主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2015, Vol. 31 ›› Issue (1): 60-66.doi:

• 论文 • 上一篇    下一篇

云量的时间精细化预报研究—以榆中为例

赵文婧  赵中军2   尚可政1   王式功1   宁贵财1   

  1. 1. 兰州大学大气科学学院,甘肃 兰州 730000;2. 中国人民解放军92493部队中心气象台,辽宁 葫芦岛,125000
  • 出版日期:2015-02-28 发布日期:2015-02-28

Refined forecast of cloud cover---a case study in Yuzhong

ZHAO Wen-jing1 ZHAO Zhong-jun2 SHANG Ke-zheng1 WANG Shi-gong1 NING Gui-cai1   

  1. 1. Department of Atmospheric Science, Lanzhou University, Lanzhou 730000, China;2. Central Meteorological Observatory of 92493 Unit of the Chinese People's Liberation Army, Huludao 125000, China
  • Online:2015-02-28 Published:2015-02-28

摘要:

利用2001年7月至2011年7月甘肃省榆中县地面测站的每日8次云量资料和同期NCEP每日4次等压面资料,由NCEP资料构造预报因子,以总云量和低云量为预报对象,分析预报因子和预报对象的相关性,采用逐步回归方法建立榆中县逐月8个时次的云量预报方程并进行回代;并利用2012年的资料检验预报方程的预报效果。结果表明:云量主要受整层湿度、垂直运动、不稳定能量、槽强度指数和700 hPa水汽通量散度影响,其中湿度状况和垂直运动是重要因素。建立的预报方程对总云量的预报效果比低云量好;总云量平均预报误差在2成左右,低云量平均预报误差在3成左右;预报值变化趋势可以部分地反映实际云量的变化趋势。

关键词: 云量, 预报, PP法

Abstract:

Based on daily cloud cover data with 3 hours interval and corresponding NCEP data with 6 hours interval from July of 2001 to July of 2011 in Yuzhong of Gansu province, relationships between forecast factors built by the NCEP data and forecast object such as the total cloud cover and low cloud cover were analyzed. A series of monthly forecast equations of cloud cover with daily 8 times was established by a stepwise regression analysis method and were tested by back substitution, and then prediction effect was checked using data of 2012. The results show that cloud cover is mainly affected by the whole layer humidity, vertical velocity, instability energy, trough intensity index and divergence of moisture flux in 700 hPa, especially the first two elements. Forecast effect of total cloud cover is better than that of low cloud cover; average errors of total cloud cover and low cloud cover are about 20% and about 30%, respectively. Tendency of predicted values can partly reflect that of observed values.

Key words: Cloud cover, Forecast, Perfect prediction (PP) method