By using a coupled land surface-atmosphere model with initial conditions of varying resolution and ensembles of systematically changed soil moisture, convective-scale simulations of a typical frontal rainstorm in the Yangtze River Basin are collected to investigate: (1) effects of different datasets on the simulated frontal mesoscale convective systems (MCSs); (2) possible linkages between soil moisture, planetary boundary layer (PBL), MCSs and precipitation in this modeled rainstorm. Firstly, initial soil moisture differences can affect the PBL, MCSs and precipitation of this frontal rainstorm. Specially, for a 90 mm precipitation forecast, the Threat score (TS) can increase 6.61% by using the Global Land Data Assimilation System (GLDAS) soil moisture. Secondly, sensitivity experiment results show that the near-surface thermodynamic conditions are more sensitive to dry soil than wet due to the initial moist surface; atmosphere conditions have suppressed the relations between soil and atmosphere; and decreased precipitation can be found over both wet and dry surfaces. Generally, a positive feedback between soil moisture and the near-surface thermodynamic conditions is identified, while the relations between soil moisture and precipitation are quite complicated. This relationship shows a daytime mixing of warm surface soil over dry surfaces and a daytime evaporation of adequate moisture over wet surfaces. The large-scale forcing can affect these relations and finally cause decreased precipitation over both wet and dry surfaces.

Automatic soil moisture data with a temporal resolution of 1 hr, a spatial resolution of 30 km, from the period June 1-September 1, 2017, and from Henan province in China, and simulation results from the Community Land Model (CLM4.0) were first compared, and a calibration study was further conducted and investigated. The operational status of the instruments was confirmed, and the data passed quality control. The corresponding simulation was performed using the CLM4.0. Small spatial scale variations in observed soil moisture contents were found, but relatively large spatial variations were observed in the simulated soil moisture contents. Since a relatively large bias and weak correlation were found between the simulation and observations, it is necessary to bias-correct the model outputs in order to improve the prediction capability of the numerical models. After bias correction was conducted, the outliers accounting for around 10% of the actual samples were identified and removed for each layer. The observed and simulated data were found to be linearly distributed, and the probability density distribution of deviations between observed and simulated values showed a normal distribution. The mean bias (BIAS) and root mean square error (RMSE) decreased by 100% and 50%, respectively, and the correlation co-efficient (CORR) increased by about 10 times. These improvements showed that the bias-corrected data met the requirements for the evaluation and application of soil moisture data, which can be of great significance in improving future land simulation and assimilation. K E Y W O R D Sautomatic soil moisture observation, bias correction, bi-weight method, land-surface model | INTRODUCTIONSoil moisture can affect the regional climate by changing the reflectance of the surface, the heat capacity of the ground, the sensible heat and latent heat which are Funding informationThe study was supported by the CMAÁHenan Key Laboratory of Agrometeorological Support and Applied Technique (AMF201904).

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.

V-modified titania nanorod-aggregates were fabricated by microwave hydrothermal route. MWV05 exhibited optimal solar activity towards PCP-Na, due to red-shift by V-doping, carriers separation by V4+/V5+ synergistic effects and charge migration along the nanorods.