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  • Author or Editor: Chang Liu x
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Lei Liu, Xue-jin Sun, Tai-chang Gao, and Shi-jun Zhao


Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July–August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.

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Yuxin Zhao, Dequan Yang, Wei Li, Chang Liu, Xiong Deng, Rixu Hao, and Zhongjie He


A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.

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Paul E. Ciesielski, Wen-Ming Chang, Shao-Chin Huang, Richard H. Johnson, Ben Jong-Dao Jou, Wen-Chau Lee, Po-Hsiung Lin, Ching-Hwang Liu, and Junhong Wang


During the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX), which coincided with Taiwan’s Southwesterly Monsoon Experiment—2008 (SoWMEX-08), the upper-air sounding network over the Taiwan region was enhanced by increasing the radiosonde (“sonde”) frequency at its operational sites and by adding several additional sites (three that were land based and two that were ship based) and aircraft dropsondes. During the special observing period of TiMREX (from 15 May to 25 June 2008), 2330 radiosonde observations were successfully taken from the enhanced network. Part of the challenge of processing the data from the 13 upsonde sites is that four different sonde types (Vaisala RS80, Vaisala RS92, Meisei, and Graw) were used. Post–field phase analyses of the sonde data revealed a significant dry bias in many of the sondes—in particular, in the data from the Vaisala RS80 sondes that were used at four sites. In addition, contamination of the sonde data by the ship’s structure resulted in poor-quality low-level thermodynamic data at a key oceanic site. This article examines the methods used to quality control the sonde data and, when possible, to correct them. Particular attention is given to the correction of the humidity field and its impact on various convective measures. Comparison of the corrected sonde humidity data with independent estimates shows good agreement, suggesting that the corrections were effective in removing many of the sonde humidity errors. Examining various measures of convection shows that use of the humidity-corrected sondes gives a much different perspective on the characteristics of convection during TiMREX. For example, at the RS80 sites, use of the corrected humidity data increases the mean CAPE by ∼500 J kg−1, decreases mean convective inhibition (CIN) by 80 J kg−1, and increases the midlevel convective mass flux by greater than 70%. Ultimately, these corrections will provide more accurate moisture fields for diagnostic analyses and modeling studies.

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