Using the satellite data in dynamic models of crop yield forecasting

Considered is a possibility to use the satellite data in the dynamic models of crop yield forecasting. The interrelation between the surface and satellite data is established. It is demonstrated that the NDVI vegetation index of arable lands indicates rather well the seasonal variations of crop photosynthesis. Obtained is the equation of computing the leaf area index using the satellite data for grain crops. Presented are the results of the use of satellite information in the operating dynamic model of spring wheat yield forecast in Krasnodar krai.     

Studies of sensitivity of a dynamic model of grain crop productivity to soil-hydrophysical information

The relationship between a target function of a mathematical model of grain crop growth and development—yield—and variations in the soil and hydrophysical information expressed by variations in the soil water-retention function is considered. The function is approximated with a formula based on soil-hydrological constants. The soil profile in the model is represented by a three-layer structure. Each layer has its own agrohydrological characteristics: maximal hygroscopic moisture, wilting moisture, the least moisture capacity, and saturation moisture. The model AGROTOOL, identified from long-term data on field experiment at the Men’kovo experiment station of the Agrophysical Research Institute (the Leningrad region), is used to estimate the influence of variations in these sets on the barley and winter rye yield. It is demonstrated that the yield is most sensitive to variations in the least moisture capacity of the soil and has the least sensitivity to the change in the maximal hygroscopic moisture.     

灰色动力模型在天气预报中的应用

本文在简述灰色系统理论的基础上,提出天气过程为灰色系统,从而探讨了灰色分析和预测方法在气象中的应用,并给出了灰色动力模型的应用实例。     

近地层臭氧对我国树木生产力和作物产量的影响:进展与展望

大气污染严重威胁了我国陆地生态系统的固碳能力,但随着减污降碳协同治理的快速推进,减缓大气污染将有利于提升陆地碳汇,并切实推动碳达峰碳中和目标的实现。为了更好地理解大气污染与生态系统固碳的关系,本文以主要空气污染物臭氧（O₃）为例,基于田间控制实验的整合分析、剂量响应关系及机理模型三种评估方法综述了近地层O₃污染对植被碳固定影响的最新进展。尽管不同作物种类以及品种、不同功能型木本植物对O₃的响应有着显著的差异,且各种方法的评估结果也不尽相同,但目前O₃浓度造成我国粮食作物减产、森林生产力降低已是不争的事实。持续升高的O₃浓度将严重威胁我国陆地生态系统的固碳能力。利用我国作物和树木的O₃剂量响应方程进行评估的结果表明,在CO₂减排和O₃污染协同治理下,预计2060年我国树木生物量和作物产量将比当前显著提高,增加陆地生态系统碳汇,助力碳中和目标。最后,对如何提高O₃污染环境下植物固碳能力也进行了展望。     

持续性强降水的区域模式动力中期预报研究

持续性强降水及其次生灾害给人民的生产和生活造成严重影响, 延伸其模式动力预报能力对防灾、减灾具有重要意义。随着对持续性强降水过程形成机理及模式动力中期预报认识的不断提高, 以减小模式初始条件误差、边界条件误差以及内场预报误差为目标提出了一系列动力中期预报技术方法, 主要包括:针对边界条件提出低通滤波技术方案, 改进了5 d以上的环流及降水预报; 针对模式预报内场进行谱逼近技术试验, 对提前3—7 d的小雨以上量级的降水预报改进明显; 针对初始条件进行多尺度混合更新初值技术预报试验, 融合全球预报的大尺度场及区域模式预报的中小尺度场进行15 d预报, 明显提高了50及100 mm以上的持续性累积降水预报时效。      

Estimation and medium-term forecasting of anthropogenic carbon dioxide and methane emission in Russia with statistical methods

A statistical method of estimation and medium-term forecasting of greenhouse gas emission is under consideration. It is shown that availability of significant enough and stable statistical relationships between greenhouse gas emission and a number of indicators of the economy development allows operational estimating and forecasting of greenhouse gas emission in the Russian economy with a lead-time out to several years. It is possible to estimate and forecast both the total emission of all greenhouse gasses in terms of CO₂-equivalent and emission of particular greenhouse gases in different sectors of economy. Both miacroeconomic and industrial indicators of the economy development can be used as predictors for building regression forecasting models.     

赫章县观测地段小麦单产预报模型研究

利用观测地段小麦单产资料和贵州省山地环境气候研究所根据赫章县旬平均降水、温度、日照时数资料加工的定量化评价结果，采用数值模拟方法，初步建立观测地段产量预报模型，取得较好的预报效果。     

Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model

The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques–the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME). Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM- 90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the forecast tracks in some of the cases are improved over that of the control. On the other hand, the EF technique has little impact on the forecasts of Flo because the control forecast is already very close to the best track. The study provides a basis for the future development of the EF technique. The limitations of this study are also addressed. For example, the above results are based on a small sample, and the study is actually a simulation, which is different than operational forecasting. Further tests of these EF techniques are proposed.     

低频环流系统的一种统计预报方法

根据实时MICAPS资料,采用经验正交函数分解和滤波方法,统计分析了欧亚大陆上空700 hPa水汽输送通量流函数和势函数主要低频分量的分布特征。结果表明：存在流函数和势函数低频环流系统变化的关键区。根据关键区环流系统演变,确定了与上海地区未来10-30 d强降水过程相对应的预报指标。以上海地区2010年7月的降水过程预报为例概述了整个预报流程,并从物理机制上证明了低层（110°E,30°N）附近的辐合以及自低空至高空向东倾斜的上升运动是造成该次降水的主要成因。     

低频环流系统的一种统计预报方法

根据实时MICAPS资料,采用经验正交函数分解和滤波方法,统计分析了欧亚大陆上空700 hPa水汽输送通量流函数和势函数主要低频分量的分布特征。结果表明：存在流函数和势函数低频环流系统变化的关键区。根据关键区环流系统演变,确定了与上海地区未来10—30 d强降水过程相对应的预报指标。以上海地区2010年7月降水过程预报为例,概述了整个预报流程,并从物理机制上证明了低层（110°E,30°N）附近的辐合以及自低空至高空向东倾斜的上升运动是造成该次降水的主要成因。     

Validation of climate model output using Bayesian statistical methods

The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM.     

基于作物生长模型及CAST分类的华北夏玉米生产力区划研究

利用试验数据校正并验证了机理性的作物生长模型WOFOST,随后模拟了华北42个站点1961—2006年夏玉米的光温和气候生产潜力。并首次运用新型统计检验聚类方法(CAST),对夏玉米光温及气候生产潜力的要素场分别进行了定量化分区。结果表明,华北夏玉米光温及气候生产潜力均分为5个不同荷载中心的区域。与农业气象传统等值线分区方法相比,将作物模型与CAST相结合进行的生产潜力区划可以更客观地反映以荷载中心台站为代表的产量的时空分布特征。这对于指导区域农业气候区划,实现区域农业可持续发展具有重要的理论及现实意义。     

A local forecasting model of formaldehyde atmospheric pollution in Tomsk based on statistical and hydrodynamic methods

A hybrid statistical hydrodynamic model of the formaldehyde pollution forecasting adapted for the town of Tomsk is presented. A statistical component of the model gives the prognostic concentration averaged for the city and the detailing of the spatial distribution is based on the hydrodynamic method. The model tests based on the independent data demonstrated the accuracy of not less than 70%. The hydrodynamic block of the model enables to obtain the relative estimates of contribution of separate enterprises or groups of sources (including the motor vehicle emissions) to the total pollution of the atmosphere in the city.     

基于统计降尺度方法的夏季降水精细化预报

利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.     

江苏省单季晚稻产量预报的分段加权动态模式

本文利用灰色系统GM(1,1)模型逐段滑动平均模拟趋势产量y_t,根据单季晚稻的穗数、粒数和粒重对最终产量的贡献确定出生育三个阶段的权重,算出各阶段的气象产量。通过MAICE逐步回归方法分别建立各介生育阶段末气候产量的预测模式,分段加权动态地进行单季晚稻的产量预测。最后还作了1986—1988年产量的试报。     

Evaluation of statistical downscaling in short range precipitation forecasting

The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”.Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...     

山西暴雨云团的卫星云图统计分析及预报研究

本文统计分析了影响山西省的暴雨云团。在分类的基础上,分别讨论它们的发生区、生命史、移动路径等,同时还分析了云团的云顶温度与3小时降水强度的相关性。利用已取得的统计分析结果,结合部分物理量和雷达回波资料,得出了一个甚短期(6小时)暴雨预报的方法。经试用,结果是令人满意的。