澳大利亚冷空气活动对西北太平洋热带气旋生成的影响

利用1980—2012年NCEP/NCAR再分析资料及中国气象局的最佳台风路径资料,研究澳大利亚冷空气活动对西北太平洋热带气旋生成的影响。研究发现,北半球夏季925 h Pa经向风超过6 m/s的频数在澳大利亚东北部海域最高,达40 d/a。为此,确定澳大利亚冷空气侵入南北半球低纬的关键区为澳大利亚东北部所罗门海地区,并用该区域经向风风速定义了一个澳大利亚冷空气活动强度指数。该指数与越赤道气流及赤道西风都有很好的相关关系,还与同期的SOI(Southern Oscillation Index,南方涛动指数)显著相关。当SOI偏低(高)时,关键区经向风风速偏强(弱)。合成分析和相关分析结果表明,澳大利亚冷空气活动强、弱年西北太平洋热带气旋生成的位置的变化与季风槽的变化一致,西北太平洋热带气旋生成总数则无显著差异。澳大利亚冷空气活动强年季风槽偏强偏东,热带气旋生成位置偏东偏南;而弱年季风槽偏弱偏西,热带气旋生成位置偏西偏北。低层涡度场、水汽输送、风垂直切变以及低纬地区对流活动的分布表明,澳大利亚冷空气活动强年有利于热带气旋生成位置偏东、偏南;弱年偏西、偏北。     

西北太平洋TC移动速度异常及预报误差特征的分析

利用国家气象局和上海台风研究所(CMA-STI)整编的西北太平洋1970—2009年热带气旋(TC,Tropical Cyclone)及TC最佳路径数据集和2005—2010年的TC路径预报误差资料,应用百分位法,确定TC移动速度异常指标,分析了40 a来西北太平洋TC移动速度及其变化异常发生的时空分布特征,研究了TC速度预报误差对路径预报误差的影响及其与大尺度引导气流之间的关系。结果显示:1)西北太平洋TC移速及移速变化累积概率达95%(5%)分位数的阈值分别为10.8 m·s~(-1)(1.43 m·s~(-1))和2.42m·s~(-1)(-1.72 m·s~(-1))。2)快速移动及加速的TC大都出现在日本海地区,而缓慢的和减速移动TC主要出现在南海区域。3)TC移动速度异常的季节变化表现为,快速移动的TC在5月出现的频率达到最高,缓慢移动的TC在10月频率达到最高,加速移动的TC在6月频率达到最高。4)近6 a的TC移速预报误差对TC路径预报误差的贡献平均约为41.6%。5)对TC路径预报误差偏大,且移速预报误差贡献大的个例分析显示,该个例大尺度环境引导气流偏弱使TC移动速度偏慢。而如果预报的大尺度环境引导气流偏强,使预报的TC移速偏快,那么就容易导致大的路径预报误差。     

Subseasonal Change in the Seesaw Pattern of Precipitation between the Yangtze River Basin and the Tropical Western North Pacific during Summer

There is a well-known seesaw pattern of precipitation between the tropical western North Pacific(WNP) and the Yangtze River basin(YRB) during summer. This study identified that this out-of-phase relationship experiences a subseasonal change;that is, the relationship is strong during early summer but much weaker during mid-summer. We investigated the large-scale circulation anomalies responsible for the YRB rainfall anomalies on the subseasonal timescale. It was found that the YRB rainfall is mainly affected by the tropical circulation anomalies during early summer, i.e., the anticyclonic or cyclonic anomaly over the subtropical WNP associated with the precipitation anomalies over the tropical WNP. During mid-summer, the YRB rainfall is mainly affected by the extratropical circulation anomalies in both the lower and upper troposphere. In the lower troposphere, the northeasterly anomaly north of the YRB favors heavier rainfall over the YRB by intensifying the meridional gradient of the equivalent potential temperature over the YRB. In the upper troposphere, the meridional displacement of the Asian westerly jet and the zonally oriented teleconnection pattern along the jet also affect the YRB rainfall. The subseasonal change in the WNP–YRB precipitation relationship illustrated by this study has important implications for the subseasonalto-seasonal forecasting of the YRB rainfall.     

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II. Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60°S and 60°N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.     

2018年7月大气环流和天气分析

2018年7月大气环流的主要特征是极涡偏强且呈单极型分布,中高纬环流呈4波型,西太平洋副热带高压强度较常年明显偏强,位置较常年明显偏北。7月全国平均气温22.9℃,较常年同期偏高1.1℃,为1961年以来历史同期第三高;全国平均降水量133.8 mm,比常年同期（120.6 mm）偏多11%,与历史同期相比呈现北多南少的分布特征。月内我国有7次区域性暴雨天气过程,多站出现极端日降水量。7月共有5个热带气旋在西北太平洋和南海活动,并有“玛莉亚”、“山神”、“安比”3个台风登陆,生成和登陆个数均较常年偏多。我国中东部出现持续性高温天气,同时强对流天气频发,影响范围较广。     

城市效应对登陆热带气旋妮妲降水影响的模拟

运用中尺度数值模式WRF耦合城市冠层模式（urban canopy model,UCM）,对2016年登陆深圳的热带气旋妮妲（1604）（以下简称妮妲）进行数值模拟。高分辨率数值模拟较好地再现了妮妲登陆前后的强度、路径和累积降水。利用城市化过程当中城市冠层对热带气旋降水的敏感性试验结果表明:城市冠层会减弱对流运动和水汽的输送,导致热带气旋登陆后珠江口城市群区域累积降水量略减少。应用最新的土地利用资料进行的城市下垫面敏感性试验结果表明:由于城市下垫面粗糙度增加,造成登陆地面风的减速,强度减弱,潜热通量与2 m高度比湿相应减小;城市下垫面粗糙度增加会加强该区域垂直对流运动以及不稳定能量增加,有利于降水增强,尤其在城市化下垫面处,热带气旋登陆后6 h累积降水增加量最大可超过20 mm。总体而言,对登陆热带气旋降水而言,耦合城市冠层使城市区域热带气旋降水减少,但在数值模拟中城市冠层影响作用不显著。城市化下垫面对登陆热带气旋暴雨的增幅作用明显,在登陆热带气旋降水预报中应重视。     

THE EFFECTS OF STATION NETWORK DENSITY ON STATISTICAL ANALYSES OF TROPICAL CYCLONE PRECIPITATION

In this paper, 1416 conventional ground-based meteorological observation stations on the mainland of China were subdivided into groups of differing spatial density. Data from each subgroup were then used to analyze variations in the tropical cyclone (TC) precipitation statistics derived from each subgroup across the mainland of China (excluding Taiwan, Hong Kong, and Macao), as well as in two regions (east China and south China) and three provinces (Guangdong, Hainan, and Jiangxi) between 1981 and 2010. The results showed that for the mainland of China, total precipitation, mean annual precipitation, mean daily precipitation, and its spatial distribution were the same regardless of the spatial density of the stations. However, some minor differences were evident with respect to precipitation extremes and their spatial distribution. Overall, there were no significant variations in the TC precipitation statistics calculated from different station density schemes for the mainland of China. The regional and provincial results showed no significant differences in mean daily precipitation, but this was not the case for the maximum daily precipitation and torrential rain frequency. The maximum daily precipitation calculated from the lower-density station data was slightly less than that based on the higher-density station schemes, and this effect should be taken into consideration when interpreting regional climate statistics. The impact of station density on TC precipitation characteristics was more obvious for Hainan than for Guangdong or Jiangxi provinces. In addition, the effects were greater for south China (including Guangxi Zhuang Autonomous region, Guangdong, and Hainan provinces) than east China (including Shandong, Jiangsu, Zhejiang, Shanghai, Fujian, Anhui, and Jiangxi provinces). Furthermore, the analysis proved that the statistical climatic characteristics began to change significantly when the station spacing was between 40 and 50 km, which are close to the mean spacing for all stations across the mainland of China. Moreover, TC areal precipitation parameters, including mean total areal precipitation and mean daily areal precipitation, also began to change significantly when the spacing was between 40 and 50 km, and were completely different when it was between 100 and 200 km.     

The Tropical Pacific–Indian Ocean Associated Mode Simulated by LICOM2.0

Oceanic general circulation models have become an important tool for the study of marine status and change. This paper reports a numerical simulation carried out using LICOM2.0 and the forcing field from CORE. When compared with SODA reanalysis data and ERSST.v3 b data, the patterns and variability of the tropical Pacific–Indian Ocean associated mode(PIOAM) are reproduced very well in this experiment. This indicates that, when the tropical central–western Indian Ocean and central–eastern Pacific are abnormally warmer/colder, the tropical eastern Indian Ocean and western Pacific are correspondingly colder/warmer. This further confirms that the tropical PIOAM is an important mode that is not only significant in the SST anomaly field, but also more obviously in the subsurface ocean temperature anomaly field. The surface associated mode index(SAMI) and the thermocline(i.e., subsurface) associated mode index(TAMI) calculated using the model output data are both consistent with the values of these indices derived from observation and reanalysis data. However, the model SAMI and TAMI are more closely and synchronously related to each other.     

A spatiotemporal analysis of tropical cyclone tracks in the Atlantic Basin and their relationship to El Niño-Southern Oscillation

There has been an enhanced focus on Atlantic tropical cyclone climatologies with the significant cyclones of the past decade and the associated loss of life and property. This study examines the geographic location of cyclone tracks and their relationship to El Niño-Southern Oscillation (ENSO). The average annual cyclone track latitude and longitude correlate positively with hurricane-season El Niño indices, indicating that during El Niño conditions, tropical cyclone tracks are shifted northward and eastward. June–November indices explain 11–22% and 3–11% of the variance in cyclone track latitude and longitude, respectively. Examination of the strongest and weakest El Niño years yields similar results. Higher sea level pressure over North America, a slight contraction of the Bermuda High, and a slight decrease in 500 mb heights during El Niño years helps to explain the observed northward and eastward movement of tropical cyclone tracks during El Niño years. Additionally, weaker easterly and stronger southerly winds on the western side of the North Atlantic Basin exist during El Niño years. Although future tropical cyclone track projection is beyond the scope of this research, these results may provide insight into forecast improvement and ultimately better responses for coastal communities.     

1949—2019 年影响山东的热带气旋时空分布及极端降水和大气环流异常

使用中国气象局热带气旋资料中心的热带气旋最佳路径数据集和NCEP/NCAR再分析资料提供的月平均数据,对北上影响山东的热带气旋（tropical cyclone,TC）及其造成的极端降水进行统计分析,并揭示了有利于 TC北移影响山东的大气环流特征。结果表明：影响山东的 TC主要出现 于 6—9 月,其中盛夏时节（7、8 月）TC对山东影响最大;TC影响山东时,强度主要为台风及以下等 级,或已发生变性;TC会引发山东极端降水事件,TC极端降水多出现在夏秋季（7—9 月）,其中8月的占比最大,9月次之,TC降水在极端降水事件中的占比约为 10%,但年际变化大,有些年份占比达60%以上,特别是1990 年以来 TC对极端降水的贡献显著增强;影响山东的 TC主要生成于西 北太平洋,多为转向型路径;当500 hPa位势高度异常场呈太平洋一日本遥相关型的正位相时,TC更易北上影响山东,此时西北太平洋副热带高压位置偏北,其外围气流会引导TC北上转向,对华东地区造成影响;850 hPa上,南海至西北太平洋存在异常气旋式环流,对流活跃,夏季风环流和季风槽加强,有利于TC的生成和发展,同时,华东、华南上空有异常上升运动,涡度增大,垂直风切变减小,水汽充沛,TC登陆后强度能得到较好的维持。