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- Author or Editor: Fan Wang x
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Abstract
The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.
Abstract
The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.
Abstract
A progression of advanced statistical methods is applied to investigate the dependence of the 6-h tropical cyclone (TC) intensity change on various environmental variables, including the recently developed ventilation index (VI). The North Atlantic (NA) and western North Pacific (WNP) observations from 1979 to 2014 are used. As a first step, a model of the intensity change is developed as a linear function of 13 variables used in operational models, obtaining statistical R 2 values of 0.26 for NA and 0.3 for WNP. Statistical variable selection techniques are then applied to significantly reduce the number of predictors (to 5–11), while keeping similar R 2 values with linear or nonlinear models. Further reduction of the number of predictors (to 5–7) and significant improvement of R 2 (0.41–0.53) are obtained with mixture modeling, indicating that the dependence of TC intensification on the environment is nonhomogeneous. Applying VI as the environmental predictor in the mixture modeling gives R 2 results (0.41–0.74) similar to or better than those with more environmental variables, confirming that VI is a dominant environmental variable, although its effect on TC intensification is quite heterogeneous. However, the overall predictive R 2 results of the mixture models are relatively low (<0.3), as the considered environmental variables have limited predictability for the occurrence of extreme/rapid intensification. Finally, nonparametric regression with six predictors [current intensity, previous intensity change, the three components of VI (maximum potential intensity, shear, and entropy deficit), and 200-hPa zonal wind] performs relatively well with predictive R 2 values of 0.37 for NA and 0.36 for WNP. The predictability of these statistical models may be further improved by adding oceanic and inner-core process predictors.
Abstract
A progression of advanced statistical methods is applied to investigate the dependence of the 6-h tropical cyclone (TC) intensity change on various environmental variables, including the recently developed ventilation index (VI). The North Atlantic (NA) and western North Pacific (WNP) observations from 1979 to 2014 are used. As a first step, a model of the intensity change is developed as a linear function of 13 variables used in operational models, obtaining statistical R 2 values of 0.26 for NA and 0.3 for WNP. Statistical variable selection techniques are then applied to significantly reduce the number of predictors (to 5–11), while keeping similar R 2 values with linear or nonlinear models. Further reduction of the number of predictors (to 5–7) and significant improvement of R 2 (0.41–0.53) are obtained with mixture modeling, indicating that the dependence of TC intensification on the environment is nonhomogeneous. Applying VI as the environmental predictor in the mixture modeling gives R 2 results (0.41–0.74) similar to or better than those with more environmental variables, confirming that VI is a dominant environmental variable, although its effect on TC intensification is quite heterogeneous. However, the overall predictive R 2 results of the mixture models are relatively low (<0.3), as the considered environmental variables have limited predictability for the occurrence of extreme/rapid intensification. Finally, nonparametric regression with six predictors [current intensity, previous intensity change, the three components of VI (maximum potential intensity, shear, and entropy deficit), and 200-hPa zonal wind] performs relatively well with predictive R 2 values of 0.37 for NA and 0.36 for WNP. The predictability of these statistical models may be further improved by adding oceanic and inner-core process predictors.
