...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of hydrometeorology >A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia
【24h】

A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia

机译：南亚和东亚日降水量的贝叶斯隐马尔可夫模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM-HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days.
机译：提出了用于多站点日降水量气候缩减的贝叶斯隐马尔可夫模型（HMM）。广义线性模型（GLM）组件允许外生变量随时间直接影响每个站点的降水分布特征，而离散状态之间的马尔可夫变迁则代表季节和季节下的天气变化。针对印度北部和巴基斯坦的旁遮普地区以及中国中南部的长江上游流域的夏季降水站网，对模型性能进行了评估。该模型很好地捕捉了两个地区的季节性和边际日分布。在旁遮普地区，极端情况的再现相对较好，但在长江地区却被低估了。就年际可变性而言，结合GLM-HMM和观测到的降雨的时空平均值作为预测因子，在站一级，特别是在旁遮普地区，显示出技能（根据降低的RMSE）。对于干旱天数，该技能是最大的;对于季节性降雨总量，该技能是最大的;对于极端潮湿的天数，该技能很小。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号