Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Leiming Ma x
  • All content x
Clear All Modify Search
Feng Zhang, Zhongping Shen, Jiangnan Li, Xiuji Zhou, and Leiming Ma

Abstract

Although single-layer solutions have been obtained for the δ-four-stream discrete ordinates method (DOM) in radiative transfer, a four-stream doubling–adding method (4DA) is lacking, which enables us to calculate the radiative transfer through a vertically inhomogeneous atmosphere with multiple layers. In this work, based on the Chandrasekhar invariance principle, an analytical method of δ-4DA is proposed.

When applying δ-4DA to an idealized medium with specified optical properties, the reflection, transmission, and absorption are the same if the medium is treated as either a single layer or dividing it into multiple layers. This indicates that δ-4DA is able to solve the multilayer connection properly in a radiative transfer process. In addition, the δ-4DA method has been systematically compared with the δ-two-stream doubling–adding method (δ-2DA) in the solar spectrum. For a realistic atmospheric profile with gaseous transmission considered, it is found that the accuracy of δ-4DA is superior to that of δ-2DA in most of cases, especially for the cloudy sky. The relative errors of δ-4DA are generally less than 1% in both the heating rate and flux, while the relative errors of δ-2DA can be as high as 6%.

Full access
Jianyong Liu, Shunan Yang, Leiming Ma, Xuwei Bao, Dongliang Wang, and Difeng Xu

Abstract

A nudging scheme for humidity fields is implemented in the Advanced Hurricane Weather Research and Forecasting model (WRF) for tropical cyclone (TC) initialization. The scheme improves TC simulation by enhancing the TC humidity profile in deep-convection regions, where it uses satellite Fengyun 2 cloud-top brightness temperatures as a judging criterion. The impacts of the nudging on predicting TC intensity and structure are evaluated through the simulation of TC Khanun (2005) during its movement toward landfall at the coast of Zhejiang Province, China. During the nudging, the humidity distributions at the TC's inner core and along its outer spiral rainbands, where deep convections occur, are both enhanced. As a result, the intensity of the vortex is enhanced, being more consistent to the best-track data from the China Meteorological Administration. Specifically, the nudging modifies the simulated distribution of humidity according to convective activities captured by the satellite and therefore adjusts the development of deep convection in the model, which then influences the intensity and size of TC vortex through diabatic heating. During WRF simulation, the TC vortex initialized from the humidity nudging is dynamically and thermodynamically balanced with the background field, favoring a steady development of the vortex's intensity and structure. Because of the better simulation of TC inner core and outer spiral rainbands, the WRF simulation skills of TC intensity and track are improved.

Full access
Chang-Jiang Zhang, Jin-Fang Qian, Lei-Ming Ma, and Xiao-Qin Lu

Abstract

An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

Full access