It is crucial to improve global precipitation estimates for understanding water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using ensemble data assimilation and the precipitation of a numerical weather prediction model. Our estimates agree with independent rain gauge observations better than the existing precipitation estimates, especially in mountainous or rain-gauge-sparse regions.

Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposed using model predictive control, known as an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrated that the system can be controlled successfully with shorter forecasts compared to previous studies.

This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.

This research found that weather control would change the chaotic behavior of an atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.

Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.