The China Meteorological Forcing Dataset (CMFD) is the first high spatial-temporal resolution gridded near-surface meteorological dataset developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis datasets and in-situ station data. Its record begins in January 1979 and is ongoing (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in the CMFD, including 2-meter air temperature, surface pressure, and specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate. Validations against observations measured at independent stations show that the CMFD is of superior quality than the GLDAS (Global Land Data Assimilation System); this is because a larger number of stations are used to generate the CMFD than are utilised in the GLDAS. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely-used climate datasets for China.

[1] The parameterization of thermal roughness length z_0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry‐land areas. The first improvement is the replacement of the model's z_0h scheme with a new one. A previous study has validated the revised Noah model at several dry‐land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer‐ (MODIS‐) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z_0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM.

A multi‐scale soil moisture and temperature monitoring network, consisting of 55 soil moisture and temperature measurement stations, has been established in central Tibetan Plateau (TP). In this study, the station‐averaged surface soil moisture data from the network are used to evaluate four soil moisture products retrieved from the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) and four land surface modeling products from the Global Land Data Assimilation System (GLDAS). Major findings are (1) none of the four AMSR‐E products provides reliable estimates in the unfrozen season, in terms of the mission requirement of the root mean square error (RMSE) < 0.06 m³m⁻³. These algorithms either evidently overestimate soil moisture or obviously underestimate it, although some of them showed the soil moisture dynamic range, indicating that the retrieval algorithms have much space to be improved for the cold semi‐arid regions. (2) The four GLDAS models tend to systematically underestimate the surface soil moisture (0–5 cm) while well simulate the soil moisture for 20–40 cm layer. In comparison with the satellite surface soil moisture products, three among the four models give low RMSE and BIAS values, but still falling out of the acceptable range. The causes for the modeling biases in this cold region were discussed.