A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

Seasonal constraint of dynamic water temperature on riverine dissolved inorganic nitrogen transport in land surface modeling

水体温度变化对河流可溶性无机氮(DIN)输送有着强烈控制作用.然而,在全球尺度上河流DIN输送量对水温度变化的响应尚不清楚.因此,本文基于陆面过程模式,耦合河流水温估算和DIN传输方案,设定有,无动态水温情景,对比研究陆面模拟中水温变化对河流DIN通量变化的影响.结果表明:在考虑水温动态变化后,在30°N和30°S之间, DIN通量年振幅减小5%–25%.在中国东部地区,水温动态变化使河流DIN通量在夏季减少1%–3%,在冬季增加1%–5%,对DIN通量具有明显的季节性约束作用,表明动态水温的表达在河流DIN输送模拟中的重要性.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

A model study on seasonal spatial-temporal variability of the Lagrangian Residual Circulations in the Bohai Sea

The spatial distribution and seasonal variation of the tide-induced Lagrangian Residual Circulations （LRC hereafter）, wind-driven LRC, and the coupling dynamic characteristics were simulated using ECOM, given the Hellerman and Rosenstein global monthly-mean wind stresses. The results showed that the tide-induced LRC of the harmonic constituent M2 bears an identical pattern in four seasons in the Bohai Sea： the surface one is weak with random directions; however, there exist a southeast current from the Bohai Strait to the Laizhou bay, and a weakly anticlockwise gyre in the south of the Bohai Strait for the bottom layer LRC. The magnitude of bottom layer tide-induced LRC is larger than the surface one, and moreover, it contributes significantly to the whole LRC in the Bohai Sea. Unlike the identical structure of the tide-induced LRC, the wind driven LRC varies seasonally under the prevailing monsoon. It forms a distinct gyre under the summer and winter monsoons in July and January respectively, but it seems weak and non-directional in April and September.

A SEASONAL INFLUENZA THEORY AND MATHEMATICAL MODEL INCORPORATING METEOROLOGICAL AND SOCIO-BEHAVIORAL FACTORS

On the basis of a comprehensive literature review and data analysis of global influenza surveillance, a transmission theory based numerical model is developed to understand the causative factors of influenza seasonality and the biodynamical mechanisms of seasonal flu. The model is applied to simulate the seasonality and weekly activity of influenza in different areas across all continents and climate zones around the world. Model solution and the good matches between model output and actual influenza indexes affirm that influenza activity is highly auto-correlative and relies on determinants of a broad spectrum. Internal dynamic resonance; variations of meteorological elements (solar radiation, precipitation and dewpoint); socio-behavioral influences and herd immunity to circulating strains prove to be the critical explanatory factors of the seasonality and weekly activity of influenza. In all climate regions, influenza activity is proportional to the exponential of the number of days with precipitation and to the negative exponential of quarter power of sunny hours. Influenza activity is a negative exponential function of dewpoint in temperate and arctic regions and an exponential function of the absolute deviation of dewpoint from its annual mean in the tropics. Epidemics of seasonal influenza could be deemed as the consequence of the dynamic resonance and interactions of determinants. Early interventions (such as opportune vaccination, prompt social distancing, and maintaining incidence well below a baseline) are key to the control and prevention of seasonal influenza. Moderate amount of sunlight exposure or Vitamin D supplementation during rainy and short-day photoperiod seasons, more outdoor activities, and appropriate indoor dewpoint deserve great attention in influenza prevention. To a considerable degree, the study reveals the mechanism of influenza seasonality, demonstrating a potential for influenza activity projection. The concept and algorithm can be explored for further applications.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

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.

A Modeling Study of Seasonal Variation of Atmospheric Aerosols over East Asia

In this study, a regional air quality model system （RAQMS） was applied to investigate the spatial distributions and seasonal variations of atmospheric aerosols in 2006 over East Asia. Model validations demonstrated that RAQMS was able to reproduce the evolution processes of aerosol components reasonably well. Ground-level PM10 （particles with aerodynamic diameter ≤10 μm） concentrations were highest in spring and lowest in summer and were characterized by three maximum centers： the Taklimakan Desert （-1000 μg m^-3）, the Gobi Desert （-400 μg m^-3）, and the Huabei Plain （- 300 μg m^-3） of China. Vertically, high PM10 concentrations ranging from 100 μg m-3 to 250 μg m-3 occurred from the surface to an altitude of 6000 m at 30°-45°N in spring. In winter, the vertical gradient was so large that most aerosols were restricted in the boundary layer. Both sulfate and ammonium reached their highest concentrations in autumn, while nitrate reached its maximum level in winter. Black carbon and organic carbon aerosol concentrations reached maximums in winter. Soil dust were strongest in spring, whereas sea salt exerted the strongest influence on the coastal regions of eastern China in summer. The estimated burden of anthropogenic aerosols was largest in winter （1621 Gg） and smallest in summer （1040 Gg）. The sulfate burden accounted for -42% of the total anthropogenic aerosol burden. The dust burden was about twice the anthropogenic aerosol burden, implying the potentially important impacts of the natural aerosols on air quality and climate over East Asia.

Technical Note on a Track-pattern-based Model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

Seasonal Evolution of Subtropical Anticyclones in the Climate System Model FGOALS-s2

The simulation characteristics of the seasonal evolution of subtropical anticyclones in the Northern Hemisphere are documented for the Flexible Global Ocean-Atmosphere-Land System model, Spectral Version 2 （FGOALS-s2）, developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, the Institute of Atmospheric Physics. An understanding of the seasonal evolution of the subtropical anticyclones is also addressed. Compared with the global analysis established by the European Centre for Medium-Range Forecasts, the ERA-40 global reanalysis data, the general features of subtropical anticyclones and their evolution are simulated well in both winter and summer, while in spring a pronounced bias in the generation of the South Asia Anticyclone（SAA） exists. Its main deviation in geopotential height from the reanalysis is consistent with the bias of temperature in the troposphere. It is found that condensation heating （CO） plays a dominant role in the seasonal development of the SAA and the subtropical anticyclone over the western Pacific （SAWP） in the middle troposphere. The CO biases in the model account for the biases in the establishment of the SAA in spring and the weaker strength of the SAA and the SAWP from spring to summer. CO is persistently overestimated in the central-east tropical Pacific from winter to summer, while it is underestimated over the area from the South China Sea to the western Pacific from spring to summer. Such biases generate an illusive anticyclonic gyre in the upper troposphere above the middle Pacific and delay the generation of the SAA over South Asia in April. In mid- summer, the simulated SAA is located farther north than in the ERA-40 data owing to excessively strong surface sensible heating （SE） to the north of the Tibetan Plateau. Whereas, the two surface subtropical anticyclones in the eastern oceans during spring to summer are controlled mainly by the surface SE over the two continents in the Northern Hemisphere, which are simulated reasonably well, albeit with their centers shifted westwards owing to the weaker longwave radiation cooling in the simulation associated with much weaker local stratiform cloud. Further improvements in the related parameterization of physical processes are therefore identified.

Seasonal Regression Models for Electricity Consumption Characteristics Analysis

This paper presents seasonal regression models of demand to investigate electricity consumption characteristics. Electricity consumption in commercial areas in Japan is analyzed by using meteorological variables, namely temperature and relative humidity. A dummy variable for holidays is also considered. We have developed models for two levels of period to analyze demand characteristics, that is, half year models and seasonal models. Some options for each model are calculated and validated by statistical tests to obtain better models. As results, half year and seasonal models present explicit information about how the variables affect the demand differently for each period. These specific information help in analyzing characteristics of studied commercial demand.

Seasonal Prediction Experiments of the Summer Droughts and Floods during the Early 1990′s in East Asia with Numerical Models

It has been shown by the observed data that during the early 1990′s, the severe disastrous climate occurred in East Asia. In the summer of 1991, severe flood occurred in the Yangtze River and the Huaihe River basin of China and in South Korea, and it also appeared in South Korea in the summer of 1993. However, in the summer of 1994, a dry and hot summer was caused in the Huaihe River basin of China and in R. O. K.. In order to investigate the seasonal predictability of the summer droughts and floods during the early 1990′s in East Asia, the seasonal prediction experiments of the summer droughts and floods in the summers of 1991-1994 in East Asia have been made by using the Institute of Atmopsheric Physics-Two-Level General Circulation Model (IAP-L2 AGCM), the IAP-Atmosphere/Ocean Coupled Model (IAP-CGCM) and the IAP-L2 AGCM including a filtering scheme, respectively. Compared with the observational facts, it is shown that the IAP-L2 AGCM or IAP-CGCM has some predictability for the summer droughts and floods during the early 1990′s in East Asia, especially for the severe droughts and floods in China and R. O. K.. In this study, a filtering scheme is used to improve the seasonal prediction experiments of the summer droughts and floods during the early 1990′s in East Asia. The predicted results show that the filtering scheme to remain the planetary-scale disturbances is an effective method for the improvement of the seasonal prediction of the summer droughts and floods in East Asia.

基于配对检验的ARIMA模型在我国甲肝发病数预测中的应用

目的:探讨基于配对检验的求和自回归移动平均(autoregressive integrated moving average,ARIMA)模型在我国甲肝发病预测中的应用,提出时间序列模型预测效果评价的新思路与方法。方法:根据2004年1月—2021年12月我国甲肝传染病月发病数建立ARIMA模型,对2022年1—8月的甲肝月发病数进行预测,通过配对t检验和误差分析评估该模型的预测效果。结果:配对t检验结果显示,ARIMA(1,1,0)(0,1,1)12模型预测的甲肝月发病数与实际月发病数差异无统计学意义(P>0.05),说明模型有较好的预测能力,预测结果的相对误差平均值为3.86%,标准差为3.25%。结论:ARIMA乘积季节模型能够较准确地预测我国甲肝的发病趋势;配对检验为时间序列模型预测效果的评价提供了客观评价依据,较好地解决了时间序列模型预测效果的评价问题。

北京市东城区2009—2019年蝇类生态学监测结果分析及其预测方法探讨

目的 掌握北京市东城区2009—2019年蝇种类、密度、分布及其季节消长规律,探讨基于生态学监测的蝇类密度预测方法,为东城区蝇类预测与科学防控提供依据。方法 收集整理东城区2009—2019年蝇类生态学监测数据并进行分析;利用MATLAB R2018b软件构建的季节性差分自回归移动平均模型(SARIMA)对2019年4—10月的蝇类密度进行预测并与实际监测值进行比较,验证模型预测效果。结果 2009—2019年东城区各生态学监测点蝇类年平均密度为7.09只/笼,优势蝇种为麻蝇科,占捕获蝇总数的56.82%,占比超过5%以上的蝇种类依次为厩腐蝇(11.74%)、家蝇(10.17%)、丝光绿蝇(8.99%)和大头金蝇(6.93%);不同生境中,宾馆饭店蝇类密度最高,为11.86只/笼,餐饮外环境最低,为2.20只/笼,麻蝇科在不同生境中均为优势种群;蝇类密度高峰主要出现在7月和8月。基于历史生态学监测数据构建的最优模型SARIMA(0,1,4)(2,1,3)12预测2019年4—10月的蝇类密度与实际密度基本一致,实际监测值均落在预测值95%置信区间内,模型评价指标均方根误差(RMSE)和平均绝对误差(MAE)分别为1.379和1.014,预测效果较好。结论 2009—2019年北京市东城区以麻蝇科为优势种群,宾馆饭店是蝇类防控的重点场所,活动高峰主要出现在7—8月;通过对SARIMA模型效果评价,该方法可用于蝇类密度短期变化趋势预测。

基于SARIMA-SVR模型的铁路货运量预测方法

鉴于铁路货运量受多种外部因素影响呈现显著的随机波动特征而难以准确预测,提出了SARIMA-SVR预测模型。首先,对全国铁路月度货运量序列进行季节时间序列(SARIMA)建模,得到模型的初始预测值及预测残差。其次,构建支持向量机(SVR)回归预测模型,将影响铁路货运量的外部因素作为模型输入项,SARIMA模型预测残差序列、月度货运量序列分别作为模型输出项,由此分别获得SARIMA模型预测残差的优化值以及SVR模型的货运量预测值。三是将优化后的SARIMA模型预测残差与其初始预测值相加,得到优化后的SARIMA模型预测值。四是再对优化后的SARIMA模型预测值和SVR模型预测值进行加权求和,得到SARIMA-SVR模型的预测结果。最后,对SARIMA-SVR模型进行消融实验验证模型有效性,并将该模型与经典预测模型进行测算精度对比。结果表明,SARIMA-SVR模型的预测精度优于单一模型和经典预测模型,在货运量预测方面具有良好的适用性。

变暖背景下砾石换填对多年冻土区机场跑道下地温场的影响

受气候变暖影响,东北北部地区冻融灾害频发,对寒区工程设施造成了重要影响。地基温度场的研究是分析与解决工程基础冻融灾害的重要手段。文中我们以漠河机场跑道为对象,通过有限单元法研究了洁净砾石换填对机场道基温度场的影响,并对运行30年内道基温度场进行预测。结果表明,换填使季节最大冻结深度(MSF)增加,且换填对道基下MSF的水平影响范围在道面中心线两侧30 m左右。之后,通过比较不同换填深度(1.5(顶)~3.5(底)、1.5~4.5、1.5~5.5和1.5~6.5 m)的道基温度场变化,发现:换填底部深度达到4.5 m时,MSF变化的速率开始减小。最后,根据IPCC第六次评估报告(AR6)未来100年间不同气候变暖速率模型模拟研究了无换填与不同换填深度下的MSF可能变化。结果发现,到2100年,在SSP2-4.5情景下,未换填及不同换填深度的道基下MSF分别为1.63、1.86、1.84、1.84和1.84 m。因此,利用换填法来减少跑道冻融灾害时换填深度应至少达到4.5 m。同时,应加强漠河机场道基地表水与该跑道区地下水的防排水设施建设与维运。研究结果有助于进一步认识换填对多年冻土和活动层水热状态的影响,可为解决道基冻胀和融沉问题提供重要科学依据。

煤矿雨季“三防”管理体系评价指标的构建

为解决煤矿雨季期间灾害频发问题,采用专家咨询和问卷调查方法,构建了涵盖防汛、防排水和防雷电三个准则层,以及排洪沟、滑坡及泥石流和雨季降水量等十三个指标层的煤矿雨季“三防”管理体系的评价指标体系,利用熵权法和灰色综合评估法进行指标权重的计算。结果表明,雨季降水量、雷电预警系统和建筑物防雷设施的权重较高,分别为0.1120、0.1095、0.1010。给出应加强排水、防雷等“三防”设施的建设和维护、地质灾害的监测和预警等针对性防治措施。该研究可提升煤矿的安全性和生产效率,降低事故发生的可能性,保障工人的生命安全。

一类具有季节交替的n维Gilpin-Ayala竞争模型的动力学

研究一类具有季节交替的n维Gilpin-Ayala竞争模型。利用单调动力系统的理论,当n=1时,系统存在着阈值动力学。根据离散竞争映射的负载单形理论,证得n维系统存在一个(n-1)维的负载单形。结果表明:(n-1)维的负载单形吸引了系统在R^(n)_(+)中的所有非平凡轨道。

基于HEC-HMS水文模型的潼三段流域2021年秋汛洪水模拟

水库精细化调度是确保防洪安全与提高水库综合效益的主要措施。以三门峡库区潼关至三门峡大坝间流域(潼三段流域)无测控区为研究区域,基于土壤、土地利用、降水、径流等数据,构建HEC-HMS水文模型,对2021年潼三段流域整个秋汛洪水过程进行模拟,分析区间各支流洪水叠加过程与径流量变化。采用径流系数法(算术平均法和泰森多边形法)计算潼三段流域径流总量并与实测值对比,验证HEC-HMS水文模型模拟结果的准确性。结果表明:HEC-HMS水文模型在潼三段流域表现出良好的适用性与可靠性,径流总量模拟值与实测值仅相差2.33%,模拟效果显著优于径流系数法。