Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models.
Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models.
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使用广义线性模型观测和模拟降雨的预测统计表示。
DOI：
10.1175/jcli-d-18-0527.1
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发表时间：
2019
影响因子：
4.9
通讯作者：
Saravanan,R
中科院分区：
文献类型：
--
作者：
Yang,Junho;Jun,Mikyoung;Schumacher,Courtney;Saravanan,R
本研究探讨了利用一组广义线性模型预测热带太平洋降水的亚日变化和气候空间格局的可行性：降水发生的logistic回归和降水量的伽玛回归。从TRMM卫星雷达观测（层状，深对流，浅对流）和CAM 5模拟（大尺度和对流）的预测分为不同的雨型。从MERRA-2和CAM 5的环境变量被用来作为TRMM和CAM 5降雨的预测，分别。在2003年期间，使用0000 UTC的环境场和0000至0600 UTC的降雨量来训练统计模型。分别用MERRA-2/TRMM和CAM 5模式对2004年降水量和降水率进行了预报。在每种情况下，湿度的第一个曲线和温度的第二个曲线对两个统计模型的预测贡献最大。逻辑回归一般表现良好的所有雨型，但在东太平洋比西太平洋更好。伽玛回归产生合理的地理降雨量分布，但降雨率概率分布没有预测，以及，这表明需要一个不同的，高阶模型来预测降雨率。这项研究的结果表明，应用于TRMM雷达观测和MERRA-2环境参数的统计模型可以预测热带降雨的空间格局和幅度的时间平均意义上。将观测训练的模型与使用CAM 5模拟训练的模型进行比较，指出CAM 5中使用的对流参数化可能存在缺陷。
This study explores the feasibility of predicting subdaily variations and the climatological spatial patterns of rain in the tropical Pacific from atmospheric profiles using a set of generalized linear models: logistic regression for rain occurrence and gamma regression for rain amount. The prediction is separated into different rain types from TRMM satellite radar observations (stratiform, deep convective, and shallow convective) and CAM5 simulations (large-scale and convective). Environmental variables from MERRA-2 and CAM5 are used as predictors for TRMM and CAM5 rainfall, respectively. The statistical models are trained using environmental fields at 0000 UTC and rainfall from 0000 to 0600 UTC during 2003. The results are used to predict 2004 rain occurrence and rate for MERRA-2/TRMM and CAM5 separately. The first EOF profile of humidity and the second EOF profile of temperature contribute most to the prediction for both statistical models in each case. The logistic regression generally performs well for all rain types, but does better in the east Pacific compared to the west Pacific. The gamma regression produces reasonable geographical rain amount distributions but rain rate probability distributions are not predicted as well, suggesting the need for a different, higher-order model to predict rain rates. The results of this study suggest that statistical models applied to TRMM radar observations and MERRA-2 environmental parameters can predict the spatial patterns and amplitudes of tropical rainfall in the time-averaged sense. Comparing the observationally trained models to models that are trained using CAM5 simulations points to possible deficiencies in the convection parameterization used in CAM5.