Utilizing the cloud parameters derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner and the near-surface rainfall detected by the TRMM Precipitation Radar, the differences of cloud parameters for precipitating clouds (PCs) and nonprecipitating clouds (NPCs) are examined in tropical cyclones (TCs) during daytime from June to September 1998–2010. A precipitation delineation scheme that is based on cloud parameter thresholds is proposed and validated using the independent TC datasets in 2011 and observational datasets from Terra/MODIS. Statistical analysis of these results shows that the differences in the effective radius of cloud particles Re are small for PCs and NPCs, while thick clouds with large cloud optical thickness (COT) and liquid water path (LWP) can be considered as candidates for PCs. The probability of precipitation increases rapidly as the LWP and COT increase, reaching ~90%, whereas the probability of precipitation reaches a peak value of only 30% as Re increases. The combined threshold of a brightness temperature at 10.8 μm (BT4) of 270 K and an LWP of 750 g m−2 shows the best performance for precipitation discrimination at the pixel levels, with the probability of detection (POD) reaching 68.2% and false-alarm ratio (FAR) reaching 31.54%. From MODIS observations, the composite scheme utilizing BT4 and LWP also proves to be a good index, with POD reaching 77.39% and FAR reaching 24.2%. The results from this study demonstrate a potential application of real-time precipitation monitoring in TCs utilizing cloud parameters from visible and infrared measurements on board geostationary weather satellites.
Waterlogging is a serious agro-meteorological disaster caused by excessive soil water, which usually causes tremendous crop yield losses. The region of middle and lower reaches of Yangtze River in China is an important production base of winter wheat, and is an area prone to waterlogging. The risk assessment of winter wheat waterlogging can provide more thorough understanding about the risk-prone environment related with food safety in this region. This study combined a variety of environmental and agricultural factors and assessed the waterlogging risk of winter wheat from the aspects of sensitivity of hazard formative environments, hazard risk, and vulnerability of hazard-affected body using multi-source data. Furthermore, it constructed a compound waterlogging risk assessment model to classify the study area into high, relatively high, moderate, and low risky areas, respectively. The results showed that the proposed model could more comprehensively reflect the occurrence mechanism of winter wheat waterlogging by synchronizing geographical, agricultural, and meteorological factors. The waterlogging regionalization based on the model could reasonably represent the spatial distribution and differentiate regional characteristics of winter wheat waterlogging in the study area. et al. Waterlogging risk assessment for winter wheat using multi-source data in the middle and lower reaches of Yangtze River. Int J Agric & Biol Eng, 2018; 11(5): 198-205.
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