一种可业务化的雷电潜势预报方案

利用2000～2005年的闪电定位资料、常规气象观测资料,实时业务应用的数值预报产品以及NCEP/NCAR再分析资料等,基于雷电发生的天气学分型和完全预报方法原理,以可业务实时实现为原则,对广州地区雷电的短期潜势预报进行了研究,引入了"马赛克化"、不确定度概念以及多元回归、线性/非线性拟合方程等数学统计方法,得到一种较有效的雷电短期潜势预报方案,该预报方案在实时业务预报中试用,预报结果对雷电潜势预报具有参考价值.     

MM5在国家气象中心CRAY-C92的实时预报试验尝试

该文利用国家气象中心CRAY-C92计算机上初步建成的一个可与实时资料连接的MM5中尺度预报试验系统,探讨在目前国家气象中心计算机资源和可获取的实时资料条件下,进行实时预报服务的可行性及可能达到的精度,采用30 km水平分辨率的模式,在1996年夏季北方汛期进行了一个半月的实时预报试验,在1997年6～7月间“庆香港回归”天气服务过程中作了实时预报服务。预报及检验结果表明:MM5模式在强降水预报方面比业务模式HLAFS有较明显的改进,能预报出某些中尺度降水的细化特征,对暴雨和大暴雨区预报有较好的参考价值;该模式可以作为城市预报的补充工具;就目前计算机和资料条件,比6 h时段更短的高频城市降水模式预报尚很难得到满意效果。     

东山县6月暴雨客观预报模型

采用文献[1]提出的客观预报技术研制模式,以1990～1999年的42个探空站的常规3层高度资料为依据,研制了本站6月暴雨客观预报模型,并对2000～2005年的实时资料进行了验证,结果表明,暴雨客观预报模型与主观预报准确率相当,且无漏报(主观预报则有一次漏报).     

黄河三角洲短期暴雨预报系统

在天气学分析的基础上,利用１９９０～１９９５年日本数值预报产品与实时资料相结合,研制黄河三角洲短期暴雨预报系统,经１９９６年使用效果较好。     

夏季温度预报综合统计释用技术

针对夏季温度预报 ,综合选择实时资料和数值预报产品与温度密切相关的因子 ,运用物理统计释用方法 ,制作了葫芦岛 6～8月温度预报方程和订正预报 ,并于2002年汛期通过业务试用效果检验。     

完全预报法在高原降水预报中的应用

通过对1993～1995年夏季08时和20时高空、地面观测资料,从降水形成的水汽、动力、热力条件进行分析,选取预报因子,分晚上、白天两个时段,分别建立降水预报方法.实际使用时完全套用实时的数值预报资料,预报方法实现自动化.     

大雪预报失误个例分析

2002年11月6～7日大雪天气过程预报有一定难度,降水预报失误较大。在分析数值预报天气形势、气象要素预报的基础上,阐述了当时预报员的预报思路,分析实时天气形势的演变,指出300hPa上亚洲中部的西北或偏西风急流轴逐渐南压,是诱发实时天气形势突变的触发物理机制。上下层高空槽从后倾转为前倾,不利于强烈的上升运动产生。高空引导气流的演变,导致地面气压路径改变、暖湿空气的配置变化,是预报失误的主要原因。     

夏季温度预报综合统计释用技术

针对夏季温度预报，综合选择实时资料和数值预报产品与温度密切相关的因子，运用物理统计释用方法，制作了葫芦岛6～8月温度预报方程和订正预报．并于2002年汛期通过业务试用效果检验。     

用HLAFS物理量分析1997年6月7～9日暴雨过程

利用1°×1°的HLAFS数值预报产品,分析了我省1997年6月7～9日暴雨过程0h部分物理量的分布特征。通过物理量0h实时场与12～48h预报场的对比分析,指出预报场的误差及对这次暴雨过程预报有较好指示意义的物理量场。     

郑州市强对流天气短期预报系统

经对郑州市1990～2000年气象资料分析,概括出短时暴雨、瞬时雷雨大风、冰雹等3种强对流天气出现的气候规律、天气形势、物理量场演变特征、单站气象要素变化规律等.在此基础上,建立了3种对流性天气的预报模型、预报指标和相应的预报方程,构成郑州市强对流天气短期预报系统.该系统根据每天08时实时资料,自动完成天气分型、物理量计算、预报指标计算和预报方程的判别,输出预报结论.     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;