Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

戏剧化结构中的现实主义和悬疑叙事——以网剧《漫长的季节》为例

近几年,悬疑剧热播。2023年播出的网剧《漫长的季节》以1997年、1998年、2016年三个时间段为叙事线索,使用典型化的符号表达时代的变迁和人性的面相,包括20世纪90年代末国企改制的社会变迁、权力关系的变化、女性所处的“第二性”地位等,体现了鲜明的现实主义特点。剧作在寻找套牌车的“总悬念”之下,通过时空转场、音乐音响等视听手段来表达分悬念,强化了惊悚、恐怖和诡异的感觉,调动了观众的思维和推理能力。为此,该剧设计了戏剧化的结构,通过寻找套牌车这一贯穿剧作始终的线索,使现实主义和悬疑得到很好的融合,扩展了戏剧叙事的“一人一事”结构,从而具备了较大的信息空间。该剧恰当地使用东北方言强化了现实感、平衡悬疑的恐怖感,使用色彩呼应了剧作的主题、丰富了叙事的感性因素,为悬疑剧表达思想、吸引观众提供了优秀的例证。

果园红壤供氮水平季节性变化及对长期套种绿肥的响应研究

本研究以柿子(Diospyros kaki)园为对象,研究了长期(近20年)套种豆科绿肥对土壤可溶性氮和氮水解酶季节性变化的影响。试验设3个处理:套种平托花生(Arachis pintoi)(TPA)、套种圆叶决明(Chamaecrista rotundifolia)(TPC)和清耕果园(CK)。结果表明:长期套种2种豆科绿肥没有改变果园红壤供氮水平的季节性变化规律,6月各处理硝态氮(NO_(3)^(-)-N)、可溶性总氮(Total soluble nitrogen,TSN)和可溶性有机氮(Soluble organic nitrogen,SON)含量最高,分别为17.71~21.81,31.40~75.12和7.77~47.65 mg·kg^(-1);12月NO_(3)^(-)-N,TSN和SON含量最低,分别为4.88~6.02,15.17~17.45和5.03~7.92 mg·kg^(-1)。3月、6月和8月铵态氮(NH_(4)^(+)-N)含量高于4月、10月和12月;6月4种氮水解酶活性最高,8月和12月活性最低。长期套种2种豆科绿肥显著提高了生长季3—8月NO_(3)^(-)N,TSN,SON和NH_(4)^(+)-N含量以及蛋白酶、天冬酰胺酶、脲酶和谷氨酰胺酶活性。因此,长期套种圆叶决明和平托花生是通过增加生长季的可溶性氮含量和氮水化酶活性来增加土壤氮供应。

面向动态交通分配的交通需求深度学习预测方法

为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。

换头节次对日光温室长季节栽培番茄生长及产量的影响

针对设施番茄长季节栽培过程中,因植株管理不当等造成的植株易早衰、产量和品质下降等问题,以圣罗兰3689为试材,以不换头为对照(CK),设置每4穗果(T1)、6穗果(T2)、8穗果(T3)进行换头一次的3个处理,研究不同换头节次对番茄生长、开花坐果时间和产量的影响。结果表明,随生育期延长,所有处理的番茄株高和茎粗均呈增加趋势,侧枝叶片光合色素含量和净光合速率均呈先升高后降低趋势。与CK相比,T1、T2、T3处理的茎秆长度和产量均有所增加,并在一定程度上延长了番茄采收期;其中,定植后240 d,T1处理的株高、茎粗和产量分别增加8.7%、3.8%和7.1%;T1处理前4穗果的转色时间和采收时间分别提前9、22、29、10 d和12、28、15、9 d,第14穗果采收时间延长了19 d,经济效益以T1处理较好。综上所述,每4穗果换头1次,有利于长季节栽培番茄生长和产量形成,能部分缓解春节和暑期番茄产品的供给,可作为番茄长季节优质高产栽培的植株调整方式。

某高速铁路沿线天然场地季节冻胀特性监测与分析

某高速铁路沿线季节性冻土大面积分布,本文依据地形地貌、土性成分及水文地质条件等特征,选定14处天然场地。通过冻融周期的监测,得到沿线地基土的冻结融化速率、最大季节冻深及地表变形量等重要数据。通过分析积雪场地及裸露场地的冻结融化过程与地表变形情况,结合气温、降水量、地表雪盖等因素,分析了高速铁路沿线季节性冻土的冻融特性。研究结果表明:(1)沿线有雪条件下天然场地最大冻结深度19~90 cm,平均值为45 cm;(2)沿线季节冻土冻结速率0.27~1.20 cm/d,融化速率普遍大于冻结速率,为0.28~1.92 cm/d;(3)积雪对保持土体温度、抑制土体冻结具有良好效果,能有效缩短冻结、融化时间,使最大冻结深度减小18%~24%;(4)冻胀量呈由西向东增大趋势,其变化规律和气候条件吻合。研究成果可为类似季节性冻土地区高速铁路设计及冻害防治提供借鉴。

高海拔深切峡谷典型季节风参数日变化特征

为确定高海拔深切峡谷典型季节风特性参数的日变化特征,依托青藏高原某横跨雅鲁藏布江的大跨度桥梁为工程背景,开展桥位风特性现场实测,选取冬季、夏季没有较强天气系统作用的典型时段,统计分析风速、风向、风攻角、风速剖面等平均风参数,采用db8小波提取时变平均风速,通过非平稳风速模型分析紊流强度、积分尺度、紊流功率谱等脉动风参数。研究结果表明:峡谷风场风速、风向均呈现以天为周期的规律波动,风速在凌晨和上午相对较小,不同季节风速起落时间不一致,夏季、冬季风速分别在10:00、12:00开始逐渐增加,在20:00、21:00后开始快速减小,夏季风向基本稳定,冬季风向在15:00存在突变,风攻角与风速、风向显著相关,在冬季先减后增、15:00取最大负值,夏季相反;冬季风速剖面显著变化、夏季趋于平稳,指数律模型不能准确描述峡谷风速的垂直分布规律;夏季紊流强度趋于平稳,冬季先减后增、12:00取最小值,实测横风向、竖向紊流强度与顺风向比值高于规范推荐值;冬季、夏季紊流积分尺度变化趋势相同且与风速变化趋势一致,实测积分尺度远小于规范推荐值;归一化功率谱峰值频率随时间变化先减后增,功率谱可采用统一形式进行描述,并考虑其在频域内分布随季节、时间改变产生的偏移效果。

区域太阳能供暖集中式与分散式组合蓄热系统形式探讨

传统区域太阳能供暖系统通常采用集中式蓄热水体,往往难以兼顾由于供暖末端不同造成的热用户用热品位的差异,而以区域内高温用热末端的供水温度确定供暖系统水温,导致区域太阳能供暖系统运行温度偏高,从而造成集热系统效率低、蓄热和输送热损失大等问题。基于此,本文提出了区域太阳能供暖集中式与分散式组合蓄热系统,介绍了该系统的工作原理,分析了组合蓄热系统与传统蓄热系统的特征与区别,探讨了组合蓄热系统运行特性、分散水箱分布形式、容量设计与匹配、运行调控策略及综合效益评价等方面需研究的关键问题。

基于周期特征提取的DLnet预测模型研究

现有的预测方法很少独立分析能源消耗的周期性特征。本文提出了一个短期办公建筑能耗预测模型(DLnet),以解决周期性能耗数据利用效率低下的问题。首先,利用STL对能耗数据的周期成分进行分解,通过网格搜索算法寻找能耗数据的最优周期;然后,根据最优周期构建周期块;再根据周期块的数据形状构建时间序列块数据;之后,利用长短期记忆(LSTM)对时间序列块数据和周期块数据进行训练和学习;最后,通过线性回归将时间序列块数据和周期块数据的预测结果进行融合。事实证明,所提出的模型的4个预测精度指标分别比LSTM模型高7%,21%,25%和26%。

农村生活污水生物-生态组合工艺净化效果及长效管理研究

相比城镇地区,农村生活污水排放分散、基础处理设施薄弱、管理维护难度大,因此本研究采用“厌氧池+生物滤池+潜流人工湿地(简称“人工湿地”)+氧化塘”的组合工艺,考察分析组合工艺各处理单元对广西桂林农村地区各污染物的去除特性。结果显示,组合工艺中生物滤池对化学需氧量(COD)、氨氮(NH_(3)-N)的去除效果最好,夏季去除贡献率分别为75.36%和66.46%,冬季分别为67.96%和57.13%;而人工湿地对总氮(TN)、总磷(TP)的去除贡献率最大,夏季去除贡献率分别为65.08%和63.97%,冬季分别为53.15%和62.87%,并且不论夏季还是冬季,芦苇混合美人蕉湿地与菖蒲混合鸢尾湿地对COD、TN、NH_(3)-N及TP去除效果均较好,二者的出水浓度均能满足一级B排放标准;氧化塘作为最后的处理单元,各生态净化功能区深度去除污染物,其中水生植物区对于TN、TP及NH_(3)-N的平均去除率最高,水生动物区则在COD的去除方面有明显的优势,夏、冬季氧化塘平均出水浓度均能较稳定达到一级A排放标准。另外,针对农村污水设施对污染物去除率随运行年限延长而下降的现象,提出对组合工艺各处理单元加强防堵塞和及时收获植物动物的长效管理建议,为保障污水处理设施长久治污效果提供参考。

考虑长周期供需不平衡风险的新型电力系统规划方法

受高比例新能源并网带来的波动性和间歇性影响,新型电力系统的长周期供需不平衡矛盾日益突出。该文将电力系统的长周期供需不平衡风险分为两部分:连续多日无风无光的极端天气场景和月电量供需不平衡风险。首先,选取连续多日无风无光的极端天气场景,提出基于条件风险价值理论(conditional value at risk,CvaR)的月电量不平衡风险评估模型。在此基础上,提出考虑长周期供需不平衡风险的新型电力系统规划方法,通过季节性储能等灵活性资源的优化配置,可有效提升电力系统的长周期平衡能力。最后,基于IEEE RTS-79算例分析论证了所提方法的有效性,并初步讨论季节性储能在平抑长周期供需不平衡风险方面的作用。

基于DWT-SARIMA-LSTM的流感预测模型研究

为提高流感预测模型的准确率,针对流感数据的季节性与波动性特点,提出利用离散小波分解(DWT)、季节性自回归综合移动平均模型(SARIMA)和长短期记忆神经网络(LSTM)综合建模,构建DWT-SARIMA-LSTM混合预测模型。首先,将流感数据分解为高频成分与低频成分,对低频成分使用SARIMA模型、高频成分使用LSTM模型分别进行预测;其次,将预测值融合得到最终的预测结果;最后,构建流行控制图预警模型。使用从中国香港卫生署官网获得的中国香港地区2010—2019年的流感数据对模型进行预测和验证,其MAE为0.3427,MAPE为8.0973%,RMSE为0.4632,预警模型的准确率为100%,该模型较于如ARIMA-LSTM等其他混合模型有更高的预测精度。

The seasonal variations in the significant wave height and sea surface wind speed of the China's seas

Long-term variations in a sea surface wind speed （WS） and a significant wave height （SWH） are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China＇s seas WS and SWH are determined based on 24 a （1988-2011） cross-calibrated, multi-platform （CCMP） wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III （WW3） wave model forced by CCMP wind data. The results show the following. （1） For the past 24 a, the China＇s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/（s.a） in the WS, 1.3 cm/a in the SWH. （2） As a whole, the increasing trend of the China＇s seas WS and SWH is strongest in March-April-May （MAM） and December-January-February （DJF）, followed by June-July-August （JJA）, and smallest in September-October-November （SON）. （3） The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China＇s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. （4） The variations in China＇s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

融合SARIMA与BiLSTM的水利设施形变预测

水利设施形变预测可以有效地判断水利设施的运行状态。水利设施安全监测数据是时间序列数据,既有趋势性又有季节性。为了获得更准确的预测结果,文中提出一种基于季节自回归差分移动平均(SARIMA)模型和双向长短时记忆(BiLSTM)网络的预测模型,以解决无法充分挖掘数据中正向与反向的关联进行预测的问题。该模型采用SARIMA模型预测变形数据中的线性分量,采用BiLSTM模型预测变形数据中的非线性分量,使得模型能够更好地提取历史数据中的非线性关系以及正向与反向关系从而提高预测准确度。结合某水电站4#引水涵洞监测数据,使用SARIMA-BiLSTM模型对裂缝计开合度时间序列进行了预测,并与反向传播神经网络模型、SARIMA模型和SARIMA-LSTM模型的预测结果进行对比,比对结果证明所提方法有效地提高了预测精度。

Improvements in Long-Lead Prediction of Early-Summer Subtropical Frontal Rainfall Based on Arctic Sea Ice

Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.

Model Analysis of Seasonal Variations in Tropospheric Ozone and Carbon Monoxide over East Asia

Temporal-spatial variations in tropospheric ozone concentrations over East Asia in the period from 1 January 2000 to 31 December 2004 were simulated by using the Models-3 Community Multi-scale Air Quality （CMAQ） modeling system with meteorological fields calculated by the Regional Atmospheric Modeling System （RAMS）. The simulated concentrations of ozone and carbon monoxide were compared with ground level observations at two remote sites, Ryori （39.03°N, 141.82°E） and Yonagunijima （24.47°N, 123.02°E）. The comparison shows that the model reproduces their seasonal variation patterns reasonably well, and simulated ozone levels are generally in good agreement with the observed ones, but carbon monoxide concentrations are underestimated. Analysis of horizontal distributions of monthly averaged ozone mixing ratios in the surface layer indicates that ozone concentrations have noticeable differences among the four seasons; they are generally higher in the spring and summer while lower in the winter, reflecting the seasonal variation of solar intensity and photochemical activity and the fact that the monsoons over East Asia are playing an important role in ozone distributions.

中国肺结核病的季节性特征、循环周期及长短期预测

目的 分析中国肺结核病的季节性特征和循环周期并进行长短期预测,为防控措施的制定提供依据。方法 采用CensusX-13季节调整法剖析中国肺结核发病的季节特征,采用Hodrick-Prescott(HP)滤波法分析发病的循环周期,基于分解后的长期趋势采用线性回归模型进行长期预测,采用Holt-Winter季节指数平滑模型进行短期预测。结果 2010—2021年中国肺结核发病具有明显季节性特征,肺结核报告发病数集中在第二季度,每年3—7月是报告发病高峰期。循环周期大致有6个,周期平均长度为21个月,整体呈现下降的长期趋势,长短期预测效果较好。结论 应针对肺结核病的季节性特征和循环周期,结合其病理特点,在发病高峰前采取有效的预防控制措施。

Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention

This paper introduces the class of seasonal fractionally integrated autoregressive moving average-generalized conditional heteroskedastisticty (SARFIMA- GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models was derived. Test statistics that are useful to examine if seasonal level shift in an SARFIMA-GARCH model is statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters was also considered.

Characterizing the Urban Temperature Trend Using Seasonal Unit Root Analysis:Hong Kong from 1970 to 2015

This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169~ C （10 yr） - 1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data -- much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865~C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.

Seasonal Difference of the Deep Currents in the Luzon Strait during October 2008-January 2009 and July-August 2009

Based on direct current measurements from two separated cruises in October 2008-January 2009 and July-August 2009, we obtained a valuable deep current observation of the Luzon Strait (LS). Rectified wavelet power spectra analysis (RWPSA) and the geostrophic current calculation are used to study the deep current. We find that the deep current differs in different seasons. The current is strongest in autumn (October-November) and weaker in summer (July-August) and in winter (December-January). The cyclonic and anti-cyclonic meander with different subtidal current directions plays an important role in the seasonal difference of the deep current in the LS. The observed seasonal difference of the deep current in the LS is connected with the deep current observed at the western boundary of the northern Philippine Basin and is also linked with the overflow near the central Bashi Channel and Luzon Trough. The RWPSA of the long observation suggests the dominant periods of 8 d, 19 d in the deep current. The dynamical cause of the resulting velocity distribution at 1850 and 1760 m is the pressure field and bottom topography steering. The observed deep current agrees well with the geostrophic current calculation.