广西太阳总辐射的计算及分布特征

利用桂林、南宁、贵阳、海口1961～2000年逐旬太阳总辐射和日照百分率资料，建立精度较高的回归方程，推算出广西各地逐旬太阳总辐射，并分析了广西四季及年太阳总辐射的空间分布特征，对研究广西太阳总辐射及其对广西气候的影响有指导意义。     

基于小波神经网络法的海南地区逐时太阳总辐射预测研究

利用2003—2012年海口市气象站不同季节逐时太阳总辐射观测资料与对应气象参数,建立基于小波BP神经网络法逐时太阳总辐射的预测模型,并利用2013年太阳总辐射数据对模型进行检验,且与建立的逐步回归模型进行对比。结果表明:小波神经网络法建立的逐时太阳总辐射预测模型精度较高,但不同季节模型预测精度存在差异,冬季预测精度最高,夏季预测精度最差,天气类型指数有利于不同季节模型预测精度的提高。春季、夏季、秋季和冬季加入天气类型指数神经网络模型的逐时太阳总辐射预测值与观测值的回归估计标准误差分别为0.32、0.47、0.35 MJ·m-2及0.23 MJ·m-2,比逐步回归模型的预报精度分别提高了28.8%、16.3%、17.9%和20.4%,说明基于小波神经网络法建立的预测模型可为海南地区逐时太阳总辐射预测提供参考。     

旬太阳总辐射气候学计算方法

太阳总辐射是地球表面最重要的能量来源,它是气候形成的主要因子。许多作者对我国的总辐射进行过研究,但是限于当时日射记录较短,其精度受到一定影响,同时又多是以年、季、月总辐射进行计算,难以满足服务需要。在农业科学实验中,要讨论农作物主要生长发育期的光能利用,在工业产品的光化试验中,需要旬(甚至日)辐射资料。为此,本文尝试作旬辐射气候学计算方法的讨论。     

复杂地形下太阳总辐射空间订正方法——以四川省为例

利用四川省158个气象站2016—2019年逐小时2 m气温、相对湿度、地面气压、能见度等观测数据,通过SMARTS模式计算并积分得到逐月晴天太阳总辐射,建立晴天太阳总辐射随海拔高度的变化关系,将该关系应用到1990—2019年太阳总辐射空间插值订正中,并对订正效果进行验证,结果表明:晴天太阳总辐射随海拔高度呈对数增加,海拔越高晴天太阳总辐射随高度增幅越小;辐射订正方面,海拔较低、地势平坦的四川盆地地区订正幅度最小,高海拔的川西高原订正幅度居中,高低海拔过渡地带订正幅度最大;交叉验证结果表明,用来验证的7个辐射站年平均绝对误差由182.77 kW·h·m~(-2)减少到145.48 kW·h·m~(-2),相对误差由13.41%减少到10.24%,冬半年订正效果好于夏半年。通过订正可有效提高复杂地形下太阳总辐射插值效果,减小插值误差。     

基于WRF模式输出统计的逐时太阳总辐射预报初探

基于WRF(weather research and forecasting model)模式逐时输出结果,设计了逐时太阳总辐射的模式输出统计(model output statistics,MOS)预报流程。主要包括:对逐时观测序列进行低通滤波再除以天文辐射,对模式输出因子的筛选和降维,以及建立MOS预报方程,并对2009年1月、4月、8月和10月武汉站逐时太阳总辐射进行预报试验。结果表明,该方案在各月预报相对稳定,拟合和预报效果均较为理想,可使平均绝对百分比误差控制在20%～30%,相对均方根误差控制在30%～40%,相对模式直接预报辐射改进了50%左右。由此可见,通过对模式输出进行解释应用,可以有效提高辐射预报的准确率。此外,客观分析所得的气温、云量、露点、比湿、相对湿度、地面气压等13个模式输出因子可以作为各地区建立MOS辐射预报方程的参考因子。     

GMS卫星资料估算地表旬太阳辐射

GMS－5静止气象卫星较宽的可见光波段，为估算到达地表的太阳辐射提供了极好的信息源。利用GMS－5可见光通道资料，分析估计太阳辐射的可行性，并给出了相应的卫星资料处理方法。利用逐时观测资料和济南日射观测站太阳总辐射小时辐照总量实测资料，建立了可见光反照率与小时辐照总量的统计关系，探讨了旬太阳辐射的估算模型。估算结果与日射站实测结果比较，旬辐射估计量的均方误差为7．7MJ.m^-1，平均相对误差为3．4％。     

广西地面太阳辐射分布特征以及对人体健康的影响

选用2001-2010年广西桂林、南宁、北海3个地面太阳辐射站的总辐射资料及日照时数资料,分析了总辐射日总量、日最大值的年变化规律,时总量的日变化规律,日最大值出现时间频率的分布区间等,结果表明：（1）广西太阳总辐射的日总量、日最大值强度的年变化规律明显。总体呈现夏季最大,春秋季次之,冬季最小的特点。大多数情况下,随着纬度由北向南递减,总辐射值递增。日总量与日照时数的年变化趋势存在正向的线性相关。（2）广西太阳总辐射的时总量的日变化规律明显。总体呈现中午前后强．早晚弱的特点。最大值一般出现在一天中的10-14时这个区间．     

最大可能蒸发量的计算分析

本文通过对彭门(Penman)原式的“干燥力”计算式是—Ea、几种主要的Ea修正式以及旬太阳总辐射气候学计算方法的研究,进行了旬最大可能蒸发量的计算,得到了较好的结果。     

广西旬太阳直接辐射和旬散射辐射的气候学计算方法及收入概况

本文根据广西南宁、桂林二个日射观测点的资料(1958—1977),运用气候学统计方法,确定了广西地区旬直接辐射和旬散射辐射的气候学计算方法及其适用范围和工作精度。 依此,计算了全区48个站的旬、月、季、年的直接辐射和散射辐射量,并且初步分析了它们的收入概况。     

辽宁省追踪式与最佳倾角斜面总辐射的推算与分析

利用辽宁省1993—2016年61个气象站的日照百分率逐月数据、6个太阳辐射观测气象站的逐时太阳总辐射数据,采用太阳辐射的气候学计算方法、Klein各向异性散射模型和Hay各向异性散射模型分别推算各站水平面、最佳倾角斜面和追踪式斜面的太阳总辐射的推算值并进行了对比与分析。同时利用1993—2016年日平均总云量数据,得到各站的累年平均晴天日数,并分析辽宁省太阳总辐射空间分布差异的原因。结果表明:全省最佳倾角角度为36°～41°,最小出现在金州,最大分别出现在康平、昌图、法库和西丰;最佳倾角斜面太阳总辐射在辽宁西部和北部地区明显偏多,其中,朝阳北部和阜新西部部分地区最多;追踪式斜面较最佳倾角斜面太阳总辐射的增加量和提升百分比的幅度均较大,分别达到约700～2300 MJ/m2和13%～41%,且存在较明显的呈区域性分布特征,其中,太阳总辐射的增加量在辽宁西部更明显,而太阳总辐射的提升百分比则以沿海地区的幅度更大,超过37%,大连南部更是超过39%;晴天日数或云量是影响辽宁省太阳能总辐射的空间分布差异的主要因素。     

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;