Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India

Abstract

Here, we investigated the impact of assimilating the satellite-based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/ rain-gauge) and observation-based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low-level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.

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

“…In addition, because computing bending angles requires a ray tracing model, assimilating of bending angles is computationally very expensive (Liu & Zou, 2003;Shao & Zou, 2002). Therefore, many studies (Boyaj et al, 2022;Chen et al, 2015Chen et al, , 2020Chen et al, , 2021Cheng et al, 2018;Healy et al, 2005;Miller et al, 2023;Singh et al, 2021;Zhou & Chen, 2014), assimilate refractivity using a less computationally expensive observation operator in regional models. We also assimilated refractivities using the computationally affordable 3D-Var assimilation technique due to our limited computational resources.…”

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

“…Chang et al (2022) showed that GPSRO refractivity can provide information about the high-moisture level (3-5 km), producing a better heavy rainfall forecast [15]. Boyaj et al (2022) demonstrated the capability of GPSRO refractivity to enhance water vapor condensation and the top levels of ice, cloud, and rainwater, all of which improve hazardous rainfall predictions [16]. Cheng et al (2018) showed that the assimilation of GPSRO refractivity alone enhances both the 24 h and 48 h regional rainfall forecasts, while the assimilation of GPSRO refractivity and conventional observations enhanced the 48 h forecast of light and heavy rain [17].…”