How much does a high-resolution global ensemble forecast improve upon deterministic prediction skill for the Indian summer monsoon?

The global Ensemble Prediction System (EPS) at NCMRWF (NEPS-G) comprises of 22 perturbed members in addition to the control (CNTL) member at 12 km horizontal resolution. Running this state-of-the-art ensemble configuration employs large computational resources compared to a deterministic system; hence it is crucial to determine if and to what extent it enhances the prediction skill of forecasts over the Indian region. In this study, we attempt to quantify the improvement in the skill of NEPS-G relative to the deterministic forecast for the 2018 Indian summer monsoon season. The ensemble mean shows substantially reduced forecast errors in the monsoon precipitation when verified against the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) data. NEPS-G mean demonstrates an improved skill for forecasts of moderate rainfall categories based on Peirce’s skill score, probability of detection and critical success index. The ensemble mean also shows an enhanced forecast skill at longer lead times, based on the anomaly correlation coefficient for both zonal winds at 850 hPa and precipitation. The model tends to underpredict very light precipitation and overpredict light precipitation. The Symmetric Extremal Dependence Index indicates a reasonable fidelity of the model in predicting heavy to very heavy rainfall. The continuous ranked probability score for NEPS-G is much lower than the mean absolute error of the CNTL forecast. The Relative Operating Characteristic curve of the ensemble distribution relative to CNTL further illustrates the value-addition by NEPS-G model to forecasts at longer lead times. Thus, through this study, the use of large computational resources for running the high-resolution NEPS-G is proved to be justified as it produces more reliable forecasts with longer lead times. [ABSTRACT FROM AUTHOR]