ISSN 1000-0526
CN 11-2282/P

Volume 40,Issue 7,2014 Table of Contents

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  • 1  Analysis of Spread Skill Relations Using the ECMWF Ensemble Prediction in Medium Term Period over China
    PENG Xiangyu DAI Kan JIN Ronghua TANG Tian
    2014, 40(7):777-786. DOI: 10.7519/j.issn.1000-0526.2014.07.001
    [Abstract](1639) [HTML](294) [PDF 2.96 M](1259)
    Abstract:
    Predictive ability of numerical model for medium term weather forecast is one of the important applications of ensemble forecast. To study the predictive ability of forecast skill of ensemble prediction in medium term period (96-360 h) in China, our analysis applies forecast data covering the period 2007-2011 from ECMWF global ensemble prediction system and chooses 500 hPa geopotential height and 850 hPa temperature as the variables, and then a comparative analysis is carried out by two different measures of spread skill relations. The results show that: (1) Forecast skill (RMSENS) and spread (SPRMSE) represented by root mean square errors show a seasonal cycle, i.e., winter (summer) is high (low), which is an inherent atmospheric property. However, the forecast skill (ACENS) and spread (SPAC) represented by anomaly correlations have no clear inherent seasonal cycle. (2) By comparing the two different measures of spread skill relations, it is known that the spread skill relations represented by anomaly correlations can reflect forecast skill better in medium term period forecast, and this spread skill relations based on T850 is stronger than based on Z500. (3) Quantitative analysis of spread skill relations indicates that good forecast skill can be reflected when spread is small, and this relationship declines about 20% from 96 to 360 h samples, while spread skill relations is weaker in the case of large spread, and this relationship does not decrease significantly with the increase of the valid time of forecast. (4) Statistic data shows that samples of the consistency of SPRMSE and SPAC account for 59% to 66% in medium term period, which does not show high consistency. The above analysis results can provide qualitative and quantitative reference for the forecast skill of ensemble prediction in medium term forecast.
    2  Study on Probability Distribution of Warm Season Hourly Rainfall with Γ Distribution
    TIAN Fuyou ZHENG Yongguang MAO Dongyan CHEN Yun ZHONG Shuixin
    2014, 40(7):787-795. DOI: 10.7519/j.issn.1000-0526.2014.07.002
    [Abstract](2106) [HTML](201) [PDF 6.48 M](1294)
    Abstract:
    Climatology and probability distribution of hourly rainfall are very important for the operational probabilistic short duration heavy rainfall forecasts. By adopting the maximum likelihood estimation, the shape parameter α and scale parameter β of 518 stations are obtained with observed warm season (May 1 to September 30) hourly rainfall data from 1991 to 2009, and Γ distribution function of every station is uniquely determined. The fitted Γ distribution and the distribution of relative frequency of observed hourly rainfall are well matched. For stations with maximum and minimum α and β, the probability distribution and the accumulative probability of hourly rainfall exceeds 0.1 mm are analyzed, and the probability distribution exceeds given thresholds are studied. The results show that the correlation coefficient of α and β gets up to 0.975, and highly dependent on the altitudes. Extreme rainfall probability that could not be revealed by observation data is well displayed by Γ distribution. Distributions of observed hourly rainfall are well depicted, and better continuity obtained. The coastal region of South China has the largest probability to have stronger hourly precipitation and the accumulated probabilities of hourly rainfall exceed 10.0 mm, 20.0 mm and 30.0 mm are 8.0%, 2.0% and 0.7%, respectively. Another remarkably high probability area is the intersectional region of Shandong, Jiangsu and Anhui, which is noticeable during the severe convective forecast. The 95% CDF hourly threshold increases from 5.0 mm at the Northwest to 20.0 mm at the Southeast of China with the maximum hourly rainfall threshold located at the south of Guangxi. Both α and β are highly affected by terrain, the relation between α and altitude can be well fitted by an exponential function with the correlation coefficient getting up to 0.709, which indicates the decisive effect of terrain on hourly rainfall distribution.
    3  Application of a Verification Method on Bias Analysis of Quantitative Precipitation Forecasts
    FU Jiaolan ZONG Zhiping DAI Kan ZHANG Fanghua GAO Dongbin
    2014, 40(7):796-805. DOI: 10.7519/j.issn.1000-0526.2014.07.003
    [Abstract](1793) [HTML](109) [PDF 12.36 M](5359)
    Abstract:
    Object oriented verification method, which analyzes the bias of location, rain volume for a rainfall event, is one of the techniques for quantitative precipitation forecast verification.Based on the object oriented verification method and weather system auto identification techenique, the errors of the quantitative precipitation forecast and synoptic scale system of high resolution version of the ECMWF medium range forecast model for 1-10 d of 5 typical severe rainfall cases over Southwest China in 2012 was determined. The results show that: The forecasted rainfall zone for all valid time is located west and north to the observed one, especially for the medium range forecast. 70% points of the forecast rainfall belt over the rainfall axis lie in two degrees north and three degrees west of observed one. The forecasted rainfall intensity with amount larger than 25 mm/24 h is weaker than the observed. The distribution of forecasted extreme rainfall is almost the same as the observed in 24-48 h. However, with the valid time becoming longer, the extreme forecasted rainfall gets obviously weaker. Area of forecasted light rain is larger than the observed, but the area of rainfall more than 25 mm/24 h is smaller. For Sichuan Basin, the forecasted shear line is situated 0.5-3 degrees west to the analyzed one, and the forecasted low level jet (LLJ) is located 0.5-1.5 degree west. The intensity error of LLJ is different for different seasons.
    4  An Artificial Intelligence Ensemble Prediction Scheme for Typhoon Intensity Using the Locally Linear Embedding
    HUANG Ying JIN Long HUANG Xiaoyan SHI Xuming JIN Jian
    2014, 40(7):806-815. DOI: 10.7519/j.issn.1000-0526.2014.07.004
    [Abstract](1420) [HTML](83) [PDF 1.14 M](1210)
    Abstract:
    A Northwest Pacific typhoon intensity prediction scheme has been developed based on multiple neural networks with the same expected output and an evolutionary Particle Swarm Optimization (PSO) algorithm. Typhoon samples during June-September spanning 2001-2012 are used for model development and Climatology and Persistence (CLIPER) factors are used as potential predictors. The new model input is constructed from potential predictors by employing both a stepwise regression method and a Locally Linear Embedding (LLE) algorithm. The LLE algorithm is able to learn and identify the underlying structure of a high dimensional vector space, and then perform dimensionality reduction and feature extraction. In this scheme, the new developed model, which is termed the PNN LLE model, is used for monthly typhoon intensity prediction at 24 and 48 h lead time. Using identical modeling samples and independent samples, predictions of the PNN LLE model are compared with the widely used CLIPER method. According to the statistics, the PNN LLE model shows reductions of the mean absolute errors of 23.34%, 24.46%, 19.41% and 27.45% relative to the CLIPER method for June-September 24 h forecasts, respectively, being 23.10% for the 4 months averagely. From June-September the mean absolute errors of the PNN LLE model are 44.82%, 16.73%, 0.89% and 49.26% more skillful than homogeneous CLIPER intensity forecasts for 48 h forecast, respectively, being 25.54% for the 4 months averagely. By adopting different numbers of nearest neighbors in the LLE algorithm, sensitivity experiments further show that the prediction results of the ensemble model are stable and reliable, and the forecast skill level of the ensemble model is better than that of the CLIPER method, potentially providing operational forecast tool and modeling method for the objective prediction of typhoon intensity.
    5  Spatio Temporal Characteristics of Rainstorm in China During 1981-2010
    LIN Jian YANG Guiming
    2014, 40(7):816-826. DOI: 10.7519/j.issn.1000-0526.2014.07.005
    [Abstract](3071) [HTML](407) [PDF 13.74 M](14352)
    Abstract:
    In term of precipitation data of 2400 stations from 1981 to 2010, annual, seasonal and monthly distribution and evolution characteristics of rainstorm were analyzed. The results show that the processes of rainstorm have been increased evidently since 21 century especially in the south of China, but the duration is relatively short. Rainstorm days have been increased, but the amount of precipitation is not as much as in 1990s. Variation trend of the annual (monthly) precipitation amount is in accordance with that of rainstorm days, but rainfall is averagely more while the rainstorm days are less during spring rainfall phase over the south of Yangtze River. Distribution of the maximum annual rainstorm days is very similar with that of the annual mean rainstorm days, revealing the feature of more in south and east but less in north and west. Maximum annual rainstorm days are more than double of annual average rainstorm days with multi centers due to the effect of topography. The months of maximum monthly rainstorm days over different regions of the same province are incompletely same as the result of the impact of different weather systems. Generally, rainstorm days have been increased since 2000, rainstorm begins earlier, ends latter and lasts longer than before. Nowadays, as the extreme rainfall events and secondary disasters happen frequently, it is conducive for the forecast of quantitative precipitation forecast (QPF) to learn the spatio temporal distribution and evolution features of rainstorm.
    6  Characteristics of Rain Season Onset Date and Its Relationship with First Rainy Season Precipitation of Guangdong in Recent 52 Years
    LIU Wei LUO Xiaoling CHEN Huihua HUANG Zhenzhu
    2014, 40(7):827-834. DOI: 10.7519/j.issn.1000-0526.2014.07.006
    [Abstract](1219) [HTML](198) [PDF 2.86 M](3675)
    Abstract:
    Based on recent 52 years (1962-2013) daily precipitation data of 86 weather stations in Guangdong, the annual rainy season onset dates (RSOD) of each station were counted, and the characteristics of the rainy season onset and its relationship with the first rainy season precipitation were analyzed. The results indicate that: (1) The RSOD is mainly concentrated in the last dekad of March to the mid dekad of April, and there is a 94 d gap between the earliest and the latest dates; (2) The rainy season begins earlier in southeast and north of Guangdong than in the southwestern part, and the latest in Leizhou Peninsula; (3) The rainy season begins in the two types of abrupt onset and gradual onset; (4) The RSOD has an inter decadal oscillation period of 15-16 years, and respective inter annual oscillation period of 8 years and 5-6 years pre and post 1986; (5) The RSOD has good correlations with late March and April precipitation but is poor with precipitation in May, June and the first rainy season; (6) The 500 hPa circulation differs greatly between the RSOD earlier years and the later years.
    7  Analysis on Persistence and Intensification Mechanism of Fog and Haze in Jiangsu in January 2013
    LIU Mei YAN Wenlian ZHANG Bei YU Jianwei JIN Xiaoxia
    2014, 40(7):835-843. DOI: 10.7519/j.issn.1000-0526.2014.07.007
    [Abstract](1599) [HTML](100) [PDF 3.34 M](1342)
    Abstract:
    By analyzing the monitoring data of visibility and environment, reanalysis data of NCEP 1°×1° and CFSV2 (0.5°×0.5°), sounding data, and meteorological elements from automatic weather stations, the paper discusses the persistence of fog and haze in Jiangsu Province in January 2013 and the intensifying burst of fog in 13-14 January 2013. The findings are as follows: Firstly, the zonal circulation in the middle high latitude is straight; Jiangsu then lies in the easterly flow at the base of high pressure; the cold high is more northward. This is the weather situation of the persistent fog and haze in January. The weak cold air caused by the weak northward meridional wind provides the favorable conditions. Secondly, the near surface relative humidity is above 85%, wind is eastward and the speed is under 3 m·s-1, and dew point difference is smaller than 3℃. Thirdly, the intensification of the fog is partly triggered by the drastic fall in temperature caused jointly by temperature drop from ground radiation and cold advection. Cold advection near the ground and the warm advection above the mixing layer provide stable atmospheric stratification conditions. The weak upward motion near surface and weak downward motion in middle high level are the dynamic mechanism of the rapid intensification of fog in the early morning of 14 January.
    8  AWS Precipitation Characteristics Based on K Means Clustering Method in Beijing Area
    LIU Weidong YOU Huanling REN Guoyu YANG Ping ZHANG Benzhi
    2014, 40(7):844-851. DOI: 10.7519/j.issn.1000-0526.2014.07.008
    [Abstract](1407) [HTML](163) [PDF 1.11 M](1398)
    Abstract:
    By using K means clustering method, hourly precipitation data from 123 automatic weather stations (AWS) in Beijing Area during the period of 2007—2010, the precipitation distribution characteristics of the four areas including North and West area, Northeast area stations, Urban area stations and Southeast area stations are analyzed. Compared with the actual topography and underlying surface types, the classification of automatic stations is more reasonable and can avoid the influence of subjective factors in the regional classification. In Urban areas the precipitation concentration period is the most prominent, mainly in 20:00—00:00 BT daily July with strongest intensity, greater amount and in fewer hours. In the North and West area precipitation mainly appears in 18:00—20:00 BT daily June and 23:00—03:00 BT the next day daily July, with weaker intensity, and less amount and in more hours. In the Northeast area precipitation mainly appears in 00:00—08:00 BT and 17:00—23:00 BT daily July, with the weakest rainfall intensity and the maximum precipitation amount and in more precipitation hours. In the Southeast area precipitation mainly appears in 02:00—04:00 BT daily July, with stronger intensity and greater amount, and in fewer hours.
    9  Study on Meteorological Index of Spring Frost Damage to Winter Wheat in Anhui Province
    XU Ying MA Xiaoqun WANG Xiaodong DU Shizhou
    2014, 40(7):852-859. DOI: 10.7519/j.issn.1000-0526.2014.07.009
    [Abstract](1074) [HTML](73) [PDF 1.77 M](1137)
    Abstract:
    Based on the data of 12 agro meteorological observation stations in Anhui Province, the variation of daily minimum temperature from 15 days before jointing stage to 20 days after jointing stage was analyzed, aiming at different varieties of winter wheat (springiness and semi winter). Taking daily minimum temperature as index, spring frost damage to winter wheat is divided into two grades: light and severe. The verification results show that, although the spring frost damage is affected by many factors such as landform, soil etc., the index based on daily minimum temperature can basically reflect the regulation of the spring frost damage to different varieties of winter wheat, and can be used in the monitoring and warning services. Then, the risk of frost was analyzed using this index, and the results show that, in the wheat producing regions of Anhui, light spring frost occurs frequently while severe spring frost hardly takes place. The high incidence areas of spring frost damage to springiness wheat is obviously further south than that of semi winter wheat. This conclusion can be used to guide the rational distribution of winter wheat varieties, and reduce the risk of spring frost injury.
    10  Research Progresses on Extreme Weather and Climate Events and Their Operational Applications in Climate Monitoring and Prediction
    REN Fumin GAO Hui LIU Lüliu SONG Yanling GAO Rong WANG Zunya GONG Zhiqiang WANG Yongguang CHEN Lijuan LI Qingquan KE Zongjian SUN Chenghu JIA Xiaolong
    2014, 40(7):860-874. DOI: 10.7519/j.issn.1000-0526.2014.07.010
    [Abstract](2314) [HTML](467) [PDF 787.50 K](3574)
    Abstract:
    Weather and climate extreme events can be divided into individual extreme events and regional extreme events. This paper reviews the progress of the studies on extreme events. Firstly, the paper pays attentions to observation study on temperature extremes, precipitation extremes, droughts and the related indices at individual station, and then reviews the study about the increasing regional extreme events in recent years and also reviews the study progress in predicting climatic extreme events. Meanwhile, a summary of the current climate monitoring and prediction operations of extreme events in China and in the world has been preliminarily carried out. The results show that the operational products in extreme event monitoring are very rich in China with a leading position in the field of regional extreme event monitoring, but in the form of products there is not a unified organization, especially in products in English. Regarding the climatic prediction of extreme events National Climate Centre has developed two methods: One is a BP CCA and OSR drought prediction method based on physical statistics, and the other is high temperature prediction method based on the National Climate Centre Monthly Dynamic Extended Range Forecast (DERF) model. Finally, an outlook of climate monitoring and prediction operations of extreme events and related scientific issues is given, and a stress is made on continuing to strengthen the frontier researches and operational capacity building of extreme events in the future.
    11  An Improved Polygons Merging Algorithm and Its Application in Weather Forecast Operation
    ZHU Wenjian MAO Dongyan ZHANG Tao
    2014, 40(7):875-880. DOI: 10.7519/j.issn.1000-0526.2014.07.011
    [Abstract](1101) [HTML](183) [PDF 3.29 M](1098)
    Abstract:
    An improved polygons merging algorithm (hereafter “PM” algorithm) was developed for the operational test bed of classified severe weather probability forecasting, based on Weiler Atherton algorithm. In this algorithm, the input points can be non directional. It is a simple and efficient polygons merging algorithm that meets the basic requirements of the meteorological operation and can also be used for polygons clipping. During the period from April to September 2013, it was tested on the test bed platform and proved to be exact, efficient and stable. In addition, it can be used in some other relevant fields of weather forecast operation, such as disaster weather forecasting, environmental weather forecasting, special weather forecasting and soon.
    12  Forgetting Factor Adaptive Least Square Algorithm and Its Application in Temperature Forecasting
    ZHAI Yumei ZHAO Ruixing GAO Jianchun WANG Liwei HAN Haidong
    2014, 40(7):881-885. DOI: 10.7519/j.issn.1000-0526.2014.07.012
    [Abstract](1107) [HTML](553) [PDF 455.34 K](1594)
    Abstract:
    A mass of data is the foundation of adaptive forecasting model. However, the role of new incoming data will be gradually reduced and the performance of the model will become poor with the data increasing. In order to overcome the influence of “data saturation” on the weather forecast, the method of adaptive linear least square modeling algorithm considering forgetting factors is developed and applied in max min temperature forecast. The results show that this adaptive linear least square modeling algorithm considering forgetting factors is superior to the traditional adaptive linear modeling algorithm, it can reduce the effect of “data saturation” by using the forgetting factor, and it is possible to improve the model’s forecast accuracy by choosing the appropriate forgetting factors.
    13  Study on the Relation Between Effective Precipitation and Landslide/Debris Flow with Probabilistic Model
    ZHANG Guoping
    2014, 40(7):886-890. DOI: 10.7519/j.issn.1000-0526.2014.07.013
    [Abstract](1305) [HTML](95) [PDF 3.41 M](1172)
    Abstract:
    Based on the assessment of susceptibility degree, the geology circumstance, geographic background and climatic factors, the zonation of the landslide and debris flow is carried out. And the research area is divided into 6 sub regions: the western part of Loess Plateau, Qinling Mountains, Daba Mountains, Dabie Mountains, Luoxiao Mountains, and Zhejiang Fujian coast. The plotting of effective precipitation and landslide/debris flow shows that effective precipitation is among a certain range instead of a critical value. The probabilistic model is adopted to describe the probabilistic relations between effective precipitation and landslide/debris flow. Correlation coefficient is calculated to evaluate the model. The result shows that effective precipitation and frequency of landslide/debris flow are normally distributed. The parameters of probabilistic model can be estimated and probability density function can be plotted. The precipitation model indicates that for each region the effective precipitation is quantitatively corresponding to the frequency of landslide/debris flow hazard. Moreover, the plotting shows that the effective precipitation increases from northwest to south east of China.
    14  Possible Causes for the Anomalous Weak East Asian Winter Monsoon in 2013/2014
    SI Dong LI Qingquan LIU Yanju WANG Zunya YUAN Yuan WANG Dongqian
    2014, 40(7):891-897. DOI: 10.7519/j.issn.1000-0526.2014.07.014
    [Abstract](1452) [HTML](129) [PDF 4.14 M](1113)
    Abstract:
    The East Asian winter monsoon was stronger than normal continuously for seven years since 2005, but it suddenly shifted to be weaker than normal in winter 2013/2014. The research results indicated that the increased Arctic sea ice extent in the last autumn was responsible for the negative sea level pressure in Siberia in winter 2013/2014, resulting in the weakening of Siberian high which was favorable for the anomalous weak East Asian winter monsoon and high temperature in China. During the winter, East Asian winter monsoon exhibited strong intraseasonal variations, weaker in the early winter and stronger in the late winter. Accompanying the intraseasonal variations of the East Asian winter monsoon, the temperature over China had two stage variations in the last winter, warmer in the early winter and colder in the late winter. Furthermore, the warmer in the early winter and colder in the late winter over China also was influenced by the blocking high activities over the North Pacific. In the late winter, the westward shifting of the blocking high to the west of date line strengthened the merional circulation over East Asia, leading the sweeping down of the cold air over East Asia, further causing the anomalous low temperature in China in the late winter. However, the westward shift of the blocking high over the North Pacific may be related to the stratospheric circulation anomalies.
    15  Analysis of the April 2014 Atmospheric Circulation and Weather
    FAN Liqiang ZHANG Tao SUN Jin
    2014, 40(7):898-904. DOI: 10.7519/j.issn.1000-0526.2014.07.015
    [Abstract](1109) [HTML](75) [PDF 2.69 M](1075)
    Abstract:
    The characteristics of general circulation of atmosphere in April 2014 are as follows. There is one single center of the Northern Hemisphere polar vortex which is located near as the northern part of Kara Sea. The Asia ridge is stronger, because of which, the mean temperature of April in China is 11℃, 1.1℃ higher than the normal, and is recorded as the fifth highest one from 1961. The position and strength of East Asia trough, south branch, and subtropical high of Northwest Pacific all are neutral compared to the normal condition. In April, the monthly mean precipitation over China is 43.7 mm, slightly less than the normal. In addition, strong convective weathers like short time severe precipitation, thunders storm high winds, etc. occur in the southern part of Jiangnan Region and South China. Moreover, there are several sand and dust events in north of China, and extreme high temperature and sharp drop in temperature are observed by some stations.

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