Impact of Varying Number of Radio Occultation Observations on Regional Weather Prediction Over India During the Summer Monsoon Season

Abstract

AbstractObserving system simulation experiments are carried out to investigate the added value of radio occultation (RO) refractivity observations with various spatial sampling scenarios on regional weather predictions across the Indian region. A full summer monsoon season (June through September 2020) was used to demonstrate how varying the numbers and horizontal resolutions of RO data impacted regional‐scale weather forecasting. The MPAS (Model for Prediction Across Scales) was used to produce a nature run at a maximum horizontal resolution of 10 km. Then the WRF model with 12‐km horizontal resolution was used to carry out assimilation/forecast experiments with varying number of simulated RO observations. When the performance of the experiments is taken into account for moisture, temperature, winds and rainfall, as well as prediction lengths, the results show that RO observations with 50‐km resolution assimilated every 6 hr would provide the best results. Increasing the horizontal resolution to 25 km per 6 hr shows little overall improvement. Furthermore, RO data with horizontal resolutions lower than 100 km per 6 hr have only a small impact on the regional numerical weather prediction system. The number of low Earth orbit satellites in low‐inclination orbits required to achieve occultations every 6 hr with 50‐km resolution based on the COSMIC‐2 mission is approximately 700. This work is relevant for the deployment of the cost‐effective RO observing system for improved weather forecasting over the Indian region.