Abstract:Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four satellite-based precipitation products (TRMM-3B42, TRMM-3B42RT, CMORPH, GSMaP) were evaluated using gauge-based rainfall analysis based on a high-density gauge network throughout the Chinese Mainland during [2003][2004][2005][2006]. To quantitatively evaluate satellite-based precipitation products, continuous (e.g., ME, RMSE, CC) and categorical (e.g., POD, FAR) verification statistics were used in this study. The results are as follows: (1) GSMaP and CMORPH underestimated precipitation (about −0.53 and −0.14 mm/day, respectively); TRMM-3B42RT overestimated precipitation (about 0.73 mm/day); TRMM-3B42, which is the only dataset corrected by gauges, had the best estimation of precipitation amongst all four products; (2) GSMaP, CMORPH and TRMM-3B42RT overestimated the frequency of low-intensity rainfall events; TRMM-3B42 underestimated the frequency of low-intensity rainfall events; GSMaP underestimated the frequency of high-intensity rainfall events; TRMM-3B42RT tended to overestimate the OPEN ACCESS Remote Sens. 2014, 6 11650 frequency of high-intensity rainfall events; TRMM-3B42 and CMORPH produced estimations of high-intensity rainfall frequency that best aligned with observations; (3) All four satellite-based precipitation products performed better in summer than in winter. They also had better performance over wet southern region than dry northern or high altitude regions. Overall, this study documented error characteristics of four satellite-based precipitation products over the Chinese Mainland. The results help to understand features of these datasets for users and improve algorithms for algorithm developers in the future.

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Background Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people’s health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. Methods A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. Results DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. Conclusions The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics. Electronic supplementary material The online version of this article (10.1186/s40249-019-0533-9) contains supplementary material, which is available to authorized users.