Assessment of WRF-3DVAR Data Assimilation on Simulation of Heavy Rainfall Events Associated with Monsoon Depressions over Bay of Bengal
The present study examines the performance of the Advance Research Weather Research and Forecasting model with three-dimensional variational data assimilation (WRF-3DVAR) associated with four heavy rainfall events (HREs) in the presence of monsoon depressions (MDs) over the Bay of Bengal (BoB). We have carried out two numerical experiments, control experiment (CNTL; without data assimilation) and 3DV (assimilation of observations from Global Telecommunication system). The resultant high-resolution analysis obtained from the successful insertion of additional observations through 3DVAR assimilation technique recaptures the better convection and synoptic features associated with the MDs. The 3DV-simulated values of hydrometeors (rainwater, cloud water, and ice + snow + graupel) are found to be reasonably well captured, compared to CNTL simulation. The MDs evolution at various phases of its life span is reasonably well simulated in the 3DV compared to the CNTL experiment. The qualitative and quantitative precipitations are examined with respect to satellite-estimated rainfall data. The quantitative validation of model simulated 24-h accumulated precipitation is evaluated through the feature-based diagnostic evaluation method. Numerous statistical skill scores are evaluated by virtue of the object-oriented tool and results revealed that the simulated rainfall is remarkably improved in 3DV experiment. The study envisages that the assimilation of observations through 3DVAR have positive impact for simulation of HREs due to the presence of MDs.
Kalra, S., Kumar, S., Mahala, B.K. et al. Assessment of WRF-3DVAR data assimilation on simulation of heavy rainfall events associated with monsoon depressions over Bay of Bengal. Meteorol Atmos Phys 134, 68 (2022). https://doi.org/10.1007/s00703-022-00892-8
This article was originally published in Meteorology and Atmospheric Physics, volume 134, in 2022. https://doi.org/10.1007/s00703-022-00892-8