本研究采用蒙特卡洛法模拟一组两参数韦伯分布函数，生成逐日平均风速。
In this paper, the average daily wind speed is generated from a set of two parameter Weibull distributions by Monte Carlo method.
模式所需的输入为初始土壤含水量、土壤物理常数、作物播种期、逐日平均气温、相对湿度和降水量。
The desired input parameters were soil physical constants, crop 's developmental date, initial soil water content and daily air temperature, relative humidity and precipitation.
气候系统存在最大可预报期限，对于超过逐日预报可预报期限后的预报，用胞映射思想证明了平均值的可预报性，并得到了定量的结果。
For the prediction beyond the daily predictability limit, it is proved by using simple cell to cell mapping idea that mean value is predictable, and the quantitative result is obtained.
聚集率则逐日增加，驯化第18天，聚集率达到最高，为91%，平均聚集率为83.9%；
The aggregation ratios were increased gradually, the maximum 91% at the 18th day and average aggregation rate of 83.9%.
从逐日序列计算出年平均温度和年极端温度序列，并把修正之前和修正之后的序列趋势进行比较。
Annual mean and extreme temperature series are deduced from the daily observations and trends in the adjusted and unadjusted series are compared.
从逐日序列计算出年平均温度和年极端温度序列，并把修正之前和修正之后的序列趋势进行比较。
Annual mean and extreme temperature series are deduced from the daily observations and trends in the adjusted and unadjusted series are compared.
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