Virtual Orbital Simulation and OSSE of Radiometer-Equipped Small Satellite Constellation for River-Basin-Scale Rainfall Prediction

Abstract Small satellite constellations (SSCs) equipped with microwave radiometers are rapidly advancing for Earth observation. The application of such technology to hydrometeorology is imminent. This study proposes a method to evaluate the impact of measurements from radiometer-equipped SSCs on river-basin-scale rainfall prediction. By combining virtual orbital simulation and observing system simulation experiments (OSSE), the method assimilates virtual but realistic measurements. It also examines the relationship between SSC configurations and prediction accuracy, thus providing the SSC design community with information for optimizing SSC setups for hydrometeorological applications. Results show that frequent assimilation of SSC data improves river-basin-scale precipitation forecasts by increasing opportunities to generate accurate initial conditions. Substantial improvements were observed also in water vapor and temperature fields, with assimilation effects lasting for over 6 h. However, increasing the number of satellites does not always lead to better prediction accuracy. Furthermore, the study reveals that evenly spaced observation intervals lead to more stable and accurate rainfall predictions, highlighting the importance of both frequency and timing in SSC configuration. These findings emphasize the need for careful consideration of SSC design to maximize the benefits of this technology for rainfall prediction. Significance Statement This study introduces a new method to validate river-basin-scale rainfall predictions by assimilating virtual measurements from a radiometer-equipped small satellite constellation (SSC) combining virtual orbital simulations and observing system simulation experiment (OSSE). It also proposes a framework for recommending optimal SSC configurations to achieve desirable prediction accuracy. The results show that frequent assimilation of SSC data has great potential to enhance rainfall prediction accuracy. The results also highlight the importance of carefully designing SSC configurations to maximize their effectiveness in improving rainfall forecasts.