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

• Published in: Meteorological applications 2022-11, Vol.29 (6), p.n/a
• Main Authors: Boyaj, Alugula, Dasari, Hari Prasad, Rao, Y. V. Rama, Ashok, Karumuri, Hoteit, Ibrahim
• Format: Article
• Language: English
• Subjects: 3DVar; Advection; Assimilation; Clouds; Condensates; Convection; data assimilation; extreme rainfall events; Extreme weather; Global positioning systems; GPS; GPS RO refractivity; Meteorological research; Motion stability; Numerical weather forecasting; Occultation; Positioning systems; Precipitation; Radio; Radio occultation; Rain; Rain and rainfall; Rain water; Rain-water (Water-supply); Rainfall; Rainfall forecasting; Rainfall measurement; Rainfall simulators; Refractive index; Refractivity; Simulation; southern India; Spatial distribution; Stability analysis; Storms; Vertical advection; Vorticity; Vorticity budget; Water vapor; Water vapour; Weather; Weather forecasting; WRF
• ISSN: 1350-4827; 1469-8080
• DOI: 10.1002/met.2103

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. Spatial distribution of observed (GPM), simulated (CTL), and assimilated (ASSIM) daily accumulated rainfall (mm/day). AWS/rain‐gauge stations rainfall magnitudes over the three study regions (black dots), Tamil Nadu (top panels), Telangana (middle panels), and Kerala (lower panels). The first column (a),(e), and (i), second column (b),(f), and (j), and third column (c),(g), and (k) show rainfall that resulted from GPM, CTL, and ASSIM experiments in Tamil Nadu, Telangana, and Kerala. In the fourth column (d),(h), and (l), brown color shade indicates the rainfall difference between the ASSIM and the CTL experiments (ASSIM‐CTL). The black dots indicate the location of AWS/rain‐gauge station and the corresponding rainfall magnitude in the bracket over the three extreme rainfall regions.