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Yizhou Zhuang, Rong Fu, and Hongqing Wang

Abstract

We developed an entraining parcel approach that partitions parcel buoyancy into contributions from different processes (e.g., adiabatic cooling, condensation, freezing, and entrainment). Applying this method to research-quality radiosonde profiles provided by the Atmospheric Radiation Measurement (ARM) program at six sites, we evaluated how atmospheric thermodynamic conditions and entrainment influence various physical processes that determine the vertical buoyancy structure across different climate regimes as represented by these sites. The differences of morning buoyancy profiles between the deep convection (DC)/transition cases and shallow convection (SC)/nontransition cases were used to assess preconditions important for shallow-to-deep convection transition. Our results show that for continental sites such as the U.S. Southern Great Plains (SGP) and west-central Africa, surface conditions alone are enough to account for the buoyancy difference between DC and SC cases, although entrainment further enhances the buoyancy difference at SGP. For oceanic sites in the tropical west Pacific, humidity dilution in the lower to middle free troposphere (~1–6 km) and temperature mixing in the middle to upper troposphere (>4 km) have the most important influences on the buoyancy difference between DC and SC cases. For the humid central Amazon region, entrainment in both the boundary layer and the lower free troposphere (~0–4 km) have significant contributions to the buoyancy difference; the upper-tropospheric influence seems unimportant. In addition, the integral of the condensation term, which represents the parcel’s ability to transform available water vapor into heat through condensation, provides a better discrimination between DC and SC cases than the integral of buoyancy or the convective available potential energy (CAPE).

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

Abstract

Although the influence of sea surface temperature (SST) forcing and large-scale teleconnection on summer droughts over the U.S. Great Plains has been suggested for decades, the underlying mechanisms are still not fully understood. Here we show a significant correlation between low-level moisture condition over the U.S. Southwest in spring and rainfall variability over the Great Plains in summer. Such a connection is due to the strong influence of the Southwest dryness on the zonal moisture advection to the Great Plains from spring to summer. This advection is an important contributor for the moisture deficit during spring to early summer, and so can initiate warm season drought over the Great Plains. In other words, the well-documented influence of cold season Pacific SST on the Southwest rainfall in spring, and the influence of the latter on the zonal moisture advection to the Great Plains from spring to summer, allows the Pacific climate variability in winter and spring to explain over 35% of the variance of the summer precipitation over the Great Plains, more than that can be explained by the previous documented west Pacific–North America (WPNA) teleconnection forced by tropical Pacific SST in early summer. Thus, this remote land surface feedback due to the Southwest dryness can potentially improve the predictability of summer precipitation and drought onsets over the Great Plains.

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Qiong Wu, Hong-Qing Wang, Yi-Zhou Zhuang, Yin-Jing Lin, Yan Zhang, and Sai-Sai Ding

Abstract

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0 and 10.8 μm (BTD12.0–10.8), and the BT difference between 6.7 and 10.8 μm (BTD6.7–10.8). Correlations among these IR indicators were investigated using MTSAT-1R images for summer 2007 over East Asia. Temporal, spatial, and numerical frequency distributions were used to represent the correlations. The results showed that large BTD12.0–10.8 values can be observed in the growth of cumulus congestus and associated with the boundary of different terrain where convection was more likely to generate and develop. The results also showed that numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, the shapes of the HT2Ds varied with latitude and the type of underlying surface. After carefully analyzing the correlations among the IR indicators, a conceptual model of the convection life cycle was constructed according to these HT2Ds. A new cloud convection index (CCI) was defined with the combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model. The preliminary test results demonstrated that CCI could effectively identify convective clouds. CCI value and its time trend could reflect the growth or decline of convective clouds.

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Qiong Wu, Hong-Qing Wang, Yin-Jing Lin, Yi-Zhou Zhuang, and Yan Zhang

Abstract

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

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