Error characterization of ARW model in Forecasting tropical cyclone rainfall over North Indian Ocean

Abstract

The hydrological extremes due to the landfalling tropical cyclones (TCs) pose a severe threat to the coastal communities over the North Indian Ocean (NIO) region. Advanced Research Weather Research and Forecasting (ARW) model is obscure in rainfall prediction, while it is extensively evaluated for track and intensity prediction over the NIO. This study focuses on estimating the model rainfall errors based on a total of 280 TC forecast cases from 42 TCs from 2007 to 2018. The model rainfall errors are studied against rain gauge and Tropical Rainfall Measuring Mission (TRMM) data as a function of TC intensity stage and model forecast length.The short-range (24 h) rainfall guidance yields fewer errors than the long-range (48–96 h) forecast when the model is initialized at any TC intensity stage. The root mean square error (RMSE) and bias of ARW rainfall is higher when the model is initialized at weaker intensity (DD or CS) stages than initialized at stronger intensity (SCS and VSCS) stages. The inland rainfall errors increase with forecast lead. The model exhibited higher errors (~2 mm h−1) in the inner-core region (0–100 km) and lesser errors (~0.5 mm h−1) in the TC environment (200–400 km). The ARW model replicates the observed radial profiles of rainfall up to 400 km with 2–5 mm h−1 overestimation at different intensity stages.Rainfall error decomposition of contiguous rain area (CRA) analysis indicates that the pattern errors contribute the maximum (~50%) to the total error, followed by the displacement error (~35%). In comparison, the volume and rotational errors are less (10% and 2%, respectively). The mean CRA horizontal shift in rainfall decreases from weaker to stronger stage initialization. The radial-distance error of categorical rainfall distribution between the ARW model and TRMM is ~ 150–200 km. This error reduced to 20–50 km after correcting the model rainfall for CRA displacement errors. The RMSE of model-rainfall after CRA correction has reduced by 3–15% for any forecast length. This study helps to advance the regional hydrological model’s ability by improved model rainfall inputs at 72–96 h lead.