabs.harvard.edu/abs/2012JCli...25.6851YNot Available

Zhai P. M., X. B. Zhang, H. Wan, and X. H. Pan, 2005: Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate,18, 1096-1108, doi: 10.1175/ JCLI-3318.1.2d61d9895684e47d5a5e3b375e6105ed

http%3A%2F%2Fjxb.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2FJCLI-3318.1%26link_type%3DDOI

http://xueshu.baidu.com/s?wd=paperuri%3A%28a54cd39e921360c2e30ad9d967965bc5%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fjxb.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2FJCLI-3318.1%26link_type%3DDOI&ie=utf-8&sc_us=1612413880034890929

Zhai P. M., A. J. Sun, F. M. Ren, X. N. Liu, B. Gao, and Q. Zhang, 1999: Changes of climate extremes in China. Climatic Change,42, 203-218, doi: 10.1023/A:1005428602279.10.1023/A:1005428602279

ae7fd00741ae2c231dab1f3e272172d8

http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1023%2FA%3A1005428602279

http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1023/A:1005428602279Changes in China's temperature and precipitation extremes have been studied by using observational data after 1950. The results reveal that mean minimum temperature has increased significantly in China during the past 40 years, especially in the winter in northern China. Meanwhile, nation-wide cold wave activity has weakened and the frequency of cold days in northern China has been reduced significantly. Mean maximum temperatures display no statistically significant trend for China as a whole. However, decreasing summer mean maximum temperatures are obvious in eastern China, where the number of hot days has been reduced. Seasonal 1-day extreme maximum temperatures mainly reflect decreasing trends, while seasonal 1-day extreme minimum temperatures are increasing. A statistically significant reduction of much above normal rain days in China has been detected. Contrarily, an increasing trend was detected in much above normal of precipitation intensity (precipitation/number of precipitation days) during the past 45 years.

Zhang H., P. M. Zhai, 2011: Temporal and spatial characteristics of extreme hourly precipitation over eastern China in the warm season. Adv. Atmos. Sci.,28, 1177-1183, doi: 10.1007/s00376-011-0020-0.10.1007/s00376-011-0020-0

e8c4b593a69b036cacf1dbda66a132e5

http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-DQJZ201105018.htm

http://d.wanfangdata.com.cn/Periodical_dqkxjz-e201105017.aspxBased on hourly precipitation data in eastern China in the warm season during 1961 2000,spatial distributions of frequency for 20 mm h-1 and 50 mm h-1 precipitation were analyzed,and the criteria of short-duration rainfall events and severe rainfall events are discussed.Furthermore,the percentile method was used to define local hourly extreme precipitation; based on this,diurnal variations and trends in extreme precipitation were further studied.The results of this study show that,over Yunnan,South China,North China,and Northeast China,the most frequent extreme precipitation events occur most frequently in late afternoon and/or early evening.In the Guizhou Plateau and the Sichuan Basin,the maximum frequency of extreme precipitation events occursin the late night and/or early morning.And in the western Sichuan Plateau,the maximum frequency occursin the middle of the night.The frequency of extreme precipitation (based on hourly rainfall measurements) has increased in mostparts of eastern China,especially in Northeast China and the middle and lower reaches of the Yangtze River,but precipitation has decreased significantly in North China in the past 50 years.In addition,stations inthe Guizhou Plateau and the middle and lower reaches of the Yangtze River exhibit significant increasing trends in hourly precipitation extremes during the nighttime more than during the daytime.

Zhang J. C., Z. G. Lin, 1985: Climate of China. Shanghai Science and Technology Press, 436 pp. (in Chinese)

Zhao Y. Y., Q. H. Zhang, Y. Du, M. Jiang, and J. P. Zhang, 2013: Objective analysis of circulation extremes during the 21 July 2012 torrential rain in Beijing. Acta Meteorologica Sinica,27, 626-635, doi: 10.1007/s13351-013-0507-y.10.1007/s13351-013-0507-y

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http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-QXXW201305002.htm

http://d.wanfangdata.com.cn/Periodical/qxxb-e201305002

Zheng Y. G., J. Chen, 2013: A climatology of deep convection over South China and the adjacent waters during summer. Journal of Tropical Meteorology, 19, 1- 15.10.1175/JTECH-D-12-00105.1

ec2f402d82c3fae3a1b5a4cabaa7d7f6

http%3A%2F%2Fwww.cqvip.com%2FQK%2F85390X%2F201301%2F1002057481.html

http://www.cnki.com.cn/Article/CJFDTotal-RQXB201301003.htmDue to the higher temporal and spatial resolution and the better integrality of long-term satellite infrared(IR) Brightness Temperature(TBB) data,a climatology of deep convection during summer over South China and the adjacent waters is presented in this paper based on the 1-hourly infrared IR TBB data during June-August of 1996-2007(except 2004).The results show that the geographic distribution of deep convection denoted by TBB over South China and the adjacent waters are basically consistent with previous statistical results based on surface thunderstorm observations and low-orbit satellite lightning observations.The monthly,ten-day,five-day and diurnal variations of deep convection in this region are focused on in this paper.There are 5 active deep-convection areas in June-August.The monthly variations of the deep convection are closely associated with the large-scale atmospheric circulations.The deep convection over the land areas of South China is more active in June while that over the South China Sea is more active in July and August.The development of deep convection is prominently intermittent and its period is about 3 to 5 five-day periods.However,the deep convection over the coastal areas in South China remains more active during summer and has no apparent intermittence.The ten-day and five-day variations of deep convection show that there are different variations of deep convection over different areas in South China and the adjacent waters.The tendency of deep convection over the land areas of South China is negatively correlated with that over the South China Sea.The diurnal variations of deep convection show that the sea-land breeze,caused by the thermal differences between land and sea,and the mountain-valley breeze,caused by the thermal differences between mountains and plains or basins,cause deep convection to propagate from sea to land in the afternoon and from land to sea after midnight,and the convection over mountains propagates from mountains to plains after midnight.The different diurnal variations of deep convection over different underlying surfaces show that not only there are general mountainous,marine and multi-peak deep convection,but also there is longer-duration deep convection over coastal areas and other deep convection triggered and maintained by larger-scale weather systems in South China during summer.

Zheng Y. G., J. Chen, and P. J. Zhu, 2008: Climatological distribution and diurnal variation of mesoscale convective systems over China and its vicinity during summer. Chinese Sci. Bull.,53, 1574-1586, doi: 10.1007/s11434-008-0116-9.10.1007/s11434-008-0116-9

acd920c76e21caadef6faf173899cc13

http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11434-008-0116-9

http://www.cnki.com.cn/Article/CJFDTotal-JXTW200810018.htmThe climatological distribution of mesoscale convective systems (MCSs) over China and its vicinity during summer is statistically analyzed, based on the 10-year (1996―2006, 2004 excluded) June-August infrared TBB (Temperature of black body) dataset. Comparing the results obtained in this paper with the distribution of thunderstorms from surface meteorological stations over China and the distribution of lightning from low-orbit satellites over China and its vicinity in the previous studies, we find that the statistic characteristics of TBB less than -52℃ can better represent the spatiotemporal distribution of MCSs over China and its vicinity during summer.The spreading pattern of the MCSs over this region shows three transmeridional bands of active MCSs, with obvious fluctuation of active MCSs in the band near 30°N. It can be explained by the atmospheric circulation that the three bands of active MCSs are associated with each other by the summer monsoon over East Asia. We focus on the diurnal variations of MCSs over different underlying surfaces, and the result shows that there are two types of MCSs over China and its vicinity during summer. One type of MCSs has only one active period all day long (single-peak MCSs), and the other has multiple active periods (multi-peak MCSs). Single-peak MCSs occur more often over plateaus or mountains, and multi-peak MCSs are more common over plains or basins. Depending on lifetimes and active periods, single-peak MCSs can be classified as Tibetan Plateau MCSs, general mountain MCSs, Ryukyu MCSs, and so on. The diurnal variation of multi-peak MCSs is very similar to that of MCCs (mesoscale convective complexes), and it reveals that multi-peak MCSs has longer life cycle and larger horizontal scale, becomes weaker after sunset, and develops again after midnight. Tibetan Plateau MCSs and general mountain MCSs both usually develop in the afternoon, but Tibetan Plateau MCSs have longer life cycle and more active MαCSs. Ryukyu MCSs generally develop after midnight, last longer time, and also have more active MαCS. The abundant moisture and favorable large-scale environment over Indian monsoon surge areas lead to active MCSs and MαCSs almost at any hour all day during summer. Due to local mountain-valley breeze circulation over the Sichuan Basin, MCSs are developed remarkably more often during the nighttime, and again there are also more active MαCSs. Because of local prominent sea-land breeze circulation over Guangxi and Guangdong, the MCSs over this region propagate from sea to land in the afternoon and from land to sea after midnight. The statistic characteristics of TBB less than -52℃ clearly display the different climatological characteristics of MCSs owing to the thermal difference among water, land and rough terrain. Not only the large-scale atmospheric circulation but also the local atmospheric circulation caused by the thermal difference among water, land and rough terrain, to a great extent, determines the climatological distribution of MCSs over China and its vicinity during summer.

Zheng Y. G., J. Chen, and Z. Y. Tao, 2014: Distribution characteristics of the intensity and extreme intensity of tropical cyclones influencing China. J. Meteor. Res.,28, 393-406, doi: 10.1007/s13351-014-3050-6.10.1007/s13351-014-3050-6

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http%3A%2F%2Fwww.cqvip.com%2FQK%2F88418X%2F201403%2F50299543.html

http://d.wanfangdata.com.cn/Periodical/qxxb-e201403006To address the deficiency of climatological research on tropical cyclones(TCs) influencing China, we analyze the distributions of TCs with different intensities in the region, based on the best-track TC data for1949-2011 provided by the Shanghai Typhoon Institute. We also present the distributions of 50- and 100-yr return-period TCs with different intensities using the Gumbel probability distribution. The results show that TCs with different intensities exert distinctive effects on various regions of China and its surrounding waters. The extreme intensity distributions of TCs over these different regions also differ. Super and severe typhoons mainly influence Taiwan Island and coastal areas of Fujian and Zhejiang provinces, while typhoons and TCs with lower intensities influence South China most frequently. The probable maximum TC intensity(PMTI) with 50- and 100-yr return periods influencing Taiwan Island is below 890 hPa; the PMTI with a50-yr return period influencing the coastal areas of Fujian and Zhejiang provinces is less than 910 hPa, and that with a 100-yr return period is less than 900 hPa; the PMTI with a 50-yr return period influencing the coastal areas of Hainan, Guangdong, and the northern part of the South China Sea is lower than 930 hPa,and that with a 100-yr return period is less than 920 hPa. The results provide a useful reference for the estimation of extreme TC intensities over different regions of China.