Precipitation over the Tibetan Plateau (TP) known as Asia’s water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998–2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004–2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research.
Satellite-based and reanalysis precipitation estimates are an alternative and an important supplement to rain gauge data. However, performance of China’s Fengyun (FY) satellite precipitation product and how it compares with other mainstream satellite and reanalysis precipitation products over China remain largely unknown. Here five satellite-based precipitation products (i.e., FY2 precipitation product, IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and one reanalysis product (i.e., ERA5) are intercompared and evaluated based on in-situ daily precipitation measurements over Mainland China during 2007–2017. Results show that the performance of these precipitation products varies with regions and seasons, with better statistical metrics over wet regions and during warm seasons. The infrared-microwave combined precipitation (i.e., IMERG, GSMaP, and CMORPH, with median KGE (Kling-Gupta efficiency) values of 0.53, 0.52, 0.59, respectively) reveals better performance than the infrared-based only product (i.e., PERSIANN-CDR, with a median KGE of 0.31) and the reanalysis product (i.e., ERA5, with a median KGE of 0.43). IMERG performs well in retrieving precipitation intensity and occurrence over China, while GSMaP performs well in the middle-low reaches of the Yangtze River basin but poorly over sparsely gauged regions, e.g., Xinjiang in Northwest China and the Tibetan Plateau. CMORPH performs well over most regions and has a greater ability to detect precipitation events than GSMaP. The FY2 precipitation product can capture the overall spatial distribution of precipitation in terms of both precipitation intensity and occurrence (median KGE and CSI of 0.54 and 0.55), and shows better performance than other satellite precipitation products in winter and over sparsely gauged regions. Annual precipitation from different products is generally consistent, though underestimation exists in the FY2 precipitation product during 2015–2017.
Abstract. Ocean color data are essential for developing our understanding of biological and ecological phenomena and processes, and also important sources of input for physical and biogeochemical ocean models. Chlorophyll-a (Chl-a) is a critical variable of ocean color in the marine environment. Quantitative retrieval from satellite remote sensing is a main way to obtain large-scale oceanic Chl-a. However, data missing is a major limitation in satellite remote sensing-based Chl-a products, due mostly to the influence of cloud, sun glint contamination, and high satellite viewing angles. The common methods to reconstruct (gap filling) missing data often consider spatiotemporal information of initial images alone, such as data interpolation empirical orthogonal function, optimal interpolation, Kriging interpolation, and extended Kalman filter. However, these methods do not perform well in the presence of large-scale missing values in the image and ignore the potential of other information on missing pixels in the data reconstruction. Here we developed a convolutional neural network (CNN) named OCNET for Chl-a concentration data reconstruction in open ocean areas, considering environmental variables that are associated with ocean phytoplankton growth and distribution. Sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) from reanalysis data and satellite observations were selected as the input of OCNET to correlate with the environment and phytoplankton mass. The developed OCNET model achieves good performance in the reconstruction of global ocean Chl-a concentration data, and captures temporal variations of these features. This study also shows the potential of machine learning in large-scale ocean color data reconstruction and offers the possibility to predict Chl-a concentration trends under a changing environment.
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