Abstract. Long-term measurements of aerosol optical depths (AODs) at 440 nm and Ångström exponents (AE) between 440 and 870 nm made for CARSNET were compiled into a climatology of aerosol optical properties for China. Quality-assured monthly mean AODs are presented for 50 sites representing remote, rural, and urban areas. AODs were 0.14, 0.34, 0.42, 0.54, and 0.74 at remote stations, rural/desert regions, the Loess Plateau, central and eastern China, and urban sites, respectively, and the corresponding AE values were 0.97, 0.55, 0.82, 1.19, and 1.05. AODs increased from north to south, with low values (< 0.20) over the Tibetan Plateau and northwestern China and high AODs (> 0.60) in central and eastern China where industrial emissions and anthropogenic activities were likely sources. AODs were 0.20–0.40 in semi-arid and arid regions and some background areas in northern and northeastern China. AEs were > 1.20 over the southern reaches of the Yangtze River and at clean sites in northeastern China. In the northwestern deserts and industrial parts of northeast China, AEs were lower (< 0.80) compared with central and eastern regions. Dust events in spring, hygroscopic particle growth during summer, and biomass burning contribute the high AODs, especially in northern and eastern China. The AODs show decreasing trends from 2006 to 2009 but increased ~ 0.03 per year from 2009 to 2013.

Soil moisture (SM) is an important index of soil drought, and it directly controls the energy balance and water cycle of the land surface. As an indicator and amplifier of global warming, the Tibetan Plateau (TP) is becoming warmer and wetter. Because of its particular geographical environment, large-scale measurements of SM on the TP can only be achieved by satellite remote sensing. The resolution of current SM product of the Soil Moisture Active Passive (SMAP) satellite is 36 km, which is insufficient for many practical applications. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model (DEM) are applied to increase the resolution of SM down to 1 km using the Random Forest (RF) algorithm. The preliminary results of the proposed algorithm are evaluated by station observations and other reanalysis products. The downscaled results are more consistent with the in situ observations, the Land Data Assimilation System (CLDAS) from China Meteorological Administration (CMA), and the Global Land Data Assimilation System (GLDAS) from National Aeronautics and Space Administration (NASA) than the original SMAP product. The downscaling algorithm is most effective for grasslands. It is demonstrated that high-resolution SM products can be generated by fusing various features using machine-learning algorithms.

Two near-real-time and one post-processing products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) were evaluated during the period from 2016 to 2018 in the wet seasons (June-November) over the Sichuan Basin, China. Results indicated the following: (1) The three products could generally replicate strong precipitation well; however, significantly large biases were observed when detecting weak precipitation. (2) All three products replicated summer precipitation more accurately than autumn precipitation. The IMERG "early run" product (IMERG-E) largely underestimated precipitation in the wet season, while the "latter run" (IMERG-L) and "final run" products (IMERG-F) could counteract this to a certain degree. (3) IMERG-F captured weak, moderate, and strong precipitation well during the wet season, and IMERG-E showed excellent potential in detecting different precipitation intensities. All the three products could replicate the diurnal cycle of the wet season precipitation. The findings of this study can facilitate the application of IMERG products in regions with complex topography and have also highlighted the potential of IMERG-E for rapid early warning and forecast systems.