1) monthly (seasonal) rainfall
月(季)降水
1.
The monthly (seasonal) rainfall distribution field which are consist of continuous monthly data is expanded by EEOF.
本文提出一种降水长期预测的新方案，用扩展经验正交函数（ＥＥＯＦ）展开连续月组成的月（季）降水分布场，求取各月的特征向量场和对应的时间权重系数，分析各场的天气学意义及前后承替的相互关系，用前期出现的特征向量场的特征来预测后期的降水场分布趋势，同时利用特征向量场所对应的时间系数作二维点聚图，估算预报月（季）份的降水总趋势。
2) Monthly-seasonal precipitation field
月季降水场
3) seasonal/monthly precipitation
月/季降水量
4) R.odorata
香水月季
1.
This study determined the probability of shoot regeneration via Protocorm-like-bodies(PLBs) in R.
以香水月季(Rosa odorata)的嫩叶为外植体,采用MS基本培养基,添加不同质量浓度的2,4-D、TDZ和IBA,研究了类原球茎再生植株的效果。
5) summer precipitation
夏季降水
1.
Analysis of abnormal characteristics of summer precipitation in Qinghai during the last 45 years;
近45年青海省夏季降水异常特征分析
2.
Numerical Simulation of the Influence of the Tibetan Plateau Winter-spring Snow Anomalies on the Summer Precipitation in China;
青藏高原冬春积雪异常影响中国夏季降水的数值模拟
3.
Trends of summer precipitation and its regional characteristics in Liaoning Province,China during 1961-2004;
辽宁省近44年夏季降水变化及区域特征分析
6) Precipitation in autumn
秋季降水
1.
Climate change characters of precipitation in autumn among decades and years on Loess Plateau;
黄土高原秋季降水年际和年代际气候变化特征
2.
Utilizing the predictor and corresponding rainfall data trains the ANN,and then the precipitation forecasting data is taken out in order to test the level forecast of Nanjing s precipitation in autumn,the results show that the correct rate of rainfall prediction is 70%～80%,10%～20% higher than HLAFS model.
把这些组合因子和对应的降水实况输入人工神经网络中进行学习和训练,最后得到南京秋季降水的人工神经网络预报方法。
补充资料：季月
1.每季的最后一月，即农历三﹑六﹑九﹑十二月。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条