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1 引言 黑龙江省大兴安岭地区面积8.46×104km2,在2006年以前大兴安岭地区气象部门有6个地面气象观测站,平均1.41×104km2有一个地面观测站,气象站在空间密度和观测频次上,不能适应中小尺度天气系统的监测要求.目前,大兴安岭地区共有52个观测站覆盖在大兴安岭的所有乡镇、林场,平均30 km有1个雨量站.实行了1 h一次的自动上传气温、雨量数据.通过对2007年1～9月的加密观测前后资料的对比分析,揭示了大兴安岭地区的气候变化规律,为林区的气象服务提供科学的依据. 相似文献
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三门峡市新一代人工影响天气综合技术系统 总被引：1，自引：0，他引：1
三门峡市新一代人工影响天气综合技术系统以地面人工增雨、防雹以及空中云水资源开发研究为目的,充分利用了micaps平台和卫星云图、新一代天气雷达、地面雨量等资料及多年人影作业经验,完善了三门峡地区人工影响天气最佳作业时机的预警和作业指标;采用合理的业务流程,实现了系统主模块以及大气监测探测、信息采集存储、通讯网络、决策指挥、效果检验和系统配置等子模块的各项功能;首创了人工影响天气作业指挥系统的立体地理模型以及连接系统软件的数字电路;该系统可以在同一平台上综合调显雷达、云图、炮位、雨量等信息,并迅速做出各种判断、设计作业方案,为实时进行地面人工增雨、防雹作业,提供了一个反应灵敏、功能齐全的决策指挥平台. 相似文献
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山东省空中水资源的初步分析 总被引：2，自引：1，他引：1
利用1997～1999年逐日探空资料和同期水文水资源资料,分析了山东省域空中水汽资源的时空分布特征及地-气系统的水量平衡关系.结果表明:山东省空中水汽资源受天气系统和季节变化的影响明显;静态水汽含量年均值为20.75 mm,动态水汽净输出量全年平均为440.91 mm,并以850 hPa层的水汽净输出为主.水汽净收支率年、季分布具有较强的地域特征.全年的水汽输入量很大,但空中水汽资源转化为地面降水的效率不足5%,而降水转化为水资源的量约为27.31%,空中水汽资源具有很大开发潜力.另外,从大气水量平衡角度分析了山东干旱气候特征的客观必然性,并提出开源增量、增加水资源的对策. 相似文献
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微型无人驾驶飞机航测试验使用微型无人驾驶飞机采集图像,用解析方法绘制地形图.微型无人驾驶飞机采用GPS导航,具有自动导航、自主飞行功能.它能在自动控制系统的控制下完成预定测绘航线的飞行,地面控制人员根据预先计算的摄影点控制机载的相机或数码相机进行拍摄.解析测图仪根据采集的图像以及外业地面控制点解析计算并绘制地形图. 相似文献
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空军部队气象台探空组 《气象》1980,6(12):19-19
目前,探空仪施放后在空中停转的现象比较普遍。我们对此进行了试验和分析,有以下几点看法。 一、停转的原因 一般说来,探空仪在空中停转的原因可有以下三种。 (一)由于两个齿轮质量不同,所以在地面上齿 相似文献
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空中人工触发闪电试验及特性分析 总被引：12，自引：10，他引：2
利用１９９６年南昌人工引雷试验观察测资料,分析了空中触发电的放电特征,估算了火箭上升时的电参数。结果表明：空中触发闪电的触发高度比地面触发闪电的蟹良高速度约大２倍。在火箭上升阶段,空中触发闪电在地面引起的电场变化为负,地面触发闪电在地面引直民的电场变化为正。 相似文献
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Summary In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation
in the Iberian Peninsula (583,551 km2). The resulting maps have a 200 m spatial resolution and a monthly temporal resolution. Multiple regression, combined with
a residual correction method, has been used to interpolate the observed data collected from the meteorological stations. This
method is attractive as it takes into account geographic information (independent variables) to interpolate the climatic data
(dependent variable). Several models have been developed using different independent variables, applying several interpolation
techniques and grouping the observed data into different subsets (drainage basin models) or into a single set (global model).
Each map is provided with its associated accuracy, which is obtained through a simple regression between independent observed
data and predicted values. This validation has shown that the most accurate results are obtained when using the global model
with multiple regression mixed with the splines interpolation of the residuals. In this optimum case, the average R
2 (mean of all the months) is 0.85. The entire process has been implemented in a GIS (Geographic Information System) which
has greatly facilitated the filtering, querying, mapping and distributing of the final cartography. 相似文献
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利用2006~2017年北京夏季（6~8月）逐日最大电力负荷和同期气象资料,分析最大电力负荷与各种气象因子的相关性,基于BP（Back Propagation）神经网络算法,建立了两种夏季日最大电力负荷预测模型并对比。结果表明:北京夏季周末基础负荷远小于工作日,剔除时应加以区分;气象因子对气象负荷的影响具有累积效应,累积2 d时两者的相关性最强;结合实际,根据自变量的不同分别建立了两种日最大电力负荷预测模型;经实际预测检验,两种预测模型均取得了较好的预测效果,能够满足电力部门的实际需求,其中自变量中加入前一日气象负荷的模型效果更优。 相似文献
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Comparative analysis of regression and artificial neural network models for wind speed prediction 总被引：1，自引：1，他引：0
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
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基于气象和水文观测资料,分析白河流域年径流量与气象要素的关系并建立拟合模型,利用CMIP5模式在不同典型浓度路径(RCPs)下的模拟结果,预估21世纪白河流域年径流量的变化特征.结果表明:1981～2012年白河流域年径流量呈先减少后增加的变化趋势,与气象站观测的年平均最高气温呈显著负相关,与降水量呈显著正相关.以红原... 相似文献
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复共线性关系对逐步回归预报方程的影响研究 总被引：1，自引：0，他引：1
针对气象预报中常用的逐步回归预报建模方法,由于没有直接考虑筛选出的预报因子之间可能存在复共线性关系会影响气象预报方程的预报性能问题,提出了在初选的大量气象预报因子(自变量)中,采用条件数计算分析方法,选择复共线性关系小的预报因子组合建立预报模型的方法.以重要气象灾害的预报难点--台风预报为例,用大样本分别建立了12个台风移动经度、纬度的条件数预报方程和逐步回归预报方程.对比分析结果表明,由于条件数计算分析有效控制了预报因子间的复共线性关系,因此,在相同的预报因子(自变量)和预报对象(因变量)条件下,分月建立的条件数台风移动路径预报方程,虽然历史建模样本的拟合精度略低于逐步回归预报方程,但是对独立样本的预报精度明显提高,其中7、8和9月条件数预报方程的预报误差平均为153.9 km,而相应的逐步回归预报误差平均为229.2 km,两者相差75.3 km.进一步研究发现,在F值分别取1.0、2.0和3.0的情况下,建立的台风移动路径的逐步回归预报方程,其预报误差也明显大于条件数预报方程.另外,由于预报因子组合的复共线性的影响,逐步回归方程还出现了在个别点预报误差极大的不合理情况. 相似文献
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Possibilities to use the non-parametric regression analysis method, named the quantile regression, for the estimation of changes
in climate characteristics are considered. When analyzing the trends of climatic series, the quantile regression method enables
to get the information on trends along the whole range of quantile values from 0 to 1 of dependent variable distributions,
that is more informative than the use of traditional regression technique, based on the least-squares method (LSM) and enabling
to obtain trend estimations for average values of the dependent variable only. Trend estimation errors for various methods
are analyzed. The computation of quantile regression parameters for real climatic series is executed. Series of meteorological
variables of the diurnal resolution, which characterize the surface climate (minimal, average, and maximal diurnal temperatures)
and free atmosphere climate (temperature of isobaric surfaces up to 30 hPa inclusive) are considered. Seasonal peculiarities
in trend manifestation at different parts of quantile range of these meteorological values are discussed. Concerning the problem
of the analysis of climate trends, the quantile regression method seems to be perspective from the point of view of more detailed
understanding of processes in the climate system, such as the surface and tropospheric warming, stratospheric cooling, long-period
changes in characteristics of climate variability and extremity. 相似文献
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Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts 总被引：1，自引：0，他引：1
Hideki Ueyama Sachiho Adachi Fujio Kimura 《Theoretical and Applied Climatology》2010,101(3-4):421-431
A new method is proposed to compile 1 km grid data of monthly mean air temperature by dynamically downscaling general circulation model (GCM) data with a regional climate model (RCM). The downscaling method used is a technique referred to as the pseudoglobal warming method to reduce GCM bias. For the grid data, RCM data were corrected with data from an existing meteorological network. The correction model for the RCM bias was developed by stepwise multiple regression analysis using the difference in the monthly mean air temperatures between the observation and RCM output as a dependent variable and the geographical factors as independent variables. Our method corrected the RCM bias from 1.69°C to 0.58°C for the month of August in the 1990s (1990–1999). 相似文献
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Prediction of soil temperature using regression and artificial neural network models 总被引：3，自引：0，他引：3
Mehmet Bilgili 《Meteorology and Atmospheric Physics》2010,110(1-2):59-70
In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献