This study mainly focuses on the effects of uncertainty in land surface information on mesoscale numerical simulation. The Weather Research and Forecasting (WRF) model was used to simulate meteorological fields over China at a spatial resolution of 10 km during 2006. Near‐surface temperature and precipitation values obtained from WRF were evaluated using site observations. The effects of accurate and updated land surface information, including Shuttle Radar Topography Mission (SRTM) data, Moderate resolution imaging spectroradiometer (MODIS) land use data, vegetation fraction derived from MODIS normalized difference vegetation index (NDVI) and Harmonized World Soil Database (HWSD) data (LAST simulation), on WRF's performance were investigated by comparison with a simulation using the default land surface information (BASE simulation). WRF reproduced the temporal and spatial variations of near‐surface temperature and precipitation over China accurately, although its performance varied significantly by season and region. WRF underestimated near‐surface temperatures in most areas of the Yunnan–Guizhou Plateau, the Tibetan Plateau, the Northeast Plain, and the southeastern coastal regions, but temperatures were overestimated in most areas of the North China Plain, the Loess Plateau, Sichuan Basin, and western Xinjiang. WRF overestimated (underestimated) precipitation in most humid (arid) areas. A positive (negative) bias in simulated precipitation is found in summer (winter). With updated land surface information, WRF's performance in terms of both daily average values and extremes improved, and the root mean squared error values for daily mean temperature and daily accumulated precipitation decreased by 7 and 2.3%. These improvements are significant for temperature, but not significant for precipitation. The uncertainty in land surface information has a greater influence on temperature than on precipitation. These findings are very important for weather forecasting and studies involving climatological analyses covering East Asia.

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Land-surface albedo plays a critical role in the Earth's radiant energy budget studies. Satellite remote sensing is an effective approach to acquire regional and global albedo observations. However, owing to cloud coverage, seasonal snow and sensor malfunctions, spatially-temporally continuous albedo datasets are often inaccessible. GLASS preliminary albedo datasets (GLASS02A2<i>x</i>, <i>x</i> = 1, 2, 3 and 4) are newly developed global daily land-surface albedo products. Like other products, GLASS02A2<i>x</i> albedo surfers from large areas of missing data. Beside this, sharp fluctuations exist in GLASS02A2<i>x</i> time series due to data noise and algorithm uncertainties. In this study, a statistics-based temporal filterer (STF) is proposed to fill the data gaps and smooth the fluctuations in GLASS02A2<i>x</i> albedo time series. The result of STF algorithm is the GLASS final albedo product (GLASS02A06). Results show that the STF method has greatly improved the integrity and smoothness of the GLASS final albedo product. Seasonal trends in albedo are well depicted by the GLASS final albedo product. Compared with MODIS product, the final GLASS albedo product is much more competent in capturing the surface albedo variations. Although the STF algorithm is designed for GLASS albedo product, it is able to incorporate other albedo products. The STF method may also be applied to other parameters, such as the LAI and soil moisture

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