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Abstract. Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

Furfurylation of wood is of interest worldwide as an environmentally friendly modification process. It is widely assumed that low-molecular weight furfuryl alcohol (FA) can penetrate into wood cells and polymerize in-situ during the process, resulting in substantial improvement in the physical-mechanical properties and durability of wood. In this study, confocal laser scanning microscopy (CLSM) was used to visualize the microscopic distribution of polymerized FA resin in the Masson pine wood cavities, and a Nanoindenter was used to probe the mechanical properties of modified wood cells. The effects of catalysts (maleic anhydride and a mixed organic acid catalyst), FA concentration, curing time, and curing temperature on the nanomechanical properties of wood cell walls were investigated. An improvement in the indentation modulus and hardness of modified wood cells demonstrated indirectly but strongly that FA indeed penetrated wood cells during the modification process. Based on the results of the cell wall nanoindentation test, a combination of 50% furfuryl alcohol, 8 h curing time, and 95 °C curing temperature were proposed as the starting processing parameters for the development of a more practical and effective wood furfurylation process using a mixed organic acid catalyst.

There is a growing need to characterize the mechanical properties of single bamboo fibers with their high potential in commercial applications. In this paper, an improved microtensile technique has been applied to measure the tensile strength of fibers isolated from Ma bamboo (Dendrocalamus latiflorus Munro) as an important commercial bamboo species in China. The property variation with respect to the age and locations within a culm was in focus. Ma bamboo fibers had superior stiffness and strength data compared with those of softwood fibers. Four-year-old Ma bamboo fibers are stiffer and stronger than 1-year-old fibers. Their in-trunk variation is rather small both in radial and longitudinal directions. This is due to the relatively constant microfibrillar angle in bamboo culms. Accordingly, the large variations in the bulk mechanical properties of bamboo are mainly attributable to fiber distribution density in the culm rather than the fiber itself.

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