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weather prediction相关的网络例句

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与 weather prediction 相关的网络例句 [注:此内容来源于网络,仅供参考]

Did you hear the weather forecaster 's prediction for rain tomorrow?

你听到说明天有雨的天气预报了吗?

A: Did you hear the weather forecaster抯 prediction for rain tomorrow?

你听到说明天有雨的天气预报了吗?

Firstly, utilize the historical weather map, adopt the gradient wind formula to calculate the wind field of this area, and correct the wind velocity and wind direction according to four common methods of wind-waves prediction and wave factors of engineering design, the Code of Hydrology for Sea Harbor included, considering the water temperature difference, ground friction resistance, crooked degree in isobar, combined by the influence of the maximum velocity from the near station.

首先利用历史天气图,采用梯度风公式计算该区域的风场,并按照4种风浪预报和工程设计波要素常用的方法,根据水气温差、地面摩阻和等压线的弯曲程度等多种因素结合测站的实测最大风速的影响进行必要的风速和风向修正。

But because the guidance thought that climate is the synthesization of weather is wrong, the short-term climate prediction is not so successful yet in the past 100 years.

在"气候是天气的综合"这一错误思想的指导下,每一次短期天气预报的成功都引出了短期气候预测的热潮,相应地出现了大型天气学、超长波控制论和短期气候的数值模式预测方法。

Using conventional survey and ground encryption observations and T213 amount prediction product and fy-2c/2d infrared satellite cloud chart data, weather power diagnosis and mesoscale analysis of gale and strong sandstorm process initiated by squall line are carried on May 2, 2008 in east of Hexi corridor.

利用常规探测、地面加密观测、T213数值预报产品和fy-2c/2 d红外卫星云图资料对2008年5月2日河西走廊东部飑线引发的强沙尘暴过程进行了天气动力诊断和中尺度分析。

Using WWW as its platform, EWSSWE generates and demonstrates a series of forecast and early warning products, including the diagnoses of the severe convection indexes, extrapolative products based on satellites/radars, mesoscale model prediction outputs as well as probability products of severe weather outlook and early warning via "the composite nesting method".

&突发性强灾害天气预警系统&以WWW为平台,融贯多种天气预报新技术,为各类用户提供了崭新的预报视角和技术含量较高的预报产品;产品包括强对流指数诊断、基于卫星和雷达的外推产品,中尺度数值模式预报产品,以及基于&综合叠套技术&给出的强天气展望和强天气临近概率预报产品等。

For strong convective weather , it classified all historical sample events into 4 weather patterns (like northwest , trough area ,west wind and southwest current), established 4 characteristic fields of 400hPa height of HLAFS , then according to the principle of the pattern match calculate and compare the real-time HLAFS forecast products-2-using the similar method inside the big and small key areas , establish forecast equation , finally gain conclusion .To duststrom weather, the historical samples were sorted into duststrom and severe duststrom types in 5 regions (they are whole area, west, central, middle-west and middle-east of Inner Mongolia ),moreover set up different sample databases about ECMWF fields (including 500hPa height, 850hPatemperature and sea-level pressure).In order to making duststrom forecast at different time level , we first filtered the real-time data by FAX data ,and then used the method of similar range degree to compare the historical data to the actual data of ECMWF .To precipitation weather ,they were divided into 2 types that suit or unsuit airplane artificial precipitation stimulation in line with their emergence time and district .The appropriate weather physical factors come from T106 were chosen to establish the artificial precipitation stimulation prediction model . In the actual application , we can get prediction result as long as use the real-time forecast data of T213 in the prediction model .

针对强对流天气将历史样本天气分为西北气流型、槽区型、西风气流型和西南气流型等4个类型,并建立其HLAFS资料400hPa四种特征场,按照模板匹配的原理,用相似分析方法在大、小两个关键区中对实时HLAFS预报产品进行计算、比较,再建立预报方程,得出结论;对沙尘暴天气将其按区域分为全区、西部、中部、中西部以及中东部5类,再按强弱分别分为强和一般2类,建立ECMWF的3个场(500hPa高度场、850hPa温度场和海平面气压场)的历史资料库,在用传真资料消空之后,用相似离度方法计算实时ECMWF资料,做出不同时次的预报;对降水天气按出现时间和区域分为适合和不适合飞机增雨作业2类,选择T106资料中恰当的气象物理量因子,用BP神经网络算法建立人工增雨降水预报模型,实际应用中将实时T213相关预报资料代入预报模型即可。

EMAN is dedicated to protection of Canada's environment and natural heritage, carries out weather and environmental prediction.

加拿大环境署生态学监测和评估网络致力于加拿大的环境和自然资源保护,进行天气和环境预报。

In a database the concept of an example might change along with time which is known as concept drift When the concept drift the classification model built by use of old data is unsuitable for classify new data Therefore concept drift has become a hot issue in data mining in recent years Although many algorithms had been proposed to resolve this problem they can not provide users with the reason of concept drift However a user might be very interested in such rules For example doctors want to find what makes disease change; researchers want to know the reason of the variety of the weather; and decision makers would like to understand why a customer's shopping habit change In this thesis we propose a Concept Drift Rule mining Tree called CDR-Tree to solve this problem CDR-Tree can not only find the rule of concept drift also build the prediction model for both old and new data at the same time

无论在大型资料库或现实生活中,同一资料样本的概念有可能会随著时间的递移而改变,也就是产生所谓的概念漂移。当样本发生概念漂移时,由旧有资料所建构的分类模组将不再适用於预测新获得的资料,因此,近年来概念漂移已成为资料探勘中一项热门的研究议题。虽然已有?多学者提出不同的技术来解决概念漂移的问题,但是这些方法都是利用修正或重建的方式来产生适合新资料的预测模组,并无法提供造成概念漂移的原因。然而对使用者而言,其感兴趣的可能正是这些引起概念漂移的规则,如医生可能想了解引起疾病变化的主因、学者会想要知道气候转变的规则、或是决策者想找出顾客购物习惯改变的因素等。因此,本论文提出概念漂移规则探勘树( Concept Drift Rule mining Tree ),简称CDR-Tree,来解决这个问题。CDR-Tree不但能探测出造成概念漂移的主要原因,亦能同时建立新旧资料的预测模组以供决策者运用使用。

However, the prediction has a limitation of underestimation and overestimation at low and high temperature or moisture conditions respectively. In order to acquire accurate estimation of soil respiration, main suggestions for further study are to: combine measurement of soil respiration by chamber with carbon flux of ecosystem by eddy covariance, measure synchronously the biotic and abiotic factors, especially pay more attention to the role of modification of biotic factors on the performance of abiotic controllers, make more measurement in typical plant phenology phase and different weather conditions, and strengthen experimental manipulation and modeling.

但是在土壤温度及湿度过高或过低的情况下会出现较大的误差,为了尽量减少土壤呼吸的误差,给出了如下建议:①加强土壤呼吸和生态系统自动碳通量的结合研究;②加强对不同生物和非生物生态环境因子的同步测定,特别重视生物因子对非生物因子的调节和影响;③加强典型物候期和不同季节典型天气土壤呼吸的测定;④加强模拟试验研究和模式研究。

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