Investigating the efficacy of a new symmetric index of agreement for evaluating WRF simulated summer monsoon rainfall ov
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ORIGINAL PAPER
Investigating the efficacy of a new symmetric index of agreement for evaluating WRF simulated summer monsoon rainfall over northeast India Aniket Chakravorty1 · Rekha Bharali Gogoi1 · Shyam Sundar Kundu1 · P. L. N. Raju1 Received: 16 October 2019 / Accepted: 6 October 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
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 ( 𝜎R ). 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.
1 Introduction The field of numerical weather prediction (NWP) has seen a multi-dimensional growth since its advent (Abbe 1901; Bauer et al. 2015; Carpenter 1979; Dudhia 2014; Richardson 1922). NWP models have become quite popular because of their ability to generate continuous and reliable data with sufficient forecast lead time (Kalnay et al. 1990; Lynch 2008; Shuman 1989). However, weather phenomena are chaotic and non-linear, and hence, numerical modelling of these phenomena requires more than a few assumptions and simplifications. Incorporating these assumptions into the modelling structure is one of the reasons for the Responsible Editor: Emilia Kyung Jin. * Aniket Chakravorty [email protected] 1
Space and Atmospheric Sciences Division, Department of Space, North Eastern Space Applications Centre, Government of India, Umiam, Meghalaya 793103, India
model error (Bjerknes 1910; Orrell et al. 2001; Tolstykh and Frolov 2005). This realisation has encouraged model evaluation studies of weather events with in-s
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