Investigating the efficacy of a new symmetric index of agreement for evaluating WRF simulated summer monsoon rainfall over northeast India

Abstract

The efficacy of the standard performance metrics [mean bias (Bias), root mean square deviation (RMSD), and correlation coefficient (CC)] compared to a new symmetric index of agreement (λ) for the evaluation of numerical weather prediction models is investigated in this study. It evaluates the weather research and forecasting (WRF) model with the global precipitation measurement’s (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and station rainfall, as the reference datasets. This study uses three IMERG products, namely: GPM infrared-microwave merged gauge corrected (GMS), GPM microwave-calibrated infrared (GIR), and GPM inter-calibrated microwave (GMW) rainfall. The analysis showed that WRF rainfall when compared to different reference datasets is producing similar RMSD values but significantly different Bias values. This behavior is because of the inverse relationship between Bias and standard deviation of residual ( $$\sigma_{{\text{R}}}$$ ). It is so because RMSD is a function of both. However, λ is able to appropriately represent the distinct performances of WRF. The regions with contradictory behavior of RMSD and CC are also appropriately represented in λ. The evaluation using λ showed that WRF is comparable to GMS and GIR, except for GMW. The performance of WRF was not found to be very promising when compared to station rainfall, which is attributed to WRFs representation efficiency and the effect of topography. However, a comparison of IMERG products with station rainfall showed that GMS was the most agreeable followed by GIR and GMW. The study also showed that the efficacy of λ is related to its non-linear relationship with Bias and CC.